Compare commits
2 Commits
| Author | SHA1 | Date |
|---|---|---|
 sunpeng
|
c6b7bb32e5 | |
 WORK\64751
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b6d5489459 |
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@ -1,4 +1 @@
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Generic single-database configuration with an async dbapi.
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alembic revision --autogenerate -m "init"
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alembic upgrade head
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@ -0,0 +1,359 @@
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"""Initial migration
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Revision ID: 424646027786
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Revises:
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Create Date: 2025-12-16 09:56:45.172954
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"""
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from typing import Sequence, Union
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from alembic import op
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import sqlalchemy as sa
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# revision identifiers, used by Alembic.
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revision: str = '424646027786'
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down_revision: Union[str, Sequence[str], None] = None
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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"""Upgrade schema."""
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# ### commands auto generated by Alembic - please adjust! ###
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op.create_table('agent_configs',
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('name', sa.String(length=100), nullable=False),
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sa.Column('description', sa.Text(), nullable=True),
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sa.Column('enabled_tools', sa.JSON(), nullable=False),
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sa.Column('max_iterations', sa.Integer(), nullable=False),
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sa.Column('temperature', sa.String(length=10), nullable=False),
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sa.Column('system_message', sa.Text(), nullable=True),
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sa.Column('verbose', sa.Boolean(), nullable=False),
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sa.Column('model_name', sa.String(length=100), nullable=False),
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sa.Column('max_tokens', sa.Integer(), nullable=False),
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sa.Column('is_active', sa.Boolean(), nullable=False),
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sa.Column('is_default', sa.Boolean(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_agent_configs'))
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)
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op.create_index(op.f('ix_agent_configs_id'), 'agent_configs', ['id'], unique=False)
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op.create_index(op.f('ix_agent_configs_name'), 'agent_configs', ['name'], unique=False)
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op.create_table('conversations',
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sa.Column('title', sa.String(length=200), nullable=False),
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sa.Column('user_id', sa.Integer(), nullable=False),
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sa.Column('knowledge_base_id', sa.Integer(), nullable=True),
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sa.Column('system_prompt', sa.Text(), nullable=True),
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sa.Column('model_name', sa.String(length=100), nullable=False),
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sa.Column('temperature', sa.String(length=10), nullable=False),
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sa.Column('max_tokens', sa.Integer(), nullable=False),
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sa.Column('is_archived', sa.Boolean(), nullable=False),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_conversations'))
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)
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op.create_index(op.f('ix_conversations_id'), 'conversations', ['id'], unique=False)
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op.create_table('database_configs',
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('name', sa.String(length=100), nullable=False),
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sa.Column('db_type', sa.String(length=20), nullable=False),
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sa.Column('host', sa.String(length=255), nullable=False),
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sa.Column('port', sa.Integer(), nullable=False),
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sa.Column('database', sa.String(length=100), nullable=False),
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sa.Column('username', sa.String(length=100), nullable=False),
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sa.Column('password', sa.Text(), nullable=False),
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sa.Column('is_active', sa.Boolean(), nullable=False),
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sa.Column('is_default', sa.Boolean(), nullable=False),
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sa.Column('connection_params', sa.JSON(), nullable=True),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_database_configs')),
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sa.UniqueConstraint('db_type', name=op.f('uq_database_configs_db_type'))
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)
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op.create_index(op.f('ix_database_configs_id'), 'database_configs', ['id'], unique=False)
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op.create_table('documents',
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sa.Column('knowledge_base_id', sa.Integer(), nullable=False),
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sa.Column('filename', sa.String(length=255), nullable=False),
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sa.Column('original_filename', sa.String(length=255), nullable=False),
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sa.Column('file_path', sa.String(length=500), nullable=False),
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sa.Column('file_size', sa.Integer(), nullable=False),
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sa.Column('file_type', sa.String(length=50), nullable=False),
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sa.Column('mime_type', sa.String(length=100), nullable=True),
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sa.Column('is_processed', sa.Boolean(), nullable=False),
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sa.Column('processing_error', sa.Text(), nullable=True),
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sa.Column('content', sa.Text(), nullable=True),
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sa.Column('doc_metadata', sa.JSON(), nullable=True),
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sa.Column('chunk_count', sa.Integer(), nullable=False),
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sa.Column('embedding_model', sa.String(length=100), nullable=True),
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sa.Column('vector_ids', sa.JSON(), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_documents'))
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)
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op.create_index(op.f('ix_documents_id'), 'documents', ['id'], unique=False)
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op.create_table('excel_files',
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sa.Column('original_filename', sa.String(length=255), nullable=False),
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sa.Column('file_path', sa.String(length=500), nullable=False),
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sa.Column('file_size', sa.Integer(), nullable=False),
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sa.Column('file_type', sa.String(length=50), nullable=False),
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sa.Column('sheet_names', sa.JSON(), nullable=False),
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sa.Column('default_sheet', sa.String(length=100), nullable=True),
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sa.Column('columns_info', sa.JSON(), nullable=False),
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sa.Column('preview_data', sa.JSON(), nullable=False),
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sa.Column('data_types', sa.JSON(), nullable=True),
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sa.Column('total_rows', sa.JSON(), nullable=True),
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sa.Column('total_columns', sa.JSON(), nullable=True),
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sa.Column('is_processed', sa.Boolean(), nullable=False),
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sa.Column('processing_error', sa.Text(), nullable=True),
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sa.Column('last_accessed', sa.DateTime(), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_excel_files'))
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)
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op.create_index(op.f('ix_excel_files_id'), 'excel_files', ['id'], unique=False)
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op.create_table('knowledge_bases',
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sa.Column('name', sa.String(length=100), nullable=False),
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sa.Column('description', sa.Text(), nullable=True),
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sa.Column('embedding_model', sa.String(length=100), nullable=False),
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sa.Column('chunk_size', sa.Integer(), nullable=False),
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sa.Column('chunk_overlap', sa.Integer(), nullable=False),
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sa.Column('is_active', sa.Boolean(), nullable=False),
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sa.Column('vector_db_type', sa.String(length=50), nullable=False),
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sa.Column('collection_name', sa.String(length=100), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_knowledge_bases'))
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)
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op.create_index(op.f('ix_knowledge_bases_id'), 'knowledge_bases', ['id'], unique=False)
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op.create_index(op.f('ix_knowledge_bases_name'), 'knowledge_bases', ['name'], unique=False)
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op.create_table('llm_configs',
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sa.Column('name', sa.String(length=100), nullable=False),
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sa.Column('provider', sa.String(length=50), nullable=False),
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sa.Column('model_name', sa.String(length=100), nullable=False),
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sa.Column('api_key', sa.String(length=500), nullable=False),
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sa.Column('base_url', sa.String(length=200), nullable=True),
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sa.Column('max_tokens', sa.Integer(), nullable=False),
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sa.Column('temperature', sa.Float(), nullable=False),
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sa.Column('top_p', sa.Float(), nullable=False),
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sa.Column('frequency_penalty', sa.Float(), nullable=False),
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sa.Column('presence_penalty', sa.Float(), nullable=False),
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sa.Column('description', sa.Text(), nullable=True),
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sa.Column('is_active', sa.Boolean(), nullable=False),
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sa.Column('is_default', sa.Boolean(), nullable=False),
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sa.Column('is_embedding', sa.Boolean(), nullable=False),
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sa.Column('extra_config', sa.JSON(), nullable=True),
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sa.Column('usage_count', sa.Integer(), nullable=False),
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sa.Column('last_used_at', sa.DateTime(), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_llm_configs'))
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)
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op.create_index(op.f('ix_llm_configs_id'), 'llm_configs', ['id'], unique=False)
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op.create_index(op.f('ix_llm_configs_name'), 'llm_configs', ['name'], unique=False)
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op.create_index(op.f('ix_llm_configs_provider'), 'llm_configs', ['provider'], unique=False)
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op.create_table('messages',
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sa.Column('conversation_id', sa.Integer(), nullable=False),
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sa.Column('role', sa.Enum('USER', 'ASSISTANT', 'SYSTEM', name='messagerole'), nullable=False),
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sa.Column('content', sa.Text(), nullable=False),
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sa.Column('message_type', sa.Enum('TEXT', 'IMAGE', 'FILE', 'AUDIO', name='messagetype'), nullable=False),
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sa.Column('message_metadata', sa.JSON(), nullable=True),
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sa.Column('context_documents', sa.JSON(), nullable=True),
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sa.Column('prompt_tokens', sa.Integer(), nullable=True),
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sa.Column('completion_tokens', sa.Integer(), nullable=True),
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sa.Column('total_tokens', sa.Integer(), nullable=True),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_messages'))
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)
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op.create_index(op.f('ix_messages_id'), 'messages', ['id'], unique=False)
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op.create_table('roles',
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sa.Column('name', sa.String(length=100), nullable=False),
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sa.Column('code', sa.String(length=100), nullable=False),
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sa.Column('description', sa.Text(), nullable=True),
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sa.Column('is_system', sa.Boolean(), nullable=False),
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sa.Column('is_active', sa.Boolean(), nullable=False),
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_roles'))
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)
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op.create_index(op.f('ix_roles_code'), 'roles', ['code'], unique=True)
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op.create_index(op.f('ix_roles_id'), 'roles', ['id'], unique=False)
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op.create_index(op.f('ix_roles_name'), 'roles', ['name'], unique=True)
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op.create_table('table_metadata',
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sa.Column('id', sa.Integer(), nullable=False),
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sa.Column('table_name', sa.String(length=100), nullable=False),
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sa.Column('table_schema', sa.String(length=50), nullable=False),
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sa.Column('table_type', sa.String(length=20), nullable=False),
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sa.Column('table_comment', sa.Text(), nullable=True),
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sa.Column('database_config_id', sa.Integer(), nullable=True),
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sa.Column('columns_info', sa.JSON(), nullable=False),
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sa.Column('primary_keys', sa.JSON(), nullable=True),
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sa.Column('foreign_keys', sa.JSON(), nullable=True),
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sa.Column('indexes', sa.JSON(), nullable=True),
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sa.Column('sample_data', sa.JSON(), nullable=True),
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sa.Column('row_count', sa.Integer(), nullable=False),
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sa.Column('is_enabled_for_qa', sa.Boolean(), nullable=False),
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sa.Column('qa_description', sa.Text(), nullable=True),
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sa.Column('business_context', sa.Text(), nullable=True),
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sa.Column('last_synced_at', sa.DateTime(timezone=True), nullable=True),
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sa.Column('created_at', sa.DateTime(), nullable=False),
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sa.Column('updated_at', sa.DateTime(), nullable=False),
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sa.Column('created_by', sa.Integer(), nullable=True),
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sa.Column('updated_by', sa.Integer(), nullable=True),
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sa.PrimaryKeyConstraint('id', name=op.f('pk_table_metadata'))
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)
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op.create_index(op.f('ix_table_metadata_id'), 'table_metadata', ['id'], unique=False)
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op.create_index(op.f('ix_table_metadata_table_name'), 'table_metadata', ['table_name'], unique=False)
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op.create_table('users',
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sa.Column('username', sa.String(length=50), nullable=False),
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sa.Column('email', sa.String(length=100), nullable=False),
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sa.Column('hashed_password', sa.String(length=255), nullable=False),
|
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sa.Column('full_name', sa.String(length=100), nullable=True),
|
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sa.Column('is_active', sa.Boolean(), nullable=False),
|
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sa.Column('avatar_url', sa.String(length=255), nullable=True),
|
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sa.Column('bio', sa.Text(), nullable=True),
|
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sa.Column('id', sa.Integer(), nullable=False),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('updated_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('created_by', sa.Integer(), nullable=True),
|
||||
sa.Column('updated_by', sa.Integer(), nullable=True),
|
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sa.PrimaryKeyConstraint('id', name=op.f('pk_users'))
|
||||
)
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op.create_index(op.f('ix_users_email'), 'users', ['email'], unique=True)
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op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
|
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op.create_index(op.f('ix_users_username'), 'users', ['username'], unique=True)
|
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op.create_table('user_roles',
|
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sa.Column('user_id', sa.Integer(), nullable=False),
|
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sa.Column('role_id', sa.Integer(), nullable=False),
|
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sa.ForeignKeyConstraint(['role_id'], ['roles.id'], name=op.f('fk_user_roles_role_id_roles')),
|
||||
sa.ForeignKeyConstraint(['user_id'], ['users.id'], name=op.f('fk_user_roles_user_id_users')),
|
||||
sa.PrimaryKeyConstraint('user_id', 'role_id', name=op.f('pk_user_roles'))
|
||||
)
|
||||
op.create_table('workflows',
|
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sa.Column('name', sa.String(length=100), nullable=False, comment='工作流名称'),
|
||||
sa.Column('description', sa.Text(), nullable=True, comment='工作流描述'),
|
||||
sa.Column('status', sa.Enum('DRAFT', 'PUBLISHED', 'ARCHIVED', name='workflowstatus'), nullable=False, comment='工作流状态'),
|
||||
sa.Column('is_active', sa.Boolean(), nullable=False, comment='是否激活'),
|
||||
sa.Column('definition', sa.JSON(), nullable=False, comment='工作流定义'),
|
||||
sa.Column('version', sa.String(length=20), nullable=False, comment='版本号'),
|
||||
sa.Column('owner_id', sa.Integer(), nullable=False, comment='所有者ID'),
|
||||
sa.Column('id', sa.Integer(), nullable=False),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('updated_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('created_by', sa.Integer(), nullable=True),
|
||||
sa.Column('updated_by', sa.Integer(), nullable=True),
|
||||
sa.ForeignKeyConstraint(['owner_id'], ['users.id'], name=op.f('fk_workflows_owner_id_users')),
|
||||
sa.PrimaryKeyConstraint('id', name=op.f('pk_workflows'))
|
||||
)
|
||||
op.create_index(op.f('ix_workflows_id'), 'workflows', ['id'], unique=False)
|
||||
op.create_table('workflow_executions',
|
||||
sa.Column('workflow_id', sa.Integer(), nullable=False, comment='工作流ID'),
|
||||
sa.Column('status', sa.Enum('PENDING', 'RUNNING', 'COMPLETED', 'FAILED', 'CANCELLED', name='executionstatus'), nullable=False, comment='执行状态'),
|
||||
sa.Column('input_data', sa.JSON(), nullable=True, comment='输入数据'),
|
||||
sa.Column('output_data', sa.JSON(), nullable=True, comment='输出数据'),
|
||||
sa.Column('started_at', sa.String(length=50), nullable=True, comment='开始时间'),
|
||||
sa.Column('completed_at', sa.String(length=50), nullable=True, comment='完成时间'),
|
||||
sa.Column('error_message', sa.Text(), nullable=True, comment='错误信息'),
|
||||
sa.Column('executor_id', sa.Integer(), nullable=False, comment='执行者ID'),
|
||||
sa.Column('id', sa.Integer(), nullable=False),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('updated_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('created_by', sa.Integer(), nullable=True),
|
||||
sa.Column('updated_by', sa.Integer(), nullable=True),
|
||||
sa.ForeignKeyConstraint(['executor_id'], ['users.id'], name=op.f('fk_workflow_executions_executor_id_users')),
|
||||
sa.ForeignKeyConstraint(['workflow_id'], ['workflows.id'], name=op.f('fk_workflow_executions_workflow_id_workflows')),
|
||||
sa.PrimaryKeyConstraint('id', name=op.f('pk_workflow_executions'))
|
||||
)
|
||||
op.create_index(op.f('ix_workflow_executions_id'), 'workflow_executions', ['id'], unique=False)
|
||||
op.create_table('node_executions',
|
||||
sa.Column('workflow_execution_id', sa.Integer(), nullable=False, comment='工作流执行ID'),
|
||||
sa.Column('node_id', sa.String(length=50), nullable=False, comment='节点ID'),
|
||||
sa.Column('node_type', sa.Enum('START', 'END', 'LLM', 'CONDITION', 'LOOP', 'CODE', 'HTTP', 'TOOL', name='nodetype'), nullable=False, comment='节点类型'),
|
||||
sa.Column('node_name', sa.String(length=100), nullable=False, comment='节点名称'),
|
||||
sa.Column('status', sa.Enum('PENDING', 'RUNNING', 'COMPLETED', 'FAILED', 'CANCELLED', name='executionstatus'), nullable=False, comment='执行状态'),
|
||||
sa.Column('input_data', sa.JSON(), nullable=True, comment='输入数据'),
|
||||
sa.Column('output_data', sa.JSON(), nullable=True, comment='输出数据'),
|
||||
sa.Column('started_at', sa.String(length=50), nullable=True, comment='开始时间'),
|
||||
sa.Column('completed_at', sa.String(length=50), nullable=True, comment='完成时间'),
|
||||
sa.Column('duration_ms', sa.Integer(), nullable=True, comment='执行时长(毫秒)'),
|
||||
sa.Column('error_message', sa.Text(), nullable=True, comment='错误信息'),
|
||||
sa.Column('id', sa.Integer(), nullable=False),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('updated_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('created_by', sa.Integer(), nullable=True),
|
||||
sa.Column('updated_by', sa.Integer(), nullable=True),
|
||||
sa.ForeignKeyConstraint(['workflow_execution_id'], ['workflow_executions.id'], name=op.f('fk_node_executions_workflow_execution_id_workflow_executions')),
|
||||
sa.PrimaryKeyConstraint('id', name=op.f('pk_node_executions'))
|
||||
)
|
||||
op.create_index(op.f('ix_node_executions_id'), 'node_executions', ['id'], unique=False)
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
"""Downgrade schema."""
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.drop_index(op.f('ix_node_executions_id'), table_name='node_executions')
|
||||
op.drop_table('node_executions')
|
||||
op.drop_index(op.f('ix_workflow_executions_id'), table_name='workflow_executions')
|
||||
op.drop_table('workflow_executions')
|
||||
op.drop_index(op.f('ix_workflows_id'), table_name='workflows')
|
||||
op.drop_table('workflows')
|
||||
op.drop_table('user_roles')
|
||||
op.drop_index(op.f('ix_users_username'), table_name='users')
|
||||
op.drop_index(op.f('ix_users_id'), table_name='users')
|
||||
op.drop_index(op.f('ix_users_email'), table_name='users')
|
||||
op.drop_table('users')
|
||||
op.drop_index(op.f('ix_table_metadata_table_name'), table_name='table_metadata')
|
||||
op.drop_index(op.f('ix_table_metadata_id'), table_name='table_metadata')
|
||||
op.drop_table('table_metadata')
|
||||
op.drop_index(op.f('ix_roles_name'), table_name='roles')
|
||||
op.drop_index(op.f('ix_roles_id'), table_name='roles')
|
||||
op.drop_index(op.f('ix_roles_code'), table_name='roles')
|
||||
op.drop_table('roles')
|
||||
op.drop_index(op.f('ix_messages_id'), table_name='messages')
|
||||
op.drop_table('messages')
|
||||
op.drop_index(op.f('ix_llm_configs_provider'), table_name='llm_configs')
|
||||
op.drop_index(op.f('ix_llm_configs_name'), table_name='llm_configs')
|
||||
op.drop_index(op.f('ix_llm_configs_id'), table_name='llm_configs')
|
||||
op.drop_table('llm_configs')
|
||||
op.drop_index(op.f('ix_knowledge_bases_name'), table_name='knowledge_bases')
|
||||
op.drop_index(op.f('ix_knowledge_bases_id'), table_name='knowledge_bases')
|
||||
op.drop_table('knowledge_bases')
|
||||
op.drop_index(op.f('ix_excel_files_id'), table_name='excel_files')
|
||||
op.drop_table('excel_files')
|
||||
op.drop_index(op.f('ix_documents_id'), table_name='documents')
|
||||
op.drop_table('documents')
|
||||
op.drop_index(op.f('ix_database_configs_id'), table_name='database_configs')
|
||||
op.drop_table('database_configs')
|
||||
op.drop_index(op.f('ix_conversations_id'), table_name='conversations')
|
||||
op.drop_table('conversations')
|
||||
op.drop_index(op.f('ix_agent_configs_name'), table_name='agent_configs')
|
||||
op.drop_index(op.f('ix_agent_configs_id'), table_name='agent_configs')
|
||||
op.drop_table('agent_configs')
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
"""Add message_count and last_message_at to conversations
|
||||
|
||||
Revision ID: 8da391c6e2b7
|
||||
Revises: 424646027786
|
||||
Create Date: 2025-12-19 16:16:29.943314
|
||||
|
||||
"""
|
||||
from typing import Sequence, Union
|
||||
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
|
||||
|
||||
# revision identifiers, used by Alembic.
|
||||
revision: str = '8da391c6e2b7'
|
||||
down_revision: Union[str, Sequence[str], None] = '424646027786'
|
||||
branch_labels: Union[str, Sequence[str], None] = None
|
||||
depends_on: Union[str, Sequence[str], None] = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
"""Upgrade schema."""
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.add_column('conversations', sa.Column('message_count', sa.Integer(), nullable=False))
|
||||
op.add_column('conversations', sa.Column('last_message_at', sa.DateTime(), nullable=True))
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
"""Downgrade schema."""
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.drop_column('conversations', 'last_message_at')
|
||||
op.drop_column('conversations', 'message_count')
|
||||
# ### end Alembic commands ###
|
||||
|
|
@ -0,0 +1,42 @@
|
|||
from sqlalchemy import create_engine, inspect
|
||||
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
import asyncio
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# 加载环境变量
|
||||
load_dotenv()
|
||||
|
||||
async def check_table_constraints():
|
||||
try:
|
||||
# 获取数据库连接字符串
|
||||
DATABASE_URL = os.getenv("DATABASE_URL", "mysql+asyncmy://root:123456@localhost:3306/th_agenter")
|
||||
|
||||
# 创建异步引擎
|
||||
engine = create_async_engine(DATABASE_URL, echo=True)
|
||||
|
||||
# 创建会话
|
||||
async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
|
||||
|
||||
async with async_session() as session:
|
||||
# 获取数据库连接
|
||||
async with session.begin():
|
||||
# 使用inspect查看表结构
|
||||
inspector = inspect(engine)
|
||||
|
||||
# 获取messages表的所有约束
|
||||
constraints = await engine.run_sync(inspector.get_table_constraints, 'messages')
|
||||
print("Messages表的所有约束:")
|
||||
for constraint in constraints:
|
||||
print(f" 约束名称: {constraint['name']}, 类型: {constraint['type']}")
|
||||
if constraint['type'] == 'PRIMARY KEY':
|
||||
print(f" 主键约束列: {constraint['constrained_columns']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"检查约束时出错: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(check_table_constraints())
|
||||
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|
|
@ -38,7 +38,7 @@ def setup_exception_handlers(app: FastAPI) -> None:
|
|||
async def http_exception_handler(request, exc):
|
||||
from utils.util_exceptions import HxfErrorResponse
|
||||
logger.exception(f"HTTP Exception: {exc.status_code} - {exc.detail} - {request.method} {request.url}")
|
||||
return HxfErrorResponse(exc.status_code, exc.detail)
|
||||
return HxfErrorResponse(exc)
|
||||
|
||||
def make_json_serializable(obj):
|
||||
"""递归地将对象转换为JSON可序列化的格式"""
|
||||
|
|
@ -127,31 +127,5 @@ def add_router(app: FastAPI) -> None:
|
|||
# Include routers
|
||||
app.include_router(router, prefix="/api")
|
||||
|
||||
|
||||
|
||||
# app.include_router(table_metadata.router)
|
||||
# # 在现有导入中添加
|
||||
# from ..api.endpoints import database_config
|
||||
|
||||
# # 在路由注册部分添加
|
||||
# app.include_router(database_config.router)
|
||||
# # Health check endpoint
|
||||
# @app.get("/health")
|
||||
# async def health_check():
|
||||
# return {"status": "healthy", "version": settings.app_version}
|
||||
|
||||
# # Root endpoint
|
||||
# @app.get("/")
|
||||
# async def root():
|
||||
# return {"message": "Chat Agent API is running"}
|
||||
|
||||
# # Test endpoint
|
||||
# @app.get("/test")
|
||||
# async def test_endpoint():
|
||||
# return {"message": "API is working"}
|
||||
|
||||
|
||||
app = create_app()
|
||||
|
||||
# from utils.util_test import test_db
|
||||
# test_db()
|
||||
# from test.example import internet_search_tool
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
# Test package for PostgreSQL agent functionality
|
||||
|
|
@ -0,0 +1,154 @@
|
|||
import os
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from deepagents import create_deep_agent
|
||||
from openai import OpenAI
|
||||
from langchain.chat_models import init_chat_model
|
||||
from langchain.agents import create_agent
|
||||
from langgraph.checkpoint.memory import InMemorySaver, MemorySaver # 导入检查点工具
|
||||
from deepagents.backends import StoreBackend
|
||||
from loguru import logger
|
||||
def internet_search_tool(query: str):
|
||||
"""Run a web search"""
|
||||
logger.info(f"Running internet search for query: {query}")
|
||||
client = OpenAI(
|
||||
api_key=os.getenv('DASHSCOPE_API_KEY'),
|
||||
base_url=os.getenv('DASHSCOPE_BASE_URL'),
|
||||
)
|
||||
logger.info(f"create OpenAI")
|
||||
completion = client.chat.completions.create(
|
||||
model="qwen-plus",
|
||||
messages=[
|
||||
{'role': 'system', 'content': 'You are a helpful assistant.'},
|
||||
{'role': 'user', 'content': query}
|
||||
],
|
||||
extra_body={
|
||||
"enable_search": True
|
||||
}
|
||||
)
|
||||
logger.info(f"create completions")
|
||||
logger.info(f"OpenAI response: {completion.choices[0].message.content}")
|
||||
return completion.choices[0].message.content
|
||||
|
||||
|
||||
|
||||
# System prompt to steer the agent to be an expert researcher
|
||||
today = datetime.now().strftime("%Y年%m月%d日")
|
||||
research_instructions = f"""你是一个智能助手。你的任务是帮助用户完成各种任务。
|
||||
|
||||
你可以使用互联网搜索工具来获取信息。
|
||||
## `internet_search`
|
||||
使用此工具对给定查询进行互联网搜索。你可以指定返回结果的最大数量、主题以及是否包含原始内容。
|
||||
|
||||
今天的日期是：{today}
|
||||
"""
|
||||
|
||||
# Create the deep agent with memory
|
||||
model = init_chat_model(
|
||||
model="gpt-4.1-mini",
|
||||
model_provider='openai',
|
||||
api_key=os.getenv('OPENAI_API_KEY'),
|
||||
base_url=os.getenv('OPENAI_BASE_URL'),
|
||||
)
|
||||
checkpointer = InMemorySaver() # 创建内存检查点，自动保存历史
|
||||
|
||||
agent = create_deep_agent( # state：thread会话级的状态
|
||||
tools=[internet_search_tool],
|
||||
system_prompt=research_instructions,
|
||||
model=model,
|
||||
checkpointer=checkpointer, # 添加检查点，启用自动记忆
|
||||
interrupt_on={'internet_search_tool':True}
|
||||
)
|
||||
|
||||
# 多轮对话循环（使用 Checkpointer 自动记忆）
|
||||
printed_msg_ids = set() # 跟踪已打印的消息ID
|
||||
thread_id = "user_session_001" # 会话 ID，区分不同用户/会话
|
||||
config = {"configurable": {"thread_id": thread_id}, "metastore": {'assistant_id': 'owenliang'}} # 配置会话
|
||||
|
||||
print("开始对话（输入 'exit' 退出）：")
|
||||
while True:
|
||||
user_input = input("\nHUMAN: ").strip()
|
||||
if user_input.lower() == 'exit':
|
||||
break
|
||||
|
||||
# 使用 values 模式多次返回完整状态，这里按 message.id 去重，并按类型分类打印
|
||||
pending_resume = None
|
||||
while True:
|
||||
if pending_resume is None:
|
||||
request = {"messages": [{"role": "user", "content": user_input}]}
|
||||
else:
|
||||
from langgraph.types import Command as _Command
|
||||
|
||||
request = _Command(resume=pending_resume)
|
||||
pending_resume = None
|
||||
|
||||
for item in agent.stream(
|
||||
request,
|
||||
config=config,
|
||||
stream_mode="values",
|
||||
):
|
||||
state = item[0] if isinstance(item, tuple) and len(item) == 2 else item
|
||||
|
||||
# 先检查是否触发了 Human-In-The-Loop 中断
|
||||
if isinstance(state, dict) and "__interrupt__" in state:
|
||||
interrupts = state["__interrupt__"] or []
|
||||
if interrupts:
|
||||
hitl_payload = interrupts[0].value
|
||||
action_requests = hitl_payload.get("action_requests", [])
|
||||
|
||||
print("\n=== 需要人工审批的工具调用 ===")
|
||||
decisions: list[dict[str, str]] = []
|
||||
for idx, ar in enumerate(action_requests):
|
||||
name = ar.get("name")
|
||||
args = ar.get("args")
|
||||
print(f"[{idx}] 工具 {name} 参数: {args}")
|
||||
while True:
|
||||
choice = input(" 决策 (a=approve, r=reject): ").strip().lower()
|
||||
if choice in ("a", "r"):
|
||||
break
|
||||
decisions.append({"type": "approve" if choice == "a" else "reject"})
|
||||
|
||||
# 下一轮调用改为 resume，同一轮用户回合继续往下跑
|
||||
pending_resume = {"decisions": decisions}
|
||||
break
|
||||
|
||||
# 兼容 dict state 和 AgentState dataclass
|
||||
messages = state.get("messages", []) if isinstance(state, dict) else getattr(state, "messages", [])
|
||||
for msg in messages:
|
||||
msg_id = getattr(msg, "id", None)
|
||||
if msg_id is not None and msg_id in printed_msg_ids:
|
||||
continue
|
||||
if msg_id is not None:
|
||||
printed_msg_ids.add(msg_id)
|
||||
|
||||
msg_type = getattr(msg, "type", None)
|
||||
|
||||
if msg_type == "human":
|
||||
# 用户输入已经在命令行里，不再重复打印
|
||||
continue
|
||||
|
||||
if msg_type == "ai":
|
||||
tool_calls = getattr(msg, "tool_calls", None) or []
|
||||
if tool_calls:
|
||||
# 这是发起工具调用的 AI 消息（TOOL CALL）
|
||||
for tc in tool_calls:
|
||||
tool_name = tc.get("name")
|
||||
args = tc.get("args")
|
||||
print(f"TOOL CALL [{tool_name}]: {args}")
|
||||
# 如果 AI 同时带有自然语言内容，也一起打印
|
||||
if getattr(msg, "content", None):
|
||||
print(f"AI: {msg.content}")
|
||||
continue
|
||||
|
||||
if msg_type == "tool":
|
||||
# 工具执行结果（TOOL RESPONSE）
|
||||
tool_name = getattr(msg, "name", None) or "tool"
|
||||
print(f"TOOL RESPONSE [{tool_name}]: {msg.content}")
|
||||
continue
|
||||
|
||||
# 兜底：其它类型直接打印出来便于调试
|
||||
print(f"[{msg_type}]: {getattr(msg, 'content', None)}")
|
||||
|
||||
# 如果没有新的中断需要 resume，则整轮结束，等待下一轮用户输入
|
||||
if pending_resume is None:
|
||||
break
|
||||
|
|
@ -95,11 +95,13 @@ async def login_oauth(
|
|||
)
|
||||
session.desc = f"用户 {user.username} OAuth2 登录成功"
|
||||
|
||||
return {
|
||||
return HxfResponse(
|
||||
{
|
||||
"access_token": access_token,
|
||||
"token_type": "bearer",
|
||||
"expires_in": settings.security.access_token_expire_minutes * 60
|
||||
}
|
||||
)
|
||||
|
||||
@router.post("/refresh", response_model=Token, summary="刷新访问token")
|
||||
async def refresh_token(
|
||||
|
|
@ -113,15 +115,17 @@ async def refresh_token(
|
|||
session, data={"sub": current_user.username}, expires_delta=access_token_expires
|
||||
)
|
||||
|
||||
return Token(
|
||||
response = Token(
|
||||
access_token=access_token,
|
||||
token_type="bearer",
|
||||
expires_in=settings.security.access_token_expire_minutes * 60
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/me", response_model=UserResponse, summary="获取当前用户信息")
|
||||
async def get_current_user_info(
|
||||
current_user = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取当前用户信息"""
|
||||
return UserResponse.model_validate(current_user, from_attributes=True)
|
||||
response = UserResponse.model_validate(current_user, from_attributes=True)
|
||||
return HxfResponse(response)
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
"""Chat endpoints for TH Agenter."""
|
||||
|
||||
import json
|
||||
from typing import List
|
||||
from fastapi import APIRouter, Depends, HTTPException, status
|
||||
from fastapi.responses import StreamingResponse
|
||||
|
|
@ -11,6 +12,8 @@ from ...models.user import User
|
|||
from ...services.auth import AuthService
|
||||
from ...services.chat import ChatService
|
||||
from ...services.conversation import ConversationService
|
||||
from utils.util_exceptions import HxfResponse
|
||||
|
||||
from utils.util_schemas import (
|
||||
ConversationCreate,
|
||||
ConversationResponse,
|
||||
|
|
@ -23,83 +26,6 @@ from utils.util_schemas import (
|
|||
|
||||
router = APIRouter()
|
||||
|
||||
# Conversation management
|
||||
@router.post("/conversations", response_model=ConversationResponse, summary="创建新对话")
|
||||
async def create_conversation(
|
||||
conversation_data: ConversationCreate,
|
||||
current_user: User = Depends(AuthService.get_current_user),
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""创建新对话"""
|
||||
session.desc = "START: 创建新对话"
|
||||
conversation_service = ConversationService(session)
|
||||
conversation = await conversation_service.create_conversation(
|
||||
user_id=current_user.id,
|
||||
conversation_data=conversation_data
|
||||
)
|
||||
session.desc = f"SUCCESS: 创建新对话完毕 >>> 当前用户ID: {current_user.id}, conversation: {conversation}"
|
||||
return ConversationResponse.model_validate(conversation)
|
||||
|
||||
@router.get("/conversations", response_model=List[ConversationResponse], summary="获取用户对话列表")
|
||||
async def list_conversations(
|
||||
skip: int = 0,
|
||||
limit: int = 50,
|
||||
search: str = None,
|
||||
include_archived: bool = False,
|
||||
order_by: str = "updated_at",
|
||||
order_desc: bool = True,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取用户对话列表"""
|
||||
session.desc = "START: 获取用户对话列表"
|
||||
conversation_service = ConversationService(session)
|
||||
conversations = await conversation_service.get_user_conversations(
|
||||
skip=skip,
|
||||
limit=limit,
|
||||
search_query=search,
|
||||
include_archived=include_archived,
|
||||
order_by=order_by,
|
||||
order_desc=order_desc
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取用户对话列表完毕 >>> {len(conversations)} 个对话"
|
||||
return [ConversationResponse.model_validate(conv) for conv in conversations]
|
||||
|
||||
@router.get("/conversations/count", summary="获取用户对话总数")
|
||||
async def get_conversations_count(
|
||||
search: str = None,
|
||||
include_archived: bool = False,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取用户对话总数"""
|
||||
session.desc = "START: 获取用户对话总数"
|
||||
conversation_service = ConversationService(session)
|
||||
count = await conversation_service.get_user_conversations_count(
|
||||
search_query=search,
|
||||
include_archived=include_archived
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取用户对话总数完毕 >>> {count} 个对话"
|
||||
return {"count": count}
|
||||
|
||||
@router.get("/conversations/{conversation_id}", response_model=ConversationResponse, summary="获取指定对话")
|
||||
async def get_conversation(
|
||||
conversation_id: int,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取指定对话"""
|
||||
session.desc = f"START: 获取指定对话 >>> conversation_id: {conversation_id}"
|
||||
conversation_service = ConversationService(session)
|
||||
conversation = await conversation_service.get_conversation(
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
if not conversation:
|
||||
session.desc = f"ERROR: 获取指定对话失败 >>> conversation_id: {conversation_id}, 未找到该对话"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Conversation not found"
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取指定对话完毕 >>> conversation_id: {conversation_id}"
|
||||
return ConversationResponse.model_validate(conversation)
|
||||
|
||||
@router.put("/conversations/{conversation_id}", response_model=ConversationResponse, summary="更新指定对话")
|
||||
async def update_conversation(
|
||||
conversation_id: int,
|
||||
|
|
@ -113,7 +39,8 @@ async def update_conversation(
|
|||
conversation_id, conversation_update
|
||||
)
|
||||
session.desc = f"SUCCESS: 更新指定对话完毕 >>> conversation_id: {conversation_id}"
|
||||
return ConversationResponse.model_validate(updated_conversation)
|
||||
response = ConversationResponse.model_validate(updated_conversation)
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
@router.delete("/conversations/{conversation_id}", summary="删除指定对话")
|
||||
|
|
@ -126,7 +53,8 @@ async def delete_conversation(
|
|||
conversation_service = ConversationService(session)
|
||||
await conversation_service.delete_conversation(conversation_id)
|
||||
session.desc = f"SUCCESS: 删除指定对话完毕 >>> conversation_id: {conversation_id}"
|
||||
return {"message": "Conversation deleted successfully"}
|
||||
response = {"message": "Conversation deleted successfully"}
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
@router.put("/conversations/{conversation_id}/archive", summary="归档指定对话")
|
||||
|
|
@ -145,7 +73,8 @@ async def archive_conversation(
|
|||
)
|
||||
|
||||
session.desc = f"SUCCESS: 归档指定对话完毕 >>> conversation_id: {conversation_id}"
|
||||
return {"message": "Conversation archived successfully"}
|
||||
response = {"message": "Conversation archived successfully"}
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
@router.put("/conversations/{conversation_id}/unarchive", summary="取消归档指定对话")
|
||||
|
|
@ -165,7 +94,8 @@ async def unarchive_conversation(
|
|||
)
|
||||
|
||||
session.desc = f"SUCCESS: 取消归档指定对话完毕 >>> conversation_id: {conversation_id}"
|
||||
return {"message": "Conversation unarchived successfully"}
|
||||
response = {"message": "Conversation unarchived successfully"}
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
# Message management
|
||||
|
|
@ -183,7 +113,8 @@ async def get_conversation_messages(
|
|||
conversation_id, skip=skip, limit=limit
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取指定对话的消息完毕 >>> conversation_id: {conversation_id}, skip: {skip}, limit: {limit}"
|
||||
return [MessageResponse.model_validate(msg) for msg in messages]
|
||||
response = [MessageResponse.model_validate(msg) for msg in messages]
|
||||
return HxfResponse(response)
|
||||
|
||||
# Chat functionality
|
||||
@router.post("/conversations/{conversation_id}/chat", response_model=ChatResponse, summary="发送消息并获取AI响应")
|
||||
|
|
@ -195,48 +126,158 @@ async def chat(
|
|||
"""发送消息并获取AI响应"""
|
||||
session.desc = f"START: 发送消息并获取AI响应 >>> conversation_id: {conversation_id}"
|
||||
chat_service = ChatService(session)
|
||||
response = await chat_service.chat(
|
||||
conversation_id=conversation_id,
|
||||
message=chat_request.message,
|
||||
stream=False,
|
||||
temperature=chat_request.temperature,
|
||||
max_tokens=chat_request.max_tokens,
|
||||
use_agent=chat_request.use_agent,
|
||||
use_langgraph=chat_request.use_langgraph,
|
||||
use_knowledge_base=chat_request.use_knowledge_base,
|
||||
knowledge_base_id=chat_request.knowledge_base_id
|
||||
)
|
||||
await chat_service.initialize(conversation_id)
|
||||
|
||||
# response = await chat_service.chat(
|
||||
# conversation_id=conversation_id,
|
||||
# message=chat_request.message,
|
||||
# stream=False,
|
||||
# temperature=chat_request.temperature,
|
||||
# max_tokens=chat_request.max_tokens,
|
||||
# use_agent=chat_request.use_agent, # 可以简化掉
|
||||
# use_langgraph=chat_request.use_langgraph, # 可以简化掉
|
||||
# use_knowledge_base=chat_request.use_knowledge_base, # 可以简化掉
|
||||
# knowledge_base_id=chat_request.knowledge_base_id # 可以简化掉
|
||||
# )
|
||||
response = "oooooooooooooooooooK"
|
||||
session.desc = f"SUCCESS: 发送消息并获取AI响应完毕 >>> conversation_id: {conversation_id}"
|
||||
|
||||
return response
|
||||
|
||||
return HxfResponse(response)
|
||||
# ------------------------------------------------------------------------
|
||||
@router.post("/conversations/{conversation_id}/chat/stream", summary="发送消息并获取流式AI响应")
|
||||
async def chat_stream(
|
||||
conversation_id: int,
|
||||
chat_request: ChatRequest,
|
||||
current_user = Depends(AuthService.get_current_user),
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""发送消息并获取流式AI响应."""
|
||||
session.title = f"对话{conversation_id} 发送消息并获取流式AI响应"
|
||||
session.desc = f"START: 对话{conversation_id} 发送消息 [{chat_request.message}] 并获取流式AI响应 >>> "
|
||||
chat_service = ChatService(session)
|
||||
await chat_service.initialize(conversation_id, streaming=True)
|
||||
|
||||
async def generate_response():
|
||||
async def generate_response(chat_service):
|
||||
try:
|
||||
async for chunk in chat_service.chat_stream(
|
||||
conversation_id=conversation_id,
|
||||
message=chat_request.message,
|
||||
temperature=chat_request.temperature,
|
||||
max_tokens=chat_request.max_tokens,
|
||||
use_agent=chat_request.use_agent,
|
||||
use_langgraph=chat_request.use_langgraph,
|
||||
use_knowledge_base=chat_request.use_knowledge_base,
|
||||
knowledge_base_id=chat_request.knowledge_base_id
|
||||
message=chat_request.message
|
||||
):
|
||||
yield f"data: {chunk}\n\n"
|
||||
yield chunk + "\n"
|
||||
except Exception as e:
|
||||
logger.error(f"{session.log_prefix()} - 流式响应生成异常: {str(e)}")
|
||||
yield {'success': False, 'data': f"data: {json.dumps({'type': 'error', 'message': f'流式响应生成异常: {str(e)}'}, ensure_ascii=False)}"}
|
||||
|
||||
return StreamingResponse(
|
||||
generate_response(),
|
||||
media_type="text/plain",
|
||||
response = StreamingResponse(
|
||||
generate_response(chat_service),
|
||||
media_type="text/stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
}
|
||||
)
|
||||
|
||||
return response
|
||||
|
||||
# Conversation management
|
||||
@router.post("/conversations", response_model=ConversationResponse, summary="创建新对话")
|
||||
async def create_conversation(
|
||||
conversation_data: ConversationCreate,
|
||||
current_user: User = Depends(AuthService.get_current_user),
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""创建新对话"""
|
||||
id = current_user.id
|
||||
session.title = f"用户{current_user.username} - 创建新对话"
|
||||
session.desc = "START: 创建新对话"
|
||||
conversation_service = ConversationService(session)
|
||||
conversation = await conversation_service.create_conversation(
|
||||
user_id=id,
|
||||
conversation_data=conversation_data
|
||||
)
|
||||
session.desc = f"SUCCESS: 创建新对话完毕 >>> 当前用户ID: {id}, conversation_id: {conversation.id}"
|
||||
response = ConversationResponse.model_validate(conversation)
|
||||
return HxfResponse(response)
|
||||
@router.get("/conversations", response_model=List[ConversationResponse], summary="获取用户对话列表")
|
||||
async def list_conversations(
|
||||
skip: int = 0,
|
||||
limit: int = 50,
|
||||
search: str = None,
|
||||
include_archived: bool = False,
|
||||
order_by: str = "updated_at",
|
||||
order_desc: bool = True,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取用户对话列表"""
|
||||
session.title = "获取用户对话列表"
|
||||
session.desc = "START: 获取用户对话列表"
|
||||
conversation_service = ConversationService(session)
|
||||
conversations = await conversation_service.get_user_conversations(
|
||||
skip=skip,
|
||||
limit=limit,
|
||||
search_query=search,
|
||||
include_archived=include_archived,
|
||||
order_by=order_by,
|
||||
order_desc=order_desc
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取用户对话列表完毕 >>> {len(conversations)} 个对话 ..."
|
||||
response = [ConversationResponse.model_validate(conv) for conv in conversations]
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/conversations/count", summary="获取用户对话总数")
|
||||
async def get_conversations_count(
|
||||
search: str = None,
|
||||
include_archived: bool = False,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取用户对话总数"""
|
||||
from th_agenter.core.context import UserContext
|
||||
user_id = UserContext.get_current_user_id()
|
||||
session.title = f"获取用户对话总数[用户id = {user_id}]"
|
||||
session.desc = "START: 获取用户对话总数"
|
||||
conversation_service = ConversationService(session)
|
||||
count = await conversation_service.get_user_conversations_count(
|
||||
search_query=search,
|
||||
include_archived=include_archived
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取用户对话总数完毕 >>> {count} 个对话"
|
||||
response = {"count": count}
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/conversations/{conversation_id}", response_model=ConversationResponse, summary="获取指定对话")
|
||||
async def get_conversation(
|
||||
conversation_id: int,
|
||||
session: Session = Depends(get_session)
|
||||
):
|
||||
"""获取指定对话"""
|
||||
session.title = f"获取指定对话[对话id = {conversation_id}]"
|
||||
session.desc = f"START: 获取指定对话 >>> 对话id = {conversation_id}"
|
||||
|
||||
conversation_service = ConversationService(session)
|
||||
conversation = await conversation_service.get_conversation(
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
if not conversation:
|
||||
session.desc = f"ERROR: 获取指定对话失败 >>> conversation_id: {conversation_id}, 未找到该对话"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Conversation not found"
|
||||
)
|
||||
session.desc = f"SUCCESS: 获取指定对话完毕 >>> conversation_id: {conversation_id} >>> {conversation}"
|
||||
|
||||
response = ConversationResponse.model_validate(conversation)
|
||||
|
||||
|
||||
# chat_service = ChatService(session)
|
||||
# await chat_service.initialize(conversation_id, streaming=False)
|
||||
# messages = await chat_service.get_conversation_history_messages(
|
||||
# conversation_id
|
||||
# )
|
||||
# response.messages = messages
|
||||
|
||||
messages = await conversation_service.get_conversation_messages(
|
||||
conversation_id, skip=0, limit=100
|
||||
)
|
||||
response.messages = [MessageResponse.model_validate(msg) for msg in messages]
|
||||
|
||||
response.message_count = len(response.messages)
|
||||
return HxfResponse(response)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from th_agenter.db.database import get_session
|
|||
from th_agenter.services.database_config_service import DatabaseConfigService
|
||||
from th_agenter.services.auth import AuthService
|
||||
from utils.util_schemas import FileListResponse,ExcelPreviewRequest,NormalResponse
|
||||
|
||||
from utils.util_exceptions import HxfResponse
|
||||
# 在文件顶部添加
|
||||
from functools import lru_cache
|
||||
|
||||
|
|
@ -68,11 +68,12 @@ async def create_database_config(
|
|||
):
|
||||
"""创建或更新数据库配置"""
|
||||
config = await service.create_or_update_config(current_user.id, config_data.model_dump())
|
||||
return NormalResponse(
|
||||
response = NormalResponse(
|
||||
success=True,
|
||||
message="保存数据库配置成功",
|
||||
data=config
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/", response_model=List[DatabaseConfigResponse], summary="获取用户的数据库配置列表")
|
||||
async def get_database_configs(
|
||||
|
|
@ -83,7 +84,7 @@ async def get_database_configs(
|
|||
configs = service.get_user_configs(current_user.id)
|
||||
|
||||
config_list = [config.to_dict(include_password=True, decrypt_service=service) for config in configs]
|
||||
return config_list
|
||||
return HxfResponse(config_list)
|
||||
|
||||
@router.post("/{config_id}/test", response_model=NormalResponse, summary="测试数据库连接")
|
||||
async def test_database_connection(
|
||||
|
|
@ -93,7 +94,7 @@ async def test_database_connection(
|
|||
):
|
||||
"""测试数据库连接"""
|
||||
result = await service.test_connection(config_id, current_user.id)
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
|
||||
@router.post("/{config_id}/connect", response_model=NormalResponse, summary="连接数据库并获取表列表")
|
||||
async def connect_database(
|
||||
|
|
@ -103,7 +104,7 @@ async def connect_database(
|
|||
):
|
||||
"""连接数据库并获取表列表"""
|
||||
result = await service.connect_and_get_tables(config_id, current_user.id)
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
|
||||
|
||||
@router.get("/tables/{table_name}/data", summary="获取表数据预览")
|
||||
|
|
@ -117,7 +118,7 @@ async def get_table_data(
|
|||
"""获取表数据预览"""
|
||||
try:
|
||||
result = await service.get_table_data(table_name, current_user.id, db_type, limit)
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
except Exception as e:
|
||||
logger.error(f"获取表数据失败: {str(e)}")
|
||||
raise HTTPException(
|
||||
|
|
@ -133,7 +134,7 @@ async def get_table_schema(
|
|||
):
|
||||
"""获取表结构信息"""
|
||||
result = await service.describe_table(table_name, current_user.id) # 这在哪里实现的？
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
|
||||
@router.get("/by-type/{db_type}", response_model=DatabaseConfigResponse, summary="根据数据库类型获取配置")
|
||||
async def get_config_by_type(
|
||||
|
|
@ -149,4 +150,4 @@ async def get_config_by_type(
|
|||
detail=f"未找到类型为 {db_type} 的配置"
|
||||
)
|
||||
# 返回包含解密密码的配置
|
||||
return config.to_dict(include_password=True, decrypt_service=service)
|
||||
return HxfResponse(config.to_dict(include_password=True, decrypt_service=service))
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
"""Knowledge base API endpoints."""
|
||||
|
||||
from utils.util_exceptions import HxfResponse
|
||||
from typing import List, Optional
|
||||
from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form, status
|
||||
from fastapi.responses import JSONResponse
|
||||
|
|
@ -35,10 +36,10 @@ async def create_knowledge_base(
|
|||
):
|
||||
"""创建新的知识库"""
|
||||
# Check if knowledge base with same name already exists for this user
|
||||
session.desc = f"START: 为用户 {current_user.username}[ID={current_user.id}] 创建新的知识库 {kb_data.name}"
|
||||
service = KnowledgeBaseService(session)
|
||||
session.desc = f"START: 为用户 {current_user.username}[ID={current_user.id}] 创建新的知识库 {kb_data}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
session.desc = f"检查用户 {current_user.username} 是否已存在知识库 {kb_data.name}"
|
||||
existing_kb = service.get_knowledge_base_by_name(kb_data.name)
|
||||
existing_kb = await kb_service.get_knowledge_base_by_name(kb_data.name)
|
||||
if existing_kb:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
|
|
@ -47,10 +48,10 @@ async def create_knowledge_base(
|
|||
|
||||
# Create knowledge base
|
||||
session.desc = f"知识库 {kb_data.name}不存在，创建之"
|
||||
kb = service.create_knowledge_base(kb_data)
|
||||
kb = await kb_service.create_knowledge_base(kb_data)
|
||||
|
||||
session.desc = f"SUCCESS: 创建知识库 {kb.name} 成功"
|
||||
return KnowledgeBaseResponse(
|
||||
response = KnowledgeBaseResponse(
|
||||
id=kb.id,
|
||||
created_at=kb.created_at,
|
||||
updated_at=kb.updated_at,
|
||||
|
|
@ -65,7 +66,7 @@ async def create_knowledge_base(
|
|||
document_count=0,
|
||||
active_document_count=0
|
||||
)
|
||||
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/", response_model=List[KnowledgeBaseResponse], summary="获取当前用户的所有知识库")
|
||||
async def list_knowledge_bases(
|
||||
|
|
@ -76,18 +77,17 @@ async def list_knowledge_bases(
|
|||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取当前用户的所有知识库"""
|
||||
session.desc = f"START: 获取用户 {current_user.username} 的所有知识库"
|
||||
service = KnowledgeBaseService(session)
|
||||
session.desc = f"获取用户 {current_user.username} 的所有知识库 (skip={skip}, limit={limit})"
|
||||
knowledge_bases = await service.get_knowledge_bases(skip=skip, limit=limit)
|
||||
session.desc = f"START: 获取用户 {current_user.username} 的所有知识库 (skip={skip}, limit={limit})"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
knowledge_bases = await kb_service.get_knowledge_bases(skip=skip, limit=limit)
|
||||
|
||||
result = []
|
||||
for kb in knowledge_bases:
|
||||
# Count documents
|
||||
# 本知识库的文档数量
|
||||
total_docs = await session.scalar(
|
||||
select(func.count()).where(Document.knowledge_base_id == kb.id)
|
||||
)
|
||||
|
||||
# 本知识库的已处理文档数量
|
||||
active_docs = await session.scalar(
|
||||
select(func.count()).where(
|
||||
Document.knowledge_base_id == kb.id,
|
||||
|
|
@ -112,7 +112,7 @@ async def list_knowledge_bases(
|
|||
))
|
||||
|
||||
session.desc = f"SUCCESS: 获取用户 {current_user.username} 的所有 {len(result)} 知识库"
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
|
||||
@router.get("/{kb_id}", response_model=KnowledgeBaseResponse, summary="根据知识库ID获取知识库详情")
|
||||
async def get_knowledge_base(
|
||||
|
|
@ -124,7 +124,7 @@ async def get_knowledge_base(
|
|||
session.desc = f"START: 获取知识库 {kb_id} 的详情"
|
||||
service = KnowledgeBaseService(session)
|
||||
session.desc = f"检查知识库 {kb_id} 是否存在"
|
||||
kb = service.get_knowledge_base(kb_id)
|
||||
kb = await service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -146,7 +146,7 @@ async def get_knowledge_base(
|
|||
)
|
||||
|
||||
session.desc = f"SUCCESS: 获取知识库 {kb_id} 的详情，共 {total_docs} 个文档，其中 {active_docs} 个已处理"
|
||||
return KnowledgeBaseResponse(
|
||||
response = KnowledgeBaseResponse(
|
||||
id=kb.id,
|
||||
created_at=kb.created_at,
|
||||
updated_at=kb.updated_at,
|
||||
|
|
@ -161,6 +161,7 @@ async def get_knowledge_base(
|
|||
document_count=total_docs,
|
||||
active_document_count=active_docs
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.put("/{kb_id}", response_model=KnowledgeBaseResponse, summary="更新知识库")
|
||||
async def update_knowledge_base(
|
||||
|
|
@ -172,7 +173,7 @@ async def update_knowledge_base(
|
|||
"""更新知识库"""
|
||||
session.desc = f"START: 更新知识库 {kb_id}"
|
||||
service = KnowledgeBaseService(session)
|
||||
kb = service.update_knowledge_base(kb_id, kb_data)
|
||||
kb = await service.update_knowledge_base(kb_id, kb_data)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -192,8 +193,8 @@ async def update_knowledge_base(
|
|||
)
|
||||
)
|
||||
|
||||
session.desc = f"SUCCESS: 更新知识库 {kb_id}，共 {total_docs} 个文档，其中 {active_docs} 个已处理"
|
||||
return KnowledgeBaseResponse(
|
||||
session.desc = f"SUCCESS: 更新知识库 {kb_id}，结果 - 共 {total_docs} 个文档，其中 {active_docs} 个已处理"
|
||||
response = KnowledgeBaseResponse(
|
||||
id=kb.id,
|
||||
created_at=kb.created_at,
|
||||
updated_at=kb.updated_at,
|
||||
|
|
@ -208,6 +209,7 @@ async def update_knowledge_base(
|
|||
document_count=total_docs,
|
||||
active_document_count=active_docs
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/{kb_id}", summary="删除知识库")
|
||||
async def delete_knowledge_base(
|
||||
|
|
@ -218,7 +220,7 @@ async def delete_knowledge_base(
|
|||
"""删除知识库"""
|
||||
session.desc = f"START: 删除知识库 {kb_id}"
|
||||
service = KnowledgeBaseService(session)
|
||||
success = service.delete_knowledge_base(kb_id)
|
||||
success = await service.delete_knowledge_base(kb_id)
|
||||
if not success:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -227,7 +229,7 @@ async def delete_knowledge_base(
|
|||
)
|
||||
|
||||
session.desc = f"SUCCESS: 删除知识库 {kb_id}"
|
||||
return {"message": "Knowledge base deleted successfully"}
|
||||
return HxfResponse({"message": "Knowledge base deleted successfully"})
|
||||
|
||||
# Document management endpoints
|
||||
@router.post("/{kb_id}/documents", response_model=DocumentResponse, summary="上传文档到知识库")
|
||||
|
|
@ -239,18 +241,18 @@ async def upload_document(
|
|||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""上传文档到知识库"""
|
||||
session.desc = f"START: 上传文档到知识库 {kb_id}"
|
||||
session.desc = f"START: 上传文档 {file.filename} ({FileUtils.format_file_size(file.size)}) 到知识库 (ID={kb_id})"
|
||||
|
||||
# Verify knowledge base exists and user has access
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
session.desc = f"获取知识库 {kb_id} 详情完毕 - 名称: {kb.name}, 描述: {kb.description}, 模型: {kb.embedding_model}"
|
||||
# Validate file
|
||||
if not FileUtils.validate_file_extension(file.filename):
|
||||
session.desc = f"ERROR: 文件 {file.filename} 类型不支持，仅支持 {', '.join(FileUtils.ALLOWED_EXTENSIONS)}"
|
||||
|
|
@ -258,7 +260,6 @@ async def upload_document(
|
|||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=f"文件类型 {file.filename.split('.')[-1]} 不支持。支持类型: {', '.join(FileUtils.ALLOWED_EXTENSIONS)}"
|
||||
)
|
||||
|
||||
# Check file size (50MB limit)
|
||||
max_size = 50 * 1024 * 1024 # 50MB
|
||||
if file.size and file.size > max_size:
|
||||
|
|
@ -268,6 +269,7 @@ async def upload_document(
|
|||
detail=f"文件 {file.filename} 大小超过 {FileUtils.format_file_size(max_size)} 限制"
|
||||
)
|
||||
|
||||
session.desc = f"文件为期望类型，处理文件 {file.filename} - "
|
||||
# Upload document
|
||||
doc_service = DocumentService(session)
|
||||
document = await doc_service.upload_document(
|
||||
|
|
@ -284,7 +286,7 @@ async def upload_document(
|
|||
session.desc = f"ERROR: 处理文档 {document.id} 时出错: {str(e)}"
|
||||
|
||||
session.desc = f"SUCCESS: 上传文档 {document.id} 到知识库 {kb_id}"
|
||||
return DocumentResponse(
|
||||
response = DocumentResponse(
|
||||
id=document.id,
|
||||
created_at=document.created_at,
|
||||
updated_at=document.updated_at,
|
||||
|
|
@ -301,6 +303,7 @@ async def upload_document(
|
|||
embedding_model=document.embedding_model,
|
||||
file_size_mb=round(document.file_size / (1024 * 1024), 2)
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{kb_id}/documents", response_model=DocumentListResponse, summary="获取知识库中的文档列表")
|
||||
async def list_documents(
|
||||
|
|
@ -314,7 +317,8 @@ async def list_documents(
|
|||
session.desc = f"START: 获取知识库 {kb_id} 中的文档列表"
|
||||
# Verify knowledge base exists and user has access
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -323,7 +327,7 @@ async def list_documents(
|
|||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
documents, total = doc_service.list_documents(kb_id, skip, limit)
|
||||
documents, total = await doc_service.list_documents(kb_id, skip, limit)
|
||||
|
||||
doc_responses = []
|
||||
for doc in documents:
|
||||
|
|
@ -346,208 +350,13 @@ async def list_documents(
|
|||
))
|
||||
|
||||
session.desc = f"SUCCESS: 获取知识库 {kb_id} 中的文档列表，共 {total} 条"
|
||||
return DocumentListResponse(
|
||||
response = DocumentListResponse(
|
||||
documents=doc_responses,
|
||||
total=total,
|
||||
page=skip // limit + 1,
|
||||
page_size=limit
|
||||
)
|
||||
|
||||
@router.get("/{kb_id}/documents/{doc_id}", response_model=DocumentResponse, summary="获取知识库中的文档详情")
|
||||
async def get_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取知识库中的文档详情。"""
|
||||
session.desc = f"START: 获取知识库 {kb_id} 中的文档 {doc_id} 详情"
|
||||
# Verify knowledge base exists and user has access
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
document = doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
session.desc = f"SUCCESS: 获取知识库 {kb_id} 中的文档 {doc_id} 详情"
|
||||
return DocumentResponse(
|
||||
id=document.id,
|
||||
created_at=document.created_at,
|
||||
updated_at=document.updated_at,
|
||||
knowledge_base_id=document.knowledge_base_id,
|
||||
filename=document.filename,
|
||||
original_filename=document.original_filename,
|
||||
file_path=document.file_path,
|
||||
file_type=document.file_type,
|
||||
file_size=document.file_size,
|
||||
mime_type=document.mime_type,
|
||||
is_processed=document.is_processed,
|
||||
processing_error=document.processing_error,
|
||||
chunk_count=document.chunk_count or 0,
|
||||
embedding_model=document.embedding_model,
|
||||
file_size_mb=round(document.file_size / (1024 * 1024), 2)
|
||||
)
|
||||
|
||||
@router.delete("/{kb_id}/documents/{doc_id}", summary="删除知识库中的文档")
|
||||
async def delete_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""删除知识库中的文档。"""
|
||||
session.desc = f"START: 删除知识库 {kb_id} 中的文档 {doc_id}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
success = doc_service.delete_document(doc_id, kb_id)
|
||||
if not success:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
session.desc = f"SUCCESS: 删除知识库 {kb_id} 中的文档 {doc_id}"
|
||||
return {"message": "Document deleted successfully"}
|
||||
|
||||
@router.post("/{kb_id}/documents/{doc_id}/process", response_model=DocumentProcessingStatus, summary="处理知识库中的文档")
|
||||
async def process_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""处理知识库中的文档，用于向量搜索。"""
|
||||
session.desc = f"START: 处理知识库 {kb_id} 中的文档 {doc_id}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
# Check if document exists
|
||||
doc_service = DocumentService(session)
|
||||
document = doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
# Process the document
|
||||
result = await doc_service.process_document(doc_id, kb_id)
|
||||
session.desc = f"SUCCESS: 处理知识库 {kb_id} 中的文档 {doc_id}"
|
||||
return DocumentProcessingStatus(
|
||||
document_id=doc_id,
|
||||
status=result["status"],
|
||||
progress=result.get("progress", 0.0),
|
||||
error_message=result.get("error_message"),
|
||||
chunks_created=result.get("chunks_created", 0)
|
||||
)
|
||||
|
||||
@router.get("/{kb_id}/documents/{doc_id}/status", response_model=DocumentProcessingStatus, summary="获取知识库中的文档处理状态")
|
||||
async def get_document_processing_status(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取知识库中的文档处理状态。"""
|
||||
# Verify knowledge base exists and user has access
|
||||
session.desc = f"START: 获取知识库 {kb_id} 中的文档 {doc_id} 处理状态"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
document = doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
# Determine status
|
||||
if document.processing_error:
|
||||
status_str = "failed"
|
||||
progress = 0.0
|
||||
session.desc = f"ERROR: 文档 {doc_id} 处理失败，错误信息：{document.processing_error}"
|
||||
elif document.is_processed:
|
||||
status_str = "completed"
|
||||
progress = 100.0
|
||||
session.desc = f"SUCCESS: 文档 {doc_id} 处理完成"
|
||||
else:
|
||||
status_str = "pending"
|
||||
progress = 0.0
|
||||
session.desc = f"文档 {doc_id} 处理pending中"
|
||||
|
||||
return DocumentProcessingStatus(
|
||||
document_id=document.id,
|
||||
status=status_str,
|
||||
progress=progress,
|
||||
error_message=document.processing_error,
|
||||
chunks_created=document.chunk_count or 0
|
||||
)
|
||||
|
||||
@router.get("/{kb_id}/search", summary="在知识库中搜索文档")
|
||||
async def search_knowledge_base(
|
||||
kb_id: int,
|
||||
query: str,
|
||||
limit: int = 5,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""在知识库中搜索文档。"""
|
||||
session.desc = f"START: 在知识库 {kb_id} 中搜索文档，查询：{query}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
# Perform search
|
||||
doc_service = DocumentService(session)
|
||||
results = doc_service.search_documents(kb_id, query, limit)
|
||||
session.desc = f"SUCCESS: 在知识库 {kb_id} 中搜索文档，查询：{query}，返回 {len(results)} 条结果"
|
||||
return {
|
||||
"knowledge_base_id": kb_id,
|
||||
"query": query,
|
||||
"results": results,
|
||||
"total_results": len(results)
|
||||
}
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{kb_id}/documents/{doc_id}/chunks", response_model=DocumentChunksResponse, summary="获取知识库中的文档块（片段）")
|
||||
async def get_document_chunks(
|
||||
|
|
@ -570,7 +379,8 @@ async def get_document_chunks(
|
|||
"""
|
||||
session.desc = f"START: 获取知识库 {kb_id} 中的文档 {doc_id} 所有文档块（片段）"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
knowledge_base = kb_service.get_knowledge_base(kb_id)
|
||||
knowledge_base = await kb_service.get_knowledge_base(kb_id)
|
||||
|
||||
if not knowledge_base:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -580,7 +390,9 @@ async def get_document_chunks(
|
|||
|
||||
# Verify document exists in the knowledge base
|
||||
doc_service = DocumentService(session)
|
||||
document = doc_service.get_document(doc_id, kb_id)
|
||||
session.desc = f"获取知识库 {kb_id} 中的文档 {doc_id} 的信息 > DocumentService"
|
||||
document = await doc_service.get_document(doc_id, kb_id)
|
||||
session.desc = f"获取知识库 {kb_id} 中的文档 {doc_id} 的信息 > get_document"
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
|
|
@ -589,11 +401,215 @@ async def get_document_chunks(
|
|||
)
|
||||
|
||||
# Get document chunks
|
||||
chunks = doc_service.get_document_chunks(doc_id)
|
||||
chunks = await doc_service.get_document_chunks(doc_id)
|
||||
|
||||
session.desc = f"SUCCESS: 获取文档 {doc_id} 共 {len(chunks)} 个文档块（片段）"
|
||||
return DocumentChunksResponse(
|
||||
response = DocumentChunksResponse(
|
||||
document_id=doc_id,
|
||||
document_name=document.filename,
|
||||
total_chunks=len(chunks),
|
||||
chunks=chunks
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{kb_id}/documents/{doc_id}", response_model=DocumentResponse, summary="获取知识库中的文档详情")
|
||||
async def get_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取知识库中的文档详情。"""
|
||||
session.desc = f"START: 获取知识库 {kb_id} 中的文档 {doc_id} 详情"
|
||||
# Verify knowledge base exists and user has access
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
document = await doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
session.desc = f"SUCCESS: 获取知识库 {kb_id} 中的文档 {doc_id} 详情"
|
||||
response = DocumentResponse(
|
||||
id=document.id,
|
||||
created_at=document.created_at,
|
||||
updated_at=document.updated_at,
|
||||
knowledge_base_id=document.knowledge_base_id,
|
||||
filename=document.filename,
|
||||
original_filename=document.original_filename,
|
||||
file_path=document.file_path,
|
||||
file_type=document.file_type,
|
||||
file_size=document.file_size,
|
||||
mime_type=document.mime_type,
|
||||
is_processed=document.is_processed,
|
||||
processing_error=document.processing_error,
|
||||
chunk_count=document.chunk_count or 0,
|
||||
embedding_model=document.embedding_model,
|
||||
file_size_mb=round(document.file_size / (1024 * 1024), 2)
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/{kb_id}/documents/{doc_id}", summary="删除知识库中的文档")
|
||||
async def delete_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""删除知识库中的文档。"""
|
||||
session.desc = f"START: 删除知识库 {kb_id} 中的文档 {doc_id}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
success = await doc_service.delete_document(doc_id, kb_id)
|
||||
if not success:
|
||||
session.desc = f"ERROR: 删除文档 {doc_id} 失败 - 文档不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
session.desc = f"SUCCESS: 删除知识库 {kb_id} 中的文档 {doc_id}"
|
||||
response = {"message": "Document deleted successfully"}
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/{kb_id}/documents/{doc_id}/process", response_model=DocumentProcessingStatus, summary="处理知识库中的文档")
|
||||
async def process_document(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""处理知识库中的文档，用于向量搜索。"""
|
||||
session.desc = f"START: 处理知识库 {kb_id} 中的文档 {doc_id}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
# Check if document exists
|
||||
doc_service = DocumentService(session)
|
||||
document = await doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
# Process the document
|
||||
result = await doc_service.process_document(doc_id, kb_id)
|
||||
await session.refresh(document)
|
||||
session.desc = f"SUCCESS: 处理知识库 {kb_id} 中的文档 {doc_id}"
|
||||
response = DocumentProcessingStatus(
|
||||
document_id=doc_id,
|
||||
status=result["status"],
|
||||
progress=result.get("progress", 0.0),
|
||||
error_message=result.get("error_message"),
|
||||
chunks_created=result.get("chunks_created", 0)
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{kb_id}/documents/{doc_id}/status", response_model=DocumentProcessingStatus, summary="获取知识库中的文档处理状态")
|
||||
async def get_document_processing_status(
|
||||
kb_id: int,
|
||||
doc_id: int,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取知识库中的文档处理状态。"""
|
||||
# Verify knowledge base exists and user has access
|
||||
session.desc = f"START: 获取知识库 {kb_id} 中的文档 {doc_id} 处理状态"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
doc_service = DocumentService(session)
|
||||
document = await doc_service.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Document not found"
|
||||
)
|
||||
|
||||
# Determine status
|
||||
if document.processing_error:
|
||||
status_str = "failed"
|
||||
progress = 0.0
|
||||
session.desc = f"ERROR: 文档 {doc_id} 处理失败，错误信息：{document.processing_error}"
|
||||
elif document.is_processed:
|
||||
status_str = "completed"
|
||||
progress = 100.0
|
||||
session.desc = f"SUCCESS: 文档 {doc_id} 处理完成"
|
||||
else:
|
||||
status_str = "pending"
|
||||
progress = 0.0
|
||||
session.desc = f"文档 {doc_id} 处理pending中"
|
||||
|
||||
response = DocumentProcessingStatus(
|
||||
document_id=document.id,
|
||||
status=status_str,
|
||||
progress=progress,
|
||||
error_message=document.processing_error,
|
||||
chunks_created=document.chunk_count or 0
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{kb_id}/search", summary="在知识库中搜索文档")
|
||||
async def search_knowledge_base(
|
||||
kb_id: int,
|
||||
query: str,
|
||||
limit: int = 5,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""在知识库中搜索文档。"""
|
||||
session.desc = f"START: 在知识库 {kb_id} 中搜索文档，查询：{query}"
|
||||
kb_service = KnowledgeBaseService(session)
|
||||
kb = await kb_service.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Knowledge base not found"
|
||||
)
|
||||
|
||||
# Perform search
|
||||
doc_service = DocumentService(session)
|
||||
results = await doc_service.search_documents(kb_id, query, limit)
|
||||
session.desc = f"SUCCESS: 在知识库 {kb_id} 中搜索文档，查询：{query}，返回 {len(results)} 条结果"
|
||||
response = {
|
||||
"knowledge_base_id": kb_id,
|
||||
"query": query,
|
||||
"results": results,
|
||||
"total_results": len(results)
|
||||
}
|
||||
return HxfResponse(response)
|
||||
|
|
|
|||
|
|
@ -1,14 +1,21 @@
|
|||
"""LLM configuration management API endpoints."""
|
||||
|
||||
from turtle import textinput
|
||||
from typing import List, Optional
|
||||
from fastapi import APIRouter, Depends, HTTPException, status, Query
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_core.messages import AIMessage
|
||||
from sqlalchemy.orm import Session
|
||||
from sqlalchemy import or_, select, delete, update
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from th_agenter.llm.embed.embed_llm import BGEEmbedLLM, EmbedLLM
|
||||
from th_agenter.llm.online.online_llm import OnlineLLM
|
||||
from ...db.database import get_session
|
||||
from ...models.user import User
|
||||
from ...models.llm_config import LLMConfig
|
||||
from th_agenter.llm.base_llm import LLMConfig_DataClass
|
||||
from ...core.simple_permissions import require_super_admin, require_authenticated_user
|
||||
from ...schemas.llm_config import (
|
||||
LLMConfigCreate, LLMConfigUpdate, LLMConfigResponse,
|
||||
|
|
@ -31,6 +38,7 @@ async def get_llm_configs(
|
|||
current_user: User = Depends(require_authenticated_user)
|
||||
):
|
||||
"""获取大模型配置列表."""
|
||||
session.title = "获取大模型配置列表"
|
||||
session.desc = f"START: 获取大模型配置列表, skip={skip}, limit={limit}, search={search}, provider={provider}, is_active={is_active}, is_embedding={is_embedding}"
|
||||
stmt = select(LLMConfig)
|
||||
|
||||
|
|
@ -62,7 +70,7 @@ async def get_llm_configs(
|
|||
# 分页
|
||||
stmt = stmt.offset(skip).limit(limit)
|
||||
configs = (await session.execute(stmt)).scalars().all()
|
||||
session.desc = f"SUCCESS: 获取 {len(configs)} 个大模型配置"
|
||||
session.desc = f"SUCCESS: 获取 {len(configs)} 个大模型配置 ..."
|
||||
return HxfResponse([config.to_dict(include_sensitive=True) for config in configs])
|
||||
|
||||
|
||||
|
|
@ -131,7 +139,7 @@ async def get_llm_config(
|
|||
):
|
||||
"""获取大模型配置详情."""
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -149,17 +157,20 @@ async def create_llm_config(
|
|||
):
|
||||
"""创建大模型配置."""
|
||||
# 检查配置名称是否已存在
|
||||
# 先保存当前用户名，避免在refresh后访问可能导致MissingGreenlet错误
|
||||
username = current_user.username
|
||||
session.desc = f"START: 创建大模型配置, name={config_data.name}"
|
||||
stmt = select(LLMConfig).where(LLMConfig.name == config_data.name)
|
||||
existing_config = session.execute(stmt).scalar_one_or_none()
|
||||
existing_config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if existing_config:
|
||||
session.desc = f"ERROR: 配置名称已存在, name={config_data.name}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="配置名称已存在"
|
||||
)
|
||||
|
||||
# 创建临时配置对象进行验证
|
||||
temp_config = LLMConfig(
|
||||
# 创建配置对象
|
||||
config = LLMConfig_DataClass(
|
||||
name=config_data.name,
|
||||
provider=config_data.provider,
|
||||
model_name=config_data.model_name,
|
||||
|
|
@ -178,7 +189,7 @@ async def create_llm_config(
|
|||
)
|
||||
|
||||
# 验证配置
|
||||
validation_result = temp_config.validate_config()
|
||||
validation_result = config.validate_config()
|
||||
if not validation_result['valid']:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
|
|
@ -190,10 +201,11 @@ async def create_llm_config(
|
|||
stmt = update(LLMConfig).where(
|
||||
LLMConfig.is_embedding == config_data.is_embedding
|
||||
).values({"is_default": False})
|
||||
session.execute(stmt)
|
||||
await session.execute(stmt)
|
||||
|
||||
session.desc = f"验证大模型配置, config_data"
|
||||
# 创建配置
|
||||
config = LLMConfig(
|
||||
config = LLMConfig_DataClass(
|
||||
name=config_data.name,
|
||||
provider=config_data.provider,
|
||||
model_name=config_data.model_name,
|
||||
|
|
@ -213,10 +225,9 @@ async def create_llm_config(
|
|||
# Audit fields are set automatically by SQLAlchemy event listener
|
||||
|
||||
session.add(config)
|
||||
session.commit()
|
||||
session.refresh(config)
|
||||
|
||||
session.desc = f"SUCCESS: 创建大模型配置, name={config.name} by user {current_user.username}"
|
||||
await session.commit()
|
||||
await session.refresh(config)
|
||||
session.desc = f"SUCCESS: 创建大模型配置, name={config.name} by user {username}"
|
||||
return HxfResponse(config.to_dict())
|
||||
|
||||
|
||||
|
|
@ -228,9 +239,10 @@ async def update_llm_config(
|
|||
current_user: User = Depends(require_super_admin)
|
||||
):
|
||||
"""更新大模型配置."""
|
||||
username = current_user.username
|
||||
session.desc = f"START: 更新大模型配置, id={config_id}"
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -243,7 +255,7 @@ async def update_llm_config(
|
|||
LLMConfig.name == config_data.name,
|
||||
LLMConfig.id != config_id
|
||||
)
|
||||
existing_config = session.execute(stmt).scalar_one_or_none()
|
||||
existing_config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if existing_config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
|
|
@ -258,17 +270,17 @@ async def update_llm_config(
|
|||
LLMConfig.is_embedding == is_embedding,
|
||||
LLMConfig.id != config_id
|
||||
).values({"is_default": False})
|
||||
session.execute(stmt)
|
||||
await session.execute(stmt)
|
||||
|
||||
# 更新字段
|
||||
update_data = config_data.model_dump(exclude_unset=True)
|
||||
for field, value in update_data.items():
|
||||
setattr(config, field, value)
|
||||
|
||||
session.commit()
|
||||
session.refresh(config)
|
||||
await session.commit()
|
||||
await session.refresh(config)
|
||||
|
||||
session.desc = f"SUCCESS: 更新大模型配置, id={config_id} by user {current_user.username}"
|
||||
session.desc = f"SUCCESS: 更新大模型配置, id={config_id} by user {username}"
|
||||
return HxfResponse(config.to_dict())
|
||||
|
||||
@router.delete("/{config_id}", status_code=status.HTTP_204_NO_CONTENT, summary="删除大模型配置")
|
||||
|
|
@ -278,22 +290,24 @@ async def delete_llm_config(
|
|||
current_user: User = Depends(require_super_admin)
|
||||
):
|
||||
"""删除大模型配置."""
|
||||
username = current_user.username
|
||||
session.desc = f"START: 删除大模型配置, id={config_id}"
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="大模型配置不存在"
|
||||
)
|
||||
|
||||
session.desc = f"待删除大模型记录 {config.to_dict()}"
|
||||
# TODO: 检查是否有对话或其他功能正在使用该配置
|
||||
# 这里可以添加相关的检查逻辑
|
||||
|
||||
session.delete(config)
|
||||
session.commit()
|
||||
# 删除配置
|
||||
await session.delete(config)
|
||||
await session.commit()
|
||||
|
||||
session.desc = f"SUCCESS: 删除大模型配置, id={config_id} by user {current_user.username}"
|
||||
session.desc = f"SUCCESS: 删除大模型配置成功, id={config_id} by user {username}"
|
||||
return HxfResponse({"message": "LLM config deleted successfully"})
|
||||
|
||||
@router.post("/{config_id}/test", summary="测试连接大模型配置")
|
||||
|
|
@ -304,17 +318,21 @@ async def test_llm_config(
|
|||
current_user: User = Depends(require_super_admin)
|
||||
):
|
||||
"""测试连接大模型配置."""
|
||||
session.desc = f"TEST: 测试连接大模型配置 {config_id} by user {current_user.username}"
|
||||
username = current_user.username
|
||||
session.desc = f"TEST: 测试连接大模型配置 {config_id} by user {username}"
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="大模型配置不存在"
|
||||
)
|
||||
|
||||
logger.info(f"TEST: 测试连接大模型配置 {config_id} by user {username}")
|
||||
config_name = config.name
|
||||
# 验证配置
|
||||
validation_result = config.validate_config()
|
||||
logger.info(f"TEST: 验证大模型配置 {config_name} validation_result = {validation_result}")
|
||||
if not validation_result["valid"]:
|
||||
return {
|
||||
"success": False,
|
||||
|
|
@ -322,41 +340,54 @@ async def test_llm_config(
|
|||
"details": validation_result
|
||||
}
|
||||
|
||||
session.desc = f"准备测试LLM功能 > 测试连接大模型配置 {config.to_dict()}"
|
||||
# 尝试创建客户端并发送测试请求
|
||||
try:
|
||||
# 这里应该根据不同的服务商创建相应的客户端
|
||||
# 由于具体的客户端实现可能因服务商而异，这里提供一个通用的框架
|
||||
# # 这里应该根据不同的服务商创建相应的客户端
|
||||
# # 由于具体的客户端实现可能因服务商而异，这里提供一个通用的框架
|
||||
|
||||
test_message = test_data.message or "Hello, this is a test message."
|
||||
session.desc = f"准备测试LLM功能 > test_message = {test_message}"
|
||||
|
||||
# TODO: 实现具体的测试逻辑
|
||||
# 例如：
|
||||
# client = config.get_client()
|
||||
# response = client.chat.completions.create(
|
||||
# model=config.model_name,
|
||||
# messages=[{"role": "user", "content": test_message}],
|
||||
# max_tokens=100
|
||||
# )
|
||||
if config.is_embedding:
|
||||
config.provider = "ollama"
|
||||
streaming_llm = BGEEmbedLLM(config)
|
||||
else:
|
||||
streaming_llm = OnlineLLM(config)
|
||||
session.desc = f"创建{'EmbeddingLLM' if config.is_embedding else 'OnlineLLM'}完毕 > 测试连接大模型配置 {config.to_dict()}"
|
||||
streaming_llm.load_model() # 加载模型
|
||||
session.desc = f"加载模型完毕，模型名称：{config.model_name}，base_url: {config.base_url}，准备测试对话..."
|
||||
|
||||
# 模拟测试成功
|
||||
session.desc = f"SUCCESS: 模拟测试连接大模型配置 {config.name} by user {current_user.username}"
|
||||
if config.is_embedding:
|
||||
# 测试嵌入模型，使用嵌入API而非聊天API
|
||||
test_text = test_message or "Hello, this is a test message for embedding"
|
||||
response = streaming_llm.embed_query(test_text)
|
||||
else:
|
||||
# 测试聊天模型
|
||||
from langchain.messages import SystemMessage, HumanMessage
|
||||
messages = [
|
||||
SystemMessage(content="你是一个简洁的助手，回答控制在50字以内"),
|
||||
HumanMessage(content=test_message)
|
||||
]
|
||||
response = streaming_llm.model.invoke(messages)
|
||||
session.desc = f"测试连接大模型配置 {config_name} 成功 >>> 响应: {type(response)}"
|
||||
|
||||
return HxfResponse({
|
||||
"success": True,
|
||||
"message": "配置测试成功",
|
||||
"test_message": test_message,
|
||||
"response": "这是一个模拟的测试响应。实际实现中，这里会是大模型的真实响应。",
|
||||
"message": "LLM测试成功",
|
||||
"request": test_message,
|
||||
"response": response.content if hasattr(response, 'content') else response, # 使用转换后的字典
|
||||
"latency_ms": 150, # 模拟延迟
|
||||
"config_info": config.get_client_config()
|
||||
"config_info": config.to_dict()
|
||||
})
|
||||
|
||||
except Exception as test_error:
|
||||
session.desc = f"ERROR: 测试连接大模型配置 {config.name} 失败, error: {str(test_error)}"
|
||||
return HxfResponse({
|
||||
"success": False,
|
||||
"message": f"配置测试失败: {str(test_error)}",
|
||||
"message": f"LLM测试失败: {str(test_error)}",
|
||||
"test_message": test_message,
|
||||
"config_info": config.get_client_config()
|
||||
"config_info": config.to_dict()
|
||||
})
|
||||
|
||||
@router.post("/{config_id}/toggle-status", summary="切换大模型配置状态")
|
||||
|
|
@ -366,10 +397,11 @@ async def toggle_llm_config_status(
|
|||
current_user: User = Depends(require_super_admin)
|
||||
):
|
||||
"""切换大模型配置状态."""
|
||||
session.desc = f"START: 切换大模型配置状态, id={config_id} by user {current_user.username}"
|
||||
username = current_user.username
|
||||
session.desc = f"START: 切换大模型配置状态, id={config_id} by user {username}"
|
||||
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -380,11 +412,11 @@ async def toggle_llm_config_status(
|
|||
config.is_active = not config.is_active
|
||||
# Audit fields are set automatically by SQLAlchemy event listener
|
||||
|
||||
session.commit()
|
||||
session.refresh(config)
|
||||
await session.commit()
|
||||
await session.refresh(config)
|
||||
|
||||
status_text = "激活" if config.is_active else "禁用"
|
||||
session.desc = f"SUCCESS: 切换大模型配置状态: {config.name} {status_text} by user {current_user.username}"
|
||||
session.desc = f"SUCCESS: 切换大模型配置状态: {config.name} {status_text} by user {username}"
|
||||
|
||||
return HxfResponse({
|
||||
"message": f"配置已{status_text}",
|
||||
|
|
@ -399,10 +431,11 @@ async def set_default_llm_config(
|
|||
current_user: User = Depends(require_super_admin)
|
||||
):
|
||||
"""设置默认大模型配置."""
|
||||
session.desc = f"START: 设置大模型配置 {config_id} 为默认 by user {current_user.username}"
|
||||
username = current_user.username
|
||||
session.desc = f"START: 设置大模型配置 {config_id} 为默认 by user {username}"
|
||||
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
config = session.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -421,19 +454,19 @@ async def set_default_llm_config(
|
|||
LLMConfig.is_embedding == config.is_embedding,
|
||||
LLMConfig.id != config_id
|
||||
).values({"is_default": False})
|
||||
session.execute(stmt)
|
||||
await session.execute(stmt)
|
||||
|
||||
# 设置当前配置为默认
|
||||
config.is_default = True
|
||||
config.set_audit_fields(current_user.id, is_update=True)
|
||||
|
||||
session.commit()
|
||||
session.refresh(config)
|
||||
await session.commit()
|
||||
await session.refresh(config)
|
||||
|
||||
model_type = "嵌入模型" if config.is_embedding else "对话模型"
|
||||
# 更新文档处理器默认embedding
|
||||
get_document_processor()._init_embeddings()
|
||||
session.desc = f"SUCCESS: 设置大模型配置 {config.name} ({model_type}) 为默认 by user {current_user.username}"
|
||||
await get_document_processor(session)._init_embeddings()
|
||||
session.desc = f"SUCCESS: 设置大模型配置 {config.name} ({model_type}) 为默认 by user {username}"
|
||||
return HxfResponse({
|
||||
"message": f"已将 {config.name} 设为默认{model_type}配置",
|
||||
"is_default": config.is_default
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
"""Role management API endpoints."""
|
||||
|
||||
from utils.util_exceptions import HxfResponse
|
||||
from loguru import logger
|
||||
from typing import List, Optional
|
||||
from fastapi import APIRouter, Depends, HTTPException, status, Query
|
||||
|
|
@ -49,7 +50,8 @@ async def get_roles(
|
|||
stmt = stmt.offset(skip).limit(limit)
|
||||
roles = (await session.execute(stmt)).scalars().all()
|
||||
session.desc = f"SUCCESS: 用户 {current_user.username} 有 {len(roles)} 个角色"
|
||||
return [role.to_dict() for role in roles]
|
||||
response = [role.to_dict() for role in roles]
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{role_id}", response_model=RoleResponse, summary="获取角色详情")
|
||||
async def get_role(
|
||||
|
|
@ -67,7 +69,8 @@ async def get_role(
|
|||
detail="角色不存在"
|
||||
)
|
||||
|
||||
return role.to_dict()
|
||||
response = role.to_dict()
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/", response_model=RoleResponse, status_code=status.HTTP_201_CREATED, summary="创建角色")
|
||||
async def create_role(
|
||||
|
|
@ -95,12 +98,13 @@ async def create_role(
|
|||
)
|
||||
role.set_audit_fields(current_user.id)
|
||||
|
||||
await session.add(role)
|
||||
session.add(role)
|
||||
await session.commit()
|
||||
await session.refresh(role)
|
||||
|
||||
logger.info(f"Role created: {role.name} by user {current_user.username}")
|
||||
return role.to_dict()
|
||||
response = role.to_dict()
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.put("/{role_id}", response_model=RoleResponse, summary="更新角色")
|
||||
async def update_role(
|
||||
|
|
@ -152,7 +156,8 @@ async def update_role(
|
|||
await session.refresh(role)
|
||||
|
||||
logger.info(f"Role updated: {role.name} by user {current_user.username}")
|
||||
return role.to_dict()
|
||||
response = role.to_dict()
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/{role_id}", status_code=status.HTTP_204_NO_CONTENT, summary="删除角色")
|
||||
async def delete_role(
|
||||
|
|
@ -190,7 +195,8 @@ async def delete_role(
|
|||
await session.commit()
|
||||
|
||||
session.desc = f"角色删除成功: {role.name} by user {current_user.username}"
|
||||
return {"message": f"Role deleted successfully: {role.name} by user {current_user.username}"}
|
||||
response = {"message": f"Role deleted successfully: {role.name} by user {current_user.username}"}
|
||||
return HxfResponse(response)
|
||||
|
||||
# 用户角色管理路由
|
||||
user_role_router = APIRouter(prefix="/user-roles", tags=["user-roles"])
|
||||
|
|
@ -230,13 +236,14 @@ async def assign_user_roles(
|
|||
user_id=assignment_data.user_id,
|
||||
role_id=role_id
|
||||
)
|
||||
await session.add(user_role)
|
||||
session.add(user_role)
|
||||
|
||||
await session.commit()
|
||||
|
||||
session.desc = f"User roles assigned: user {user.username}, roles {assignment_data.role_ids} by user {current_user.username}"
|
||||
|
||||
return {"message": "角色分配成功"}
|
||||
response = {"message": "角色分配成功"}
|
||||
return HxfResponse(response)
|
||||
|
||||
@user_role_router.get("/user/{user_id}", response_model=List[RoleResponse], summary="获取用户角色列表")
|
||||
async def get_user_roles(
|
||||
|
|
@ -267,7 +274,8 @@ async def get_user_roles(
|
|||
)
|
||||
roles = (await session.execute(stmt)).scalars().all()
|
||||
|
||||
return [role.to_dict() for role in roles]
|
||||
response = [role.to_dict() for role in roles]
|
||||
return HxfResponse(response)
|
||||
|
||||
# 将子路由添加到主路由
|
||||
router.include_router(user_role_router)
|
||||
|
|
@ -12,6 +12,7 @@ from th_agenter.services.conversation_context import conversation_context_servic
|
|||
from utils.util_schemas import BaseResponse
|
||||
from pydantic import BaseModel
|
||||
from loguru import logger
|
||||
from utils.util_exceptions import HxfResponse
|
||||
|
||||
router = APIRouter(prefix="/smart-chat", tags=["smart-chat"])
|
||||
security = HTTPBearer()
|
||||
|
|
@ -65,6 +66,8 @@ async def smart_query(
|
|||
|
||||
# 初始化工作流管理器
|
||||
workflow_manager = SmartWorkflowManager(session)
|
||||
await workflow_manager.initialize()
|
||||
|
||||
conversation_service = ConversationService(session)
|
||||
|
||||
# 处理对话上下文
|
||||
|
|
@ -126,7 +129,7 @@ async def smart_query(
|
|||
except Exception as e:
|
||||
session.desc = f"ERROR: 智能查询执行失败: {e}"
|
||||
# 返回结构化的错误响应
|
||||
return SmartQueryResponse(
|
||||
response = SmartQueryResponse(
|
||||
success=False,
|
||||
message=f"查询执行失败: {str(e)}",
|
||||
data={'error_type': 'query_execution_error'},
|
||||
|
|
@ -137,6 +140,7 @@ async def smart_query(
|
|||
}],
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
# 如果查询成功，保存助手回复和更新上下文
|
||||
if result['success'] and conversation_id:
|
||||
|
|
@ -169,21 +173,22 @@ async def smart_query(
|
|||
if conversation_id:
|
||||
response_data['conversation_id'] = conversation_id
|
||||
session.desc = f"SUCCESS: 保存助手回复和更新上下文，对话ID: {conversation_id}"
|
||||
return SmartQueryResponse(
|
||||
response = SmartQueryResponse(
|
||||
success=result['success'],
|
||||
message=result.get('message', '查询完成'),
|
||||
data=response_data,
|
||||
workflow_steps=result.get('workflow_steps', []),
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except HTTPException:
|
||||
except HTTPException as e:
|
||||
session.desc = f"EXCEPTION: HTTP异常: {e}"
|
||||
raise
|
||||
raise e
|
||||
except Exception as e:
|
||||
session.desc = f"ERROR: 智能查询接口异常: {e}"
|
||||
# 返回通用错误响应
|
||||
return SmartQueryResponse(
|
||||
response = SmartQueryResponse(
|
||||
success=False,
|
||||
message="服务器内部错误，请稍后重试",
|
||||
data={'error_type': 'internal_server_error'},
|
||||
|
|
@ -194,6 +199,7 @@ async def smart_query(
|
|||
}],
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/conversation/{conversation_id}/context", response_model=ConversationContextResponse, summary="获取对话上下文")
|
||||
async def get_conversation_context(
|
||||
|
|
@ -227,11 +233,12 @@ async def get_conversation_context(
|
|||
history = await conversation_context_service.get_conversation_history(conversation_id)
|
||||
context['message_history'] = history
|
||||
session.desc = f"SUCCESS: 获取对话上下文成功，对话ID: {conversation_id}"
|
||||
return ConversationContextResponse(
|
||||
response = ConversationContextResponse(
|
||||
success=True,
|
||||
message="获取对话上下文成功",
|
||||
data=context
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
@router.get("/files/status", response_model=ConversationContextResponse, summary="获取用户当前的文件状态和统计信息")
|
||||
|
|
@ -244,6 +251,7 @@ async def get_files_status(
|
|||
"""
|
||||
session.desc = f"START: 获取用户文件状态和统计信息，用户ID: {current_user.id}"
|
||||
workflow_manager = SmartWorkflowManager()
|
||||
await workflow_manager.initialize()
|
||||
|
||||
# 获取用户文件列表
|
||||
file_list = await workflow_manager.excel_workflow._load_user_file_list(current_user.id)
|
||||
|
|
@ -277,11 +285,12 @@ async def get_files_status(
|
|||
}
|
||||
|
||||
session.desc = f"SUCCESS: 获取用户文件状态和统计信息成功，用户ID: {current_user.id}"
|
||||
return ConversationContextResponse(
|
||||
response = ConversationContextResponse(
|
||||
success=True,
|
||||
message=f"当前有{total_files}个可用文件" if total_files > 0 else "暂无可用文件，请先上传Excel文件",
|
||||
data=status_data
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/conversation/{conversation_id}/reset", summary="重置对话上下文")
|
||||
async def reset_conversation_context(
|
||||
|
|
@ -315,10 +324,11 @@ async def reset_conversation_context(
|
|||
|
||||
if success:
|
||||
session.desc = f"SUCCESS: 重置对话上下文成功，对话ID: {conversation_id}"
|
||||
return {
|
||||
"success": True,
|
||||
"message": "对话上下文已重置，可以开始新的数据分析会话"
|
||||
}
|
||||
response = ConversationContextResponse(
|
||||
success=True,
|
||||
message="对话上下文已重置，可以开始新的数据分析会话"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
else:
|
||||
session.desc = f"EXCEPTION: 重置对话上下文失败，对话ID: {conversation_id}"
|
||||
raise HTTPException(
|
||||
|
|
|
|||
|
|
@ -30,6 +30,7 @@ from th_agenter.services.smart_workflow import SmartWorkflowManager
|
|||
from th_agenter.services.conversation_context import ConversationContextService
|
||||
from pydantic import BaseModel
|
||||
from loguru import logger
|
||||
from utils.util_exceptions import HxfResponse
|
||||
|
||||
router = APIRouter(prefix="/smart-query", tags=["smart-query"])
|
||||
security = HTTPBearer()
|
||||
|
|
@ -163,12 +164,13 @@ async def upload_excel(
|
|||
})
|
||||
|
||||
session.desc = f"SUCCESS: 用户 {current_user.username} 上传的文件 {file.filename} 预处理成功，文件ID: {excel_file.id}"
|
||||
return ExcelUploadResponse(
|
||||
response = ExcelUploadResponse(
|
||||
file_id=excel_file.id,
|
||||
success=True,
|
||||
message="Excel文件上传成功",
|
||||
data=analysis_result
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/preview-excel", response_model=QueryResponse, summary="预览Excel文件数据")
|
||||
async def preview_excel(
|
||||
|
|
@ -233,7 +235,7 @@ async def preview_excel(
|
|||
data = paginated_df.fillna('').to_dict('records')
|
||||
columns = df.columns.tolist()
|
||||
session.desc = f"SUCCESS: 用户 {current_user.username} 预览文件 {request.file_id} 加载成功，共 {total_rows} 行数据"
|
||||
return QueryResponse(
|
||||
response = QueryResponse(
|
||||
success=True,
|
||||
message="Excel文件预览加载成功",
|
||||
data={
|
||||
|
|
@ -245,6 +247,7 @@ async def preview_excel(
|
|||
'total_pages': (total_rows + request.page_size - 1) // request.page_size
|
||||
}
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/test-db-connection", response_model=NormalResponse, summary="测试数据库连接")
|
||||
async def test_database_connection(
|
||||
|
|
@ -264,10 +267,11 @@ async def test_database_connection(
|
|||
message="数据库连接测试成功"
|
||||
)
|
||||
else:
|
||||
return NormalResponse(
|
||||
response = NormalResponse(
|
||||
success=False,
|
||||
message="数据库连接测试失败"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except Exception as e:
|
||||
return NormalResponse(
|
||||
|
|
@ -298,22 +302,25 @@ async def get_table_schema(
|
|||
schema_result = await db_service.get_table_schema(request.table_name, current_user.id)
|
||||
|
||||
if schema_result['success']:
|
||||
return QueryResponse(
|
||||
response = QueryResponse(
|
||||
success=True,
|
||||
message="获取表结构成功",
|
||||
data=schema_result['data']
|
||||
)
|
||||
return HxfResponse(response)
|
||||
else:
|
||||
return QueryResponse(
|
||||
response = QueryResponse(
|
||||
success=False,
|
||||
message=schema_result['message']
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except Exception as e:
|
||||
return QueryResponse(
|
||||
response = QueryResponse(
|
||||
success=False,
|
||||
message=f"获取表结构失败: {str(e)}"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
class StreamQueryRequest(BaseModel):
|
||||
query: str
|
||||
|
|
@ -355,6 +362,8 @@ async def stream_smart_query(
|
|||
|
||||
# 初始化服务
|
||||
workflow_manager = SmartWorkflowManager(session)
|
||||
await workflow_manager.initialize()
|
||||
|
||||
conversation_context_service = ConversationContextService()
|
||||
|
||||
# 处理对话上下文
|
||||
|
|
@ -435,7 +444,7 @@ async def stream_smart_query(
|
|||
except:
|
||||
pass
|
||||
|
||||
return StreamingResponse(
|
||||
response = StreamingResponse(
|
||||
generate_stream(),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
|
|
@ -447,6 +456,7 @@ async def stream_smart_query(
|
|||
"Access-Control-Allow-Methods": "*"
|
||||
}
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/execute-db-query", summary="流式数据库查询")
|
||||
async def execute_database_query(
|
||||
|
|
@ -477,6 +487,7 @@ async def execute_database_query(
|
|||
|
||||
# 初始化服务
|
||||
workflow_manager = SmartWorkflowManager(session)
|
||||
await workflow_manager.initialize()
|
||||
conversation_context_service = ConversationContextService()
|
||||
|
||||
# 处理对话上下文
|
||||
|
|
@ -556,7 +567,7 @@ async def execute_database_query(
|
|||
except:
|
||||
pass
|
||||
|
||||
return StreamingResponse(
|
||||
response = StreamingResponse(
|
||||
generate_stream(),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
|
|
@ -568,6 +579,7 @@ async def execute_database_query(
|
|||
"Access-Control-Allow-Methods": "*"
|
||||
}
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/cleanup-temp-files", summary="清理临时文件")
|
||||
async def cleanup_temp_files(
|
||||
|
|
@ -590,16 +602,18 @@ async def cleanup_temp_files(
|
|||
except OSError:
|
||||
pass
|
||||
|
||||
return BaseResponse(
|
||||
response = BaseResponse(
|
||||
success=True,
|
||||
message=f"已清理 {cleaned_count} 个临时文件"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except Exception as e:
|
||||
return BaseResponse(
|
||||
response = BaseResponse(
|
||||
success=False,
|
||||
message=f"清理临时文件失败: {str(e)}"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/files", response_model=FileListResponse, summary="获取用户上传的Excel文件列表")
|
||||
async def get_file_list(
|
||||
|
|
@ -634,7 +648,7 @@ async def get_file_list(
|
|||
file_list.append(file_info)
|
||||
|
||||
session.desc = f"SUCCESS: 获取用户 {current_user.id} 的文件列表，共 {total} 个文件"
|
||||
return FileListResponse(
|
||||
response = FileListResponse(
|
||||
success=True,
|
||||
message="获取文件列表成功",
|
||||
data={
|
||||
|
|
@ -645,12 +659,14 @@ async def get_file_list(
|
|||
'total_pages': (total + page_size - 1) // page_size
|
||||
}
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except Exception as e:
|
||||
return FileListResponse(
|
||||
response = FileListResponse(
|
||||
success=False,
|
||||
message=f"获取文件列表失败: {str(e)}"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/files/{file_id}", response_model=NormalResponse, summary="删除指定的Excel文件")
|
||||
async def delete_file(
|
||||
|
|
@ -668,22 +684,26 @@ async def delete_file(
|
|||
|
||||
if success:
|
||||
session.desc = f"SUCCESS: 删除用户 {current_user.id} 的文件 {file_id}"
|
||||
return NormalResponse(
|
||||
response = NormalResponse(
|
||||
success=True,
|
||||
message="文件删除成功"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
else:
|
||||
session.desc = f"ERROR: 删除用户 {current_user.id} 的文件 {file_id}，文件不存在或删除失败"
|
||||
return NormalResponse(
|
||||
response = NormalResponse(
|
||||
success=False,
|
||||
message="文件不存在或删除失败"
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
except Exception as e:
|
||||
return NormalResponse(
|
||||
response = NormalResponse(
|
||||
success=True,
|
||||
message=str(e)
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/files/{file_id}/info", response_model=QueryResponse, summary="获取指定文件的详细信息")
|
||||
async def get_file_info(
|
||||
|
|
@ -725,9 +745,11 @@ async def get_file_info(
|
|||
'sheets_summary': excel_file.get_all_sheets_summary()
|
||||
}
|
||||
|
||||
return QueryResponse(
|
||||
session.desc = f"SUCCESS: 获取用户 {current_user.id} 的文件 {file_id} 信息"
|
||||
response = QueryResponse(
|
||||
success=True,
|
||||
message="获取文件信息成功",
|
||||
data=file_info
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
|
|
@ -9,6 +9,7 @@ from th_agenter.models.user import User
|
|||
from th_agenter.db.database import get_session
|
||||
from th_agenter.services.table_metadata_service import TableMetadataService
|
||||
from th_agenter.services.auth import AuthService
|
||||
from utils.util_exceptions import HxfResponse
|
||||
|
||||
router = APIRouter(prefix="/api/table-metadata", tags=["table-metadata"])
|
||||
|
||||
|
|
@ -54,8 +55,7 @@ async def collect_table_metadata(
|
|||
request.table_names
|
||||
)
|
||||
session.desc = f"SUCCESS: 用户 {current_user.id} 收集表元数据"
|
||||
return result
|
||||
|
||||
return HxfResponse(result)
|
||||
|
||||
@router.get("/", summary="获取用户表元数据列表")
|
||||
async def get_table_metadata(
|
||||
|
|
@ -66,7 +66,7 @@ async def get_table_metadata(
|
|||
"""获取表元数据列表"""
|
||||
try:
|
||||
service = TableMetadataService(session)
|
||||
metadata_list = service.get_user_table_metadata(
|
||||
metadata_list = await service.get_user_table_metadata(
|
||||
current_user.id,
|
||||
database_config_id
|
||||
)
|
||||
|
|
@ -96,17 +96,17 @@ async def get_table_metadata(
|
|||
for meta in metadata_list
|
||||
]
|
||||
|
||||
return {
|
||||
return HxfResponse({
|
||||
"success": True,
|
||||
"data": data
|
||||
}
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取表元数据失败: {str(e)}")
|
||||
return {
|
||||
return HxfResponse({
|
||||
"success": False,
|
||||
"message": str(e)
|
||||
}
|
||||
})
|
||||
|
||||
|
||||
@router.post("/by-table", summary="根据表名获取表元数据")
|
||||
|
|
@ -118,7 +118,7 @@ async def get_table_metadata_by_name(
|
|||
"""根据表名获取表元数据"""
|
||||
try:
|
||||
service = TableMetadataService(session)
|
||||
metadata = service.get_table_metadata_by_name(
|
||||
metadata = await service.get_table_metadata_by_name(
|
||||
current_user.id,
|
||||
request.database_config_id,
|
||||
request.table_name
|
||||
|
|
@ -146,27 +146,30 @@ async def get_table_metadata_by_name(
|
|||
"business_context": metadata.business_context or ""
|
||||
}
|
||||
}
|
||||
return {"success": True, "data": data}
|
||||
else:
|
||||
return {"success": False, "data": None, "message": "表元数据不存在"}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取表元数据失败: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"message": str(e)
|
||||
}
|
||||
|
||||
return {
|
||||
return HxfResponse({
|
||||
"success": True,
|
||||
"data": data
|
||||
}
|
||||
})
|
||||
else:
|
||||
return HxfResponse({
|
||||
"success": False,
|
||||
"data": None,
|
||||
"message": "表元数据不存在"
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取表元数据失败: {str(e)}")
|
||||
return {
|
||||
return HxfResponse({
|
||||
"success": False,
|
||||
"message": str(e)
|
||||
}
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取表元数据失败: {str(e)}")
|
||||
return HxfResponse({
|
||||
"success": False,
|
||||
"message": str(e)
|
||||
})
|
||||
|
||||
|
||||
@router.put("/{metadata_id}/qa-settings", summary="更新表的问答设置")
|
||||
|
|
@ -179,14 +182,17 @@ async def update_qa_settings(
|
|||
"""更新表的问答设置"""
|
||||
try:
|
||||
service = TableMetadataService(session)
|
||||
success = service.update_table_qa_settings(
|
||||
success = await service.update_table_qa_settings(
|
||||
current_user.id,
|
||||
metadata_id,
|
||||
settings.dict()
|
||||
settings.model_dump()
|
||||
)
|
||||
|
||||
if success:
|
||||
return {"success": True, "message": "设置更新成功"}
|
||||
return HxfResponse({
|
||||
"success": True,
|
||||
"message": "设置更新成功"
|
||||
})
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
|
|
@ -221,9 +227,9 @@ async def save_table_metadata(
|
|||
|
||||
session.desc = f"用户 {current_user.id} 保存了 {len(request.table_names)} 个表的配置"
|
||||
|
||||
return {
|
||||
return HxfResponse({
|
||||
"success": True,
|
||||
"message": f"成功保存 {len(result['saved_tables'])} 个表的配置",
|
||||
"saved_tables": result['saved_tables'],
|
||||
"failed_tables": result.get('failed_tables', [])
|
||||
}
|
||||
})
|
||||
|
|
@ -9,6 +9,7 @@ from ...core.simple_permissions import require_super_admin
|
|||
from ...services.auth import AuthService
|
||||
from ...services.user import UserService
|
||||
from ...schemas.user import UserResponse, UserUpdate, UserCreate, ChangePasswordRequest, ResetPasswordRequest
|
||||
from utils.util_exceptions import HxfResponse
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
|
@ -17,7 +18,8 @@ async def get_user_profile(
|
|||
current_user = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取当前用户的个人信息."""
|
||||
return UserResponse.model_validate(current_user)
|
||||
response = UserResponse.model_validate(current_user)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.put("/profile", response_model=UserResponse, summary="更新当前用户的个人信息")
|
||||
async def update_user_profile(
|
||||
|
|
@ -39,7 +41,8 @@ async def update_user_profile(
|
|||
|
||||
# Update user
|
||||
updated_user = await user_service.update_user(current_user.id, user_update)
|
||||
return UserResponse.model_validate(updated_user)
|
||||
response = UserResponse.model_validate(updated_user)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/profile", summary="删除当前用户的账户")
|
||||
async def delete_user_account(
|
||||
|
|
@ -51,7 +54,8 @@ async def delete_user_account(
|
|||
user_service = UserService(session)
|
||||
await user_service.delete_user(current_user.id)
|
||||
session.desc = f"删除用户 [{username}] 成功"
|
||||
return {"message": f"删除用户 {username} 成功"}
|
||||
response = {"message": f"删除用户 {username} 成功"}
|
||||
return HxfResponse(response)
|
||||
|
||||
# Admin endpoints
|
||||
@router.post("/", response_model=UserResponse, summary="创建新用户 (需要有管理员权限)")
|
||||
|
|
@ -83,7 +87,8 @@ async def create_user(
|
|||
|
||||
# Create user
|
||||
new_user = await user_service.create_user(user_create)
|
||||
return UserResponse.model_validate(new_user)
|
||||
response = UserResponse.model_validate(new_user)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/", summary="列出所有用户，支持分页和筛选 (仅管理员权限)")
|
||||
async def list_users(
|
||||
|
|
@ -111,7 +116,7 @@ async def list_users(
|
|||
"page": page,
|
||||
"page_size": size
|
||||
}
|
||||
return result
|
||||
return HxfResponse(result)
|
||||
|
||||
|
||||
@router.get("/{user_id}", response_model=UserResponse, summary="通过ID获取用户信息 (仅管理员权限)")
|
||||
|
|
@ -128,7 +133,8 @@ async def get_user(
|
|||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="User not found"
|
||||
)
|
||||
return UserResponse.model_validate(user)
|
||||
response = UserResponse.model_validate(user)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.put("/change-password", summary="修改当前用户的密码")
|
||||
async def change_password(
|
||||
|
|
@ -145,7 +151,8 @@ async def change_password(
|
|||
current_password=request.current_password,
|
||||
new_password=request.new_password
|
||||
)
|
||||
return {"message": "Password changed successfully"}
|
||||
response = {"message": "Password changed successfully"}
|
||||
return HxfResponse(response)
|
||||
except Exception as e:
|
||||
if "Current password is incorrect" in str(e):
|
||||
raise HTTPException(
|
||||
|
|
@ -178,7 +185,8 @@ async def reset_user_password(
|
|||
user_id=user_id,
|
||||
new_password=request.new_password
|
||||
)
|
||||
return {"message": "Password reset successfully"}
|
||||
response = {"message": "Password reset successfully"}
|
||||
return HxfResponse(response)
|
||||
except Exception as e:
|
||||
if "User not found" in str(e):
|
||||
raise HTTPException(
|
||||
|
|
@ -215,7 +223,8 @@ async def update_user(
|
|||
)
|
||||
|
||||
updated_user = await user_service.update_user(user_id, user_update)
|
||||
return UserResponse.model_validate(updated_user)
|
||||
response = UserResponse.model_validate(updated_user)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/{user_id}", summary="删除用户 (仅管理员权限)")
|
||||
async def delete_user(
|
||||
|
|
@ -234,4 +243,5 @@ async def delete_user(
|
|||
)
|
||||
|
||||
await user_service.delete_user(user_id)
|
||||
return {"message": "User deleted successfully"}
|
||||
response = {"message": "User deleted successfully"}
|
||||
return HxfResponse(response)
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ from ...services.workflow_engine import get_workflow_engine
|
|||
from ...services.auth import AuthService
|
||||
from ...models.user import User
|
||||
from loguru import logger
|
||||
|
||||
from utils.util_exceptions import HxfResponse
|
||||
router = APIRouter()
|
||||
|
||||
def convert_workflow_for_response(workflow_dict):
|
||||
|
|
@ -31,37 +31,6 @@ def convert_workflow_for_response(workflow_dict):
|
|||
conn['to'] = conn.pop('to_node')
|
||||
return workflow_dict
|
||||
|
||||
@router.post("/", response_model=WorkflowResponse)
|
||||
async def create_workflow(
|
||||
workflow_data: WorkflowCreate,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""创建工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
|
||||
# 创建工作流
|
||||
workflow = Workflow(
|
||||
name=workflow_data.name,
|
||||
description=workflow_data.description,
|
||||
definition=workflow_data.definition.model_dump(),
|
||||
version="1.0.0",
|
||||
status=workflow_data.status,
|
||||
owner_id=current_user.id
|
||||
)
|
||||
workflow.set_audit_fields(current_user.id)
|
||||
|
||||
await session.add(workflow)
|
||||
await session.commit()
|
||||
await session.refresh(workflow)
|
||||
|
||||
# 转换definition中的字段映射
|
||||
workflow_dict = convert_workflow_for_response(workflow.to_dict())
|
||||
|
||||
logger.info(f"Created workflow: {workflow.name} by user {current_user.username}")
|
||||
return WorkflowResponse(**workflow_dict)
|
||||
|
||||
|
||||
@router.get("/", response_model=WorkflowListResponse)
|
||||
async def list_workflows(
|
||||
skip: Optional[int] = Query(None, ge=0),
|
||||
|
|
@ -73,6 +42,7 @@ async def list_workflows(
|
|||
):
|
||||
"""获取工作流列表"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 获取用户 {current_user.username} 的所有工作流 (skip={skip}, limit={limit})"
|
||||
|
||||
# 构建查询
|
||||
stmt = select(Workflow).where(Workflow.owner_id == current_user.id)
|
||||
|
|
@ -90,17 +60,22 @@ async def list_workflows(
|
|||
count_query = count_query.where(Workflow.status == workflow_status)
|
||||
if search:
|
||||
count_query = count_query.where(Workflow.name.ilike(f"%{search}%"))
|
||||
total = session.scalar(count_query)
|
||||
|
||||
session.desc = f"查询条件: 状态={workflow_status}, 搜索={search}"
|
||||
total = await session.scalar(count_query)
|
||||
session.desc = f"查询结果: 共 {total} 条"
|
||||
|
||||
# 如果没有传分页参数，返回所有数据
|
||||
if skip is None and limit is None:
|
||||
workflows = session.scalars(stmt).all()
|
||||
return WorkflowListResponse(
|
||||
workflows = (await session.scalars(stmt)).all()
|
||||
session.desc = f"SUCCESS: 没有传分页参数，返回所有数据 - 共 {len(workflows)} 条"
|
||||
response = WorkflowListResponse(
|
||||
workflows=[WorkflowResponse(**convert_workflow_for_response(w.to_dict())) for w in workflows],
|
||||
total=total,
|
||||
page=1,
|
||||
size=total
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
# 使用默认分页参数
|
||||
if skip is None:
|
||||
|
|
@ -109,14 +84,16 @@ async def list_workflows(
|
|||
limit = 10
|
||||
|
||||
# 分页查询
|
||||
workflows = session.scalars(stmt.offset(skip).limit(limit)).all()
|
||||
workflows = (await session.scalars(stmt.offset(skip).limit(limit))).all()
|
||||
session.desc = f"SUCCESS: 分页查询 - 共 {len(workflows)} 条"
|
||||
|
||||
return WorkflowListResponse(
|
||||
response = WorkflowListResponse(
|
||||
workflows=[WorkflowResponse(**convert_workflow_for_response(w.to_dict())) for w in workflows],
|
||||
total=total,
|
||||
page=skip // limit + 1, # 计算页码
|
||||
size=limit
|
||||
)
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.get("/{workflow_id}", response_model=WorkflowResponse)
|
||||
async def get_workflow(
|
||||
|
|
@ -126,8 +103,9 @@ async def get_workflow(
|
|||
):
|
||||
"""获取工作流详情"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 获取工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).where(
|
||||
Workflow.id == workflow_id,
|
||||
Workflow.owner_id == current_user.id
|
||||
|
|
@ -135,12 +113,15 @@ async def get_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 获取工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
)
|
||||
|
||||
return WorkflowResponse(**convert_workflow_for_response(workflow.to_dict()))
|
||||
session.desc = f"SUCCESS: 获取工作流数据 {workflow_id}"
|
||||
response = WorkflowResponse(**convert_workflow_for_response(workflow.to_dict()))
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.put("/{workflow_id}", response_model=WorkflowResponse)
|
||||
async def update_workflow(
|
||||
|
|
@ -151,8 +132,9 @@ async def update_workflow(
|
|||
):
|
||||
"""更新工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 更新工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).where(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -162,13 +144,15 @@ async def update_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 更新工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
)
|
||||
|
||||
# 更新字段
|
||||
update_data = workflow_data.dict(exclude_unset=True)
|
||||
session.desc = f"UPDATE: 工作流 {workflow_id} 更新字段 {workflow_data.model_dump(exclude_unset=True)}"
|
||||
update_data = workflow_data.model_dump(exclude_unset=True)
|
||||
for field, value in update_data.items():
|
||||
if field == "definition" and value:
|
||||
# 如果value是Pydantic模型，转换为字典；如果已经是字典，直接使用
|
||||
|
|
@ -181,12 +165,11 @@ async def update_workflow(
|
|||
|
||||
workflow.set_audit_fields(current_user.id, is_update=True)
|
||||
|
||||
session.commit()
|
||||
session.refresh(workflow)
|
||||
|
||||
logger.info(f"Updated workflow: {workflow.name} by user {current_user.username}")
|
||||
return WorkflowResponse(**convert_workflow_for_response(workflow.to_dict()))
|
||||
|
||||
await session.commit()
|
||||
await session.refresh(workflow)
|
||||
session.desc = f"SUCCESS: 更新工作流数据 commit & refresh {workflow_id}"
|
||||
response = WorkflowResponse(**convert_workflow_for_response(workflow.to_dict()))
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.delete("/{workflow_id}")
|
||||
async def delete_workflow(
|
||||
|
|
@ -196,8 +179,9 @@ async def delete_workflow(
|
|||
):
|
||||
"""删除工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 删除工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).filter(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -207,16 +191,18 @@ async def delete_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 删除工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
)
|
||||
session.desc = f"删除工作流: {workflow.name}"
|
||||
await session.delete(workflow)
|
||||
await session.commit()
|
||||
|
||||
session.delete(workflow)
|
||||
session.commit()
|
||||
|
||||
logger.info(f"Deleted workflow: {workflow.name} by user {current_user.username}")
|
||||
return {"message": "工作流删除成功"}
|
||||
session.desc = f"SUCCESS: 删除工作流数据 commit {workflow_id}"
|
||||
response = {"message": "工作流删除成功"}
|
||||
return HxfResponse(response)
|
||||
|
||||
|
||||
@router.post("/{workflow_id}/activate")
|
||||
|
|
@ -227,8 +213,9 @@ async def activate_workflow(
|
|||
):
|
||||
"""激活工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 激活工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).filter(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -238,6 +225,7 @@ async def activate_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 激活工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
|
|
@ -245,11 +233,11 @@ async def activate_workflow(
|
|||
|
||||
workflow.status = ModelWorkflowStatus.PUBLISHED
|
||||
workflow.set_audit_fields(current_user.id, is_update=True)
|
||||
await session.commit()
|
||||
|
||||
session.commit()
|
||||
|
||||
logger.info(f"Activated workflow: {workflow.name} by user {current_user.username}")
|
||||
return {"message": "工作流激活成功"}
|
||||
session.desc = f"SUCCESS: 激活工作流数据 commit {workflow_id}"
|
||||
response = {"message": "工作流激活成功"}
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/{workflow_id}/deactivate")
|
||||
async def deactivate_workflow(
|
||||
|
|
@ -259,8 +247,9 @@ async def deactivate_workflow(
|
|||
):
|
||||
"""停用工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 停用工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).filter(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -270,6 +259,7 @@ async def deactivate_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 停用工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
|
|
@ -278,10 +268,11 @@ async def deactivate_workflow(
|
|||
workflow.status = ModelWorkflowStatus.ARCHIVED
|
||||
workflow.set_audit_fields(current_user.id, is_update=True)
|
||||
|
||||
session.commit()
|
||||
await session.commit()
|
||||
|
||||
logger.info(f"Deactivated workflow: {workflow.name} by user {current_user.username}")
|
||||
return {"message": "工作流停用成功"}
|
||||
session.desc = f"SUCCESS: 停用工作流数据 commit {workflow_id}"
|
||||
response = {"message": "工作流停用成功"}
|
||||
return HxfResponse(response)
|
||||
|
||||
@router.post("/{workflow_id}/execute", response_model=WorkflowExecutionResponse)
|
||||
async def execute_workflow(
|
||||
|
|
@ -292,8 +283,9 @@ async def execute_workflow(
|
|||
):
|
||||
"""执行工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 执行工作流 {workflow_id}"
|
||||
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).filter(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -303,6 +295,7 @@ async def execute_workflow(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 执行工作流数据 - 工作流不存在 {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
|
|
@ -323,8 +316,8 @@ async def execute_workflow(
|
|||
session=session
|
||||
)
|
||||
|
||||
logger.info(f"Executed workflow: {workflow.name} by user {current_user.username}")
|
||||
return execution_result
|
||||
session.desc = f"SUCCESS: 执行工作流数据 commit {workflow_id}"
|
||||
return HxfResponse(execution_result)
|
||||
|
||||
|
||||
@router.get("/{workflow_id}/executions", response_model=List[WorkflowExecutionResponse])
|
||||
|
|
@ -336,11 +329,12 @@ async def list_workflow_executions(
|
|||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取工作流执行历史"""
|
||||
session.desc = f"START: 获取工作流执行历史 {workflow_id}"
|
||||
try:
|
||||
from ...models.workflow import Workflow, WorkflowExecution
|
||||
|
||||
# 验证工作流所有权
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).where(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -350,24 +344,25 @@ async def list_workflow_executions(
|
|||
)
|
||||
|
||||
if not workflow:
|
||||
session.desc = f"ERROR: 获取工作流执行历史数据 - 工作流不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="工作流不存在"
|
||||
)
|
||||
|
||||
# 获取执行历史
|
||||
executions = session.scalars(
|
||||
executions = (await session.scalars(
|
||||
select(WorkflowExecution).where(
|
||||
WorkflowExecution.workflow_id == workflow_id
|
||||
).order_by(WorkflowExecution.created_at.desc()).offset(skip).limit(limit)
|
||||
).all()
|
||||
)).all()
|
||||
|
||||
return [WorkflowExecutionResponse.model_validate(execution) for execution in executions]
|
||||
session.desc = f"SUCCESS: 获取工作流执行历史数据 commit {workflow_id}"
|
||||
response = [WorkflowExecutionResponse.model_validate(execution) for execution in executions]
|
||||
return HxfResponse(response)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Error listing workflow executions {workflow_id}: {str(e)}")
|
||||
session.desc = f"ERROR: 获取工作流执行历史数据 commit {workflow_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="获取执行历史失败"
|
||||
|
|
@ -380,10 +375,11 @@ async def get_workflow_execution(
|
|||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""获取工作流执行详情"""
|
||||
session.desc = f"START: 获取工作流执行详情 {execution_id}"
|
||||
try:
|
||||
from ...models.workflow import WorkflowExecution, Workflow
|
||||
|
||||
execution = session.scalar(
|
||||
execution = await session.scalar(
|
||||
select(WorkflowExecution).join(
|
||||
Workflow, WorkflowExecution.workflow_id == Workflow.id
|
||||
).where(
|
||||
|
|
@ -393,17 +389,18 @@ async def get_workflow_execution(
|
|||
)
|
||||
|
||||
if not execution:
|
||||
session.desc = f"ERROR: 获取工作流执行详情数据 - 执行记录不存在"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="执行记录不存在"
|
||||
)
|
||||
|
||||
return WorkflowExecutionResponse.model_validate(execution)
|
||||
response = WorkflowExecutionResponse.model_validate(execution)
|
||||
session.desc = f"SUCCESS: 获取工作流执行详情数据 commit {execution_id}"
|
||||
return HxfResponse(response)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting workflow execution {execution_id}: {str(e)}")
|
||||
session.desc = f"ERROR: 获取工作流执行详情数据 commit {execution_id}"
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="获取执行详情失败"
|
||||
|
|
@ -418,7 +415,7 @@ async def execute_workflow_stream(
|
|||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""流式执行工作流，实时推送节点执行状态"""
|
||||
|
||||
session.desc = f"START: 流式执行工作流 {workflow_id}"
|
||||
async def generate_stream() -> AsyncGenerator[str, None]:
|
||||
workflow_engine = None
|
||||
|
||||
|
|
@ -426,7 +423,7 @@ async def execute_workflow_stream(
|
|||
from ...models.workflow import Workflow
|
||||
|
||||
# 验证工作流
|
||||
workflow = session.scalar(
|
||||
workflow = await session.scalar(
|
||||
select(Workflow).filter(
|
||||
and_(
|
||||
Workflow.id == workflow_id,
|
||||
|
|
@ -466,7 +463,7 @@ async def execute_workflow_stream(
|
|||
logger.error(f"流式工作流执行异常: {e}", exc_info=True)
|
||||
yield f"data: {json.dumps({'type': 'error', 'message': f'工作流执行失败: {str(e)}'}, ensure_ascii=False)}\n\n"
|
||||
|
||||
return StreamingResponse(
|
||||
response = StreamingResponse(
|
||||
generate_stream(),
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
|
|
@ -478,3 +475,42 @@ async def execute_workflow_stream(
|
|||
"Access-Control-Allow-Methods": "*"
|
||||
}
|
||||
)
|
||||
session.desc = f"SUCCESS: 流式执行工作流 {workflow_id} 完毕"
|
||||
return HxfResponse(response)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
|
||||
@router.post("/", response_model=WorkflowResponse)
|
||||
async def create_workflow(
|
||||
workflow_data: WorkflowCreate,
|
||||
session: Session = Depends(get_session),
|
||||
current_user: User = Depends(AuthService.get_current_user)
|
||||
):
|
||||
"""创建工作流"""
|
||||
from ...models.workflow import Workflow
|
||||
session.desc = f"START: 创建工作流 {workflow_data.name}"
|
||||
# 创建工作流
|
||||
workflow = Workflow(
|
||||
name=workflow_data.name,
|
||||
description=workflow_data.description,
|
||||
definition=workflow_data.definition.model_dump(),
|
||||
version="1.0.0",
|
||||
status=workflow_data.status,
|
||||
owner_id=current_user.id
|
||||
)
|
||||
session.desc = f"创建工作流实例 - Workflow()， {workflow_data.name}"
|
||||
workflow.set_audit_fields(current_user.id)
|
||||
session.desc = f"保存工作流 - set_audit_fields {workflow_data.name}"
|
||||
|
||||
session.add(workflow)
|
||||
await session.commit()
|
||||
await session.refresh(workflow)
|
||||
session.desc = f"保存工作流 - commit & refresh {workflow_data.name}"
|
||||
# 转换definition中的字段映射
|
||||
workflow_dict = convert_workflow_for_response(workflow.to_dict())
|
||||
session.desc = f"转换工作流数据 - convert_workflow_for_response {workflow_data.name}"
|
||||
|
||||
response = WorkflowResponse(**workflow_dict)
|
||||
session.desc = f"SUCCESS: 返回工作流数据 - WorkflowResponse {workflow_data.name}"
|
||||
return HxfResponse(response)
|
||||
|
||||
|
|
@ -69,13 +69,6 @@ router.include_router(
|
|||
tags=["smart-chat"]
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
router.include_router(
|
||||
workflow.router,
|
||||
prefix="/workflows",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
"""Configuration management for TH Agenter."""
|
||||
|
||||
import os
|
||||
from requests import Session
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
from loguru import logger
|
||||
|
|
@ -88,13 +89,15 @@ class LLMSettings(BaseSettings):
|
|||
"extra": "ignore"
|
||||
}
|
||||
|
||||
def get_current_config(self) -> dict:
|
||||
async def get_current_config(self, session: Session) -> dict:
|
||||
"""获取当前选择的提供商配置 - 优先从数据库读取默认配置."""
|
||||
try:
|
||||
# 尝试从数据库读取默认聊天模型配置
|
||||
from th_agenter.services.llm_config_service import LLMConfigService
|
||||
# 尝试从数据库读取默认聊天模型配置
|
||||
llm_service = LLMConfigService()
|
||||
db_config = llm_service.get_default_chat_config()
|
||||
db_config = None
|
||||
if session:
|
||||
db_config = await llm_service.get_default_chat_config(session)
|
||||
|
||||
if db_config:
|
||||
# 如果数据库中有默认配置，使用数据库配置
|
||||
|
|
@ -105,10 +108,17 @@ class LLMSettings(BaseSettings):
|
|||
"max_tokens": self.max_tokens,
|
||||
"temperature": self.temperature
|
||||
}
|
||||
if session:
|
||||
session.desc = f"使用LLM配置(get_default_chat_config)> {config}"
|
||||
else:
|
||||
logger.info(f"使用LLM配置(get_default_chat_config) > {config}")
|
||||
return config
|
||||
except Exception as e:
|
||||
# 如果数据库读取失败，记录错误并回退到环境变量
|
||||
logger.warning(f"Failed to read LLM config from database, falling back to env vars: {e}")
|
||||
if session:
|
||||
session.desc = f"EXCEPTION: 获取默认对话模型配置失败: {str(e)}"
|
||||
else:
|
||||
logger.error(f"获取默认对话模型配置失败: {str(e)}")
|
||||
|
||||
# 回退到原有的环境变量配置
|
||||
provider_configs = {
|
||||
|
|
@ -182,13 +192,15 @@ class EmbeddingSettings(BaseSettings):
|
|||
"extra": "ignore"
|
||||
}
|
||||
|
||||
def get_current_config(self) -> dict:
|
||||
async def get_current_config(self, session: Session) -> dict:
|
||||
"""获取当前选择的embedding提供商配置 - 优先从数据库读取默认配置."""
|
||||
try:
|
||||
if session:
|
||||
session.desc = "尝试从数据库读取默认嵌入模型配置 ... >>> get_current_config";
|
||||
# 尝试从数据库读取默认嵌入模型配置
|
||||
from th_agenter.services.llm_config_service import LLMConfigService
|
||||
llm_service = LLMConfigService()
|
||||
db_config = llm_service.get_default_embedding_config()
|
||||
db_config = await llm_service.get_default_embedding_config(session)
|
||||
|
||||
if db_config:
|
||||
# 如果数据库中有默认配置，使用数据库配置
|
||||
|
|
@ -200,7 +212,10 @@ class EmbeddingSettings(BaseSettings):
|
|||
return config
|
||||
except Exception as e:
|
||||
# 如果数据库读取失败，记录错误并回退到环境变量
|
||||
logger.warning(f"Failed to read embedding config from database, falling back to env vars: {e}")
|
||||
if session:
|
||||
session.error(f"Failed to read embedding config from database, falling back to env vars: {e}")
|
||||
else:
|
||||
logger.error(f"Failed to read embedding config from database, falling back to env vars: {e}")
|
||||
|
||||
# 回退到原有的环境变量配置
|
||||
provider_configs = {
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from ..models.user import User
|
|||
from loguru import logger
|
||||
|
||||
# Context variable to store current user
|
||||
current_user_context: ContextVar[Optional[User]] = ContextVar('current_user', default=None)
|
||||
current_user_context: ContextVar[Optional[dict]] = ContextVar('current_user', default=None)
|
||||
|
||||
# Thread-local storage as backup
|
||||
_thread_local = threading.local()
|
||||
|
|
@ -19,34 +19,56 @@ class UserContext:
|
|||
"""User context manager for accessing current user globally."""
|
||||
|
||||
@staticmethod
|
||||
def set_current_user(user: User) -> None:
|
||||
def set_current_user(user: User, canLog: bool = False) -> None:
|
||||
"""Set current user in context."""
|
||||
logger.info(f"[UserContext] - Setting user in context: {user.username} (ID: {user.id})")
|
||||
if canLog:
|
||||
logger.info(f"[UserContext] - 设置用户上下文 {user.username} (ID: {user.id})")
|
||||
|
||||
# Store user information as a dictionary instead of the SQLAlchemy model
|
||||
user_dict = {
|
||||
'id': user.id,
|
||||
'username': user.username,
|
||||
'email': user.email,
|
||||
'full_name': user.full_name,
|
||||
'is_active': user.is_active
|
||||
}
|
||||
|
||||
# Set in ContextVar
|
||||
current_user_context.set(user)
|
||||
current_user_context.set(user_dict)
|
||||
|
||||
# Also set in thread-local as backup
|
||||
_thread_local.current_user = user
|
||||
_thread_local.current_user = user_dict
|
||||
|
||||
# Verify it was set
|
||||
verify_user = current_user_context.get()
|
||||
logger.info(f"[UserContext] - Verification - ContextVar user: {verify_user.username if verify_user else None}")
|
||||
if canLog:
|
||||
logger.info(f"[UserContext] - 验证 - ContextVar 用户: {verify_user.get('username') if verify_user else None}")
|
||||
|
||||
@staticmethod
|
||||
def set_current_user_with_token(user: User):
|
||||
def set_current_user_with_token(user: User, canLog: bool = False):
|
||||
"""Set current user in context and return token for cleanup."""
|
||||
logger.info(f"[UserContext] - Setting user in context with token: {user.username} (ID: {user.id})")
|
||||
if canLog:
|
||||
logger.info(f"[UserContext] - 设置用户上下文 {user.username} (ID: {user.id})")
|
||||
|
||||
# Store user information as a dictionary instead of the SQLAlchemy model
|
||||
user_dict = {
|
||||
'id': user.id,
|
||||
'username': user.username,
|
||||
'email': user.email,
|
||||
'full_name': user.full_name,
|
||||
'is_active': user.is_active
|
||||
}
|
||||
|
||||
# Set in ContextVar and get token
|
||||
token = current_user_context.set(user)
|
||||
token = current_user_context.set(user_dict)
|
||||
|
||||
# Also set in thread-local as backup
|
||||
_thread_local.current_user = user
|
||||
_thread_local.current_user = user_dict
|
||||
|
||||
# Verify it was set
|
||||
verify_user = current_user_context.get()
|
||||
logger.info(f"[UserContext] - Verification - ContextVar user: {verify_user.username if verify_user else None}")
|
||||
if canLog:
|
||||
logger.info(f"[UserContext] - 验证 - ContextVar 用户: {verify_user.get('username') if verify_user else None}")
|
||||
|
||||
return token
|
||||
|
||||
|
|
@ -63,34 +85,37 @@ class UserContext:
|
|||
delattr(_thread_local, 'current_user')
|
||||
|
||||
@staticmethod
|
||||
def get_current_user() -> Optional[User]:
|
||||
def get_current_user() -> Optional[dict]:
|
||||
"""Get current user from context."""
|
||||
logger.debug("[UserContext] - Attempting to get user from context")
|
||||
|
||||
# Try ContextVar first
|
||||
user = current_user_context.get()
|
||||
if user:
|
||||
logger.debug(f"[UserContext] - Got user from ContextVar: {user.username} (ID: {user.id})")
|
||||
# logger.info(f"[UserContext] - 取得当前用户为 ContextVar 用户: {user.get('username') if user else None}")
|
||||
return user
|
||||
|
||||
# Fallback to thread-local
|
||||
user = getattr(_thread_local, 'current_user', None)
|
||||
if user:
|
||||
logger.debug(f"[UserContext] - Got user from thread-local: {user.username} (ID: {user.id})")
|
||||
# logger.info(f"[UserContext] - 取得当前用户为线程本地用户: {user.get('username') if user else None}")
|
||||
return user
|
||||
|
||||
logger.debug("[UserContext] - No user found in context (neither ContextVar nor thread-local)")
|
||||
logger.error("[UserContext] - 上下文未找到当前用户 (neither ContextVar nor thread-local)")
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def get_current_user_id() -> Optional[int]:
|
||||
"""Get current user ID from context."""
|
||||
try:
|
||||
user = UserContext.get_current_user()
|
||||
return user.id if user else None
|
||||
return user.get('id') if user else None
|
||||
except Exception as e:
|
||||
logger.error(f"[UserContext] - Error getting current user ID: {e}")
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def clear_current_user() -> None:
|
||||
def clear_current_user(canLog: bool = False) -> None:
|
||||
"""Clear current user from context."""
|
||||
if canLog:
|
||||
logger.info("[UserContext] - 清除当前用户上下文")
|
||||
|
||||
current_user_context.set(None)
|
||||
|
|
@ -98,7 +123,7 @@ class UserContext:
|
|||
delattr(_thread_local, 'current_user')
|
||||
|
||||
@staticmethod
|
||||
def require_current_user() -> User:
|
||||
def require_current_user() -> dict:
|
||||
"""Get current user from context, raise exception if not found."""
|
||||
# Use the same logic as get_current_user to check both ContextVar and thread-local
|
||||
user = UserContext.get_current_user()
|
||||
|
|
@ -114,4 +139,4 @@ class UserContext:
|
|||
def require_current_user_id() -> int:
|
||||
"""Get current user ID from context, raise exception if not found."""
|
||||
user = UserContext.require_current_user()
|
||||
return user.id
|
||||
return user.get('id')
|
||||
|
|
@ -20,7 +20,7 @@ class UserContextMiddleware(BaseHTTPMiddleware):
|
|||
|
||||
def __init__(self, app, exclude_paths: list = None):
|
||||
super().__init__(app)
|
||||
self.canLog = True
|
||||
self.canLog = False
|
||||
# Paths that don't require authentication
|
||||
self.exclude_paths = exclude_paths or [
|
||||
"/docs",
|
||||
|
|
@ -77,7 +77,7 @@ class UserContextMiddleware(BaseHTTPMiddleware):
|
|||
logger.info(f"[MIDDLEWARE] - 路由 {path} 需要认证，开始处理")
|
||||
|
||||
# Always clear any existing user context to ensure fresh authentication
|
||||
UserContext.clear_current_user()
|
||||
UserContext.clear_current_user(self.canLog)
|
||||
|
||||
# Initialize context token
|
||||
user_token = None
|
||||
|
|
@ -96,6 +96,7 @@ class UserContextMiddleware(BaseHTTPMiddleware):
|
|||
# Extract token
|
||||
token = authorization.split(" ")[1]
|
||||
|
||||
|
||||
# Verify token
|
||||
payload = AuthService.verify_token(token)
|
||||
if payload is None:
|
||||
|
|
@ -136,7 +137,7 @@ class UserContextMiddleware(BaseHTTPMiddleware):
|
|||
)
|
||||
|
||||
# Set user in context using token mechanism
|
||||
user_token = UserContext.set_current_user_with_token(user)
|
||||
user_token = UserContext.set_current_user_with_token(user, self.canLog)
|
||||
if self.canLog:
|
||||
logger.info(f"[MIDDLEWARE] - 用户 {user.username} (ID: {user.id}) 已通过认证并设置到上下文")
|
||||
|
||||
|
|
@ -160,8 +161,13 @@ class UserContextMiddleware(BaseHTTPMiddleware):
|
|||
try:
|
||||
response = await call_next(request)
|
||||
return response
|
||||
except Exception as e:
|
||||
# Log error but don't fail the request
|
||||
logger.error(f"[MIDDLEWARE] - 请求处理出错: {e}")
|
||||
# Return 500 error
|
||||
return HxfErrorResponse(e)
|
||||
finally:
|
||||
# Always clear user context after request processing
|
||||
UserContext.clear_current_user()
|
||||
UserContext.clear_current_user(self.canLog)
|
||||
if self.canLog:
|
||||
logger.debug(f"[MIDDLEWARE] - 已清除请求处理后的用户上下文: {path}")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,73 @@
|
|||
"""LLM工厂类，用于创建和管理LLM实例"""
|
||||
|
||||
from typing import Optional
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain.agents import create_agent
|
||||
from loguru import logger
|
||||
from requests import Session
|
||||
from .config import get_settings
|
||||
|
||||
async def new_llm(session: Session = None, model: Optional[str] = None,
|
||||
temperature: Optional[float] = None,
|
||||
streaming: bool = False) -> ChatOpenAI:
|
||||
"""创建LLM实例
|
||||
|
||||
Args:
|
||||
model: 可选，指定使用的模型名称。如果不指定，将使用配置文件中的默认模型
|
||||
temperature: 可选，模型温度参数
|
||||
streaming: 是否启用流式响应，默认False
|
||||
|
||||
Returns:
|
||||
ChatOpenAI实例
|
||||
"""
|
||||
settings = get_settings()
|
||||
llm_config = await settings.llm.get_current_config(session)
|
||||
|
||||
if model:
|
||||
# 根据指定的模型获取对应配置
|
||||
if model.startswith('deepseek'):
|
||||
llm_config['model'] = settings.llm.deepseek_model
|
||||
llm_config['api_key'] = settings.llm.deepseek_api_key
|
||||
llm_config['base_url'] = settings.llm.deepseek_base_url
|
||||
elif model.startswith('doubao'):
|
||||
llm_config['model'] = settings.llm.doubao_model
|
||||
llm_config['api_key'] = settings.llm.doubao_api_key
|
||||
llm_config['base_url'] = settings.llm.doubao_base_url
|
||||
elif model.startswith('glm'):
|
||||
llm_config['model'] = settings.llm.zhipu_model
|
||||
llm_config['api_key'] = settings.llm.zhipu_api_key
|
||||
llm_config['base_url'] = settings.llm.zhipu_base_url
|
||||
elif model.startswith('moonshot'):
|
||||
llm_config['model'] = settings.llm.moonshot_model
|
||||
llm_config['api_key'] = settings.llm.moonshot_api_key
|
||||
llm_config['base_url'] = settings.llm.moonshot_base_url
|
||||
|
||||
llm = ChatOpenAI(
|
||||
model=llm_config['model'],
|
||||
api_key=llm_config['api_key'],
|
||||
base_url=llm_config['base_url'],
|
||||
temperature=temperature if temperature is not None else llm_config['temperature'],
|
||||
max_tokens=llm_config['max_tokens'],
|
||||
streaming=streaming
|
||||
)
|
||||
|
||||
return llm
|
||||
|
||||
async def new_agent(session: Session = None, model: Optional[str] = None,
|
||||
temperature: Optional[float] = None,
|
||||
streaming: bool = False) -> ChatOpenAI:
|
||||
"""创建LLM实例
|
||||
|
||||
Args:
|
||||
model: 可选，指定使用的模型名称。如果不指定，将使用配置文件中的默认模型
|
||||
temperature: 可选，模型温度参数
|
||||
streaming: 是否启用流式响应，默认False
|
||||
|
||||
Returns:
|
||||
ChatOpenAI实例
|
||||
"""
|
||||
llm = await new_llm(session, model, temperature, streaming)
|
||||
result = create_agent(
|
||||
model=llm,
|
||||
)
|
||||
return result
|
||||
|
|
@ -3,6 +3,7 @@
|
|||
from functools import wraps
|
||||
from typing import Optional
|
||||
from fastapi import HTTPException, Depends
|
||||
from loguru import logger
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from ..db.database import get_session
|
||||
|
|
@ -11,45 +12,40 @@ from ..models.permission import Role
|
|||
from ..services.auth import AuthService
|
||||
|
||||
|
||||
def is_super_admin(user: User, db: Session) -> bool:
|
||||
async def is_super_admin(user: User, session: Session) -> bool:
|
||||
"""检查用户是否为超级管理员."""
|
||||
session.desc = f"检查用户 {user.id} 是否为超级管理员"
|
||||
if not user or not user.is_active:
|
||||
session.desc = f"用户 {user.id} 不是活跃状态"
|
||||
return False
|
||||
|
||||
# 检查用户是否有超级管理员角色
|
||||
try:
|
||||
# 尝试访问已加载的角色
|
||||
for role in user.roles:
|
||||
if role.code == "SUPER_ADMIN":
|
||||
return True
|
||||
return False
|
||||
except Exception:
|
||||
# 如果角色未加载或访问失败，直接从数据库查询
|
||||
from sqlalchemy import select, and_
|
||||
from ..models.permission import Role, UserRole
|
||||
# 直接使用提供的session查询，避免MissingGreenlet错误
|
||||
from sqlalchemy import select
|
||||
from ..models.permission import UserRole, Role
|
||||
|
||||
try:
|
||||
# 直接查询用户角色
|
||||
stmt = select(Role).join(UserRole).filter(
|
||||
and_(
|
||||
stmt = select(UserRole).join(Role).filter(
|
||||
UserRole.user_id == user.id,
|
||||
Role.code == "SUPER_ADMIN",
|
||||
Role.code == 'SUPER_ADMIN',
|
||||
Role.is_active == True
|
||||
)
|
||||
)
|
||||
super_admin_role = db.execute(stmt).scalar_one_or_none()
|
||||
return super_admin_role is not None
|
||||
except Exception:
|
||||
# 如果查询失败，返回False
|
||||
user_role = await session.execute(stmt)
|
||||
result = user_role.scalar_one_or_none() is not None
|
||||
session.desc = f"用户 {user.id} 超级管理员角色查询结果: {result}"
|
||||
return result
|
||||
except Exception as e:
|
||||
# 如果调用失败，记录错误并返回False
|
||||
session.desc = f"EXCEPTION: 用户 {user.id} 超级管理员角色查询失败: {str(e)}"
|
||||
logger.error(f"检查用户 {user.id} 超级管理员角色失败: {str(e)}")
|
||||
return False
|
||||
|
||||
|
||||
def require_super_admin(
|
||||
async def require_super_admin(
|
||||
current_user: User = Depends(AuthService.get_current_user),
|
||||
session: Session = Depends(get_session)
|
||||
) -> User:
|
||||
"""要求超级管理员权限的依赖项."""
|
||||
if not is_super_admin(current_user, session):
|
||||
if not await is_super_admin(current_user, session):
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail="需要超级管理员权限"
|
||||
|
|
@ -75,17 +71,17 @@ class SimplePermissionChecker:
|
|||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
|
||||
def check_super_admin(self, user: User) -> bool:
|
||||
async def check_super_admin(self, user: User) -> bool:
|
||||
"""检查是否为超级管理员."""
|
||||
return is_super_admin(user, self.db)
|
||||
return await is_super_admin(user, self.db)
|
||||
|
||||
def check_user_access(self, user: User, target_user_id: int) -> bool:
|
||||
async def check_user_access(self, user: User, target_user_id: int) -> bool:
|
||||
"""检查用户访问权限（自己或超级管理员）."""
|
||||
if not user or not user.is_active:
|
||||
return False
|
||||
|
||||
# 超级管理员可以访问所有用户
|
||||
if self.check_super_admin(user):
|
||||
if await self.check_super_admin(user):
|
||||
return True
|
||||
|
||||
# 用户只能访问自己的信息
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ class BaseModel(Base):
|
|||
def to_dict(self):
|
||||
"""Convert model to dictionary."""
|
||||
return {
|
||||
column.name: getattr(self, column.name)
|
||||
column.name: getattr(self, column.name).isoformat() if hasattr(getattr(self, column.name), 'isoformat') else getattr(self, column.name)
|
||||
for column in self.__table__.columns
|
||||
}
|
||||
|
||||
|
|
@ -60,31 +60,31 @@ class BaseModel(Base):
|
|||
return cls(**filtered_data)
|
||||
|
||||
def set_audit_fields(self, user_id: Optional[int] = None, is_update: bool = False):
|
||||
"""Set audit fields for create/update operations.
|
||||
"""对创建/更新操作设置created_by/updated_by字段。
|
||||
|
||||
Args:
|
||||
user_id: ID of the user performing the operation (optional, will use context if not provided)
|
||||
is_update: True for update operations, False for create operations
|
||||
user_id: 用户ID，用于设置创建/更新操作的审计字段（可选，默认从上下文获取）
|
||||
is_update: True 表示更新操作，False 表示创建操作
|
||||
"""
|
||||
# Get user_id from context if not provided
|
||||
# 如果未提供user_id，则从上下文获取
|
||||
if user_id is None:
|
||||
from ..core.context import UserContext
|
||||
try:
|
||||
user_id = UserContext.get_current_user_id()
|
||||
except Exception:
|
||||
# If no user in context, skip setting audit fields
|
||||
# 如果上下文没有用户ID，则跳过设置审计字段
|
||||
return
|
||||
|
||||
# Skip if still no user_id
|
||||
# 如果仍未提供user_id，则跳过设置审计字段
|
||||
if user_id is None:
|
||||
return
|
||||
|
||||
if not is_update:
|
||||
# For create operations, set both create_by and update_by
|
||||
# 对于创建操作，同时设置created_by和updated_by
|
||||
self.created_by = user_id
|
||||
self.updated_by = user_id
|
||||
else:
|
||||
# For update operations, only set update_by
|
||||
# 对于更新操作，仅设置updated_by
|
||||
self.updated_by = user_id
|
||||
|
||||
# @event.listens_for(Session, 'before_flush')
|
||||
|
|
|
|||
|
|
@ -18,12 +18,27 @@ class DrSession(AsyncSession):
|
|||
def __init__(self, **kwargs):
|
||||
"""Initialize DrSession with unique ID."""
|
||||
super().__init__(**kwargs)
|
||||
self.title = ""
|
||||
self.descs = []
|
||||
# 确保info属性存在
|
||||
if not hasattr(self, 'info'):
|
||||
self.info = {}
|
||||
self.info['session_id'] = str(uuid.uuid4()).split('-')[0]
|
||||
self.stepIndex = 0
|
||||
|
||||
@property
|
||||
def title(self) -> Optional[str]:
|
||||
"""Get work brief from session info."""
|
||||
return self.info.get('title')
|
||||
|
||||
@title.setter
|
||||
def title(self, value: str) -> None:
|
||||
"""Set work brief in session info."""
|
||||
if('title' not in self.info or self.info['title'].strip() == ""):
|
||||
self.info['title'] = value # 确保title属性存在
|
||||
else:
|
||||
self.info['title'] = value + " >>> " + self.info['title']
|
||||
|
||||
@property
|
||||
def desc(self) -> Optional[str]:
|
||||
"""Get work brief from session info."""
|
||||
|
|
@ -34,6 +49,25 @@ class DrSession(AsyncSession):
|
|||
"""Set work brief in session info."""
|
||||
self.stepIndex += 1
|
||||
|
||||
match = re.search(r";(-\d+)", value);
|
||||
level = -3
|
||||
if match:
|
||||
level = int(match.group(1))
|
||||
value = value.replace(f";{level}", "")
|
||||
level = -3 + level
|
||||
|
||||
if "警告" in value or value.startswith("WARNING"):
|
||||
self.log_warning(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
elif "异常" in value or value.startswith("EXCEPTION"):
|
||||
self.log_exception(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
elif "成功" in value or value.startswith("SUCCESS"):
|
||||
self.log_success(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
elif "开始" in value or value.startswith("START"):
|
||||
self.log_success(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
elif "失败" in value or value.startswith("ERROR"):
|
||||
self.log_error(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
else:
|
||||
self.log_info(f"第 {self.stepIndex} 步 - {value}", level = level)
|
||||
|
||||
def log_prefix(self) -> str:
|
||||
"""Get log prefix with session ID and desc."""
|
||||
|
|
@ -74,14 +108,14 @@ class DrSession(AsyncSession):
|
|||
|
||||
engine_async = create_async_engine(
|
||||
get_settings().database.url,
|
||||
echo=True, # get_settings().database.echo,
|
||||
echo=False, # get_settings().database.echo,
|
||||
future=True,
|
||||
pool_size=get_settings().database.pool_size,
|
||||
max_overflow=get_settings().database.max_overflow,
|
||||
pool_pre_ping=True,
|
||||
pool_recycle=3600,
|
||||
)
|
||||
from fastapi import Request
|
||||
from fastapi import HTTPException, Request
|
||||
|
||||
AsyncSessionFactory = sessionmaker(
|
||||
bind=engine_async,
|
||||
|
|
@ -95,10 +129,15 @@ async def get_session(request: Request = None):
|
|||
if request:
|
||||
url = f"{request.method} {request.url.path}"# .split("://")[-1]
|
||||
# session = AsyncSessionFactory()
|
||||
|
||||
print(url)
|
||||
# 取得request的来源IP
|
||||
if request:
|
||||
client_host = request.client.host
|
||||
else:
|
||||
client_host = "无request"
|
||||
session = DrSession(bind=engine_async)
|
||||
|
||||
session.desc = f"SUCCESS: 创建数据库 session >>> {url}"
|
||||
session.title = f"{url} - {client_host}"
|
||||
|
||||
# 设置request属性
|
||||
if request:
|
||||
|
|
@ -111,8 +150,9 @@ async def get_session(request: Request = None):
|
|||
errMsg = f"数据库 session 异常 >>> {e}"
|
||||
session.desc = f"EXCEPTION: {errMsg}"
|
||||
await session.rollback()
|
||||
raise e
|
||||
# DatabaseError(e)
|
||||
# 重新抛出原始异常，不转换为 HTTPException
|
||||
raise e # HTTPException(status_code=e.status_code, detail=errMsg) # main.py中将捕获本异常
|
||||
finally:
|
||||
session.desc = f"数据库 session 关闭"
|
||||
# session.desc = f"数据库 session 关闭"
|
||||
session.desc = ""
|
||||
await session.close()
|
||||
|
|
|
|||
|
|
@ -11,8 +11,8 @@ sys.path.insert(0, str(backend_dir))
|
|||
|
||||
from sqlalchemy import create_engine, text
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
from th_agenter.core.config import settings
|
||||
from th_agenter.db.database import Base, get_session
|
||||
from th_agenter.core.config import get_settings
|
||||
from th_agenter.db.database import Base
|
||||
from th_agenter.models import * # Import all models to ensure they're registered
|
||||
from th_agenter.utils.logger import get_logger
|
||||
from th_agenter.models.resource import Resource
|
||||
|
|
@ -25,8 +25,25 @@ def migrate_hardcoded_resources():
|
|||
"""Migrate hardcoded resources from init_resource_data.py to database."""
|
||||
db = None
|
||||
try:
|
||||
# Get database settings
|
||||
settings = get_settings()
|
||||
|
||||
# Create synchronous engine (remove asyncpg from URL)
|
||||
sync_db_url = settings.database.url.replace('postgresql+asyncpg://', 'postgresql://')
|
||||
sync_engine = create_engine(
|
||||
sync_db_url,
|
||||
echo=False,
|
||||
pool_size=settings.database.pool_size,
|
||||
max_overflow=settings.database.max_overflow,
|
||||
pool_pre_ping=True,
|
||||
pool_recycle=3600,
|
||||
)
|
||||
|
||||
# Create synchronous session factory
|
||||
SyncSessionFactory = sessionmaker(bind=sync_engine)
|
||||
|
||||
# Get database session
|
||||
db = get_session() # xxxx
|
||||
db = SyncSessionFactory()
|
||||
|
||||
if db is None:
|
||||
logger.error("Failed to create database session")
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ branch_labels = None
|
|||
depends_on = None
|
||||
|
||||
|
||||
def upgrade():
|
||||
async def upgrade():
|
||||
"""删除权限相关表."""
|
||||
|
||||
# 获取数据库连接
|
||||
|
|
@ -44,7 +44,7 @@ def upgrade():
|
|||
WHERE table_name = '{table_name}'
|
||||
);
|
||||
"""))
|
||||
table_exists = result.scalar()
|
||||
table_exists = await result.scalar()
|
||||
|
||||
if table_exists:
|
||||
print(f"删除表: {table_name}")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,198 @@
|
|||
from loguru import logger
|
||||
from typing import List, Dict, Optional, Union, AsyncGenerator, Generator, Any
|
||||
|
||||
# 核心：导入 LangChain 的基础语言模型抽象类
|
||||
from langchain_core.language_models import BaseLanguageModel
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.messages import BaseMessage
|
||||
from langchain_core.outputs import ChatResult
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Optional, Dict, Any, List
|
||||
from datetime import datetime
|
||||
|
||||
@dataclass
|
||||
class LLMConfig_DataClass:
|
||||
"""
|
||||
统一的LLM配置基类，覆盖在线/本地/嵌入式模型所有配置，映射数据库完整字段
|
||||
通过 provider + is_embedding 区分模型类型：
|
||||
- 在线模型：provider in ['openai', 'zhipu', 'baidu'] + is_embedding=False
|
||||
- 本地模型：provider in ['llama', 'qwen', 'yi'] + is_embedding=False
|
||||
- 嵌入式模型：provider in ['bge', 'text2vec'] + is_embedding=True
|
||||
"""
|
||||
# ====================== 数据库核心公共字段（必选/可选） ======================
|
||||
# 基础标识字段
|
||||
name: str # 模型自定义名称（如 "gpt-5"）
|
||||
model_name: str # 模型官方标识名（如 "gpt-5"、"BAAI/bge-small-zh-v1.5"）
|
||||
provider: str # 提供商（openai/llama/bge/zhipu 等）
|
||||
id: Optional[int] = None # 数据库主键ID
|
||||
description: Optional[str] = None # 模型描述
|
||||
is_active: bool = True # 是否启用
|
||||
is_default: bool = False # 是否默认模型
|
||||
is_embedding: bool = False # 是否为嵌入式模型（核心区分标识）
|
||||
|
||||
# ====================== 通用生成参数（所有推理模型共用） ======================
|
||||
temperature: float = 0.7 # 生成温度（默认值对齐数据库示例）
|
||||
max_tokens: int = 3000 # 最大生成长度（默认值对齐数据库示例）
|
||||
top_p: float = 0.6 # 采样Top-P
|
||||
frequency_penalty: float = 0.0 # 频率惩罚
|
||||
presence_penalty: float = 0.0 # 存在惩罚
|
||||
|
||||
# ====================== 在线模型专属参数（非必填，仅在线模型生效） ======================
|
||||
api_key: Optional[str] = None # API密钥（在线模型必填）
|
||||
base_url: Optional[str] = None # API代理地址（如 https://api.openai-proxy.org/v1）
|
||||
# timeout: int = 30 # 请求超时时间（秒）
|
||||
max_retries: int = 3 # 最大重试次数
|
||||
api_version: Optional[str] = None # API版本（如 OpenAI 的 2024-02-15-preview）
|
||||
|
||||
# ====================== 本地模型专属参数（非必填，仅本地模型生效） ======================
|
||||
model_path: Optional[str] = None # 本地模型文件路径（本地模型必填）
|
||||
device: str = "cpu" # 运行设备（cpu/cuda/mps）
|
||||
n_ctx: int = 2048 # 上下文窗口大小
|
||||
n_threads: int = 8 # 推理线程数
|
||||
quantization: str = "q4_0" # 量化级别（q4_0/q8_0/f16）
|
||||
load_in_8bit: bool = False # 是否8bit加载
|
||||
load_in_4bit: bool = False # 是否4bit加载
|
||||
prompt_template: Optional[str] = None # 自定义Prompt模板
|
||||
|
||||
# ====================== 嵌入式模型专属参数（非必填，仅嵌入式模型生效） ======================
|
||||
normalize_embeddings: bool = True # 是否归一化向量
|
||||
batch_size: int = 32 # 批量编码大小
|
||||
encode_kwargs: Dict[str, Any] = field(default_factory=dict) # 编码扩展参数
|
||||
dimension: Optional[int] = None # 向量维度（如 768）
|
||||
|
||||
# ====================== 元数据字段（数据库自动维护） ======================
|
||||
extra_config: Dict[str, Any] = field(default_factory=dict) # 额外扩展配置
|
||||
usage_count: int = 0 # 使用次数
|
||||
last_used_at: Optional[datetime] = None # 最后使用时间
|
||||
created_at: Optional[datetime] = None # 创建时间
|
||||
updated_at: Optional[datetime] = None # 更新时间
|
||||
created_by: Optional[int] = None # 创建人ID
|
||||
updated_by: Optional[int] = None # 更新人ID
|
||||
|
||||
api_key_masked: Optional[str] = "" # 掩码后的API密钥（数据库存储）
|
||||
|
||||
# ====================== 核心工具方法 ======================
|
||||
def __post_init__(self):
|
||||
"""后置初始化：自动校验和修正配置"""
|
||||
# 1. 嵌入式模型强制清空推理参数（避免误用）
|
||||
if self.is_embedding:
|
||||
self.max_tokens = 0
|
||||
self.temperature = 0.0
|
||||
self.top_p = 0.0
|
||||
|
||||
# 2. 校验必填参数（按模型类型）
|
||||
self._validate_required_fields()
|
||||
|
||||
def _validate_required_fields(self):
|
||||
"""按模型类型校验必填参数"""
|
||||
# 在线模型校验
|
||||
if not self.is_embedding and self.provider in ['openai', 'zhipu', 'baidu', 'anthropic']:
|
||||
if not self.api_key:
|
||||
raise ValueError(f"[{self.name}] 在线模型({self.provider})必须配置 api_key")
|
||||
|
||||
# 本地模型校验
|
||||
if not self.is_embedding and self.provider in ['llama', 'qwen', 'yi', 'glm', 'mistral']:
|
||||
if not self.model_path:
|
||||
raise ValueError(f"[{self.name}] 本地模型({self.provider})必须配置 model_path")
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""转换为字典（用于存入/更新数据库）"""
|
||||
return {
|
||||
key: value for key, value in self.__dict__.items()
|
||||
if not key.startswith('_') # 排除私有属性
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_db_dict(cls, db_dict: Dict[str, Any]) -> "LLMConfig_DataClass":
|
||||
"""从数据库字典初始化配置（核心方法）"""
|
||||
# 1. 时间字段转换：字符串 → datetime
|
||||
time_fields = ['last_used_at', 'created_at', 'updated_at']
|
||||
for field_name in time_fields:
|
||||
val = db_dict.get(field_name)
|
||||
if val and isinstance(val, str):
|
||||
try:
|
||||
db_dict[field_name] = datetime.fromisoformat(val.replace('Z', '+00:00'))
|
||||
except (ValueError, TypeError):
|
||||
db_dict[field_name] = None
|
||||
|
||||
# 2. 过滤数据库中无关字段（如 api_key_masked）
|
||||
valid_fields = cls.__dataclass_fields__.keys()
|
||||
filtered_dict = {k: v for k, v in db_dict.items() if k in valid_fields}
|
||||
|
||||
# 3. 初始化并返回配置实例
|
||||
return cls(**filtered_dict)
|
||||
|
||||
def get_model_type(self) -> str:
|
||||
"""快速判断模型类型（返回：online/local/embedding）"""
|
||||
if self.is_embedding:
|
||||
return "embedding"
|
||||
if self.provider in ['openai', 'zhipu', 'baidu', 'anthropic']:
|
||||
return "online"
|
||||
if self.provider in ['llama', 'qwen', 'yi', 'glm', 'mistral']:
|
||||
return "local"
|
||||
return "unknown"
|
||||
|
||||
|
||||
class BaseLLM(BaseChatModel):
|
||||
"""
|
||||
继承 LangChain 的 BaseChatModel（BaseLanguageModel 的子类）
|
||||
使其能直接用于 create_agent
|
||||
"""
|
||||
# 配置参数（通过 __init__ 初始化）
|
||||
config: Any = None
|
||||
model: Any = None
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__() # 必须调用父类构造函数
|
||||
self.config = config
|
||||
self.model = None
|
||||
self._validate_config()
|
||||
logger.info(f"初始化 {self.__class__.__name__}，模型: {config.model_name}")
|
||||
|
||||
# ---------------------- 必须实现的核心抽象方法（LangChain 协议） ----------------------
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
"""
|
||||
核心同步生成方法（LangChain 要求必须实现）
|
||||
messages: 消息列表（如 [HumanMessage(content="你好")]）
|
||||
返回 ChatResult 类型（LangChain 标准输出）
|
||||
"""
|
||||
logger.error(f"{self.__class__.__name__} 未实现 同步 _generate 方法")
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
** kwargs: Any,
|
||||
) -> ChatResult:
|
||||
"""异步生成方法（LangChain 异步协议）"""
|
||||
logger.error(f"{self.__class__.__name__} 未实现 异步 _agenerate 方法")
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""返回模型类型标识（如 "openai"、"llama"、"bge"）"""
|
||||
return self.__class__.__name__
|
||||
|
||||
def load_model(self) -> None:
|
||||
"""加载模型（自定义逻辑）"""
|
||||
logger.error(f"{self.__class__.__name__} 未实现 load_model 方法")
|
||||
|
||||
def close(self) -> None:
|
||||
"""释放资源（自定义逻辑）"""
|
||||
if self.model:
|
||||
logger.info(f"释放 {self.__class__.__name__} 模型资源")
|
||||
self.model = None
|
||||
|
||||
def __enter__(self):
|
||||
self.load_model()
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
self.close()
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
from typing import List
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from loguru import logger
|
||||
from th_agenter.llm.base_llm import BaseLLM
|
||||
|
||||
class EmbedLLM(BaseLLM, Embeddings):
|
||||
"""嵌入式模型继承 LangChain 的 Embeddings 抽象类，而非 BaseLanguageModel"""
|
||||
def __init__(self, config):
|
||||
logger.info(f"初始化 EmbedLLM 模型: {config.model_name}")
|
||||
super().__init__(config)
|
||||
logger.info(f"已加载 EmbedLLM 模型: {config.model_name}")
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""LangChain 要求的核心方法：批量文档向量化"""
|
||||
pass
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""异步批量向量化"""
|
||||
pass
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""单查询文本向量化"""
|
||||
pass
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
"""异步单查询向量化"""
|
||||
pass
|
||||
|
||||
# 具体实现 BGE 嵌入式模型
|
||||
class BGEEmbedLLM(EmbedLLM):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
def _validate_config(self):
|
||||
if not self.config.model_name:
|
||||
raise ValueError("必须配置 model_name")
|
||||
|
||||
def load_model(self):
|
||||
logger.info(f"正在加载 嵌入 模型: {self.config.model_name}")
|
||||
if hasattr(self.config, 'provider') and self.config.provider == 'ollama':
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
self.model = OllamaEmbeddings(
|
||||
model=self.config.model_name,
|
||||
base_url=self.config.base_url if hasattr(self.config, 'base_url') else None
|
||||
)
|
||||
else:
|
||||
try:
|
||||
from langchain_huggingface import HuggingFaceEmbeddings
|
||||
self.model = HuggingFaceEmbeddings(
|
||||
model_name=self.config.model_name,
|
||||
model_kwargs={"device": self.config.device if hasattr(self.config, 'device') else "cpu"},
|
||||
encode_kwargs={"normalize_embeddings": self.config.normalize_embeddings if hasattr(self.config, 'normalize_embeddings') else True}
|
||||
)
|
||||
except ImportError as e:
|
||||
logger.error(f"Failed to load HuggingFaceEmbeddings: {e}")
|
||||
logger.error("Please install sentence-transformers: pip install sentence-transformers")
|
||||
raise
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
return self.model.embed_documents(texts)
|
||||
|
||||
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
return await self.model.aembed_documents(texts)
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
return self.model.embed_query(text)
|
||||
|
||||
async def aembed_query(self, text: str) -> List[float]:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
return await self.model.aembed_query(text)
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
import os, dotenv
|
||||
from loguru import logger
|
||||
from utils.Constant import Constant
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_core.messages import HumanMessage
|
||||
# 加载环境变量
|
||||
dotenv.load_dotenv()
|
||||
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
|
||||
os.environ["OPENAI_BASE_URL"] = os.getenv("OPENAI_BASE_URL")
|
||||
|
||||
class LLM_Model_Base(object):
|
||||
'''
|
||||
语言模型基类
|
||||
所有语言模型类的基类，定义了语言模型的基本属性和方法。
|
||||
- 语言模型名称， 缺省为"gpt-4o-mini"
|
||||
- 温度，缺省为0.7
|
||||
- 语言模型实例， 由子类实现
|
||||
- 语言模型模式， 由子类实现
|
||||
- 语言模型名称， 用于描述语言模型， 在人机界面中显示
|
||||
|
||||
author: DrGraph
|
||||
date: 2025-11-20
|
||||
'''
|
||||
def __init__(self, model_name: str = "gpt-4o-mini", temperature: float = 0.7):
|
||||
self.model_name = model_name # 0.15 0.6
|
||||
self.temperature = temperature
|
||||
self.llmModel = None
|
||||
self.mode = Constant.LLM_MODE_NONE
|
||||
self.name = '未知模型'
|
||||
|
||||
def buildPromptTemplateValue(self, prompt: str, methodType: str, valueType: str):
|
||||
logger.info(f"{self.name} >>> 1.1 用户输入: {type(prompt)}")
|
||||
prompt_template = PromptTemplate.from_template(
|
||||
template="请回答以下问题: {question}",
|
||||
)
|
||||
prompt_template_value = None
|
||||
if methodType == "format":
|
||||
# 方式1 - 使用format方法，取得字符串
|
||||
prompt_str = prompt_template.format(question=prompt) # prompt 为 字符串
|
||||
logger.info(f"{self.name} >>> 1.2 通过PromptTemplate实例 format 方法，取得字符串prompt_str, 然后再处理 - {type(prompt_str)} - {prompt_str}")
|
||||
|
||||
if valueType == "str":
|
||||
# 1.1 直接用字符串进行调用LLM的invoke
|
||||
prompt_template_value = prompt_str
|
||||
logger.info(f"{self.name} >>> 1.2.1 直接使用字符串")
|
||||
|
||||
elif valueType == "messages":
|
||||
# 1.2 由字符串，创建HumanMessage对象列表
|
||||
prompt_template_value = [HumanMessage(content=prompt)]
|
||||
logger.info(f"{self.name} >>> 1.2.2 创建HumanMessage对象列表")
|
||||
|
||||
elif methodType == "invoke":
|
||||
# 方式2 - 使用invoke方法，取得PromptValue
|
||||
prompt_value = prompt_template.invoke(input={"question" : prompt}) # prompt 为 langchain_core.prompt_values.StringPromptValue
|
||||
logger.info(f"{self.name} >>> 1.2 通过PromptTemplate实例 invoke 方法，取得PromptValue, 然后再处理 - {type(prompt_value)} - {prompt_value}")
|
||||
if valueType == "str":
|
||||
# 2.1 再倒回字符串方式
|
||||
prompt_template_value = prompt_value.to_string()
|
||||
logger.info(f"{self.name} >>> 1.2.1 由 PromptValue 转换为字符串")
|
||||
elif valueType == "promptValue":
|
||||
# 2.2 直接使用 prompt_value 作为 prompt_template_value
|
||||
prompt_template_value = prompt_value
|
||||
logger.info(f"{self.name} >>> 1.2.2 直接使用 PromptValue 作为 prompt_template_value")
|
||||
elif valueType == "messages":
|
||||
# 2.3 使用 prompt_value.to_messages() 方法，将 PromptValue 转换为 HumanMessage 对象列表
|
||||
prompt_template_value = prompt_value.to_messages()
|
||||
logger.info(f"{self.name} >>> 1.2.3 使用 PromptValue.to_messages() 方法，将 PromptValue 转换为 HumanMessage 对象列表")
|
||||
|
||||
logger.info(f"{self.name} >>> 1.3 用户输入 最终包装为(PromptValue/str/list of BaseMessages): {type(prompt_template_value)}\n{prompt_template_value}")
|
||||
return prompt_template_value
|
||||
|
|
@ -0,0 +1,29 @@
|
|||
from langchain_openai import ChatOpenAI
|
||||
from loguru import logger
|
||||
|
||||
from DrGraph.utils.Constant import Constant
|
||||
from LLM.llm_model_base import LLM_Model_Base
|
||||
|
||||
class Chat_LLM(LLM_Model_Base):
|
||||
def __init__(self, model_name: str = "gpt-4o-mini", temperature: float = 0.7):
|
||||
super().__init__(model_name, temperature)
|
||||
self.name = '聊天模型'
|
||||
self.mode = Constant.LLM_MODE_CHAT
|
||||
self.llmModel = ChatOpenAI(
|
||||
model_name=self.model_name,
|
||||
temperature=self.temperature,
|
||||
)
|
||||
|
||||
# 返回消息格式，以便在chatbot中显示
|
||||
def invoke(self, prompt: str):
|
||||
prompt_template_value = self.buildPromptTemplateValue(
|
||||
prompt=prompt,
|
||||
methodType=Constant.LLM_PROMPT_TEMPLATE_METHOD_INVOKE,
|
||||
valueType=Constant.LLM_PROMPT_VALUE_MESSAGES)
|
||||
try:
|
||||
response = self.llmModel.invoke(prompt_template_value)
|
||||
logger.info(f"{self.name} >>> 2. 助手回复: {type(response)}\n{response}")
|
||||
# response = {"role": "assistant", "content": response.content}
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
return response
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
'''
|
||||
非聊天模型类,继承自 LLM_Model_Base
|
||||
|
||||
author: DrGraph
|
||||
date: 2025-11-20
|
||||
'''
|
||||
from loguru import logger
|
||||
from langchain_openai import OpenAI
|
||||
from langchain_core.messages import AIMessage
|
||||
from DrGraph.utils.Constant import Constant
|
||||
from LLM.llm_model_base import LLM_Model_Base
|
||||
|
||||
|
||||
class NonChat_LLM(LLM_Model_Base):
|
||||
'''
|
||||
非聊天模型类，继承自 LLM_Model_Base，调用这个非聊天模型OpenAI
|
||||
- 语言模型名称， 缺省为"gpt-4o-mini"
|
||||
- 温度，缺省为0.7
|
||||
- 语言模型名称 = "非聊天模型"， 在人机界面中显示
|
||||
'''
|
||||
def __init__(self, model_name: str = "gpt-4o-mini", temperature: float = 0.7):
|
||||
super().__init__(model_name, temperature)
|
||||
self.name = '非聊天模型'
|
||||
self.mode = Constant.LLM_MODE_NONCHAT
|
||||
self.llmModel = OpenAI(
|
||||
model_name=self.model_name,
|
||||
temperature=self.temperature,
|
||||
)
|
||||
# 返回消息格式，以便在chatbot中显示
|
||||
def invoke(self, prompt: str):
|
||||
'''
|
||||
调用非聊天模型，返回消息格式，以便在chatbot中显示
|
||||
prompt: 用户输入，为字符串类型
|
||||
return: 助手回复，为字符串类型
|
||||
'''
|
||||
logger.info(f"{self.name} >>> 1.1 用户输入: {type(prompt)}")
|
||||
try:
|
||||
response = self.llmModel.invoke(prompt)
|
||||
logger.info(f"{self.name} >>> 1.2 助手回复: {type(response)}")
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
return response
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
from loguru import logger
|
||||
|
||||
from DrGraph.utils.Constant import Constant
|
||||
from LLM.llm_model_base import LLM_Model_Base
|
||||
from langchain_ollama import ChatOllama
|
||||
class Chat_Ollama(LLM_Model_Base):
|
||||
def __init__(self, base_url="http://127.0.0.1:11434", model_name: str = "OxW/Qwen3-0.6B-GGUF:latest", temperature: float = 0.7):
|
||||
super().__init__(model_name, temperature)
|
||||
self.name = '私有化Ollama模型'
|
||||
self.base_url = base_url
|
||||
self.llmModel = ChatOllama(
|
||||
base_url = self.base_url,
|
||||
model=model_name,
|
||||
temperature=temperature
|
||||
)
|
||||
self.mode = Constant.LLM_MODE_LOCAL_OLLAMA
|
||||
|
||||
def invoke(self, prompt: str):
|
||||
prompt_template_value = self.buildPromptTemplateValue(
|
||||
prompt=prompt,
|
||||
methodType=Constant.LLM_PROMPT_TEMPLATE_METHOD_INVOKE,
|
||||
valueType=Constant.LLM_PROMPT_VALUE_MESSAGES)
|
||||
try:
|
||||
response = self.llmModel.invoke(prompt_template_value)
|
||||
logger.info(f"{self.name} >>> 2. 助手回复: {type(response)}\n{response}")
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
return response
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
from typing import List, Optional
|
||||
from th_agenter.llm.base_llm import BaseLLM
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.messages import BaseMessage, AIMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatResult, ChatGeneration
|
||||
|
||||
|
||||
class LocalLLM(BaseLLM):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.local_config = config
|
||||
|
||||
def _validate_config(self):
|
||||
if not self.local_config.model_path:
|
||||
raise ValueError("LocalLLM 必须配置 model_path")
|
||||
|
||||
def load_model(self):
|
||||
from langchain_community.llms import LlamaCpp
|
||||
self.model = LlamaCpp(
|
||||
model_path=self.local_config.model_path,
|
||||
temperature=self.local_config.temperature,
|
||||
max_tokens=self.local_config.max_tokens,
|
||||
n_ctx=self.local_config.n_ctx,
|
||||
n_threads=self.local_config.n_threads,
|
||||
verbose=False
|
||||
)
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "llama"
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,** kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
# 适配 LlamaCpp（非 Chat 模型）的调用方式
|
||||
prompt = self._format_messages(messages)
|
||||
text = self.model.invoke(prompt, stop=stop, **kwargs)
|
||||
# 构造 ChatResult（LangChain 标准格式）
|
||||
generation = ChatGeneration(message=AIMessage(content=text))
|
||||
return ChatResult(generations=[generation])
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,** kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
prompt = self._format_messages(messages)
|
||||
text = await self.model.ainvoke(prompt, stop=stop, **kwargs)
|
||||
generation = ChatGeneration(message=AIMessage(content=text))
|
||||
return ChatResult(generations=[generation])
|
||||
|
||||
def _format_messages(self, messages: List[BaseMessage]) -> str:
|
||||
"""将 LangChain 消息列表格式化为本地模型的 Prompt"""
|
||||
prompt_parts = []
|
||||
for msg in messages:
|
||||
if isinstance(msg, HumanMessage):
|
||||
prompt_parts.append(f"<s>[INST] {msg.content} [/INST]")
|
||||
elif isinstance(msg, AIMessage):
|
||||
prompt_parts.append(msg.content)
|
||||
return "".join(prompt_parts)
|
||||
|
|
@ -0,0 +1,80 @@
|
|||
from langchain_openai import ChatOpenAI
|
||||
from langchain_core.messages import HumanMessage, BaseMessage
|
||||
from typing import List, Optional, Any, Union
|
||||
from langchain_core.outputs import ChatResult
|
||||
from th_agenter.llm.base_llm import BaseLLM
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
|
||||
class OnlineLLM(BaseLLM):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
def _validate_config(self):
|
||||
if not self.config.api_key:
|
||||
raise ValueError("OnlineLLM 必须配置 api_key")
|
||||
|
||||
def load_model(self):
|
||||
# from langchain.chat_models import init_chat_model
|
||||
# self.model = init_chat_model(
|
||||
# self.config.model_name,
|
||||
# self.config.api_key)
|
||||
from langchain_openai import ChatOpenAI
|
||||
self.model = ChatOpenAI(
|
||||
api_key=self.config.api_key,
|
||||
model_name=self.config.model_name,
|
||||
temperature=self.config.temperature,
|
||||
max_tokens=self.config.max_tokens,
|
||||
base_url=self.config.base_url,
|
||||
)
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "openai" # 标识模型类型
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,** kwargs: Any,
|
||||
) -> ChatResult:
|
||||
"""委托给底层 LangChain 模型的 _generate 方法"""
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
# 复用底层模型的实现
|
||||
return self.model._generate(
|
||||
messages=messages,
|
||||
stop=stop,
|
||||
run_manager=run_manager,** kwargs
|
||||
)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,** kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if not self.model:
|
||||
self.load_model()
|
||||
return await self.model._agenerate(
|
||||
messages=messages,
|
||||
stop=stop,
|
||||
run_manager=run_manager,** kwargs
|
||||
)
|
||||
|
||||
# ---------------------- 保留自定义的便捷方法 ----------------------
|
||||
def generate(self, prompt: Union[str, List[BaseMessage]], **kwargs) -> str:
|
||||
"""自定义便捷方法：直接传入字符串 prompt 或消息列表"""
|
||||
if isinstance(prompt, str):
|
||||
messages = [HumanMessage(content=prompt)]
|
||||
else:
|
||||
messages = prompt
|
||||
result = self._generate(messages, **kwargs)
|
||||
return result.generations[0].text
|
||||
|
||||
async def async_generate(self, prompt: Union[str, List[BaseMessage]], **kwargs) -> str:
|
||||
"""自定义便捷异步方法：直接传入字符串 prompt 或消息列表"""
|
||||
if isinstance(prompt, str):
|
||||
messages = [HumanMessage(content=prompt)]
|
||||
else:
|
||||
messages = prompt
|
||||
result = await self._agenerate(messages, **kwargs)
|
||||
return result.generations[0].text
|
||||
|
|
@ -24,15 +24,20 @@ class Conversation(BaseModel):
|
|||
|
||||
# Relationships removed to eliminate foreign key constraints
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Convert conversation to a dictionary."""
|
||||
return {
|
||||
"id": self.id,
|
||||
"title": self.title,
|
||||
"user_id": self.user_id,
|
||||
"knowledge_base_id": self.knowledge_base_id,
|
||||
"system_prompt": self.system_prompt,
|
||||
"model_name": self.model_name,
|
||||
"temperature": self.temperature,
|
||||
"max_tokens": self.max_tokens,
|
||||
"is_archived": self.is_archived,
|
||||
"message_count": self.message_count,
|
||||
"last_message_at": self.last_message_at,
|
||||
}
|
||||
def __repr__(self):
|
||||
return f"<Conversation(id={self.id}, title='{self.title}', user_id={self.user_id})>"
|
||||
|
||||
@property
|
||||
def message_count(self):
|
||||
"""Get the number of messages in this conversation."""
|
||||
return len(self.messages)
|
||||
|
||||
@property
|
||||
def last_message_at(self):
|
||||
"""Get the timestamp of the last message."""
|
||||
return self.messages[-1].created_at or self.created_at
|
||||
return f"<Conversation(id={self.id}, title='{self.title}', user_id={self.user_id}, system_prompt={self.system_prompt}, model_name='{self.model_name}', temperature='{self.temperature}', message_count={self.message_count})>"
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ class LLMConfig(BaseModel):
|
|||
last_used_at: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True) # 最后使用时间
|
||||
|
||||
def __repr__(self):
|
||||
return f"<LLMConfig(id={self.id}, name='{self.name}', provider='{self.provider}', model='{self.model_name}')>"
|
||||
return f"<LLMConfig(id={self.id}, name='{self.name}', provider='{self.provider}', model_name='{self.model_name}', base_url='{self.base_url}')>"
|
||||
|
||||
def to_dict(self, include_sensitive=False):
|
||||
"""Convert to dictionary, optionally excluding sensitive data."""
|
||||
|
|
@ -60,7 +60,7 @@ class LLMConfig(BaseModel):
|
|||
'is_embedding': self.is_embedding,
|
||||
'extra_config': self.extra_config,
|
||||
'usage_count': self.usage_count,
|
||||
'last_used_at': self.last_used_at
|
||||
'last_used_at': self.last_used_at.isoformat() if self.last_used_at else None
|
||||
})
|
||||
|
||||
if include_sensitive:
|
||||
|
|
@ -102,8 +102,8 @@ class LLMConfig(BaseModel):
|
|||
if not self.name or not self.name.strip():
|
||||
return {"valid": False, "error": "配置名称不能为空"}
|
||||
|
||||
if not self.provider or self.provider not in ['openai', 'deepseek', 'doubao', 'zhipu', 'moonshot', 'baidu']:
|
||||
return {"valid": False, "error": "不支持的服务商"}
|
||||
if not self.provider or self.provider not in ['openai', 'deepseek', 'doubao', 'zhipu', 'moonshot', 'baidu', 'ollama']:
|
||||
return {"valid": False, "error": f"不支持的服务商 {self.provider}"}
|
||||
|
||||
if not self.model_name or not self.model_name.strip():
|
||||
return {"valid": False, "error": "模型名称不能为空"}
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ class LLMConfigCreate(LLMConfigBase):
|
|||
allowed_providers = [
|
||||
'openai', 'azure', 'anthropic', 'google', 'baidu',
|
||||
'alibaba', 'tencent', 'zhipu', 'moonshot', 'deepseek',
|
||||
'ollama', 'custom', "doubao"
|
||||
'ollama', 'custom', "doubao", "ollama"
|
||||
]
|
||||
if v.lower() not in allowed_providers:
|
||||
raise ValueError(f'不支持的服务商: {v}，支持的服务商: {", ".join(allowed_providers)}')
|
||||
|
|
@ -74,7 +74,7 @@ class LLMConfigUpdate(BaseModel):
|
|||
allowed_providers = [
|
||||
'openai', 'azure', 'anthropic', 'google', 'baidu',
|
||||
'alibaba', 'tencent', 'zhipu', 'moonshot', 'deepseek',
|
||||
'ollama', 'custom',"doubao"
|
||||
'ollama', 'custom',"doubao", "ollama"
|
||||
]
|
||||
if v.lower() not in allowed_providers:
|
||||
raise ValueError(f'不支持的服务商: {v}，支持的服务商: {", ".join(allowed_providers)}')
|
||||
|
|
|
|||
|
|
@ -33,14 +33,16 @@ class AgentConfig(BaseModel):
|
|||
class AgentService:
|
||||
"""LangChain Agent service with tool calling capabilities."""
|
||||
|
||||
def __init__(self, db_session=None):
|
||||
def __init__(self):
|
||||
self.settings = get_settings()
|
||||
|
||||
async def initialize(self, session=None):
|
||||
self.tool_registry = ToolRegistry()
|
||||
self.config = AgentConfig()
|
||||
self.db_session = db_session
|
||||
self.config_service = AgentConfigService(db_session) if db_session else None
|
||||
self.session = session
|
||||
self.config_service = AgentConfigService(session) if session else None
|
||||
self._initialize_tools()
|
||||
self._load_config()
|
||||
await self._load_config()
|
||||
|
||||
def _initialize_tools(self):
|
||||
"""Initialize and register all available tools."""
|
||||
|
|
@ -56,18 +58,17 @@ class AgentService:
|
|||
self.tool_registry.register(tool)
|
||||
logger.info(f"Registered tool: {tool.get_name()}")
|
||||
|
||||
def _load_config(self):
|
||||
async def _load_config(self):
|
||||
"""Load configuration from database if available."""
|
||||
if self.config_service:
|
||||
try:
|
||||
config_dict = self.config_service.get_config_dict()
|
||||
config_dict = await self.config_service.get_config_dict()
|
||||
# Update config with database values
|
||||
for key, value in config_dict.items():
|
||||
if hasattr(self.config, key):
|
||||
setattr(self.config, key, value)
|
||||
logger.info("Loaded agent configuration from database")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load config from database, using defaults: {str(e)}")
|
||||
logger.error(f"Failed to load config from database, using defaults: {str(e)}")
|
||||
|
||||
def _get_enabled_tools(self) -> List[Any]:
|
||||
"""Get list of enabled LangChain tools."""
|
||||
|
|
@ -83,11 +84,11 @@ class AgentService:
|
|||
|
||||
return enabled_tools
|
||||
|
||||
def _create_agent_executor(self) -> Any:
|
||||
async def _create_agent_executor(self) -> Any:
|
||||
"""Create LangChain agent executor."""
|
||||
# Get LLM configuration
|
||||
from ...core.llm import create_llm
|
||||
llm = create_llm()
|
||||
from ...core.new_agent import new_agent
|
||||
llm = await new_agent()
|
||||
|
||||
# Get enabled tools
|
||||
tools = self._get_enabled_tools()
|
||||
|
|
@ -114,7 +115,7 @@ class AgentService:
|
|||
logger.info(f"Processing agent chat message: {message[:100]}...")
|
||||
|
||||
# Create agent
|
||||
agent = self._create_agent_executor()
|
||||
agent = await self._create_agent_executor()
|
||||
|
||||
# Convert chat history to LangChain format
|
||||
langchain_history = []
|
||||
|
|
@ -155,7 +156,7 @@ class AgentService:
|
|||
logger.info(f"Processing agent chat stream: {message[:100]}...")
|
||||
|
||||
# Create agent
|
||||
agent = self._create_agent_executor()
|
||||
agent = await self._create_agent_executor()
|
||||
|
||||
# Convert chat history to LangChain format
|
||||
langchain_history = []
|
||||
|
|
@ -263,17 +264,18 @@ class AgentService:
|
|||
|
||||
|
||||
# Global agent service instance
|
||||
_agent_service: Optional[AgentService] = None
|
||||
_global_agent_service: Optional[AgentService] = None
|
||||
|
||||
|
||||
def get_agent_service(db_session=None) -> AgentService:
|
||||
async def get_agent_service(session=None) -> AgentService:
|
||||
"""Get global agent service instance."""
|
||||
global _agent_service
|
||||
if _agent_service is None:
|
||||
_agent_service = AgentService(db_session)
|
||||
elif db_session and not _agent_service.db_session:
|
||||
global _global_agent_service
|
||||
if _global_agent_service is None:
|
||||
_global_agent_service = AgentService()
|
||||
await _global_agent_service.initialize(session)
|
||||
elif session and session != _global_agent_service.session:
|
||||
# Update with database session if not already set
|
||||
_agent_service.db_session = db_session
|
||||
_agent_service.config_service = AgentConfigService(db_session)
|
||||
_agent_service._load_config()
|
||||
return _agent_service
|
||||
_global_agent_service.session = session
|
||||
_global_agent_service.config_service = AgentConfigService(session)
|
||||
_global_agent_service._load_config()
|
||||
return _global_agent_service
|
||||
|
|
|
|||
|
|
@ -41,16 +41,18 @@ class LangGraphAgentConfig(BaseModel):
|
|||
class LangGraphAgentService:
|
||||
"""LangGraph Agent service using low-level LangGraph graph (React pattern)."""
|
||||
|
||||
def __init__(self, db_session=None):
|
||||
def __init__(self):
|
||||
self.settings = get_settings()
|
||||
|
||||
async def initialize(self, session=None):
|
||||
self.tool_registry = ToolRegistry()
|
||||
self.config = LangGraphAgentConfig()
|
||||
self.tools = []
|
||||
self.db_session = db_session
|
||||
self.config_service = AgentConfigService(db_session) if db_session else None
|
||||
self.session = session
|
||||
self.config_service = AgentConfigService(session) if session else None
|
||||
self._initialize_tools()
|
||||
self._load_config()
|
||||
self._create_react_agent()
|
||||
await self._load_config()
|
||||
await self._create_react_agent()
|
||||
|
||||
def _initialize_tools(self):
|
||||
"""Initialize available tools."""
|
||||
|
|
@ -76,28 +78,29 @@ class LangGraphAgentService:
|
|||
|
||||
|
||||
|
||||
def _load_config(self):
|
||||
async def _load_config(self):
|
||||
"""Load configuration from database if available."""
|
||||
if self.config_service:
|
||||
try:
|
||||
db_config = self.config_service.get_active_config()
|
||||
if db_config:
|
||||
# Update config with database values
|
||||
config_dict = db_config.config_data
|
||||
for key, value in config_dict.items():
|
||||
if hasattr(self.config, key):
|
||||
setattr(self.config, key, value)
|
||||
logger.info("Loaded configuration from database")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load config from database: {e}")
|
||||
pass
|
||||
# if self.config_service:
|
||||
# try:
|
||||
# db_config = self.config_service.get_active_config()
|
||||
# if db_config:
|
||||
# # Update config with database values
|
||||
# config_dict = db_config.config_data
|
||||
# for key, value in config_dict.items():
|
||||
# if hasattr(self.config, key):
|
||||
# setattr(self.config, key, value)
|
||||
# logger.info("Loaded configuration from database")
|
||||
# except Exception as e:
|
||||
# logger.exception(f"Failed to load config from database: {e}")
|
||||
|
||||
|
||||
|
||||
def _create_react_agent(self):
|
||||
async def _create_react_agent(self):
|
||||
"""Create LangGraph agent using low-level StateGraph with explicit nodes/edges."""
|
||||
try:
|
||||
# Initialize the model
|
||||
llm_config = get_settings().llm.get_current_config()
|
||||
llm_config = await get_settings().llm.get_current_config(self.db_session)
|
||||
self.model = init_chat_model(
|
||||
model=llm_config['model'],
|
||||
model_provider='openai',
|
||||
|
|
@ -183,7 +186,7 @@ class LangGraphAgentService:
|
|||
# Compile graph and store as self.agent for compatibility with existing code
|
||||
self.react_agent = graph.compile()
|
||||
|
||||
logger.info("LangGraph low-level React agent created successfully")
|
||||
logger.info("LangGraph 底层 React 智能体创建成功")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create agent: {str(e)}")
|
||||
|
|
@ -723,15 +726,14 @@ class LangGraphAgentService:
|
|||
|
||||
|
||||
# Global instance
|
||||
_langgraph_agent_service: Optional[LangGraphAgentService] = None
|
||||
_global_langgraph_agent_service: Optional[LangGraphAgentService] = None
|
||||
|
||||
|
||||
def get_langgraph_agent_service(db_session=None) -> LangGraphAgentService:
|
||||
async def get_langgraph_agent_service(session=None) -> LangGraphAgentService:
|
||||
"""Get or create LangGraph agent service instance."""
|
||||
global _langgraph_agent_service
|
||||
global _global_langgraph_agent_service
|
||||
|
||||
if _langgraph_agent_service is None:
|
||||
_langgraph_agent_service = LangGraphAgentService(db_session)
|
||||
logger.info("LangGraph Agent service initialized")
|
||||
if _global_langgraph_agent_service is None:
|
||||
_global_langgraph_agent_service = LangGraphAgentService()
|
||||
await _global_langgraph_agent_service.initialize(session)
|
||||
|
||||
return _langgraph_agent_service
|
||||
return _global_langgraph_agent_service
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
from typing import List, Dict, Any, Optional
|
||||
from sqlalchemy.orm import Session
|
||||
from sqlalchemy import and_
|
||||
from sqlalchemy import and_, select, update
|
||||
|
||||
from ..models.agent_config import AgentConfig
|
||||
from utils.util_exceptions import ValidationError, NotFoundError
|
||||
|
|
@ -15,7 +15,7 @@ class AgentConfigService:
|
|||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
|
||||
def create_config(self, config_data: Dict[str, Any]) -> AgentConfig:
|
||||
async def create_config(self, config_data: Dict[str, Any]) -> AgentConfig:
|
||||
"""Create a new agent configuration."""
|
||||
try:
|
||||
# Validate required fields
|
||||
|
|
@ -23,9 +23,8 @@ class AgentConfigService:
|
|||
raise ValidationError("Configuration name is required")
|
||||
|
||||
# Check if name already exists
|
||||
existing = self.db.query(AgentConfig).filter(
|
||||
AgentConfig.name == config_data["name"]
|
||||
).first()
|
||||
stmt = select(AgentConfig).where(AgentConfig.name == config_data["name"])
|
||||
existing = (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
if existing:
|
||||
raise ValidationError(f"Configuration with name '{config_data['name']}' already exists")
|
||||
|
||||
|
|
@ -44,62 +43,60 @@ class AgentConfigService:
|
|||
|
||||
# If this is set as default, unset other defaults
|
||||
if config.is_default:
|
||||
self.db.query(AgentConfig).filter(
|
||||
AgentConfig.is_default == True
|
||||
).update({"is_default": False})
|
||||
stmt = update(AgentConfig).where(AgentConfig.is_default == True).values({"is_default": False})
|
||||
await self.db.execute(stmt)
|
||||
|
||||
self.db.add(config)
|
||||
self.db.commit()
|
||||
self.db.refresh(config)
|
||||
await self.db.commit()
|
||||
await self.db.refresh(config)
|
||||
|
||||
logger.info(f"Created agent configuration: {config.name}")
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.db.rollback()
|
||||
await self.db.rollback()
|
||||
logger.error(f"Error creating agent configuration: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_config(self, config_id: int) -> Optional[AgentConfig]:
|
||||
async def get_config(self, config_id: int) -> Optional[AgentConfig]:
|
||||
"""Get agent configuration by ID."""
|
||||
return self.db.query(AgentConfig).filter(
|
||||
AgentConfig.id == config_id
|
||||
).first()
|
||||
stmt = select(AgentConfig).where(AgentConfig.id == config_id)
|
||||
return (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
def get_config_by_name(self, name: str) -> Optional[AgentConfig]:
|
||||
async def get_config_by_name(self, name: str) -> Optional[AgentConfig]:
|
||||
"""Get agent configuration by name."""
|
||||
return self.db.query(AgentConfig).filter(
|
||||
AgentConfig.name == name
|
||||
).first()
|
||||
stmt = select(AgentConfig).where(AgentConfig.name == name)
|
||||
return (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
def get_default_config(self) -> Optional[AgentConfig]:
|
||||
async def get_default_config(self) -> Optional[AgentConfig]:
|
||||
"""Get default agent configuration."""
|
||||
return self.db.query(AgentConfig).filter(
|
||||
and_(AgentConfig.is_default == True, AgentConfig.is_active == True)
|
||||
).first()
|
||||
stmt = select(AgentConfig).where(and_(AgentConfig.is_default == True, AgentConfig.is_active == True))
|
||||
return (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
def list_configs(self, active_only: bool = True) -> List[AgentConfig]:
|
||||
"""List all agent configurations."""
|
||||
query = self.db.query(AgentConfig)
|
||||
stmt = select(AgentConfig)
|
||||
if active_only:
|
||||
query = query.filter(AgentConfig.is_active == True)
|
||||
return query.order_by(AgentConfig.created_at.desc()).all()
|
||||
stmt = stmt.where(AgentConfig.is_active == True)
|
||||
stmt = stmt.order_by(AgentConfig.created_at.desc())
|
||||
return self.db.execute(stmt).scalars().all()
|
||||
|
||||
def update_config(self, config_id: int, config_data: Dict[str, Any]) -> AgentConfig:
|
||||
async def update_config(self, config_id: int, config_data: Dict[str, Any]) -> AgentConfig:
|
||||
"""Update agent configuration."""
|
||||
try:
|
||||
config = self.get_config(config_id)
|
||||
config = await self.get_config(config_id)
|
||||
if not config:
|
||||
raise NotFoundError(f"Agent configuration with ID {config_id} not found")
|
||||
|
||||
# Check if name change conflicts with existing
|
||||
if "name" in config_data and config_data["name"] != config.name:
|
||||
existing = self.db.query(AgentConfig).filter(
|
||||
stmt = select(AgentConfig).where(
|
||||
and_(
|
||||
AgentConfig.name == config_data["name"],
|
||||
AgentConfig.id != config_id
|
||||
)
|
||||
).first()
|
||||
)
|
||||
existing = (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
if existing:
|
||||
raise ValidationError(f"Configuration with name '{config_data['name']}' already exists")
|
||||
|
||||
|
|
@ -110,28 +107,29 @@ class AgentConfigService:
|
|||
|
||||
# If this is set as default, unset other defaults
|
||||
if config_data.get("is_default", False):
|
||||
self.db.query(AgentConfig).filter(
|
||||
stmt = update(AgentConfig).where(
|
||||
and_(
|
||||
AgentConfig.is_default == True,
|
||||
AgentConfig.id != config_id
|
||||
)
|
||||
).update({"is_default": False})
|
||||
).values({"is_default": False})
|
||||
await self.db.execute(stmt)
|
||||
|
||||
self.db.commit()
|
||||
self.db.refresh(config)
|
||||
await self.db.commit()
|
||||
await self.db.refresh(config)
|
||||
|
||||
logger.info(f"Updated agent configuration: {config.name}")
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.db.rollback()
|
||||
await self.db.rollback()
|
||||
logger.error(f"Error updating agent configuration: {str(e)}")
|
||||
raise
|
||||
|
||||
def delete_config(self, config_id: int) -> bool:
|
||||
async def delete_config(self, config_id: int) -> bool:
|
||||
"""Delete agent configuration (soft delete by setting is_active=False)."""
|
||||
try:
|
||||
config = self.get_config(config_id)
|
||||
config = await self.get_config(config_id)
|
||||
if not config:
|
||||
raise NotFoundError(f"Agent configuration with ID {config_id} not found")
|
||||
|
||||
|
|
@ -146,14 +144,14 @@ class AgentConfigService:
|
|||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.db.rollback()
|
||||
await self.db.rollback()
|
||||
logger.error(f"Error deleting agent configuration: {str(e)}")
|
||||
raise
|
||||
|
||||
def set_default_config(self, config_id: int) -> AgentConfig:
|
||||
async def set_default_config(self, config_id: int) -> AgentConfig:
|
||||
"""Set a configuration as default."""
|
||||
try:
|
||||
config = self.get_config(config_id)
|
||||
config = await self.get_config(config_id)
|
||||
if not config:
|
||||
raise NotFoundError(f"Agent configuration with ID {config_id} not found")
|
||||
|
||||
|
|
@ -161,29 +159,28 @@ class AgentConfigService:
|
|||
raise ValidationError("Cannot set inactive configuration as default")
|
||||
|
||||
# Unset other defaults
|
||||
self.db.query(AgentConfig).filter(
|
||||
AgentConfig.is_default == True
|
||||
).update({"is_default": False})
|
||||
stmt = update(AgentConfig).where(AgentConfig.is_default == True).values({"is_default": False})
|
||||
await self.db.execute(stmt)
|
||||
|
||||
# Set this as default
|
||||
config.is_default = True
|
||||
self.db.commit()
|
||||
self.db.refresh(config)
|
||||
await self.db.commit()
|
||||
await self.db.refresh(config)
|
||||
|
||||
logger.info(f"Set default agent configuration: {config.name}")
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
self.db.rollback()
|
||||
await self.db.rollback()
|
||||
logger.error(f"Error setting default agent configuration: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_config_dict(self, config_id: Optional[int] = None) -> Dict[str, Any]:
|
||||
async def get_config_dict(self, config_id: Optional[int] = None) -> Dict[str, Any]:
|
||||
"""Get configuration as dictionary. If no ID provided, returns default config."""
|
||||
if config_id:
|
||||
config = self.get_config(config_id)
|
||||
config = await self.get_config(config_id)
|
||||
else:
|
||||
config = self.get_default_config()
|
||||
config = await self.get_default_config()
|
||||
|
||||
if not config:
|
||||
# Return default values if no configuration found
|
||||
|
|
|
|||
|
|
@ -33,19 +33,16 @@ class AuthService:
|
|||
)
|
||||
|
||||
token = credentials.credentials
|
||||
session.desc = f"[AuthService] 取得token: {token[:50]}..."
|
||||
payload = AuthService.verify_token(token)
|
||||
if payload is None:
|
||||
session.desc = "ERROR: 令牌验证失败"
|
||||
session.desc = f"ERROR: 令牌验证失败 - 令牌: {token[:50]}..."
|
||||
raise credentials_exception
|
||||
|
||||
session.desc = f"[AuthService] 令牌有效 - 解析得到有效载荷: {payload}"
|
||||
username: str = payload.get("sub")
|
||||
if username is None:
|
||||
session.desc = "ERROR: 令牌中没有用户名"
|
||||
raise credentials_exception
|
||||
|
||||
session.desc = f"[AuthService] 获取当前用户 - 查找名为 {username} 的用户"
|
||||
stmt = select(User).where(User.username == username)
|
||||
user = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if user is None:
|
||||
|
|
@ -53,8 +50,8 @@ class AuthService:
|
|||
raise credentials_exception
|
||||
|
||||
# Set user in context for global access
|
||||
UserContext.set_current_user(user)
|
||||
session.desc = f"[AuthService] 用户 {user.username} (ID: {user.id}) 已设置为当前用户"
|
||||
UserContext.set_current_user(user, canLog=True)
|
||||
# session.desc = f"[AuthService] 用户 {user.username} (ID: {user.id}) 已设置为当前用户"
|
||||
|
||||
return user
|
||||
|
||||
|
|
@ -138,6 +135,4 @@ class AuthService:
|
|||
except jwt.PyJWTError as e:
|
||||
logger.error(f"Token verification failed: {e}")
|
||||
logger.error(f"Token: {token[:50]}...")
|
||||
logger.error(f"Secret key: {settings.security.secret_key[:20]}...")
|
||||
logger.error(f"Algorithm: {settings.security.algorithm}")
|
||||
return None
|
||||
|
|
@ -1,44 +1,36 @@
|
|||
"""Chat service for AI model integration using LangChain."""
|
||||
|
||||
from th_agenter import db
|
||||
import json
|
||||
import asyncio
|
||||
import os
|
||||
from typing import AsyncGenerator, Optional, List, Dict, Any
|
||||
from typing import AsyncGenerator, Optional, List, Dict, Any, TypedDict
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from th_agenter.core.new_agent import new_agent, new_llm
|
||||
from ..core.config import settings
|
||||
from ..models.message import MessageRole
|
||||
from utils.util_schemas import ChatResponse, StreamChunk, MessageResponse
|
||||
from utils.util_exceptions import ChatServiceError, OpenAIError
|
||||
from utils.util_exceptions import ChatServiceError, HxfResponse, OpenAIError
|
||||
from .conversation import ConversationService
|
||||
from .langchain_chat import LangChainChatService
|
||||
from .knowledge_chat import KnowledgeChatService
|
||||
from .agent.agent_service import get_agent_service
|
||||
from .agent.langgraph_agent_service import get_langgraph_agent_service
|
||||
|
||||
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
|
||||
from langgraph.checkpoint.postgres import PostgresSaver
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.state import CompiledStateGraph
|
||||
class AgentState(TypedDict):
|
||||
messages: List[dict] # 存储对话消息（核心记忆）
|
||||
|
||||
class ChatService:
|
||||
"""Service for handling AI chat functionality using LangChain."""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.conversation_service = ConversationService(db)
|
||||
|
||||
# Initialize LangChain chat service
|
||||
self.langchain_service = LangChainChatService(db)
|
||||
|
||||
# Initialize Knowledge chat service
|
||||
self.knowledge_service = KnowledgeChatService(db)
|
||||
|
||||
# Initialize Agent service with database session
|
||||
self.agent_service = get_agent_service(db)
|
||||
|
||||
# Initialize LangGraph Agent service with database session
|
||||
self.langgraph_service = get_langgraph_agent_service(db)
|
||||
|
||||
logger.info("ChatService initialized with LangChain backend and Agent support")
|
||||
|
||||
|
||||
_checkpointer_initialized = False
|
||||
_conn_string = None
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
|
|
@ -57,7 +49,7 @@ class ChatService:
|
|||
logger.info(f"Processing chat request for conversation {conversation_id} via Knowledge Base {knowledge_base_id}")
|
||||
|
||||
# Use knowledge base chat service
|
||||
return await self.knowledge_service.chat_with_knowledge_base(
|
||||
return await self.knowledge_chat_service.chat_with_knowledge_base(
|
||||
conversation_id=conversation_id,
|
||||
message=message,
|
||||
knowledge_base_id=knowledge_base_id,
|
||||
|
|
@ -69,29 +61,29 @@ class ChatService:
|
|||
logger.info(f"Processing chat request for conversation {conversation_id} via LangGraph Agent")
|
||||
|
||||
# Get conversation history for LangGraph agent
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
conversation = await self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError(f"Conversation {conversation_id} not found")
|
||||
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
messages = await self.conversation_service.get_conversation_messages(conversation_id)
|
||||
chat_history = [{
|
||||
"role": "user" if msg.role == MessageRole.USER else "assistant",
|
||||
"content": msg.content
|
||||
} for msg in messages]
|
||||
|
||||
# Use LangGraph agent service
|
||||
agent_result = await self.langgraph_service.chat(message, chat_history)
|
||||
agent_result = await self.langgraph_agent_service.chat(message, chat_history)
|
||||
|
||||
if agent_result["success"]:
|
||||
# Save user message
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Save assistant response
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=agent_result["response"],
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -114,11 +106,11 @@ class ChatService:
|
|||
logger.info(f"Processing chat request for conversation {conversation_id} via Agent")
|
||||
|
||||
# Get conversation history for agent
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
conversation = await self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError(f"Conversation {conversation_id} not found")
|
||||
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
messages = await self.conversation_service.get_conversation_messages(conversation_id)
|
||||
chat_history = [{
|
||||
"role": "user" if msg.role == MessageRole.USER else "assistant",
|
||||
"content": msg.content
|
||||
|
|
@ -129,14 +121,14 @@ class ChatService:
|
|||
|
||||
if agent_result["success"]:
|
||||
# Save user message
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Save assistant response
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=agent_result["response"],
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -159,7 +151,7 @@ class ChatService:
|
|||
logger.info(f"Processing chat request for conversation {conversation_id} via LangChain")
|
||||
|
||||
# Delegate to LangChain service
|
||||
return await self.langchain_service.chat(
|
||||
return await self.langchain_chat_service.chat(
|
||||
conversation_id=conversation_id,
|
||||
message=message,
|
||||
stream=stream,
|
||||
|
|
@ -167,156 +159,12 @@ class ChatService:
|
|||
max_tokens=max_tokens
|
||||
)
|
||||
|
||||
async def chat_stream(
|
||||
self,
|
||||
conversation_id: int,
|
||||
message: str,
|
||||
temperature: Optional[float] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
use_agent: bool = False,
|
||||
use_langgraph: bool = False,
|
||||
use_knowledge_base: bool = False,
|
||||
knowledge_base_id: Optional[int] = None
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Send a message and get streaming AI response using LangChain, Agent, or Knowledge Base."""
|
||||
if use_knowledge_base and knowledge_base_id:
|
||||
logger.info(f"Processing streaming chat request for conversation {conversation_id} via Knowledge Base {knowledge_base_id}")
|
||||
|
||||
# Use knowledge base chat service streaming
|
||||
async for content in self.knowledge_service.chat_stream_with_knowledge_base(
|
||||
conversation_id=conversation_id,
|
||||
message=message,
|
||||
knowledge_base_id=knowledge_base_id,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
):
|
||||
# Create stream chunk for compatibility with existing API
|
||||
stream_chunk = StreamChunk(
|
||||
content=content,
|
||||
role=MessageRole.ASSISTANT
|
||||
)
|
||||
yield json.dumps(stream_chunk.dict(), ensure_ascii=False)
|
||||
elif use_langgraph:
|
||||
logger.info(f"Processing streaming chat request for conversation {conversation_id} via LangGraph Agent")
|
||||
|
||||
# Get conversation history for LangGraph agent
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError(f"Conversation {conversation_id} not found")
|
||||
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
chat_history = [{
|
||||
"role": "user" if msg.role == MessageRole.USER else "assistant",
|
||||
"content": msg.content
|
||||
} for msg in messages]
|
||||
|
||||
# Save user message first
|
||||
user_message = self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Use LangGraph agent service streaming
|
||||
full_response = ""
|
||||
intermediate_steps = []
|
||||
|
||||
async for chunk in self.langgraph_service.chat_stream(message, chat_history):
|
||||
if chunk["type"] == "response":
|
||||
full_response = chunk["content"]
|
||||
intermediate_steps = chunk.get("intermediate_steps", [])
|
||||
|
||||
# Return the chunk as-is to maintain type information
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
|
||||
elif chunk["type"] == "error":
|
||||
# Return the chunk as-is to maintain type information
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
return
|
||||
else:
|
||||
# For other types (status, step, etc.), pass through
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
|
||||
# Save assistant response
|
||||
if full_response:
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=full_response,
|
||||
role=MessageRole.ASSISTANT,
|
||||
message_metadata={"intermediate_steps": intermediate_steps}
|
||||
)
|
||||
elif use_agent:
|
||||
logger.info(f"Processing streaming chat request for conversation {conversation_id} via Agent")
|
||||
|
||||
# Get conversation history for agent
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError(f"Conversation {conversation_id} not found")
|
||||
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
chat_history = [{
|
||||
"role": "user" if msg.role == MessageRole.USER else "assistant",
|
||||
"content": msg.content
|
||||
} for msg in messages]
|
||||
|
||||
# Save user message first
|
||||
user_message = self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Use agent service streaming
|
||||
full_response = ""
|
||||
tool_calls = []
|
||||
|
||||
async for chunk in self.agent_service.chat_stream(message, chat_history):
|
||||
if chunk["type"] == "response":
|
||||
full_response = chunk["content"]
|
||||
tool_calls = chunk.get("tool_calls", [])
|
||||
|
||||
# Return the chunk as-is to maintain type information
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
|
||||
elif chunk["type"] == "error":
|
||||
# Return the chunk as-is to maintain type information
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
return
|
||||
else:
|
||||
# For other types (status, tool_start, etc.), pass through
|
||||
yield json.dumps(chunk, ensure_ascii=False)
|
||||
|
||||
# Save assistant response
|
||||
if full_response:
|
||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=full_response,
|
||||
role=MessageRole.ASSISTANT,
|
||||
message_metadata={"tool_calls": tool_calls}
|
||||
)
|
||||
else:
|
||||
logger.info(f"Processing streaming chat request for conversation {conversation_id} via LangChain")
|
||||
|
||||
# Delegate to LangChain service and wrap response in JSON format
|
||||
async for content in self.langchain_service.chat_stream(
|
||||
conversation_id=conversation_id,
|
||||
message=message,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
):
|
||||
# Create stream chunk for compatibility with existing API
|
||||
stream_chunk = StreamChunk(
|
||||
content=content,
|
||||
role=MessageRole.ASSISTANT
|
||||
)
|
||||
yield json.dumps(stream_chunk.dict(), ensure_ascii=False)
|
||||
|
||||
async def get_available_models(self) -> List[str]:
|
||||
"""Get list of available models from LangChain."""
|
||||
logger.info("Getting available models via LangChain")
|
||||
|
||||
# Delegate to LangChain service
|
||||
return await self.langchain_service.get_available_models()
|
||||
return await self.langchain_chat_service.get_available_models()
|
||||
|
||||
def update_model_config(
|
||||
self,
|
||||
|
|
@ -328,8 +176,135 @@ class ChatService:
|
|||
logger.info(f"Updating model config via LangChain: model={model}, temperature={temperature}, max_tokens={max_tokens}")
|
||||
|
||||
# Delegate to LangChain service
|
||||
self.langchain_service.update_model_config(
|
||||
self.langchain_chat_service.update_model_config(
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens
|
||||
)
|
||||
# -------------------------------------------------------------------------
|
||||
def __init__(self, session: Session):
|
||||
self.session = session
|
||||
|
||||
async def initialize(self, conversation_id: int, streaming: bool = False):
|
||||
self.conversation_service = ConversationService(self.session)
|
||||
self.session.desc = "ChatService初始化 - ConversationService 实例化完毕"
|
||||
self.conversation = await self.conversation_service.get_conversation(
|
||||
conversation_id=conversation_id
|
||||
)
|
||||
if not self.conversation:
|
||||
raise ChatServiceError(f"Conversation {conversation_id} not found")
|
||||
|
||||
if not ChatService._checkpointer_initialized:
|
||||
from langgraph.checkpoint.postgres import PostgresSaver
|
||||
import psycopg2
|
||||
CONN_STRING = "postgresql://postgres:postgres@localhost:5433/postgres"
|
||||
ChatService._conn_string = CONN_STRING
|
||||
|
||||
# 检查必要的表是否已存在
|
||||
tables_need_setup = True
|
||||
try:
|
||||
# 连接到数据库并检查表是否存在
|
||||
conn = psycopg2.connect(CONN_STRING)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 检查langgraph需要的表是否存在
|
||||
cursor.execute("""
|
||||
SELECT table_name
|
||||
FROM information_schema.tables
|
||||
WHERE table_schema = 'public'
|
||||
AND table_name IN ('checkpoints', 'checkpoint_writes', 'checkpoint_blobs')
|
||||
""")
|
||||
existing_tables = [row[0] for row in cursor.fetchall()]
|
||||
|
||||
# 检查是否所有必要的表都存在
|
||||
required_tables = ['checkpoints', 'checkpoint_writes', 'checkpoint_blobs']
|
||||
if all(table in existing_tables for table in required_tables):
|
||||
tables_need_setup = False
|
||||
self.session.desc = "ChatService初始化 - 检测到langgraph表已存在，跳过setup"
|
||||
|
||||
cursor.close()
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
self.session.desc = f"ChatService初始化 - 检查表存在性失败: {str(e)}，将进行setup"
|
||||
tables_need_setup = True
|
||||
|
||||
# 只有在需要时才进行setup
|
||||
if tables_need_setup:
|
||||
self.session.desc = "ChatService初始化 - 正在进行PostgresSaver setup"
|
||||
try:
|
||||
async with AsyncPostgresSaver.from_conn_string(CONN_STRING) as checkpointer:
|
||||
await checkpointer.setup()
|
||||
self.session.desc = "ChatService初始化 - PostgresSaver setup完成"
|
||||
logger.info("PostgresSaver setup完成")
|
||||
except Exception as e:
|
||||
self.session.desc = f"ChatService初始化 - PostgresSaver setup失败: {str(e)}"
|
||||
logger.error(f"PostgresSaver setup失败: {e}")
|
||||
raise
|
||||
else:
|
||||
self.session.desc = "ChatService初始化 - 使用现有的langgraph表"
|
||||
|
||||
# 存储连接字符串供后续使用
|
||||
ChatService._checkpointer_initialized = True
|
||||
|
||||
self.llm = await new_llm(session=self.session, streaming=streaming)
|
||||
self.session.desc = f"ChatService初始化 - 获取对话实例完毕 > {self.conversation}"
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"configurable": {
|
||||
"thread_id": str(self.conversation.id),
|
||||
"checkpoint_ns": "drgraph"
|
||||
}
|
||||
}
|
||||
return config
|
||||
|
||||
async def chat_stream(
|
||||
self,
|
||||
message: str
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Send a message and get streaming AI response using LangChain, Agent, or Knowledge Base."""
|
||||
self.session.desc = f"ChatService - 发送消息 {message} >>> 流式对话请求，会话 ID: {self.conversation.id}"
|
||||
await self.conversation_service.add_message(
|
||||
conversation_id=self.conversation.id,
|
||||
role=MessageRole.USER,
|
||||
content=message
|
||||
)
|
||||
full_assistant_content = ""
|
||||
|
||||
async with AsyncPostgresSaver.from_conn_string(conn_string=self._conn_string) as checkpointer:
|
||||
from langchain.agents import create_agent
|
||||
agent = create_agent(
|
||||
model=self.llm, # await new_llm(session=self.session, streaming=self.streaming),
|
||||
checkpointer=checkpointer
|
||||
)
|
||||
async for chunk in agent.astream(
|
||||
{"messages": [{"role": "user", "content": message}]},
|
||||
config=self.get_config(),
|
||||
stream_mode="messages"
|
||||
):
|
||||
full_assistant_content += chunk[0].content
|
||||
json_result = {"data": {"v": chunk[0].content }}
|
||||
yield json.dumps(
|
||||
json_result,
|
||||
ensure_ascii=True
|
||||
)
|
||||
|
||||
if len(full_assistant_content) > 0:
|
||||
await self.conversation_service.add_message(
|
||||
conversation_id=self.conversation.id,
|
||||
role=MessageRole.ASSISTANT,
|
||||
content=full_assistant_content
|
||||
)
|
||||
|
||||
def get_conversation_history_messages(
|
||||
self, conversation_id: int, skip: int = 0, limit: int = 100
|
||||
):
|
||||
"""Get conversation history messages with pagination."""
|
||||
result = []
|
||||
with PostgresSaver.from_conn_string(conn_string=self._conn_string) as checkpointer:
|
||||
checkpoints = checkpointer.list(self.get_config())
|
||||
for checkpoint in checkpoints:
|
||||
print(checkpoint)
|
||||
result.append(checkpoint.messages)
|
||||
return result
|
||||
|
|
|
|||
|
|
@ -3,6 +3,9 @@
|
|||
from typing import List, Optional
|
||||
from sqlalchemy.orm import Session
|
||||
from sqlalchemy import select, desc, func, or_
|
||||
from langchain_core.messages import HumanMessage, AIMessage
|
||||
|
||||
from th_agenter.db.database import AsyncSessionFactory
|
||||
|
||||
from ..models.conversation import Conversation
|
||||
from ..models.message import Message, MessageRole
|
||||
|
|
@ -24,7 +27,7 @@ class ConversationService:
|
|||
conversation_data: ConversationCreate
|
||||
) -> Conversation:
|
||||
"""Create a new conversation."""
|
||||
logger.info(f"Creating new conversation for user {user_id}: {conversation_data}")
|
||||
self.session.desc = f"创建新会话 - 用户ID: {user_id}，会话数据: {conversation_data}"
|
||||
|
||||
try:
|
||||
conversation = Conversation(
|
||||
|
|
@ -39,18 +42,23 @@ class ConversationService:
|
|||
await self.session.commit()
|
||||
await self.session.refresh(conversation)
|
||||
|
||||
logger.info(f"Successfully created conversation {conversation.id} for user {user_id}")
|
||||
self.session.desc = f"创建新会话 Conversation ID: {conversation.id}，用户ID: {user_id}"
|
||||
return conversation
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create conversation: {str(e)}", exc_info=True)
|
||||
self.session.desc = f"ERROR: 创建会话失败 - 用户ID: {user_id}，错误: {str(e)}"
|
||||
await self.session.rollback()
|
||||
raise DatabaseError(f"Failed to create conversation: {str(e)}")
|
||||
raise DatabaseError(f"创建会话失败: {str(e)}")
|
||||
|
||||
async def get_conversation(self, conversation_id: int) -> Optional[Conversation]:
|
||||
"""Get a conversation by ID."""
|
||||
try:
|
||||
user_id = UserContext.get_current_user_id()
|
||||
self.session.desc = f"获取会话 - 会话ID: {conversation_id}，用户ID: {user_id}"
|
||||
if user_id is None:
|
||||
logger.error(f"Failed to get conversation {conversation_id}: No user context available")
|
||||
return None
|
||||
|
||||
conversation = await self.session.scalar(
|
||||
select(Conversation).where(
|
||||
Conversation.id == conversation_id,
|
||||
|
|
@ -59,12 +67,11 @@ class ConversationService:
|
|||
)
|
||||
|
||||
if not conversation:
|
||||
logger.warning(f"Conversation {conversation_id} not found")
|
||||
|
||||
self.session.desc = f"警告: 会话 {conversation_id} 不存在，用户ID: {user_id}"
|
||||
return conversation
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get conversation {conversation_id}: {str(e)}", exc_info=True)
|
||||
self.session.desc = f"ERROR: 获取会话失败 - 会话ID: {conversation_id}，用户ID: {user_id}，错误: {str(e)}"
|
||||
raise DatabaseError(f"Failed to get conversation: {str(e)}")
|
||||
|
||||
async def get_user_conversations(
|
||||
|
|
@ -78,6 +85,10 @@ class ConversationService:
|
|||
) -> List[Conversation]:
|
||||
"""Get user's conversations with search and filtering."""
|
||||
user_id = UserContext.get_current_user_id()
|
||||
if user_id is None:
|
||||
logger.error("Failed to get user conversations: No user context available")
|
||||
return []
|
||||
|
||||
query = select(Conversation).where(
|
||||
Conversation.user_id == user_id
|
||||
)
|
||||
|
|
@ -137,7 +148,7 @@ class ConversationService:
|
|||
if not conversation:
|
||||
return False
|
||||
|
||||
self.session.delete(conversation)
|
||||
await self.session.delete(conversation)
|
||||
await self.session.commit()
|
||||
return True
|
||||
|
||||
|
|
@ -180,19 +191,42 @@ class ConversationService:
|
|||
# Set audit fields
|
||||
message.set_audit_fields()
|
||||
|
||||
self.session.add(message)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(message)
|
||||
session = AsyncSessionFactory()
|
||||
session.begin()
|
||||
try:
|
||||
session.add(message)
|
||||
await session.commit()
|
||||
await session.refresh(message)
|
||||
|
||||
# Update conversation's updated_at timestamp
|
||||
conversation = await self.get_conversation(conversation_id)
|
||||
if conversation:
|
||||
conversation.updated_at = datetime.now(timezone.utc)
|
||||
conversation.set_audit_fields(user_id=conversation.user_id, is_update=True)
|
||||
await self.session.commit()
|
||||
await session.commit()
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to add message to conversation {conversation_id}: {str(e)}", exc_info=True)
|
||||
await session.rollback()
|
||||
finally:
|
||||
await session.close()
|
||||
|
||||
return message
|
||||
|
||||
async def get_conversation_history_messages(
|
||||
self,
|
||||
conversation_id: int,
|
||||
limit: int = 20
|
||||
) -> List[Message]:
|
||||
"""Get recent conversation history messages."""
|
||||
history = await self.get_conversation_history(conversation_id, limit)
|
||||
history_messages = []
|
||||
for message in history:
|
||||
if message.role == MessageRole.USER:
|
||||
history_messages.append(HumanMessage(content=message.content))
|
||||
elif message.role == MessageRole.ASSISTANT:
|
||||
history_messages.append(AIMessage(content=message.content))
|
||||
return history_messages
|
||||
|
||||
async def get_conversation_history(
|
||||
self,
|
||||
conversation_id: int,
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class ConversationContextService:
|
|||
新创建的对话ID
|
||||
"""
|
||||
try:
|
||||
session = next(get_session())
|
||||
session = await anext(get_session())
|
||||
|
||||
conversation = Conversation(
|
||||
user_id=user_id,
|
||||
|
|
@ -37,8 +37,8 @@ class ConversationContextService:
|
|||
)
|
||||
|
||||
session.add(conversation)
|
||||
session.commit()
|
||||
session.refresh(conversation)
|
||||
await session.commit()
|
||||
await session.refresh(conversation)
|
||||
|
||||
# 初始化对话上下文
|
||||
self.context_cache[conversation.id] = {
|
||||
|
|
@ -74,7 +74,7 @@ class ConversationContextService:
|
|||
|
||||
# 从数据库加载
|
||||
try:
|
||||
session = next(get_session())
|
||||
session = await anext(get_session())
|
||||
|
||||
conversation = session.query(Conversation).filter(
|
||||
Conversation.id == conversation_id
|
||||
|
|
@ -194,7 +194,7 @@ class ConversationContextService:
|
|||
保存是否成功
|
||||
"""
|
||||
try:
|
||||
session = next(get_session())
|
||||
session = await anext(get_session())
|
||||
|
||||
message = Message(
|
||||
conversation_id=conversation_id,
|
||||
|
|
@ -205,7 +205,7 @@ class ConversationContextService:
|
|||
)
|
||||
|
||||
session.add(message)
|
||||
session.commit()
|
||||
await session.commit()
|
||||
|
||||
# 更新对话的最后更新时间
|
||||
conversation = session.query(Conversation).filter(
|
||||
|
|
@ -214,7 +214,7 @@ class ConversationContextService:
|
|||
|
||||
if conversation:
|
||||
conversation.updated_at = datetime.utcnow()
|
||||
session.commit()
|
||||
await session.commit()
|
||||
|
||||
return True
|
||||
|
||||
|
|
@ -262,7 +262,7 @@ class ConversationContextService:
|
|||
消息历史列表
|
||||
"""
|
||||
try:
|
||||
session = next(get_session())
|
||||
session = await anext(get_session())
|
||||
|
||||
messages = session.query(Message).filter(
|
||||
Message.conversation_id == conversation_id
|
||||
|
|
|
|||
|
|
@ -102,41 +102,41 @@ class DatabaseConfigService:
|
|||
)
|
||||
|
||||
self.session.add(db_config)
|
||||
self.session.commit()
|
||||
self.session.refresh(db_config)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(db_config)
|
||||
|
||||
logger.info(f"创建数据库配置成功: {db_config.name} (ID: {db_config.id})")
|
||||
return db_config
|
||||
|
||||
except Exception as e:
|
||||
self.session.rollback()
|
||||
await self.session.rollback()
|
||||
logger.error(f"创建数据库配置失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_user_configs(self, user_id: int, active_only: bool = True) -> List[DatabaseConfig]:
|
||||
async def get_user_configs(self, user_id: int, active_only: bool = True) -> List[DatabaseConfig]:
|
||||
"""获取用户的数据库配置列表"""
|
||||
stmt = select(DatabaseConfig).where(DatabaseConfig.created_by == user_id)
|
||||
if active_only:
|
||||
stmt = stmt.where(DatabaseConfig.is_active == True)
|
||||
stmt = stmt.order_by(DatabaseConfig.created_at.desc())
|
||||
return self.session.scalars(stmt).all()
|
||||
return (await self.session.execute(stmt)).scalars().all()
|
||||
|
||||
def get_config_by_id(self, config_id: int, user_id: int) -> Optional[DatabaseConfig]:
|
||||
async def get_config_by_id(self, config_id: int, user_id: int) -> Optional[DatabaseConfig]:
|
||||
"""根据ID获取配置"""
|
||||
stmt = select(DatabaseConfig).where(
|
||||
DatabaseConfig.id == config_id,
|
||||
DatabaseConfig.created_by == user_id
|
||||
)
|
||||
return self.session.scalar(stmt)
|
||||
return (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
def get_default_config(self, user_id: int) -> Optional[DatabaseConfig]:
|
||||
async def get_default_config(self, user_id: int) -> Optional[DatabaseConfig]:
|
||||
"""获取用户的默认配置"""
|
||||
stmt = select(DatabaseConfig).where(
|
||||
DatabaseConfig.created_by == user_id,
|
||||
# DatabaseConfig.is_default == True,
|
||||
DatabaseConfig.is_active == True
|
||||
)
|
||||
return self.session.scalar(stmt)
|
||||
return (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
async def test_connection(self, config_id: int, user_id: int) -> Dict[str, Any]:
|
||||
"""测试数据库连接"""
|
||||
|
|
@ -278,14 +278,14 @@ class DatabaseConfigService:
|
|||
'message': f'断开连接失败: {str(e)}'
|
||||
}
|
||||
|
||||
def get_config_by_type(self, user_id: int, db_type: str) -> Optional[DatabaseConfig]:
|
||||
async def get_config_by_type(self, user_id: int, db_type: str) -> Optional[DatabaseConfig]:
|
||||
"""根据数据库类型获取用户配置"""
|
||||
stmt = select(DatabaseConfig).where(
|
||||
DatabaseConfig.created_by == user_id,
|
||||
DatabaseConfig.db_type == db_type,
|
||||
DatabaseConfig.is_active == True
|
||||
)
|
||||
return self.session.scalar(stmt)
|
||||
return await self.session.scalar(stmt)
|
||||
|
||||
async def create_or_update_config(self, user_id: int, config_data: Dict[str, Any]) -> DatabaseConfig:
|
||||
"""创建或更新数据库配置（保证db_type唯一性）"""
|
||||
|
|
@ -301,8 +301,8 @@ class DatabaseConfigService:
|
|||
elif hasattr(existing_config, key):
|
||||
setattr(existing_config, key, value)
|
||||
|
||||
self.session.commit()
|
||||
self.session.refresh(existing_config)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(existing_config)
|
||||
logger.info(f"更新数据库配置成功: {existing_config.name} (ID: {existing_config.id})")
|
||||
return existing_config
|
||||
else:
|
||||
|
|
@ -310,7 +310,7 @@ class DatabaseConfigService:
|
|||
return await self.create_config(user_id, config_data)
|
||||
|
||||
except Exception as e:
|
||||
self.session.rollback()
|
||||
await self.session.rollback()
|
||||
logger.error(f"创建或更新数据库配置失败: {str(e)}")
|
||||
raise
|
||||
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ class DocumentService:
|
|||
self.session.desc = f"上传文档 {file.filename} 到知识库 {kb_id}"
|
||||
# Validate knowledge base exists
|
||||
stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
|
||||
kb = self.session.scalar(stmt)
|
||||
kb = await self.session.scalar(stmt)
|
||||
if not kb:
|
||||
self.session.desc = f"ERROR: 知识库 {kb_id} 不存在"
|
||||
raise ValueError(f"知识库 {kb_id} 不存在")
|
||||
|
|
@ -48,6 +48,7 @@ class DocumentService:
|
|||
|
||||
# Upload file using storage service
|
||||
storage_info = await storage_service.upload_file(file, kb_id)
|
||||
self.session.desc = f"文档 {file.filename} 上传到 {storage_info}"
|
||||
|
||||
# Create document record
|
||||
document = Document(
|
||||
|
|
@ -65,21 +66,21 @@ class DocumentService:
|
|||
document.set_audit_fields()
|
||||
|
||||
self.session.add(document)
|
||||
self.session.commit()
|
||||
self.session.refresh(document)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(document)
|
||||
|
||||
self.session.desc = f"SUCCESS: 成功上传文档 {file.filename} 到知识库 {kb_id} (Doc ID: {document.id})"
|
||||
self.session.desc = f"上传文档 {file.filename} 到知识库 {kb_id} (Doc ID: {document.id})"
|
||||
return document
|
||||
|
||||
def get_document(self, doc_id: int, kb_id: int = None) -> Optional[Document]:
|
||||
async def get_document(self, doc_id: int, kb_id: int = None) -> Optional[Document]:
|
||||
"""根据文档ID查询文档，可选地根据知识库ID过滤。"""
|
||||
self.session.desc = f"查询文档 {doc_id}"
|
||||
self.session.desc = f"根据文档ID查询文档 {doc_id}"
|
||||
stmt = select(Document).where(Document.id == doc_id)
|
||||
if kb_id is not None:
|
||||
stmt = stmt.where(Document.knowledge_base_id == kb_id)
|
||||
return self.session.scalar(stmt)
|
||||
return await self.session.scalar(stmt)
|
||||
|
||||
def get_documents(self, kb_id: int, skip: int = 0, limit: int = 50) -> List[Document]:
|
||||
async def get_documents(self, kb_id: int, skip: int = 0, limit: int = 50) -> List[Document]:
|
||||
"""根据知识库ID查询文档，支持分页。"""
|
||||
self.session.desc = f"查询知识库 {kb_id} 中的文档 (跳过 {skip} 条，限制 {limit} 条)"
|
||||
stmt = (
|
||||
|
|
@ -88,14 +89,14 @@ class DocumentService:
|
|||
.offset(skip)
|
||||
.limit(limit)
|
||||
)
|
||||
return self.session.scalars(stmt).all()
|
||||
return (await self.session.scalars(stmt)).all()
|
||||
|
||||
def list_documents(self, kb_id: int, skip: int = 0, limit: int = 50) -> tuple[List[Document], int]:
|
||||
async def list_documents(self, kb_id: int, skip: int = 0, limit: int = 50) -> tuple[List[Document], int]:
|
||||
"""根据知识库ID查询文档，支持分页，并返回总文档数。"""
|
||||
self.session.desc = f"查询知识库 {kb_id} 中的文档 (跳过 {skip} 条，限制 {limit} 条)"
|
||||
# Get total count
|
||||
count_stmt = select(func.count(Document.id)).where(Document.knowledge_base_id == kb_id)
|
||||
total = self.session.scalar(count_stmt)
|
||||
total = await self.session.scalar(count_stmt)
|
||||
|
||||
# Get documents with pagination
|
||||
documents_stmt = (
|
||||
|
|
@ -104,44 +105,52 @@ class DocumentService:
|
|||
.offset(skip)
|
||||
.limit(limit)
|
||||
)
|
||||
documents = self.session.scalars(documents_stmt).all()
|
||||
documents = (await self.session.scalars(documents_stmt)).all()
|
||||
|
||||
return documents, total
|
||||
|
||||
def delete_document(self, doc_id: int, kb_id: int = None) -> bool:
|
||||
async def delete_document(self, doc_id: int, kb_id: int = None) -> bool:
|
||||
"""根据文档ID删除文档，可选地根据知识库ID过滤。"""
|
||||
self.session.desc = f"删除文档 {doc_id}"
|
||||
document = self.get_document(doc_id, kb_id)
|
||||
document = await self.get_document(doc_id, kb_id)
|
||||
if not document:
|
||||
self.session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
return False
|
||||
|
||||
# Delete file from storage
|
||||
try:
|
||||
storage_service.delete_file(document.file_path)
|
||||
logger.info(f"Deleted file: {document.file_path}")
|
||||
await storage_service.delete_file(document.file_path)
|
||||
self.session.desc = f"SUCCESS: 删除文档 {doc_id} 关联文件 {document.file_path}"
|
||||
except Exception as e:
|
||||
self.session.desc = f"EXCEPTION: 删除文档 {doc_id} 关联文件时失败: {e}"
|
||||
|
||||
# TODO: Remove from vector database
|
||||
# This should be implemented when vector database service is ready
|
||||
get_document_processor().delete_document_from_vector_store(kb_id,doc_id)
|
||||
self.session.desc = f"从向量数据库删除文档 {doc_id}"
|
||||
(await get_document_processor(self.session)).delete_document_from_vector_store(kb_id,doc_id)
|
||||
# Delete database record
|
||||
self.session.delete(document)
|
||||
self.session.commit()
|
||||
self.session.desc = f"删除数据库记录 {doc_id}"
|
||||
await self.session.delete(document)
|
||||
await self.session.commit()
|
||||
self.session.desc = f"SUCCESS: 成功删除文档 {doc_id}"
|
||||
return True
|
||||
|
||||
async def process_document(self, doc_id: int, kb_id: int = None) -> Dict[str, Any]:
|
||||
"""处理文档，提取文本并创建嵌入向量。"""
|
||||
try:
|
||||
self.session.desc = f"处理文档 {doc_id}"
|
||||
document = self.get_document(doc_id, kb_id)
|
||||
self.session.desc = f"处理文档 {doc_id} - 提取文本并创建嵌入向量"
|
||||
document = await self.get_document(doc_id, kb_id)
|
||||
self.session.desc = f"获取文档 {doc_id} >>> {document}"
|
||||
if not document:
|
||||
self.session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
raise ValueError(f"Document {doc_id} not found")
|
||||
|
||||
if document.is_processed:
|
||||
# document.file_path[为('C:\\DrGraph\\TH_Backend\\data\\uploads\\kb_1\\997eccbb-9081-4ddf-879e-bc7d781fab50_答辩.txt',) ，需要取第一个元素
|
||||
file_path = document.file_path
|
||||
knowledge_base_id=document.knowledge_base_id
|
||||
is_processed=document.is_processed
|
||||
|
||||
if is_processed:
|
||||
self.session.desc = f"INFO: 文档 {doc_id} 已处理"
|
||||
return {
|
||||
"document_id": doc_id,
|
||||
|
|
@ -149,38 +158,43 @@ class DocumentService:
|
|||
"message": "文档已处理"
|
||||
}
|
||||
|
||||
self.session.desc = f"查询文档完毕 {doc_id} >>> is_processed = {is_processed}"
|
||||
# 更新文档状态为处理中
|
||||
document.processing_error = None
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
self.session.desc = f"更新文档状态为处理中 {doc_id}"
|
||||
|
||||
# 调用文档处理器进行处理
|
||||
result = get_document_processor().process_document(
|
||||
document_processor = await get_document_processor(self.session)
|
||||
self.session.desc = f"调用文档处理器进行处理=== {doc_id} >>> {document_processor}"
|
||||
result = await document_processor.process_document(
|
||||
session=self.session,
|
||||
document_id=doc_id,
|
||||
file_path=document.file_path,
|
||||
knowledge_base_id=document.knowledge_base_id
|
||||
file_path=file_path,
|
||||
knowledge_base_id=knowledge_base_id
|
||||
)
|
||||
self.session.desc = f"SUCCESS: 成功处理文档 {doc_id}"
|
||||
self.session.desc = f"处理文档完毕 {doc_id}"
|
||||
|
||||
# 如果处理成功，更新文档状态
|
||||
if result["status"] == "success":
|
||||
document.is_processed = True
|
||||
document.chunk_count = result.get("chunks_count", 0)
|
||||
self.session.commit()
|
||||
self.session.refresh(document)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(document)
|
||||
logger.info(f"Processed document: {document.filename} (ID: {doc_id})")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
self.session.rollback()
|
||||
await self.session.rollback()
|
||||
self.session.desc = f"EXCEPTION: 处理文档 {doc_id} 时失败: {e}"
|
||||
|
||||
# Update document with error
|
||||
try:
|
||||
document = self.get_document(doc_id)
|
||||
document = await self.get_document(doc_id, kb_id)
|
||||
if document:
|
||||
document.processing_error = str(e)
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
except Exception as db_error:
|
||||
logger.error(f"Failed to update document error status: {db_error}")
|
||||
|
||||
|
|
@ -217,10 +231,10 @@ class DocumentService:
|
|||
self.session.desc = f"EXCEPTION: 从文档 {document.file_path} 提取文本时失败: {e}"
|
||||
raise
|
||||
|
||||
def update_document_status(self, doc_id: int, is_processed: bool, error: Optional[str] = None) -> bool:
|
||||
async def update_document_status(self, doc_id: int, is_processed: bool, error: Optional[str] = None) -> bool:
|
||||
"""更新文档处理状态。"""
|
||||
self.session.desc = f"更新文档 {doc_id} 处理状态为 {is_processed}"
|
||||
document = self.get_document(doc_id)
|
||||
document = await self.get_document(doc_id)
|
||||
if not document:
|
||||
self.session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
return False
|
||||
|
|
@ -228,16 +242,16 @@ class DocumentService:
|
|||
document.is_processed = is_processed
|
||||
document.processing_error = error
|
||||
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
self.session.desc = f"SUCCESS: 更新文档 {doc_id} 处理状态为 {is_processed}"
|
||||
return True
|
||||
|
||||
def search_documents(self, kb_id: int, query: str, limit: int = 5) -> List[Dict[str, Any]]:
|
||||
async def search_documents(self, kb_id: int, query: str, limit: int = 5) -> List[Dict[str, Any]]:
|
||||
"""在知识库中搜索文档使用向量相似度。"""
|
||||
try:
|
||||
# 使用文档处理器进行相似性搜索
|
||||
self.session.desc = f"搜索知识库 {kb_id} 中的文档使用向量相似度: {query}"
|
||||
results = get_document_processor().search_similar_documents(kb_id, query, limit)
|
||||
self.session.desc = f"搜索知识库 {kb_id} 中的文档使用向量相似度: {query} >>> {limit}条"
|
||||
results = (await get_document_processor(self.session)).search_similar_documents(kb_id, query, limit)
|
||||
self.session.desc = f"SUCCESS: 搜索知识库 {kb_id} 中的文档使用向量相似度: {query} >>> {len(results)} 条结果"
|
||||
return results
|
||||
except Exception as e:
|
||||
|
|
@ -245,9 +259,9 @@ class DocumentService:
|
|||
logger.error(f"查找知识库 {kb_id} 中的文档使用向量相似度时失败: {e}")
|
||||
return []
|
||||
|
||||
def get_document_stats(self, kb_id: int) -> Dict[str, Any]:
|
||||
async def get_document_stats(self, kb_id: int) -> Dict[str, Any]:
|
||||
"""获取知识库中的文档统计信息。"""
|
||||
documents = self.get_documents(kb_id, limit=1000) # Get all documents
|
||||
documents = await self.get_documents(kb_id, limit=1000) # Get all documents
|
||||
|
||||
total_count = len(documents)
|
||||
processed_count = len([doc for doc in documents if doc.is_processed])
|
||||
|
|
@ -267,19 +281,21 @@ class DocumentService:
|
|||
"file_types": file_types
|
||||
}
|
||||
|
||||
def get_document_chunks(self, doc_id: int) -> List[DocumentChunk]:
|
||||
async def get_document_chunks(self, doc_id: int) -> List[DocumentChunk]:
|
||||
"""获取特定文档的文档块。"""
|
||||
try:
|
||||
self.session.desc = f"获取文档 {doc_id} 的文档块"
|
||||
stmt = select(Document).where(Document.id == doc_id)
|
||||
document = self.session.scalar(stmt)
|
||||
document = await self.session.scalar(stmt)
|
||||
if not document:
|
||||
self.session.desc = f"ERROR: 文档 {doc_id} 不存在"
|
||||
return []
|
||||
|
||||
self.session.desc = f"获取文档 {doc_id} 的文档块 > document"
|
||||
# Get chunks from document processor
|
||||
chunks_data = get_document_processor().get_document_chunks(document.knowledge_base_id, doc_id)
|
||||
chunks_data = (await get_document_processor(self.session)).get_document_chunks(document.knowledge_base_id, doc_id)
|
||||
|
||||
self.session.desc = f"获取文档 {doc_id} 的文档块 > chunks_data"
|
||||
# Convert to DocumentChunk objects
|
||||
chunks = []
|
||||
for chunk_data in chunks_data:
|
||||
|
|
@ -290,7 +306,8 @@ class DocumentService:
|
|||
page_number=chunk_data.get("page_number"),
|
||||
chunk_index=chunk_data["chunk_index"],
|
||||
start_char=chunk_data.get("start_char"),
|
||||
end_char=chunk_data.get("end_char")
|
||||
end_char=chunk_data.get("end_char"),
|
||||
vector_id=chunk_data.get("vector_id")
|
||||
)
|
||||
chunks.append(chunk)
|
||||
|
||||
|
|
|
|||
|
|
@ -3,10 +3,9 @@
|
|||
import os
|
||||
from typing import List, Dict, Any, Optional
|
||||
from pathlib import Path
|
||||
from urllib.parse import quote
|
||||
from sqlalchemy import create_engine, text
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
from sqlalchemy.pool import QueuePool
|
||||
from fastapi import HTTPException
|
||||
from requests import Session
|
||||
from sqlalchemy import text
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
from langchain_community.document_loaders import (
|
||||
TextLoader,
|
||||
|
|
@ -18,7 +17,7 @@ import pdfplumber
|
|||
from langchain_core.documents import Document
|
||||
from langchain_postgres import PGVector
|
||||
from typing import List
|
||||
# 旧的ZhipuEmbeddings类已移除，现在统一使用EmbeddingFactory创建embedding实例
|
||||
|
||||
|
||||
from ..core.config import BaseSettings, get_settings
|
||||
from ..models.knowledge_base import Document as DocumentModel
|
||||
|
|
@ -26,64 +25,12 @@ from ..db.database import get_session
|
|||
from loguru import logger
|
||||
|
||||
settings = get_settings()
|
||||
class PGVectorConnectionPool:
|
||||
"""PGVector连接池管理器"""
|
||||
|
||||
def __init__(self):
|
||||
logger.error("PGVector连接池管理器 -==== 待异步方式实现")
|
||||
self.engine = None
|
||||
self.SessionLocal = None
|
||||
# self._init_connection_pool()
|
||||
|
||||
# def _init_connection_pool(self):
|
||||
# """初始化连接池"""
|
||||
# if settings.vector_db.type == "pgvector":
|
||||
# # 构建连接字符串，对密码进行URL编码以处理特殊字符（如@符号）
|
||||
# encoded_password = quote(settings.vector_db.pgvector_password, safe="")
|
||||
# connection_string = (
|
||||
# f"postgresql://{settings.vector_db.pgvector_user}:"
|
||||
# f"{encoded_password}@"
|
||||
# f"{settings.vector_db.pgvector_host}:"
|
||||
# f"{settings.vector_db.pgvector_port}/"
|
||||
# f"{settings.vector_db.pgvector_database}"
|
||||
# )
|
||||
|
||||
# # 创建SQLAlchemy引擎，配置连接池
|
||||
# self.engine = create_engine(
|
||||
# connection_string,
|
||||
# poolclass=QueuePool,
|
||||
# pool_size=5, # 连接池大小
|
||||
# max_overflow=10, # 最大溢出连接数
|
||||
# pool_pre_ping=True, # 连接前ping检查
|
||||
# pool_recycle=3600, # 连接回收时间（秒）
|
||||
# echo=False # 是否打印SQL语句
|
||||
# )
|
||||
|
||||
# # 创建会话工厂
|
||||
# self.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=self.engine)
|
||||
# logger.info(f"PGVector连接池已初始化: {settings.vector_db.pgvector_host}:{settings.vector_db.pgvector_port}")
|
||||
|
||||
# def get_session(self):
|
||||
# """获取数据库会话"""
|
||||
# if self.SessionLocal is None:
|
||||
# raise RuntimeError("连接池未初始化")
|
||||
# return self.SessionLocal()
|
||||
|
||||
# def execute_query(self, query: str, params: tuple = None):
|
||||
# """执行查询并返回结果"""
|
||||
# session = self.get_session()
|
||||
# try:
|
||||
# result = session.execute(text(query), params or {})
|
||||
# return result.fetchall()
|
||||
# finally:
|
||||
# session.close()
|
||||
|
||||
|
||||
class DocumentProcessor:
|
||||
"""文档处理器，负责文档的加载、分段和向量化"""
|
||||
|
||||
def __init__(self):
|
||||
# 初始化语义分割器配置
|
||||
self.embeddings = None
|
||||
self.semantic_splitter_enabled = settings.file.semantic_splitter_enabled
|
||||
self.text_splitter = RecursiveCharacterTextSplitter(
|
||||
chunk_size=settings.file.chunk_size,
|
||||
|
|
@ -92,28 +39,29 @@ class DocumentProcessor:
|
|||
separators=["\n\n", "\n", " ", ""]
|
||||
)
|
||||
|
||||
async def initialize(self, session: Session = None):
|
||||
# 初始化嵌入模型 - 根据配置选择提供商
|
||||
self._init_embeddings()
|
||||
await self._init_embeddings(session)
|
||||
|
||||
# 初始化连接池（仅对PGVector）
|
||||
self.pgvector_pool = None
|
||||
|
||||
# PostgreSQL pgvector连接配置
|
||||
print('settings.vector_db.type=============', settings.vector_db.type)
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# 新版本PGVector使用psycopg3连接字符串
|
||||
# 对密码进行URL编码以处理特殊字符（如@符号）
|
||||
encoded_password = quote(settings.vector_db.pgvector_password, safe="")
|
||||
self.connection_string = (
|
||||
f"postgresql+psycopg://{settings.vector_db.pgvector_user}:"
|
||||
f"{encoded_password}@"
|
||||
f"{settings.vector_db.pgvector_host}:"
|
||||
f"{settings.vector_db.pgvector_port}/"
|
||||
f"{settings.vector_db.pgvector_database}"
|
||||
)
|
||||
# 初始化连接池
|
||||
self.pgvector_pool = PGVectorConnectionPool()
|
||||
else:
|
||||
# if settings.vector_db.type == "pgvector":
|
||||
# # 新版本PGVector使用psycopg3连接字符串
|
||||
# # 对密码进行URL编码以处理特殊字符（如@符号）
|
||||
# encoded_password = quote(settings.vector_db.pgvector_password, safe="")
|
||||
# self.connection_string = (
|
||||
# f"postgresql+psycopg://{settings.vector_db.pgvector_user}:"
|
||||
# f"{encoded_password}@"
|
||||
# f"{settings.vector_db.pgvector_host}:"
|
||||
# f"{settings.vector_db.pgvector_port}/"
|
||||
# f"{settings.vector_db.pgvector_database}"
|
||||
# )
|
||||
# # 初始化连接池
|
||||
# self.pgvector_pool = PGVectorConnectionPool()
|
||||
# logger.info("新版本PGVector使用psycopg3连接字符串: %s", self.connection_string)
|
||||
# else:
|
||||
# 向量数据库存储路径（Chroma兼容）
|
||||
vector_db_path = settings.vector_db.persist_directory
|
||||
if not os.path.isabs(vector_db_path):
|
||||
|
|
@ -122,38 +70,105 @@ class DocumentProcessor:
|
|||
backend_dir = Path(__file__).parent.parent.parent
|
||||
vector_db_path = str(backend_dir / vector_db_path)
|
||||
self.vector_db_path = vector_db_path
|
||||
session.desc = f"初始化向量数据库 - 路径 = {self.vector_db_path}"
|
||||
|
||||
def _init_embeddings(self):
|
||||
"""根据配置初始化embedding模型"""
|
||||
from .embedding_factory import EmbeddingFactory
|
||||
self.embeddings = EmbeddingFactory.create_embeddings()
|
||||
async def _init_embeddings(self, session: Optional[Any] = None):
|
||||
"""初始化嵌入模型。"""
|
||||
try:
|
||||
if not self.embeddings:
|
||||
# 使用llm_config_service获取嵌入配置
|
||||
from .llm_config_service import LLMConfigService
|
||||
llm_config_service = LLMConfigService()
|
||||
|
||||
def load_document(self, file_path: str) -> List[Document]:
|
||||
# 获取嵌入配置
|
||||
config = None
|
||||
if session:
|
||||
config = await llm_config_service.get_default_embedding_config(session)
|
||||
if config:
|
||||
if(session != None):
|
||||
session.desc = f"获取默认嵌入模型配置: {config}"
|
||||
# # 转换配置格式
|
||||
# config = {
|
||||
# "provider": config.provider,
|
||||
# "api_key": config.api_key,
|
||||
# "model": config.model_name
|
||||
# }
|
||||
|
||||
# 如果未找到配置，使用默认配置
|
||||
if not config:
|
||||
session.desc = f"ERROR: 未找到嵌入模型配置"
|
||||
raise HTTPException(status_code=400, detail="未找到嵌入模型配置")
|
||||
session.desc = f"获取嵌入模型配置 > 结果：{config}"
|
||||
|
||||
# 根据配置创建嵌入模型
|
||||
if config.provider == "openai":
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
self.embeddings = OpenAIEmbeddings(
|
||||
model=config.get("model", "text-embedding-3-small"),
|
||||
api_key=config.get("api_key")
|
||||
)
|
||||
session.desc = f"创建嵌入模型 - OpenAIEmbeddings(model={config.get('model', 'text-embedding-3-small')})"
|
||||
elif config.provider == "ollama":
|
||||
from langchain_ollama import OllamaEmbeddings
|
||||
self.embeddings = OllamaEmbeddings(
|
||||
model=config.model_name,
|
||||
base_url=config.base_url
|
||||
)
|
||||
session.desc = f"创建嵌入模型 - OllamaEmbeddings({self.embeddings.base_url} - {self.embeddings.model})"
|
||||
elif config.provider == "local":
|
||||
from langchain_huggingface import HuggingFaceEmbeddings
|
||||
self.embeddings = HuggingFaceEmbeddings(
|
||||
model_name=config.get("model", "sentence-transformers/all-MiniLM-L6-v2")
|
||||
)
|
||||
session.desc = f"创建嵌入模型 - HuggingFaceEmbeddings(model={config.get('model', 'sentence-transformers/all-MiniLM-L6-v2')})"
|
||||
else:
|
||||
# 默认使用OpenAI
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
self.embeddings = OpenAIEmbeddings(
|
||||
model=config.get("model", "text-embedding-3-small"),
|
||||
api_key=config.get("api_key")
|
||||
)
|
||||
session.desc = f"ERROR: 未支持的嵌入提供者: {config['provider']}，已使用默认的 OpenAIEmbeddings - 可能不正确或无效"
|
||||
|
||||
return self.embeddings
|
||||
except Exception as e:
|
||||
logger.error(f"初始化嵌入模型时出错: {e}")
|
||||
raise
|
||||
|
||||
def load_document(self, session: Session, file_path: str) -> List[Document]:
|
||||
"""根据文件类型加载文档"""
|
||||
file_extension = Path(file_path).suffix.lower()
|
||||
|
||||
try:
|
||||
if file_extension == '.txt':
|
||||
session.desc = f"加载文档 - 文件路径: {file_path} - 类型: txt"
|
||||
loader = TextLoader(file_path, encoding='utf-8')
|
||||
documents = loader.load()
|
||||
elif file_extension == '.pdf':
|
||||
# 使用pdfplumber处理PDF文件，更稳定
|
||||
documents = self._load_pdf_with_pdfplumber(file_path)
|
||||
session.desc = f"加载文档 - 文件路径: {file_path} - 类型: pdf"
|
||||
from langchain_community.document_loaders import PyPDFLoader
|
||||
loader = PyPDFLoader(file_path)
|
||||
documents = loader.load()
|
||||
# documents = self._load_pdf_with_pdfplumber(file_path)
|
||||
elif file_extension == '.docx':
|
||||
session.desc = f"加载文档 - 文件路径: {file_path} - 类型: docx"
|
||||
loader = Docx2txtLoader(file_path)
|
||||
documents = loader.load()
|
||||
elif file_extension == '.md':
|
||||
session.desc = f"加载文档 - 文件路径: {file_path} - 类型: md"
|
||||
loader = UnstructuredMarkdownLoader(file_path)
|
||||
documents = loader.load()
|
||||
else:
|
||||
raise ValueError(f"不支持的文件类型: {file_extension}")
|
||||
|
||||
logger.info(f"成功加载文档: {file_path}, 页数: {len(documents)}")
|
||||
session.desc = f"已载文档: {file_path}, 页数: {len(documents)}"
|
||||
# if len(documents) > 0:
|
||||
# session.desc = f"文档内容示例: {type(documents[0])} - {documents[0]}"
|
||||
return documents
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"加载文档失败 {file_path}: {str(e)}")
|
||||
raise
|
||||
session.desc = f"ERROR: 加载文档失败 {file_path}: {str(e)}"
|
||||
raise e
|
||||
|
||||
def _load_pdf_with_pdfplumber(self, file_path: str) -> List[Document]:
|
||||
"""使用pdfplumber加载PDF文档"""
|
||||
|
|
@ -196,77 +211,6 @@ class DocumentProcessor:
|
|||
|
||||
return Document(page_content=merged_text, metadata=merged_metadata)
|
||||
|
||||
def _get_semantic_split_points(self, text: str) -> List[str]:
|
||||
"""使用大模型分析文档内容，返回合适的分割点列表"""
|
||||
try:
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from ..core.config import get_settings
|
||||
|
||||
|
||||
|
||||
prompt = f"""
|
||||
# 任务说明
|
||||
请分析文档内容，识别出适合作为分割点的关键位置。分割点应该是能够将文档划分为有意义段落的文本片段。
|
||||
|
||||
|
||||
# 分割规则
|
||||
请严格按照以下规则识别分割点：
|
||||
|
||||
## 基本要求
|
||||
1. 分割点必须是完整的句子开头或段落开头
|
||||
2. 每个分割后的部分应包含相对完整的语义内容
|
||||
3. 每个分割部分的理想长度控制在500字以内，严禁超过1000字。如果超过了1000字，要强制分段。
|
||||
|
||||
## 短段落处理
|
||||
4. 如果某部分长度可能小于50字，应将其与后续内容合并，避免产生过短片段
|
||||
|
||||
## 唯一性保证（重要）
|
||||
5. 确保每个分割点在文档中具有唯一性：
|
||||
- 检查文内是否存在相同的文本片段
|
||||
- 如果存在重复，需要扩展分割点字符串，直到获得唯一标识
|
||||
- 扩展方法：在当前分割点后追加几个字符，形成更长的唯一字符串
|
||||
|
||||
## 示例说明
|
||||
原始文档：
|
||||
"目录：
|
||||
第一章 标题一
|
||||
第二章 标题二
|
||||
正文
|
||||
第一章 标题一
|
||||
这是第一章的内容
|
||||
|
||||
第二章 标题二
|
||||
这是第二章的内容"
|
||||
|
||||
错误分割点："第一章 标题一"（在目录和正文中重复出现）
|
||||
|
||||
正确分割点："第一章 标题一\n这是第"（通过追加内容确保唯一性）
|
||||
|
||||
# 输出格式
|
||||
- 只返回分割点文本字符串
|
||||
- 每个分割点用~~分隔
|
||||
- 不要包含任何其他内容或解释
|
||||
|
||||
示例输出：分割点1~~分割点2~~分割点3
|
||||
|
||||
|
||||
文档内容：
|
||||
{text[:10000]} # 限制输入长度
|
||||
"""
|
||||
from ..core.llm import create_llm
|
||||
llm = create_llm(temperature=0.2)
|
||||
|
||||
response = llm.invoke(prompt)
|
||||
|
||||
# 解析响应获取分割点列表
|
||||
split_points = [point.strip() for point in response.content.split('~~') if point.strip()]
|
||||
logger.info(f"语义分析得到 {len(split_points)} 个分割点")
|
||||
return split_points
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取语义分割点失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def _split_by_semantic_points(self, text: str, split_points: List[str]) -> List[str]:
|
||||
"""根据语义分割点切分文本"""
|
||||
chunks = []
|
||||
|
|
@ -291,76 +235,19 @@ class DocumentProcessor:
|
|||
|
||||
return chunks
|
||||
|
||||
def split_documents(self, documents: List[Document]) -> List[Document]:
|
||||
async def split_documents(self, session: Session, documents: List[Document]) -> List[Document]:
|
||||
"""将文档分割成小块（含短段落合并和超长强制分割功能）"""
|
||||
try:
|
||||
if self.semantic_splitter_enabled and documents:
|
||||
# 1. 合并文档
|
||||
merged_doc = self._merge_documents(documents)
|
||||
|
||||
# 2. 获取语义分割点
|
||||
split_points = self._get_semantic_split_points(merged_doc.page_content)
|
||||
|
||||
if split_points:
|
||||
# 3. 根据语义分割点切分文本
|
||||
text_chunks = self._split_by_semantic_points(merged_doc.page_content, split_points)
|
||||
|
||||
# 4. 处理短段落合并和超长强制分割（新增逻辑）
|
||||
processed_chunks = []
|
||||
buffer = ""
|
||||
for chunk in text_chunks:
|
||||
# 先检查当前chunk是否超长（超过1000字符）
|
||||
if len(chunk) > 1000:
|
||||
# 如果有缓冲内容，先处理缓冲
|
||||
if buffer:
|
||||
processed_chunks.append(buffer)
|
||||
buffer = ""
|
||||
|
||||
# 对超长chunk进行强制分割
|
||||
forced_splits = self._force_split_long_chunk(chunk)
|
||||
processed_chunks.extend(forced_splits)
|
||||
else:
|
||||
# 正常处理短段落合并
|
||||
if not buffer:
|
||||
buffer = chunk
|
||||
else:
|
||||
if len(buffer) < 100:
|
||||
buffer = f"{buffer}\n{chunk}"
|
||||
else:
|
||||
processed_chunks.append(buffer)
|
||||
buffer = chunk
|
||||
|
||||
# 添加最后剩余的缓冲内容
|
||||
if buffer:
|
||||
processed_chunks.append(buffer)
|
||||
|
||||
# 5. 创建Document对象
|
||||
chunks = []
|
||||
for i, chunk in enumerate(processed_chunks):
|
||||
doc = Document(
|
||||
page_content=chunk,
|
||||
metadata={
|
||||
**merged_doc.metadata,
|
||||
'chunk_index': i,
|
||||
'merged': len(chunk) > 100, # 标记是否经过合并
|
||||
'forced_split': len(chunk) > 1000 # 标记是否经过强制分割
|
||||
}
|
||||
)
|
||||
chunks.append(doc)
|
||||
else:
|
||||
# 如果获取分割点失败，回退到默认分割器
|
||||
logger.warning("语义分割失败，使用默认分割器")
|
||||
chunks = self.text_splitter.split_documents(documents)
|
||||
else:
|
||||
# 使用默认分割器
|
||||
chunks = self.text_splitter.split_documents(documents)
|
||||
|
||||
logger.info(f"文档分割完成，共生成 {len(chunks)} 个文档块")
|
||||
session.desc = f"文档分割完成，共生成 {len(chunks)} 个文档块"
|
||||
if len(chunks) > 0:
|
||||
session.desc = f"文档块内容示例: {type(chunks[0])} - {chunks[0]}"
|
||||
return chunks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"文档分割失败: {str(e)}")
|
||||
raise
|
||||
session.desc = f"ERROR: 文档分割失败: {str(e)}"
|
||||
raise e
|
||||
|
||||
def _force_split_long_chunk(self, chunk: str) -> List[str]:
|
||||
"""强制分割超长段落（超过1000字符）"""
|
||||
|
|
@ -395,35 +282,35 @@ class DocumentProcessor:
|
|||
def create_vector_store(self, knowledge_base_id: int, documents: List[Document], document_id: int = None) -> str:
|
||||
"""为知识库创建向量存储"""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# 添加元数据
|
||||
for i, doc in enumerate(documents):
|
||||
doc.metadata.update({
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"document_id": str(document_id) if document_id else "unknown",
|
||||
"chunk_id": f"{knowledge_base_id}_{document_id}_{i}",
|
||||
"chunk_index": i
|
||||
})
|
||||
# if settings.vector_db.type == "pgvector":
|
||||
# # 添加元数据
|
||||
# for i, doc in enumerate(documents):
|
||||
# doc.metadata.update({
|
||||
# "knowledge_base_id": knowledge_base_id,
|
||||
# "document_id": str(document_id) if document_id else "unknown",
|
||||
# "chunk_id": f"{knowledge_base_id}_{document_id}_{i}",
|
||||
# "chunk_index": i
|
||||
# })
|
||||
|
||||
# 创建PostgreSQL pgvector存储
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
# # 创建PostgreSQL pgvector存储
|
||||
# collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
# 创建新版本PGVector实例
|
||||
vector_store = PGVector(
|
||||
connection=self.connection_string,
|
||||
embeddings=self.embeddings,
|
||||
collection_name=collection_name,
|
||||
use_jsonb=True # 使用JSONB存储元数据
|
||||
)
|
||||
# # 创建新版本PGVector实例
|
||||
# vector_store = PGVector(
|
||||
# connection=self.connection_string,
|
||||
# embeddings=self.embeddings,
|
||||
# collection_name=collection_name,
|
||||
# use_jsonb=True # 使用JSONB存储元数据
|
||||
# )
|
||||
|
||||
# 手动添加文档
|
||||
vector_store.add_documents(documents)
|
||||
# # 手动添加文档
|
||||
# vector_store.add_documents(documents)
|
||||
|
||||
logger.info(f"PostgreSQL pgvector存储创建成功: {collection_name}")
|
||||
return collection_name
|
||||
else:
|
||||
# logger.info(f"PostgreSQL pgvector存储创建成功: {collection_name}")
|
||||
# return collection_name
|
||||
# else:
|
||||
# Chroma兼容模式
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_chroma import Chroma
|
||||
kb_vector_path = os.path.join(self.vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
# 添加元数据
|
||||
|
|
@ -442,9 +329,6 @@ class DocumentProcessor:
|
|||
persist_directory=kb_vector_path
|
||||
)
|
||||
|
||||
# 持久化向量存储
|
||||
vector_store.persist()
|
||||
|
||||
logger.info(f"向量存储创建成功: {kb_vector_path}")
|
||||
return kb_vector_path
|
||||
|
||||
|
|
@ -452,49 +336,22 @@ class DocumentProcessor:
|
|||
logger.error(f"创建向量存储失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def add_documents_to_vector_store(self, knowledge_base_id: int, documents: List[Document], document_id: int = None) -> None:
|
||||
def add_documents_to_vector_store(self, session: Session, knowledge_base_id: int, documents: List[Document], document_id: int = None) -> None:
|
||||
"""向现有向量存储添加文档"""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# 添加元数据
|
||||
for i, doc in enumerate(documents):
|
||||
doc.metadata.update({
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"document_id": str(document_id) if document_id else "unknown",
|
||||
"chunk_id": f"{knowledge_base_id}_{document_id}_{i}",
|
||||
"chunk_index": i
|
||||
})
|
||||
|
||||
# PostgreSQL pgvector存储
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
try:
|
||||
# 连接到现有集合
|
||||
vector_store = PGVector(
|
||||
connection=self.connection_string,
|
||||
embeddings=self.embeddings,
|
||||
collection_name=collection_name,
|
||||
use_jsonb=True
|
||||
)
|
||||
# 添加新文档
|
||||
vector_store.add_documents(documents)
|
||||
except Exception as e:
|
||||
# 如果集合不存在，创建新的向量存储
|
||||
logger.warning(f"连接现有向量存储失败，创建新的向量存储: {e}")
|
||||
self.create_vector_store(knowledge_base_id, documents, document_id)
|
||||
if len(documents) == 0:
|
||||
session.desc = f"WARNING: 文档列表为空，不执行添加操作"
|
||||
return
|
||||
from langchain_chroma import Chroma
|
||||
|
||||
logger.info(f"文档已添加到PostgreSQL pgvector存储: {collection_name}")
|
||||
else:
|
||||
# Chroma兼容模式
|
||||
from langchain_community.vectorstores import Chroma
|
||||
kb_vector_path = os.path.join(self.vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
session.desc = f"添加文档到向量存储: {kb_vector_path} - documents number: {len(documents)}"
|
||||
# 检查向量存储是否存在
|
||||
if not os.path.exists(kb_vector_path):
|
||||
# 如果不存在，创建新的向量存储
|
||||
session.desc = f"WARNING: 向量存储不存在，创建新的向量存储"
|
||||
self.create_vector_store(knowledge_base_id, documents, document_id)
|
||||
return
|
||||
|
||||
session.desc = f"添加文档到向量存储: exists"
|
||||
# 添加元数据
|
||||
for i, doc in enumerate(documents):
|
||||
doc.metadata.update({
|
||||
|
|
@ -504,43 +361,44 @@ class DocumentProcessor:
|
|||
"chunk_index": i
|
||||
})
|
||||
|
||||
session.desc = f"添加文档到向量存储: enumerate"
|
||||
# 加载现有向量存储
|
||||
vector_store = Chroma(
|
||||
persist_directory=kb_vector_path,
|
||||
embedding_function=self.embeddings
|
||||
)
|
||||
|
||||
session.desc = f"添加文档到向量存储: Chroma"
|
||||
# 添加新文档
|
||||
vector_store.add_documents(documents)
|
||||
vector_store.persist()
|
||||
ids = vector_store.add_documents(documents)
|
||||
session.desc = f"文档已添加到向量存储: {kb_vector_path} -> {len(ids)} IDS - \n{ids}"
|
||||
|
||||
logger.info(f"文档已添加到向量存储: {kb_vector_path}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"添加文档到向量存储失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def process_document(self, document_id: int, file_path: str, knowledge_base_id: int) -> Dict[str, Any]:
|
||||
async def process_document(self, session: Session, document_id: int, file_path: str, knowledge_base_id: int) -> Dict[str, Any]:
|
||||
"""处理单个文档：加载、分段、向量化"""
|
||||
try:
|
||||
logger.info(f"开始处理文档 ID: {document_id}, 路径: {file_path}")
|
||||
session.desc = f"处理文档 ID: {document_id} 文件路径: {file_path}"
|
||||
|
||||
# 1. 加载文档
|
||||
documents = self.load_document(file_path)
|
||||
documents = self.load_document(session, file_path)
|
||||
|
||||
# 2. 分割文档
|
||||
chunks = self.split_documents(documents)
|
||||
chunks = await self.split_documents(session, documents)
|
||||
|
||||
# 3. 添加到向量存储
|
||||
self.add_documents_to_vector_store(knowledge_base_id, chunks, document_id)
|
||||
self.add_documents_to_vector_store(session, knowledge_base_id, chunks, document_id)
|
||||
|
||||
# 4. 更新文档状态
|
||||
with next(get_session()) as session:
|
||||
document = session.query(DocumentModel).filter(DocumentModel.id == document_id).first()
|
||||
session = await anext(get_session())
|
||||
try:
|
||||
from sqlalchemy import select
|
||||
document = await session.scalar(select(DocumentModel).where(DocumentModel.id == document_id))
|
||||
|
||||
if document:
|
||||
document.status = "processed"
|
||||
document.chunk_count = len(chunks)
|
||||
session.commit()
|
||||
await session.commit()
|
||||
finally:
|
||||
await session.close()
|
||||
|
||||
result = {
|
||||
"document_id": document_id,
|
||||
|
|
@ -549,22 +407,27 @@ class DocumentProcessor:
|
|||
"message": "文档处理完成"
|
||||
}
|
||||
|
||||
logger.info(f"文档处理完成: {result}")
|
||||
|
||||
session.desc = f"文档处理完成: {result}"
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"文档处理失败 ID: {document_id}: {str(e)}")
|
||||
session.desc = f"ERROR: 文档处理失败 ID: {document_id}: {str(e)}"
|
||||
|
||||
# 更新文档状态为失败
|
||||
try:
|
||||
with next(get_session()) as session:
|
||||
document = session.query(DocumentModel).filter(DocumentModel.id == document_id).first()
|
||||
session = await anext(get_session())
|
||||
try:
|
||||
from sqlalchemy import select
|
||||
document = await session.scalar(select(DocumentModel).where(DocumentModel.id == document_id))
|
||||
if document:
|
||||
document.status = "failed"
|
||||
document.error_message = str(e)
|
||||
session.commit()
|
||||
await session.commit()
|
||||
finally:
|
||||
await session.close()
|
||||
except Exception as db_error:
|
||||
logger.error(f"更新文档状态失败: {str(db_error)}")
|
||||
session.desc = f"ERROR: 更新文档状态失败: {str(db_error)}"
|
||||
|
||||
return {
|
||||
"document_id": document_id,
|
||||
|
|
@ -573,98 +436,34 @@ class DocumentProcessor:
|
|||
"message": "文档处理失败"
|
||||
}
|
||||
|
||||
def _get_document_ids_from_vector_store(self, knowledge_base_id: int, document_id: int) -> List[str]:
|
||||
"""查询指定document_id的所有向量记录的uuid"""
|
||||
try:
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
# 使用连接池执行查询
|
||||
if self.pgvector_pool:
|
||||
query = f"""
|
||||
SELECT uuid FROM langchain_pg_embedding
|
||||
WHERE collection_id = (
|
||||
SELECT uuid FROM langchain_pg_collection
|
||||
WHERE name = %s
|
||||
) AND cmetadata->>'document_id' = %s
|
||||
"""
|
||||
|
||||
result = self.pgvector_pool.execute_query(query, (collection_name, str(document_id)))
|
||||
return [row[0] for row in result] if result else []
|
||||
else:
|
||||
logger.warning("PGVector连接池未初始化")
|
||||
return []
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"查询文档向量记录失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def delete_document_from_vector_store(self, knowledge_base_id: int, document_id: int) -> None:
|
||||
"""从向量存储中删除文档"""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# PostgreSQL pgvector存储
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
try:
|
||||
# 创建新版本PGVector实例
|
||||
vector_store = PGVector(
|
||||
connection=self.connection_string,
|
||||
embeddings=self.embeddings,
|
||||
collection_name=collection_name,
|
||||
use_jsonb=True
|
||||
)
|
||||
|
||||
# 直接从数据库查询要删除的文档UUID
|
||||
try:
|
||||
from sqlalchemy import text
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
# 获取数据库引擎
|
||||
engine = vector_store._engine
|
||||
|
||||
with Session(engine) as session:
|
||||
# 查询匹配document_id的所有记录的ID
|
||||
query_sql = text(
|
||||
f"SELECT id FROM langchain_pg_embedding "
|
||||
f"WHERE cmetadata->>'document_id' = :doc_id"
|
||||
)
|
||||
result = session.execute(query_sql, {"doc_id": str(document_id)})
|
||||
ids_to_delete = [row[0] for row in result.fetchall()]
|
||||
|
||||
if ids_to_delete:
|
||||
# 使用ID删除文档
|
||||
vector_store.delete(ids=ids_to_delete)
|
||||
logger.info(f"成功删除 {len(ids_to_delete)} 个文档块: document_id={document_id}")
|
||||
else:
|
||||
logger.warning(f"未找到要删除的文档ID: document_id={document_id}")
|
||||
|
||||
except Exception as query_error:
|
||||
logger.error(f"查询要删除的文档时出错: {query_error}")
|
||||
# 如果查询失败，说明文档可能不存在
|
||||
logger.warning(f"无法查询到要删除的文档: document_id={document_id}")
|
||||
return
|
||||
|
||||
logger.info(f"文档已从PostgreSQL pgvector存储中删除: document_id={document_id}")
|
||||
except Exception as e:
|
||||
logger.warning(f"PostgreSQL pgvector存储不存在或删除失败: {collection_name}, {str(e)}")
|
||||
else:
|
||||
# Chroma兼容模式
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_chroma import Chroma
|
||||
kb_vector_path = os.path.join(self.vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
if not os.path.exists(kb_vector_path):
|
||||
logger.warning(f"向量存储不存在: {kb_vector_path}")
|
||||
return
|
||||
|
||||
chunks = self.get_document_chunks(knowledge_base_id, document_id)
|
||||
# 加载向量存储
|
||||
vector_store = Chroma(
|
||||
persist_directory=kb_vector_path,
|
||||
embedding_function=self.embeddings
|
||||
)
|
||||
|
||||
# 删除相关文档块（这里需要根据实际的Chroma API来实现）
|
||||
count_before = vector_store._collection.count()
|
||||
count_after = count_before
|
||||
|
||||
if len(chunks) > 0:
|
||||
where_filter = {"document_id": str(document_id)}
|
||||
vector_store.delete(where=where_filter)
|
||||
count_after = vector_store._collection.count()
|
||||
|
||||
# 注意：Chroma的删除功能可能需要特定的实现方式
|
||||
logger.info(f"文档已从向量存储中删除: document_id={document_id}")
|
||||
logger.info(f"文档已从向量存储中删除: document_id={document_id}，删除前有 {count_before} 个向量，删除后有 {count_after} 个向量")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"从向量存储删除文档失败: {str(e)}")
|
||||
|
|
@ -679,25 +478,6 @@ class DocumentProcessor:
|
|||
- 确保结果按chunk_index排序
|
||||
"""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# PostgreSQL pgvector存储 - 使用直接SQL查询避免相似性搜索
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
try:
|
||||
# 尝试直接SQL查询（推荐方法）
|
||||
chunks = self._get_chunks_by_sql(knowledge_base_id, document_id)
|
||||
if chunks:
|
||||
return chunks
|
||||
|
||||
# 如果SQL查询失败，回退到改进的LangChain方法
|
||||
logger.info("SQL查询失败，使用LangChain回退方案")
|
||||
return self._get_chunks_by_langchain_improved(knowledge_base_id, document_id, collection_name)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"PostgreSQL pgvector存储访问失败: {collection_name}, {str(e)}")
|
||||
return []
|
||||
else:
|
||||
# Chroma兼容模式
|
||||
return self._get_chunks_chroma(knowledge_base_id, document_id)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -826,9 +606,7 @@ class DocumentProcessor:
|
|||
|
||||
def _get_chunks_chroma(self, knowledge_base_id: int, document_id: int) -> List[Dict[str, Any]]:
|
||||
"""Chroma存储的处理逻辑"""
|
||||
try:
|
||||
from langchain_community.vectorstores import Chroma
|
||||
|
||||
from langchain_chroma import Chroma
|
||||
# 构建向量数据库路径
|
||||
vector_db_path = os.path.join(self.vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
|
|
@ -845,6 +623,7 @@ class DocumentProcessor:
|
|||
# 获取所有文档的元数据，筛选出指定文档的分段
|
||||
collection = vectorstore._collection
|
||||
all_docs = collection.get(include=["metadatas", "documents"])
|
||||
all_ids_data = collection.get()
|
||||
|
||||
chunks = []
|
||||
chunk_index = 0
|
||||
|
|
@ -852,6 +631,7 @@ class DocumentProcessor:
|
|||
for i, metadata in enumerate(all_docs["metadatas"]):
|
||||
if metadata.get("document_id") == str(document_id):
|
||||
chunk_content = all_docs["documents"][i]
|
||||
vector_id = all_ids_data["ids"][i]
|
||||
|
||||
chunk = {
|
||||
"id": f"chunk_{document_id}_{chunk_index}",
|
||||
|
|
@ -860,65 +640,61 @@ class DocumentProcessor:
|
|||
"page_number": metadata.get("page"),
|
||||
"chunk_index": chunk_index,
|
||||
"start_char": metadata.get("start_char"),
|
||||
"end_char": metadata.get("end_char")
|
||||
"end_char": metadata.get("end_char"),
|
||||
"vector_id": vector_id
|
||||
}
|
||||
chunks.append(chunk)
|
||||
chunk_index += 1
|
||||
|
||||
logger.info(f"获取到文档 {document_id} 的 {len(chunks)} 个分段")
|
||||
return chunks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Chroma存储处理失败: {e}")
|
||||
return []
|
||||
|
||||
def search_similar_documents(self, knowledge_base_id: int, query: str, k: int = 5) -> List[Dict[str, Any]]:
|
||||
"""在知识库中搜索相似文档"""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# PostgreSQL pgvector存储
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
# if settings.vector_db.type == "pgvector":
|
||||
# # PostgreSQL pgvector存储
|
||||
# collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
try:
|
||||
vector_store = PGVector(
|
||||
connection=self.connection_string,
|
||||
embeddings=self.embeddings,
|
||||
collection_name=collection_name,
|
||||
use_jsonb=True
|
||||
)
|
||||
# try:
|
||||
# vector_store = PGVector(
|
||||
# connection=self.connection_string,
|
||||
# embeddings=self.embeddings,
|
||||
# collection_name=collection_name,
|
||||
# use_jsonb=True
|
||||
# )
|
||||
|
||||
# 执行相似性搜索
|
||||
results = vector_store.similarity_search_with_score(query, k=k)
|
||||
# # 执行相似性搜索
|
||||
# results = vector_store.similarity_search_with_score(query, k=k)
|
||||
|
||||
# 格式化结果
|
||||
formatted_results = []
|
||||
for doc, distance_score in results:
|
||||
# pgvector使用余弦距离，距离越小相似度越高
|
||||
# 将距离转换为0-1之间的相似度分数
|
||||
similarity_score = 1.0 / (1.0 + distance_score)
|
||||
# # 格式化结果
|
||||
# formatted_results = []
|
||||
# for doc, distance_score in results:
|
||||
# # pgvector使用余弦距离，距离越小相似度越高
|
||||
# # 将距离转换为0-1之间的相似度分数
|
||||
# similarity_score = 1.0 / (1.0 + distance_score)
|
||||
|
||||
formatted_results.append({
|
||||
"content": doc.page_content,
|
||||
"metadata": doc.metadata,
|
||||
"similarity_score": distance_score, # 保留原始距离分数
|
||||
"normalized_score": similarity_score, # 归一化相似度分数
|
||||
"source": doc.metadata.get('filename', 'unknown'),
|
||||
"document_id": doc.metadata.get('document_id', 'unknown'),
|
||||
"chunk_id": doc.metadata.get('chunk_id', 'unknown')
|
||||
})
|
||||
# formatted_results.append({
|
||||
# "content": doc.page_content,
|
||||
# "metadata": doc.metadata,
|
||||
# "similarity_score": distance_score, # 保留原始距离分数
|
||||
# "normalized_score": similarity_score, # 归一化相似度分数
|
||||
# "source": doc.metadata.get('filename', 'unknown'),
|
||||
# "document_id": doc.metadata.get('document_id', 'unknown'),
|
||||
# "chunk_id": doc.metadata.get('chunk_id', 'unknown')
|
||||
# })
|
||||
|
||||
# 按相似度分数排序（距离越小越相似）
|
||||
formatted_results.sort(key=lambda x: x['similarity_score'])
|
||||
# # 按相似度分数排序（距离越小越相似）
|
||||
# formatted_results.sort(key=lambda x: x['similarity_score'])
|
||||
|
||||
logger.info(f"PostgreSQL pgvector搜索完成，找到 {len(formatted_results)} 个相关文档")
|
||||
return formatted_results
|
||||
# logger.info(f"PostgreSQL pgvector搜索完成，找到 {len(formatted_results)} 个相关文档")
|
||||
# return formatted_results
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"PostgreSQL pgvector存储不存在: {collection_name}, {str(e)}")
|
||||
return []
|
||||
else:
|
||||
# except Exception as e:
|
||||
# logger.warning(f"PostgreSQL pgvector存储不存在: {collection_name}, {str(e)}")
|
||||
# return []
|
||||
# else:
|
||||
# Chroma兼容模式
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_chroma import Chroma
|
||||
kb_vector_path = os.path.join(self.vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
if not os.path.exists(kb_vector_path):
|
||||
|
|
@ -961,13 +737,15 @@ class DocumentProcessor:
|
|||
logger.error(f"搜索文档失败: {str(e)}")
|
||||
return [] # 返回空列表而不是抛出异常
|
||||
|
||||
|
||||
# 全局文档处理器实例（延迟初始化）
|
||||
document_processor = None
|
||||
|
||||
def get_document_processor():
|
||||
async def get_document_processor(session: Session = None):
|
||||
"""获取文档处理器实例（延迟初始化）"""
|
||||
global document_processor
|
||||
if session:
|
||||
session.desc = "获取文档处理器实例"
|
||||
if document_processor is None:
|
||||
document_processor = DocumentProcessor()
|
||||
await document_processor.initialize(session)
|
||||
return document_processor
|
||||
|
|
@ -4,6 +4,7 @@ from typing import Optional
|
|||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
from langchain_community.embeddings import HuggingFaceEmbeddings
|
||||
from requests import Session
|
||||
from .zhipu_embeddings import ZhipuOpenAIEmbeddings
|
||||
from ..core.config import settings
|
||||
from loguru import logger
|
||||
|
|
@ -12,7 +13,8 @@ class EmbeddingFactory:
|
|||
"""Factory class for creating embedding instances based on provider."""
|
||||
|
||||
@staticmethod
|
||||
def create_embeddings(
|
||||
async def create_embeddings(
|
||||
session: Session = None,
|
||||
provider: Optional[str] = None,
|
||||
model: Optional[str] = None,
|
||||
dimensions: Optional[int] = None
|
||||
|
|
@ -28,12 +30,12 @@ class EmbeddingFactory:
|
|||
Embeddings instance
|
||||
"""
|
||||
# 使用新的embedding配置
|
||||
embedding_config = settings.embedding.get_current_config()
|
||||
embedding_config = await settings.embedding.get_current_config(session)
|
||||
provider = provider or settings.embedding.provider
|
||||
model = model or embedding_config.get("model")
|
||||
dimensions = dimensions or settings.vector_db.embedding_dimension
|
||||
|
||||
logger.info(f"Creating embeddings with provider: {provider}, model: {model}")
|
||||
session.desc = f"创建嵌入模型: {provider}, {model}"
|
||||
|
||||
if provider == "openai":
|
||||
return EmbeddingFactory._create_openai_embeddings(embedding_config, model, dimensions)
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import os
|
|||
import pandas as pd
|
||||
from typing import Dict, List, Any, Optional, Tuple
|
||||
from sqlalchemy.orm import Session
|
||||
from sqlalchemy import select, func
|
||||
from ..models.excel_file import ExcelFile
|
||||
from ..db.database import get_session
|
||||
from loguru import logger
|
||||
|
|
@ -102,7 +103,7 @@ class ExcelMetadataService:
|
|||
'processing_error': str(e)
|
||||
}
|
||||
|
||||
def save_file_metadata(self, file_path: str, original_filename: str,
|
||||
async def save_file_metadata(self, file_path: str, original_filename: str,
|
||||
user_id: int, file_size: int) -> ExcelFile:
|
||||
"""Extract and save Excel file metadata to database."""
|
||||
try:
|
||||
|
|
@ -131,31 +132,30 @@ class ExcelMetadataService:
|
|||
|
||||
|
||||
# Save to database
|
||||
self.db.add(excel_file)
|
||||
self.db.commit()
|
||||
self.db.refresh(excel_file)
|
||||
self.session.add(excel_file)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(excel_file)
|
||||
|
||||
logger.info(f"Saved metadata for file {original_filename} with ID {excel_file.id}")
|
||||
return excel_file
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving metadata for {original_filename}: {str(e)}")
|
||||
self.db.rollback()
|
||||
await self.session.rollback()
|
||||
raise
|
||||
|
||||
def get_user_files(self, user_id: int, skip: int = 0, limit: int = 50) -> Tuple[List[ExcelFile], int]:
|
||||
async def get_user_files(self, user_id: int, skip: int = 0, limit: int = 50) -> Tuple[List[ExcelFile], int]:
|
||||
"""Get Excel files for a user with pagination."""
|
||||
try:
|
||||
# Get total count
|
||||
total = self.db.query(ExcelFile).filter(ExcelFile.created_by == user_id).count()
|
||||
stmt = select(func.count(ExcelFile.id)).where(ExcelFile.created_by == user_id)
|
||||
result = await self.session.execute(stmt)
|
||||
total = result.scalar_one()
|
||||
|
||||
# Get files with pagination
|
||||
files = (self.db.query(ExcelFile)
|
||||
.filter(ExcelFile.created_by == user_id)
|
||||
.order_by(ExcelFile.created_at.desc())
|
||||
.offset(skip)
|
||||
.limit(limit)
|
||||
.all())
|
||||
stmt = select(ExcelFile).where(ExcelFile.created_by == user_id).order_by(ExcelFile.created_at.desc()).offset(skip).limit(limit)
|
||||
result = await self.session.execute(stmt)
|
||||
files = result.scalars().all()
|
||||
|
||||
return files, total
|
||||
|
||||
|
|
@ -163,21 +163,21 @@ class ExcelMetadataService:
|
|||
logger.error(f"Error getting user files for user {user_id}: {str(e)}")
|
||||
return [], 0
|
||||
|
||||
def get_file_by_id(self, file_id: int, user_id: int) -> Optional[ExcelFile]:
|
||||
async def get_file_by_id(self, file_id: int, user_id: int) -> Optional[ExcelFile]:
|
||||
"""Get Excel file by ID and user ID."""
|
||||
try:
|
||||
return (self.db.query(ExcelFile)
|
||||
.filter(ExcelFile.id == file_id, ExcelFile.created_by == user_id)
|
||||
.first())
|
||||
stmt = select(ExcelFile).where(ExcelFile.id == file_id, ExcelFile.created_by == user_id)
|
||||
result = await self.session.execute(stmt)
|
||||
return result.scalar_one_or_none()
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting file {file_id} for user {user_id}: {str(e)}")
|
||||
return None
|
||||
|
||||
def delete_file(self, file_id: int, user_id: int) -> bool:
|
||||
async def delete_file(self, file_id: int, user_id: int) -> bool:
|
||||
"""Delete Excel file record and physical file."""
|
||||
try:
|
||||
# Get file record
|
||||
excel_file = self.get_file_by_id(file_id, user_id)
|
||||
excel_file = await self.get_file_by_id(file_id, user_id)
|
||||
if not excel_file:
|
||||
return False
|
||||
|
||||
|
|
@ -187,39 +187,40 @@ class ExcelMetadataService:
|
|||
logger.info(f"Deleted physical file: {excel_file.file_path}")
|
||||
|
||||
# Delete database record
|
||||
self.db.delete(excel_file)
|
||||
self.db.commit()
|
||||
await self.session.delete(excel_file)
|
||||
await self.session.commit()
|
||||
|
||||
logger.info(f"Deleted Excel file record with ID {file_id}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error deleting file {file_id}: {str(e)}")
|
||||
self.db.rollback()
|
||||
await self.session.rollback()
|
||||
return False
|
||||
|
||||
def update_last_accessed(self, file_id: int, user_id: int) -> bool:
|
||||
async def update_last_accessed(self, file_id: int, user_id: int) -> bool:
|
||||
"""Update last accessed time for a file."""
|
||||
try:
|
||||
excel_file = self.get_file_by_id(file_id, user_id)
|
||||
excel_file = await self.get_file_by_id(file_id, user_id)
|
||||
if not excel_file:
|
||||
return False
|
||||
|
||||
from sqlalchemy.sql import func
|
||||
excel_file.last_accessed = func.now()
|
||||
self.db.commit()
|
||||
await self.session.commit()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating last accessed for file {file_id}: {str(e)}")
|
||||
self.db.rollback()
|
||||
await self.session.rollback()
|
||||
return False
|
||||
|
||||
def get_file_summary_for_llm(self, user_id: int) -> List[Dict[str, Any]]:
|
||||
async def get_file_summary_for_llm(self, user_id: int) -> List[Dict[str, Any]]:
|
||||
"""Get file summary information for LLM context."""
|
||||
try:
|
||||
files = self.db.query(ExcelFile).filter(ExcelFile.user_id == user_id).all()
|
||||
stmt = select(ExcelFile).where(ExcelFile.user_id == user_id)
|
||||
result = await self.session.execute(stmt)
|
||||
files = result.scalars().all()
|
||||
|
||||
summary = []
|
||||
for file in files:
|
||||
|
|
|
|||
|
|
@ -29,9 +29,11 @@ class KnowledgeBaseService:
|
|||
Args:
|
||||
session (Session): 数据库会话，用于执行ORM操作。
|
||||
"""
|
||||
if session is None:
|
||||
logger.error("session为空，session must be an instance of Session")
|
||||
self.session = session
|
||||
|
||||
def create_knowledge_base(self, kb_data: KnowledgeBaseCreate) -> KnowledgeBase:
|
||||
async def create_knowledge_base(self, kb_data: KnowledgeBaseCreate) -> KnowledgeBase:
|
||||
"""创建一个新的知识库实例。
|
||||
|
||||
Args:
|
||||
|
|
@ -57,130 +59,22 @@ class KnowledgeBaseService:
|
|||
collection_name=collection_name
|
||||
)
|
||||
|
||||
# Set audit fields
|
||||
# 自动更新created_by和updated_by字段
|
||||
kb.set_audit_fields()
|
||||
|
||||
self.session.add(kb)
|
||||
self.session.commit()
|
||||
self.session.refresh(kb)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(kb)
|
||||
|
||||
logger.info(f"Created knowledge base: {kb.name} (ID: {kb.id})")
|
||||
self.session.desc = f"Created knowledge base: {kb.name} - collection_name = {collection_name}, embedding_model = {kb.embedding_model}"
|
||||
return kb
|
||||
|
||||
except Exception as e:
|
||||
self.session.rollback()
|
||||
await self.session.rollback()
|
||||
logger.error(f"Failed to create knowledge base: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_knowledge_base(self, kb_id: int) -> Optional[KnowledgeBase]:
|
||||
"""根据ID获取知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 知识库实例的ID。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回知识库实例，否则返回None。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
|
||||
return self.session.execute(stmt).scalar_one_or_none()
|
||||
|
||||
def get_knowledge_base_by_name(self, name: str) -> Optional[KnowledgeBase]:
|
||||
"""根据名称获取当前用户的知识库实例。
|
||||
|
||||
Args:
|
||||
name (str): 知识库实例的名称。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回知识库实例，否则返回None。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(
|
||||
KnowledgeBase.name == name,
|
||||
KnowledgeBase.created_by == UserContext.get_current_user().id
|
||||
)
|
||||
return self.session.execute(stmt).scalar_one_or_none()
|
||||
|
||||
async def get_knowledge_bases(self, skip: int = 0, limit: int = 50, active_only: bool = True) -> List[KnowledgeBase]:
|
||||
"""获取当前用户的所有知识库的列表。
|
||||
|
||||
Args:
|
||||
skip (int, optional): 跳过的记录数。默认值为0。
|
||||
limit (int, optional): 返回的最大记录数。默认值为50。
|
||||
active_only (bool, optional): 是否仅返回活动的知识库。默认值为True。
|
||||
|
||||
Returns:
|
||||
List[KnowledgeBase]: 当前用户的知识库列表。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(KnowledgeBase.created_by == UserContext.get_current_user().id)
|
||||
|
||||
if active_only:
|
||||
stmt = stmt.where(KnowledgeBase.is_active == True)
|
||||
|
||||
stmt = stmt.offset(skip).limit(limit)
|
||||
return (await self.session.execute(stmt)).scalars().all()
|
||||
|
||||
|
||||
def update_knowledge_base(self, kb_id: int, kb_update: KnowledgeBaseUpdate) -> Optional[KnowledgeBase]:
|
||||
"""更新知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 待更新的知识库实例ID。
|
||||
kb_update (KnowledgeBaseUpdate): 用于更新知识库实例的数据。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回更新后的知识库实例，否则返回None。
|
||||
|
||||
Raises:
|
||||
Exception: 如果更新过程中发生错误。
|
||||
"""
|
||||
try:
|
||||
kb = self.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
return None
|
||||
|
||||
# Update fields
|
||||
update_data = kb_update.model_dump(exclude_unset=True)
|
||||
for field, value in update_data.items():
|
||||
setattr(kb, field, value)
|
||||
|
||||
# Set audit fields
|
||||
kb.set_audit_fields(is_update=True)
|
||||
|
||||
self.session.commit()
|
||||
self.session.refresh(kb)
|
||||
|
||||
self.session.desc = f"[KNOWLEDGE_BASE] 更新知识库 {kb.name} (ID: {kb.id})"
|
||||
return kb
|
||||
|
||||
except Exception as e:
|
||||
self.session.rollback()
|
||||
self.session.desc = f"[KNOWLEDGE_BASE] 更新知识库 {kb_id} 失败: {str(e)}"
|
||||
raise
|
||||
|
||||
def delete_knowledge_base(self, kb_id: int) -> bool:
|
||||
"""删除知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 待删除的知识库实例ID。
|
||||
|
||||
Returns:
|
||||
bool: 如果知识库实例被成功删除则返回True，否则返回False。
|
||||
|
||||
Raises:
|
||||
Exception: 如果删除过程中发生错误。
|
||||
"""
|
||||
kb = self.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
return False
|
||||
|
||||
# TODO: Clean up vector database collection
|
||||
# This should be implemented when vector database service is ready
|
||||
|
||||
self.session.delete(kb)
|
||||
self.session.commit()
|
||||
|
||||
return True
|
||||
|
||||
def search_knowledge_bases(self, query: str, skip: int = 0, limit: int = 50) -> List[KnowledgeBase]:
|
||||
async def search_knowledge_bases(self, query: str, skip: int = 0, limit: int = 50) -> List[KnowledgeBase]:
|
||||
"""Search knowledge bases by name or description for the current user.
|
||||
|
||||
Args:
|
||||
|
|
@ -192,7 +86,7 @@ class KnowledgeBaseService:
|
|||
List[KnowledgeBase]: List of matching knowledge bases.
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(
|
||||
KnowledgeBase.created_by == UserContext.get_current_user().id,
|
||||
KnowledgeBase.created_by == UserContext.get_current_user()['id'],
|
||||
KnowledgeBase.is_active == True,
|
||||
or_(
|
||||
KnowledgeBase.name.ilike(f"%{query}%"),
|
||||
|
|
@ -201,7 +95,7 @@ class KnowledgeBaseService:
|
|||
)
|
||||
|
||||
stmt = stmt.offset(skip).limit(limit)
|
||||
return self.session.execute(stmt).scalars().all()
|
||||
return (await self.session.execute(stmt)).scalars().all()
|
||||
|
||||
async def search(self, kb_id: int, query: str, top_k: int = 5, similarity_threshold: float = 0.7) -> List[Dict[str, Any]]:
|
||||
"""Search in knowledge base using vector similarity.
|
||||
|
|
@ -219,7 +113,7 @@ class KnowledgeBaseService:
|
|||
logger.info(f"Searching in knowledge base {kb_id} for: {query}")
|
||||
|
||||
# Use document processor for vector search
|
||||
search_results = get_document_processor().search_similar_documents(
|
||||
search_results = (await get_document_processor(self.session)).search_similar_documents(
|
||||
knowledge_base_id=kb_id,
|
||||
query=query,
|
||||
k=top_k
|
||||
|
|
@ -247,3 +141,104 @@ class KnowledgeBaseService:
|
|||
except Exception as e:
|
||||
logger.error(f"Search failed for knowledge base {kb_id}: {str(e)}")
|
||||
return []
|
||||
|
||||
# ----------------------------------------------------------------------------------
|
||||
async def get_knowledge_base_by_name(self, name: str) -> Optional[KnowledgeBase]:
|
||||
"""根据名称获取当前用户的知识库实例。
|
||||
|
||||
Args:
|
||||
name (str): 知识库实例的名称。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回知识库实例，否则返回None。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(
|
||||
KnowledgeBase.name == name,
|
||||
KnowledgeBase.created_by == UserContext.get_current_user()['id']
|
||||
)
|
||||
result = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
return result
|
||||
|
||||
async def get_knowledge_bases(self, skip: int = 0, limit: int = 50, active_only: bool = True) -> List[KnowledgeBase]:
|
||||
"""获取当前用户的所有知识库的列表。
|
||||
|
||||
Args:
|
||||
skip (int, optional): 跳过的记录数。默认值为0。
|
||||
limit (int, optional): 返回的最大记录数。默认值为50。
|
||||
active_only (bool, optional): 是否仅返回活动的知识库。默认值为True。
|
||||
|
||||
Returns:
|
||||
List[KnowledgeBase]: 当前用户的知识库列表。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(KnowledgeBase.created_by == UserContext.get_current_user()['id']) # 使用字典键索引访问用户ID
|
||||
if active_only:
|
||||
stmt = stmt.where(KnowledgeBase.is_active == True)
|
||||
stmt = stmt.offset(skip).limit(limit)
|
||||
return (await self.session.execute(stmt)).scalars().all()
|
||||
|
||||
async def get_knowledge_base(self, kb_id: int) -> Optional[KnowledgeBase]:
|
||||
"""根据ID获取知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 知识库实例的ID。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回知识库实例，否则返回None。
|
||||
"""
|
||||
stmt = select(KnowledgeBase).where(KnowledgeBase.id == kb_id)
|
||||
return (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
async def update_knowledge_base(self, kb_id: int, kb_update: KnowledgeBaseUpdate) -> Optional[KnowledgeBase]:
|
||||
"""更新知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 待更新的知识库实例ID。
|
||||
kb_update (KnowledgeBaseUpdate): 用于更新知识库实例的数据。
|
||||
|
||||
Returns:
|
||||
Optional[KnowledgeBase]: 如果找到则返回更新后的知识库实例，否则返回None。
|
||||
|
||||
Raises:
|
||||
Exception: 如果更新过程中发生错误。
|
||||
"""
|
||||
kb = await self.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
return None
|
||||
|
||||
# Update fields
|
||||
update_data = kb_update.model_dump(exclude_unset=True)
|
||||
for field, value in update_data.items():
|
||||
setattr(kb, field, value)
|
||||
|
||||
# Set audit fields
|
||||
kb.set_audit_fields(is_update=True)
|
||||
|
||||
await self.session.commit()
|
||||
await self.session.refresh(kb)
|
||||
|
||||
self.session.desc = f"[KNOWLEDGE_BASE] 更新知识库 {kb.name} (ID: {kb.id})"
|
||||
return kb
|
||||
|
||||
async def delete_knowledge_base(self, kb_id: int) -> bool:
|
||||
"""删除知识库实例。
|
||||
|
||||
Args:
|
||||
kb_id (int): 待删除的知识库实例ID。
|
||||
|
||||
Returns:
|
||||
bool: 如果知识库实例被成功删除则返回True，否则返回False。
|
||||
|
||||
Raises:
|
||||
Exception: 如果删除过程中发生错误。
|
||||
"""
|
||||
kb = await self.get_knowledge_base(kb_id)
|
||||
if not kb:
|
||||
return False
|
||||
|
||||
# TODO: Clean up vector database collection
|
||||
# This should be implemented when vector database service is ready
|
||||
|
||||
await self.session.delete(kb)
|
||||
await self.session.commit()
|
||||
|
||||
return True
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
|
|||
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_chroma import Chroma
|
||||
from langchain_postgres import PGVector
|
||||
from .embedding_factory import EmbeddingFactory
|
||||
|
||||
|
|
@ -21,16 +21,16 @@ from .conversation import ConversationService
|
|||
from .document_processor import get_document_processor
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class KnowledgeChatService:
|
||||
"""Knowledge base chat service using LangChain RAG."""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.conversation_service = ConversationService(db)
|
||||
def __init__(self, session: Session):
|
||||
self.session = session
|
||||
self.conversation_service = ConversationService(session)
|
||||
|
||||
async def initialize(self):
|
||||
# 获取当前LLM配置
|
||||
llm_config = settings.llm.get_current_config()
|
||||
llm_config = await settings.llm.get_current_config(self.session)
|
||||
|
||||
# Initialize LangChain ChatOpenAI
|
||||
self.llm = ChatOpenAI(
|
||||
|
|
@ -53,30 +53,12 @@ class KnowledgeChatService:
|
|||
)
|
||||
|
||||
# Initialize embeddings based on provider
|
||||
self.embeddings = EmbeddingFactory.create_embeddings()
|
||||
|
||||
logger.info(f"Knowledge Chat Service initialized with model: {self.llm.model_name}")
|
||||
|
||||
def _get_vector_store(self, knowledge_base_id: int) -> Optional[PGVector]:
|
||||
self.embeddings = await EmbeddingFactory.create_embeddings(self.session)
|
||||
async def _get_vector_store(self, knowledge_base_id: int) -> Optional[PGVector]:
|
||||
"""Get vector store for knowledge base."""
|
||||
try:
|
||||
if settings.vector_db.type == "pgvector":
|
||||
# 使用PGVector
|
||||
doc_processor = get_document_processor()
|
||||
collection_name = f"{settings.vector_db.pgvector_table_name}_kb_{knowledge_base_id}"
|
||||
|
||||
vector_store = PGVector(
|
||||
connection=doc_processor.connection_string,
|
||||
embeddings=self.embeddings,
|
||||
collection_name=collection_name,
|
||||
use_jsonb=True
|
||||
)
|
||||
|
||||
return vector_store
|
||||
else:
|
||||
# 兼容Chroma模式
|
||||
import os
|
||||
kb_vector_path = os.path.join(get_document_processor().vector_db_path, f"kb_{knowledge_base_id}")
|
||||
kb_vector_path = os.path.join((await get_document_processor(self.session)).vector_db_path, f"kb_{knowledge_base_id}")
|
||||
|
||||
if not os.path.exists(kb_vector_path):
|
||||
logger.warning(f"Vector store not found for knowledge base {knowledge_base_id}")
|
||||
|
|
@ -159,7 +141,7 @@ class KnowledgeChatService:
|
|||
|
||||
try:
|
||||
# Get conversation and validate
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
conversation = await self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError("Conversation not found")
|
||||
|
||||
|
|
@ -169,14 +151,14 @@ class KnowledgeChatService:
|
|||
raise ChatServiceError(f"Knowledge base {knowledge_base_id} not found or not processed")
|
||||
|
||||
# Save user message
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Get conversation history
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
messages = await self.conversation_service.get_conversation_messages(conversation_id)
|
||||
conversation_history = self._prepare_conversation_history(messages)
|
||||
|
||||
# Create RAG chain
|
||||
|
|
@ -203,7 +185,7 @@ class KnowledgeChatService:
|
|||
response_content = await asyncio.to_thread(rag_chain.invoke, message)
|
||||
|
||||
# Save assistant message with context
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=response_content,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -251,14 +233,14 @@ class KnowledgeChatService:
|
|||
raise ChatServiceError(f"Knowledge base {knowledge_base_id} not found or not processed")
|
||||
|
||||
# Get conversation history
|
||||
messages = self.conversation_service.get_conversation_messages(conversation_id)
|
||||
messages = await self.conversation_service.get_conversation_messages(conversation_id)
|
||||
conversation_history = self._prepare_conversation_history(messages)
|
||||
|
||||
# Create RAG chain
|
||||
rag_chain, retriever = self._create_rag_chain(vector_store, conversation_history)
|
||||
|
||||
# Save user message
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
|
|
@ -269,7 +251,7 @@ class KnowledgeChatService:
|
|||
context = "\n\n".join([doc.page_content for doc in relevant_docs])
|
||||
|
||||
# Create streaming LLM
|
||||
llm_config = settings.llm.get_current_config()
|
||||
llm_config = await settings.llm.get_current_config()
|
||||
streaming_llm = ChatOpenAI(
|
||||
model=llm_config["model"],
|
||||
temperature=temperature or llm_config["temperature"],
|
||||
|
|
@ -315,7 +297,7 @@ class KnowledgeChatService:
|
|||
|
||||
# Save assistant response
|
||||
if full_response:
|
||||
self.conversation_service.add_message(
|
||||
await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=full_response,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -331,7 +313,7 @@ class KnowledgeChatService:
|
|||
yield error_message
|
||||
|
||||
# Save error message
|
||||
self.conversation_service.add_message(
|
||||
await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=error_message,
|
||||
role=MessageRole.ASSISTANT
|
||||
|
|
|
|||
|
|
@ -41,24 +41,23 @@ class StreamingCallbackHandler(BaseCallbackHandler):
|
|||
class LangChainChatService:
|
||||
"""LangChain-based chat service for AI model integration."""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.conversation_service = ConversationService(db)
|
||||
def __init__(self, session: Session):
|
||||
self.session = session
|
||||
self.conversation_service = ConversationService(session)
|
||||
|
||||
from ..core.llm import create_llm
|
||||
|
||||
# 添加调试日志
|
||||
logger.info(f"LLM Provider: {settings.llm.provider}")
|
||||
async def initialize(self):
|
||||
from ..core.new_agent import new_agent
|
||||
|
||||
# Initialize LangChain ChatOpenAI
|
||||
self.llm = create_llm(streaming=False)
|
||||
self.llm = await new_agent(self.session, streaming=False)
|
||||
self.session.desc = "LangChainChatService初始化 - llm 实例化完毕"
|
||||
|
||||
# Streaming LLM for stream responses
|
||||
self.streaming_llm = create_llm(streaming=True)
|
||||
self.streaming_llm = await new_agent(self.session, streaming=True)
|
||||
self.session.desc = "LangChainChatService初始化 - streaming_llm 实例化完毕"
|
||||
|
||||
self.streaming_handler = StreamingCallbackHandler()
|
||||
|
||||
logger.info(f"LangChain ChatService initialized with model: {self.llm.model_name}")
|
||||
self.session.desc = "LangChainChatService初始化 - streaming_handler 实例化完毕"
|
||||
|
||||
def _prepare_langchain_messages(self, conversation, history: List) -> List:
|
||||
"""Prepare messages for LangChain format."""
|
||||
|
|
@ -101,19 +100,19 @@ class LangChainChatService:
|
|||
|
||||
try:
|
||||
# Get conversation details
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
conversation = await self.conversation_service.get_conversation(conversation_id)
|
||||
if not conversation:
|
||||
raise ChatServiceError("Conversation not found")
|
||||
|
||||
# Add user message to database
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Get conversation history for context
|
||||
history = self.conversation_service.get_conversation_history(
|
||||
history = await self.conversation_service.get_conversation_history(
|
||||
conversation_id, limit=20
|
||||
)
|
||||
|
||||
|
|
@ -123,7 +122,7 @@ class LangChainChatService:
|
|||
# Update LLM parameters if provided
|
||||
llm_to_use = self.llm
|
||||
if temperature is not None or max_tokens is not None:
|
||||
llm_config = settings.llm.get_current_config()
|
||||
llm_config = await settings.llm.get_current_config()
|
||||
llm_to_use = ChatOpenAI(
|
||||
model=llm_config["model"],
|
||||
openai_api_key=llm_config["api_key"],
|
||||
|
|
@ -140,7 +139,7 @@ class LangChainChatService:
|
|||
assistant_content = response.content
|
||||
|
||||
# Add assistant message to database
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=assistant_content,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -152,7 +151,7 @@ class LangChainChatService:
|
|||
)
|
||||
|
||||
# Update conversation timestamp
|
||||
self.conversation_service.update_conversation_timestamp(conversation_id)
|
||||
await self.conversation_service.update_conversation_timestamp(conversation_id)
|
||||
|
||||
logger.info(f"Successfully processed LangChain chat request for conversation {conversation_id}")
|
||||
|
||||
|
|
@ -171,7 +170,7 @@ class LangChainChatService:
|
|||
error_message = self._format_error_message(e)
|
||||
|
||||
# Add error message to database
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=error_message,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -206,33 +205,33 @@ class LangChainChatService:
|
|||
max_tokens: Optional[int] = None
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Send a message and get streaming AI response using LangChain."""
|
||||
logger.info(f"Processing LangChain streaming chat request for conversation {conversation_id}")
|
||||
logger.info(f"通过 LangChain 进行流式处理对话 请求，会话 ID: {conversation_id}")
|
||||
|
||||
try:
|
||||
# Get conversation details
|
||||
conversation = self.conversation_service.get_conversation(conversation_id)
|
||||
conversation = await self.conversation_service.get_conversation(conversation_id)
|
||||
conv = conversation.to_dict()
|
||||
if not conversation:
|
||||
raise ChatServiceError("Conversation not found")
|
||||
|
||||
# Add user message to database
|
||||
user_message = self.conversation_service.add_message(
|
||||
user_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=message,
|
||||
role=MessageRole.USER
|
||||
)
|
||||
|
||||
# Get conversation history for context
|
||||
history = self.conversation_service.get_conversation_history(
|
||||
history = await self.conversation_service.get_conversation_history(
|
||||
conversation_id, limit=20
|
||||
)
|
||||
|
||||
# Prepare messages for LangChain
|
||||
langchain_messages = self._prepare_langchain_messages(conversation, history)
|
||||
|
||||
langchain_messages = self._prepare_langchain_messages(conv, history)
|
||||
# Update streaming LLM parameters if provided
|
||||
streaming_llm_to_use = self.streaming_llm
|
||||
if temperature is not None or max_tokens is not None:
|
||||
llm_config = settings.llm.get_current_config()
|
||||
llm_config = await settings.llm.get_current_config()
|
||||
streaming_llm_to_use = ChatOpenAI(
|
||||
model=llm_config["model"],
|
||||
openai_api_key=llm_config["api_key"],
|
||||
|
|
@ -241,13 +240,13 @@ class LangChainChatService:
|
|||
max_tokens=max_tokens if max_tokens is not None else conversation.max_tokens,
|
||||
streaming=True
|
||||
)
|
||||
|
||||
# Clear previous streaming handler state
|
||||
self.streaming_handler.clear()
|
||||
|
||||
# Stream response
|
||||
full_response = ""
|
||||
async for chunk in streaming_llm_to_use.astream(langchain_messages):
|
||||
try:
|
||||
async for chunk in streaming_llm_to_use._astream(langchain_messages):
|
||||
# Handle different chunk types to avoid KeyError
|
||||
chunk_content = None
|
||||
if hasattr(chunk, 'content'):
|
||||
|
|
@ -265,9 +264,11 @@ class LangChainChatService:
|
|||
if chunk_content:
|
||||
full_response += chunk_content
|
||||
yield chunk_content
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in LLM streaming: {e}")
|
||||
yield f"{self._format_error_message(e)} >>> {e}"
|
||||
# Add complete assistant message to database
|
||||
assistant_message = self.conversation_service.add_message(
|
||||
assistant_message = await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=full_response,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -280,13 +281,12 @@ class LangChainChatService:
|
|||
)
|
||||
|
||||
# Update conversation timestamp
|
||||
self.conversation_service.update_conversation_timestamp(conversation_id)
|
||||
|
||||
logger.info(f"Successfully processed LangChain streaming chat request for conversation {conversation_id}")
|
||||
await self.conversation_service.update_conversation_timestamp(conversation_id)
|
||||
logger.info(f"完成 LangChain 流式处理对话，会话 ID: {conversation_id}")
|
||||
|
||||
except Exception as e:
|
||||
# 安全地格式化异常信息，避免再次引发KeyError
|
||||
error_info = f"Failed to process LangChain streaming chat request for conversation {conversation_id}"
|
||||
error_info = f"Failed to process LangChain streaming chat request for conversation {conversation_id} >>> {e}"
|
||||
logger.error(error_info, exc_info=True)
|
||||
|
||||
# Format error message for user
|
||||
|
|
@ -294,7 +294,7 @@ class LangChainChatService:
|
|||
yield error_message
|
||||
|
||||
# Add error message to database
|
||||
self.conversation_service.add_message(
|
||||
await self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
||||
content=error_message,
|
||||
role=MessageRole.ASSISTANT,
|
||||
|
|
@ -324,23 +324,23 @@ class LangChainChatService:
|
|||
logger.error(f"Failed to get available models: {str(e)}")
|
||||
return ["gpt-3.5-turbo"]
|
||||
|
||||
def update_model_config(
|
||||
async def update_model_config(
|
||||
self,
|
||||
model: Optional[str] = None,
|
||||
temperature: Optional[float] = None,
|
||||
max_tokens: Optional[int] = None
|
||||
):
|
||||
"""Update LLM configuration."""
|
||||
from ..core.llm import create_llm
|
||||
from ..core.new_agent import new_agent
|
||||
|
||||
# 重新创建LLM实例
|
||||
self.llm = create_llm(
|
||||
self.llm = await new_agent(
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
streaming=False
|
||||
)
|
||||
|
||||
self.streaming_llm = create_llm(
|
||||
self.streaming_llm = await new_agent(
|
||||
model=model,
|
||||
temperature=temperature,
|
||||
streaming=True
|
||||
|
|
@ -357,7 +357,7 @@ class LangChainChatService:
|
|||
if "rate limit" in error_str.lower():
|
||||
return "服务器繁忙，请稍后再试。"
|
||||
elif "api key" in error_str.lower() or "authentication" in error_str.lower():
|
||||
return "API认证失败，请检查配置。"
|
||||
return f"API认证失败，请检查配置文件。"
|
||||
elif "timeout" in error_str.lower():
|
||||
return "请求超时，请重试。"
|
||||
elif "connection" in error_str.lower():
|
||||
|
|
|
|||
|
|
@ -11,11 +11,9 @@ from loguru import logger
|
|||
class LLMConfigService:
|
||||
"""LLM配置管理服务"""
|
||||
|
||||
def __init__(self, db_session: Optional[Session] = None):
|
||||
self.db = db_session or get_session() # TODO DrGraph:检查异步
|
||||
|
||||
def get_default_chat_config(self) -> Optional[LLMConfig]:
|
||||
async def get_default_chat_config(self, session: Session) -> Optional[LLMConfig]:
|
||||
"""获取默认对话模型配置"""
|
||||
# async for session in get_session():
|
||||
try:
|
||||
stmt = select(LLMConfig).where(
|
||||
and_(
|
||||
|
|
@ -24,7 +22,7 @@ class LLMConfigService:
|
|||
LLMConfig.is_active == True
|
||||
)
|
||||
)
|
||||
config = self.db.execute(stmt).scalar_one_or_none()
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if not config:
|
||||
logger.warning("未找到默认对话模型配置")
|
||||
|
|
@ -36,7 +34,7 @@ class LLMConfigService:
|
|||
logger.error(f"获取默认对话模型配置失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def get_default_embedding_config(self) -> Optional[LLMConfig]:
|
||||
async def get_default_embedding_config(self, session: Session) -> Optional[LLMConfig]:
|
||||
"""获取默认嵌入模型配置"""
|
||||
try:
|
||||
stmt = select(LLMConfig).where(
|
||||
|
|
@ -46,23 +44,27 @@ class LLMConfigService:
|
|||
LLMConfig.is_active == True
|
||||
)
|
||||
)
|
||||
config = self.db.execute(stmt).scalar_one_or_none()
|
||||
|
||||
config = None
|
||||
if session != None:
|
||||
config = (await session.execute(stmt)).scalar_one_or_none()
|
||||
if not config:
|
||||
logger.warning("未找到默认嵌入模型配置")
|
||||
if session != None:
|
||||
session.desc = "ERROR: 未找到默认嵌入模型配置"
|
||||
return None
|
||||
|
||||
session.desc = f"获取默认嵌入模型配置 > 结果：{config}"
|
||||
return config
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取默认嵌入模型配置失败: {str(e)}")
|
||||
if session != None:
|
||||
session.desc = f"ERROR: 获取默认嵌入模型配置失败: {str(e)}"
|
||||
return None
|
||||
|
||||
def get_config_by_id(self, config_id: int) -> Optional[LLMConfig]:
|
||||
async def get_config_by_id(self, config_id: int) -> Optional[LLMConfig]:
|
||||
"""根据ID获取配置"""
|
||||
try:
|
||||
stmt = select(LLMConfig).where(LLMConfig.id == config_id)
|
||||
return self.db.execute(stmt).scalar_one_or_none()
|
||||
return (await self.db.execute(stmt)).scalar_one_or_none()
|
||||
except Exception as e:
|
||||
logger.error(f"获取配置失败: {str(e)}")
|
||||
return None
|
||||
|
|
@ -82,17 +84,17 @@ class LLMConfigService:
|
|||
logger.error(f"获取激活配置失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def _get_fallback_chat_config(self) -> Dict[str, Any]:
|
||||
async def _get_fallback_chat_config(self) -> Dict[str, Any]:
|
||||
"""获取fallback对话模型配置（从环境变量）"""
|
||||
from ..core.config import get_settings
|
||||
settings = get_settings()
|
||||
return settings.llm.get_current_config()
|
||||
return await settings.llm.get_current_config()
|
||||
|
||||
def _get_fallback_embedding_config(self) -> Dict[str, Any]:
|
||||
async def _get_fallback_embedding_config(self) -> Dict[str, Any]:
|
||||
"""获取fallback嵌入模型配置（从环境变量）"""
|
||||
from ..core.config import get_settings
|
||||
settings = get_settings()
|
||||
return settings.embedding.get_current_config()
|
||||
return await settings.embedding.get_current_config()
|
||||
|
||||
def test_config(self, config_id: int, test_message: str = "Hello") -> Dict[str, Any]:
|
||||
"""测试配置连接"""
|
||||
|
|
@ -110,12 +112,12 @@ class LLMConfigService:
|
|||
logger.error(f"测试配置失败: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
# 全局实例
|
||||
_llm_config_service = None
|
||||
# # 全局实例
|
||||
# _llm_config_service = None
|
||||
|
||||
def get_llm_config_service(db_session: Optional[Session] = None) -> LLMConfigService:
|
||||
"""获取LLM配置服务实例"""
|
||||
global _llm_config_service
|
||||
if _llm_config_service is None or db_session is not None:
|
||||
_llm_config_service = LLMConfigService(db_session)
|
||||
return _llm_config_service
|
||||
# def get_llm_config_service(db_session: Optional[Session] = None) -> LLMConfigService:
|
||||
# """获取LLM配置服务实例"""
|
||||
# global _llm_config_service
|
||||
# if _llm_config_service is None or db_session is not None:
|
||||
# _llm_config_service = LLMConfigService(db_session)
|
||||
# return _llm_config_service
|
||||
|
|
@ -49,8 +49,9 @@ class SmartDatabaseWorkflowManager:
|
|||
self.db = db
|
||||
self.table_metadata_service = TableMetadataService(db) if db else None
|
||||
|
||||
from ..core.llm import create_llm
|
||||
self.llm = create_llm()
|
||||
async def initialize(self):
|
||||
from ..core.new_agent import new_agent
|
||||
self.llm = await new_agent()
|
||||
|
||||
def _get_database_tool(self, db_type: str):
|
||||
"""根据数据库类型获取对应的数据库工具"""
|
||||
|
|
|
|||
|
|
@ -54,9 +54,10 @@ class SmartExcelWorkflowManager:
|
|||
else:
|
||||
self.metadata_service = None
|
||||
|
||||
from ..core.llm import create_llm
|
||||
async def initialize(self):
|
||||
from ..core.new_agent import new_agent
|
||||
# 禁用流式响应，避免pandas代理兼容性问题
|
||||
self.llm = create_llm(streaming=False)
|
||||
self.llm = await new_agent(streaming=False)
|
||||
|
||||
async def _run_in_executor(self, func, *args):
|
||||
"""在线程池中运行阻塞函数"""
|
||||
|
|
|
|||
|
|
@ -15,8 +15,12 @@ class SmartWorkflowManager:
|
|||
|
||||
def __init__(self, db=None):
|
||||
self.db = db
|
||||
self.excel_workflow = SmartExcelWorkflowManager(db)
|
||||
self.database_workflow = SmartDatabaseWorkflowManager(db)
|
||||
|
||||
async def initialize(self):
|
||||
self.excel_workflow = SmartExcelWorkflowManager(self.db)
|
||||
await self.excel_workflow.initialize()
|
||||
self.database_workflow = SmartDatabaseWorkflowManager(self.db)
|
||||
await self.database_workflow.initialize()
|
||||
|
||||
async def process_excel_query_stream(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ class TableMetadataService:
|
|||
DatabaseConfig.id == database_config_id,
|
||||
DatabaseConfig.created_by == user_id
|
||||
)
|
||||
db_config = self.session.scalar_one_or_none(stmt)
|
||||
db_config = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if not db_config:
|
||||
self.session.desc = "ERROR: 数据库配置不存在"
|
||||
|
|
@ -202,7 +202,7 @@ class TableMetadataService:
|
|||
TableMetadata.database_config_id == database_config_id,
|
||||
TableMetadata.table_name == table_name
|
||||
)
|
||||
existing = self.session.scalar_one_or_none(stmt)
|
||||
existing = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if existing:
|
||||
self.session.desc = f"更新用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 的元数据"
|
||||
|
|
@ -216,8 +216,8 @@ class TableMetadataService:
|
|||
existing.table_comment = metadata['table_comment']
|
||||
existing.last_synced_at = datetime.utcnow()
|
||||
|
||||
self.session.commit()
|
||||
self.session.refresh(existing)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(existing)
|
||||
return existing
|
||||
else:
|
||||
self.session.desc = f"创建用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 的元数据"
|
||||
|
|
@ -240,8 +240,8 @@ class TableMetadataService:
|
|||
)
|
||||
|
||||
self.session.add(table_metadata)
|
||||
self.session.commit()
|
||||
self.session.refresh(table_metadata)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(table_metadata)
|
||||
self.session.desc = f"SUCCESS: 创建用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 的元数据"
|
||||
return table_metadata
|
||||
|
||||
|
|
@ -258,7 +258,7 @@ class TableMetadataService:
|
|||
DatabaseConfig.id == database_config_id,
|
||||
DatabaseConfig.user_id == user_id
|
||||
)
|
||||
db_config = self.session.scalar_one_or_none(stmt)
|
||||
db_config = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if not db_config:
|
||||
self.session.desc = f"用户 {user_id} 数据库配置 {database_config_id} 不存在"
|
||||
|
|
@ -275,7 +275,7 @@ class TableMetadataService:
|
|||
TableMetadata.database_config_id == database_config_id,
|
||||
TableMetadata.table_name == table_name
|
||||
)
|
||||
existing = self.session.scalar_one_or_none(stmt)
|
||||
existing = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if existing:
|
||||
self.session.desc = f"更新用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 的元数据配置"
|
||||
|
|
@ -320,7 +320,7 @@ class TableMetadataService:
|
|||
})
|
||||
|
||||
# 提交事务
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
self.session.desc = f"SUCCESS: 保存用户 {user_id} 数据库配置 {database_config_id} 表 {table_names} 的元数据配置"
|
||||
return {
|
||||
'saved_tables': saved_tables,
|
||||
|
|
@ -330,7 +330,7 @@ class TableMetadataService:
|
|||
}
|
||||
|
||||
|
||||
def get_user_table_metadata(
|
||||
async def get_user_table_metadata(
|
||||
self,
|
||||
user_id: int,
|
||||
database_config_id: Optional[int] = None
|
||||
|
|
@ -345,9 +345,9 @@ class TableMetadataService:
|
|||
self.session.desc = f"用户 {user_id} 数据库配置 {database_config_id} 不存在"
|
||||
raise NotFoundError("数据库配置不存在")
|
||||
stmt = stmt.where(TableMetadata.is_enabled_for_qa == True)
|
||||
return self.session.scalars(stmt).all()
|
||||
return (await self.session.scalars(stmt)).all()
|
||||
|
||||
def get_table_metadata_by_name(
|
||||
async def get_table_metadata_by_name(
|
||||
self,
|
||||
user_id: int,
|
||||
database_config_id: int,
|
||||
|
|
@ -360,9 +360,9 @@ class TableMetadataService:
|
|||
TableMetadata.database_config_id == database_config_id,
|
||||
TableMetadata.table_name == table_name
|
||||
)
|
||||
return self.session.scalar_one_or_none(stmt)
|
||||
return (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
def update_table_qa_settings(
|
||||
async def update_table_qa_settings(
|
||||
self,
|
||||
user_id: int,
|
||||
metadata_id: int,
|
||||
|
|
@ -375,7 +375,7 @@ class TableMetadataService:
|
|||
TableMetadata.id == metadata_id,
|
||||
TableMetadata.created_by == user_id
|
||||
)
|
||||
metadata = self.session.scalar_one_or_none(stmt)
|
||||
metadata = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if not metadata:
|
||||
self.session.desc = f"用户 {user_id} 数据库库配置表 metadata_id={metadata_id} 不存在"
|
||||
|
|
@ -388,15 +388,15 @@ class TableMetadataService:
|
|||
if 'business_context' in settings:
|
||||
metadata.business_context = settings['business_context']
|
||||
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.session.desc = f"ERROR: 更新用户 {user_id} 数据库配置表 metadata_id={metadata_id} 的问答设置失败: {str(e)}"
|
||||
self.session.rollback()
|
||||
await self.session.rollback()
|
||||
return False
|
||||
|
||||
def save_table_metadata(
|
||||
async def save_table_metadata(
|
||||
self,
|
||||
user_id: int,
|
||||
database_config_id: int,
|
||||
|
|
@ -414,7 +414,7 @@ class TableMetadataService:
|
|||
TableMetadata.database_config_id == database_config_id,
|
||||
TableMetadata.table_name == table_name
|
||||
)
|
||||
existing = self.session.scalar_one_or_none(stmt)
|
||||
existing = (await self.session.execute(stmt)).scalar_one_or_none()
|
||||
|
||||
if existing:
|
||||
self.session.desc = f"用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 已存在，更新其元数据"
|
||||
|
|
@ -424,7 +424,7 @@ class TableMetadataService:
|
|||
existing.row_count = row_count
|
||||
existing.table_comment = table_comment
|
||||
existing.last_synced_at = datetime.utcnow()
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
return existing
|
||||
else:
|
||||
self.session.desc = f"用户 {user_id} 数据库配置 {database_config_id} 表 {table_name} 不存在，创建新记录"
|
||||
|
|
@ -444,8 +444,8 @@ class TableMetadataService:
|
|||
)
|
||||
|
||||
self.session.add(metadata)
|
||||
self.session.commit()
|
||||
self.session.refresh(metadata)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(metadata)
|
||||
return metadata
|
||||
|
||||
def _decrypt_password(self, encrypted_password: str) -> str:
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ from langchain.tools import BaseTool
|
|||
from langchain_community.tools.tavily_search import TavilySearchResults
|
||||
from pydantic import BaseModel, Field, PrivateAttr
|
||||
from typing import Optional, Type, ClassVar
|
||||
from langchain_tavily import TavilySearch
|
||||
|
||||
# 定义输入参数模型（替代原get_parameters()）
|
||||
class SearchInput(BaseModel):
|
||||
|
|
@ -35,7 +36,7 @@ class TavilySearchTool(BaseTool):
|
|||
raise ValueError("Tavily API key not found in settings")
|
||||
|
||||
# 初始化Tavily客户端
|
||||
self._search_client = TavilySearchResults(
|
||||
self._search_client = TavilySearch(
|
||||
tavily_api_key=self._tavily_api_key
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -126,10 +126,10 @@ class UserService:
|
|||
self.session.desc = f"用户ID为{user_id}的密码已成功更改"
|
||||
return True
|
||||
|
||||
def reset_password(self, user_id: int, new_password: str) -> bool: ### DrGraph: OK
|
||||
async def reset_password(self, user_id: int, new_password: str) -> bool: ### DrGraph: OK
|
||||
"""Reset user password (admin only, no current password required)."""
|
||||
self.session.desc = f"重置用户ID为{user_id}的密码"
|
||||
user = self.get_user_by_id(user_id)
|
||||
user = await self.get_user_by_id(user_id)
|
||||
if not user:
|
||||
raise ValidationError("User not found")
|
||||
|
||||
|
|
@ -141,7 +141,7 @@ class UserService:
|
|||
hashed_password = self.get_password_hash(new_password)
|
||||
# Update password
|
||||
user.hashed_password = hashed_password
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
|
||||
self.session.desc = f"用户ID为{user_id}的密码已成功重置"
|
||||
return True
|
||||
|
|
|
|||
|
|
@ -42,8 +42,8 @@ class WorkflowEngine:
|
|||
execution.set_audit_fields(user_id)
|
||||
|
||||
self.session.add(execution)
|
||||
self.session.commit()
|
||||
self.session.refresh(execution)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(execution)
|
||||
|
||||
|
||||
|
||||
|
|
@ -75,8 +75,8 @@ class WorkflowEngine:
|
|||
|
||||
|
||||
execution.set_audit_fields(user_id, is_update=True)
|
||||
self.session.commit()
|
||||
self.session.refresh(execution)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(execution)
|
||||
|
||||
return WorkflowExecutionResponse.from_orm(execution)
|
||||
|
||||
|
|
@ -100,8 +100,8 @@ class WorkflowEngine:
|
|||
execution.set_audit_fields(user_id)
|
||||
|
||||
self.session.add(execution)
|
||||
self.session.commit()
|
||||
self.session.refresh(execution)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(execution)
|
||||
|
||||
# 发送工作流开始执行的消息
|
||||
yield {
|
||||
|
|
@ -170,11 +170,8 @@ class WorkflowEngine:
|
|||
}
|
||||
|
||||
execution.set_audit_fields(user_id, is_update=True)
|
||||
self.session.commit()
|
||||
self.session.refresh(execution)
|
||||
|
||||
self.session.commit()
|
||||
self.session.refresh(execution)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(execution)
|
||||
|
||||
def _build_node_graph(self, nodes: Dict[str, Any], connections: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
|
||||
"""构建节点依赖图"""
|
||||
|
|
@ -385,8 +382,8 @@ class WorkflowEngine:
|
|||
started_at=datetime.now().isoformat()
|
||||
)
|
||||
self.session.add(node_execution)
|
||||
self.session.commit()
|
||||
self.session.refresh(node_execution)
|
||||
await self.session.commit()
|
||||
await self.session.refresh(node_execution)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
|
|
@ -419,7 +416,7 @@ class WorkflowEngine:
|
|||
}
|
||||
|
||||
node_execution.input_data = display_input_data
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
|
||||
|
||||
|
||||
|
|
@ -446,7 +443,7 @@ class WorkflowEngine:
|
|||
node_execution.completed_at = datetime.now().isoformat()
|
||||
node_execution.duration_ms = int((end_time - start_time) * 1000)
|
||||
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
|
||||
|
||||
|
||||
|
|
@ -459,7 +456,7 @@ class WorkflowEngine:
|
|||
node_execution.error_message = str(e)
|
||||
node_execution.completed_at = datetime.now().isoformat()
|
||||
node_execution.duration_ms = int((end_time - start_time) * 1000)
|
||||
self.session.commit()
|
||||
await self.session.commit()
|
||||
|
||||
|
||||
|
||||
|
|
@ -918,8 +915,8 @@ class WorkflowEngine:
|
|||
|
||||
|
||||
# 工作流引擎实例
|
||||
def get_workflow_engine(session: Session = None) -> WorkflowEngine:
|
||||
async def get_workflow_engine(session: Session = None) -> WorkflowEngine:
|
||||
"""获取工作流引擎实例"""
|
||||
if session is None:
|
||||
session = next(get_session())
|
||||
session = await anext(get_session())
|
||||
return WorkflowEngine(session)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,490 @@
|
|||
from PySide6.QtCore import QPointF, QRectF, QSizeF
|
||||
# -*- coding: utf-8 -*-
|
||||
class Constant:
|
||||
invalid_point = QPointF(-123.4, -234.71) # 无效点
|
||||
invalid_rect = QRectF(invalid_point, QSizeF(0, 0)) # 无效矩形
|
||||
service_yml_path = 'config/service/dsp_%s_service.yml' # 服务配置路径
|
||||
kafka_yml_path = 'config/kafka/dsp_%s_kafka.yml' # kafka配置路径
|
||||
aliyun_yml_path = "config/aliyun/dsp_%s_aliyun.yml" # 阿里云配置路径
|
||||
baidu_yml_path = 'config/baidu/dsp_%s_baidu.yml' # 百度配置路径
|
||||
pull_frame_width = 1400 # 拉流帧宽度
|
||||
|
||||
LLM_MODE_NONE = 0
|
||||
LLM_MODE_NONCHAT = 1
|
||||
LLM_MODE_CHAT = 2
|
||||
LLM_MODE_EMBEDDING = 3
|
||||
LLM_MODE_LOCAL_OLLAMA = 4
|
||||
|
||||
LLM_PROMPT_TEMPLATE_METHOD_FORMAT = "format"
|
||||
LLM_PROMPT_TEMPLATE_METHOD_INVOKE = "invoke"
|
||||
|
||||
LLM_PROMPT_VALUE_STR = "str"
|
||||
LLM_PROMPT_VALUE_MESSAGES = "messages"
|
||||
LLM_PROMPT_VALUE_VALUE = "promptValue"
|
||||
|
||||
INPUT_NONE = 0
|
||||
INPUT_PULL_STREAM = 1
|
||||
INPUT_NET_CAMERA = 2
|
||||
INPUT_USB_CAMERA = 3
|
||||
INPUT_LOCAL_VIDEO = 4
|
||||
INPUT_LOCAL_DIR = 5
|
||||
INPUT_LOCAL_FILE = 6
|
||||
INPUT_LOCAL_LABELLED_DIR = 7
|
||||
INPUT_LOCAL_LABELLED_ZIP = 8
|
||||
|
||||
SYSTEM_BUTTON_SNAP = 1
|
||||
SYSTEM_BUTTON_CLEAR_LOG = 2
|
||||
|
||||
MODE_OCR = 7
|
||||
|
||||
WEB_OWNER_NONE = 0
|
||||
WEB_OWNER_ALG_TEST = 1
|
||||
WEB_OWNER_LLM = 2
|
||||
|
||||
PAGE_MODE_NONE = 0
|
||||
PAGE_MODE_VIDEO = 1
|
||||
PAGE_MODE_LABELLING = 2
|
||||
PAGE_MODE_ALG_TEST = 3
|
||||
PAGE_MODE_WEB = 4
|
||||
PAGE_MODE_LLM = 5
|
||||
PAGE_MODE_CONFIG = 6
|
||||
|
||||
ALG_TEST_LOAD_DATA = 1
|
||||
ALG_TEST_TRAIN = 2
|
||||
ALG_TEST_INFER = 3
|
||||
|
||||
ALG_STATUS_NOTHING = 0 # 啥也没干
|
||||
ALG_STATUS_MODEL_READY = 1 # 模型已就绪
|
||||
ALG_STATUS_DATA_LOADED = 2 # 加载已数据/加载中
|
||||
ALG_STATUS_TRAINED = 4 # 训练完成/训练中
|
||||
ALG_STATUS_INFER = 8 # 推理完成/推理中
|
||||
|
||||
UTF_8 = "utf-8" # 编码格式
|
||||
|
||||
COLOR = (
|
||||
[0, 0, 255],
|
||||
[255, 0, 0],
|
||||
[211, 0, 148],
|
||||
[0, 127, 0],
|
||||
[0, 69, 255],
|
||||
[0, 255, 0],
|
||||
[255, 0, 255],
|
||||
[0, 0, 127],
|
||||
[127, 0, 255],
|
||||
[255, 129, 0],
|
||||
[139, 139, 0],
|
||||
[255, 255, 0],
|
||||
[127, 255, 0],
|
||||
[0, 127, 255],
|
||||
[0, 255, 127],
|
||||
[255, 127, 255],
|
||||
[8, 101, 139],
|
||||
[171, 130, 255],
|
||||
[139, 112, 74],
|
||||
[205, 205, 180])
|
||||
|
||||
ONLINE = "online"
|
||||
OFFLINE = "offline"
|
||||
PHOTO = "photo"
|
||||
RECORDING = "recording"
|
||||
|
||||
ONLINE_START_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start"]
|
||||
},
|
||||
"pull_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 255
|
||||
},
|
||||
"push_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 255
|
||||
},
|
||||
"logo_url": {
|
||||
'type': 'string',
|
||||
'required': False,
|
||||
'nullable': True,
|
||||
'maxlength': 255
|
||||
},
|
||||
"models": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'nullable': False,
|
||||
'minlength': 1,
|
||||
'maxlength': 3,
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"code": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "categories",
|
||||
'regex': r'^[a-zA-Z0-9]{1,255}$'
|
||||
},
|
||||
"is_video": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"is_image": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"categories": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'dependencies': "code",
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{0,255}$'},
|
||||
"config": {
|
||||
'type': 'dict',
|
||||
'required': False,
|
||||
'dependencies': "id",
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ONLINE_STOP_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["stop"]
|
||||
}
|
||||
}
|
||||
|
||||
OFFLINE_START_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start"]
|
||||
},
|
||||
"push_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 255
|
||||
},
|
||||
"pull_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 255
|
||||
},
|
||||
"logo_url": {
|
||||
'type': 'string',
|
||||
'required': False,
|
||||
'nullable': True,
|
||||
'maxlength': 255
|
||||
},
|
||||
"models": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'maxlength': 3,
|
||||
'minlength': 1,
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"code": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "categories",
|
||||
'regex': r'^[a-zA-Z0-9]{1,255}$'
|
||||
},
|
||||
"is_video": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"is_image": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"categories": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'dependencies': "code",
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{0,255}$'},
|
||||
"config": {
|
||||
'type': 'dict',
|
||||
'required': False,
|
||||
'dependencies': "id",
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
OFFLINE_STOP_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["stop"]
|
||||
}
|
||||
}
|
||||
|
||||
IMAGE_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start"]
|
||||
},
|
||||
"logo_url": {
|
||||
'type': 'string',
|
||||
'required': False,
|
||||
'nullable': True,
|
||||
'maxlength': 255
|
||||
},
|
||||
"image_urls": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'minlength': 1,
|
||||
'schema': {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 5000
|
||||
}
|
||||
},
|
||||
"models": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"code": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "categories",
|
||||
'regex': r'^[a-zA-Z0-9]{1,255}$'
|
||||
},
|
||||
"is_video": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"is_image": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "code",
|
||||
'allowed': ["0", "1"]
|
||||
},
|
||||
"categories": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'dependencies': "code",
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{0,255}$'},
|
||||
"config": {
|
||||
'type': 'dict',
|
||||
'required': False,
|
||||
'dependencies': "id",
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
RECORDING_START_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start"]
|
||||
},
|
||||
"pull_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'maxlength': 255
|
||||
},
|
||||
"push_url": {
|
||||
'type': 'string',
|
||||
'required': False,
|
||||
'empty': True,
|
||||
'maxlength': 255
|
||||
},
|
||||
"logo_url": {
|
||||
'type': 'string',
|
||||
'required': False,
|
||||
'nullable': True,
|
||||
'maxlength': 255
|
||||
}
|
||||
}
|
||||
|
||||
RECORDING_STOP_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["stop"]
|
||||
}
|
||||
}
|
||||
|
||||
PULL2PUSH_START_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start"]
|
||||
},
|
||||
"video_urls": {
|
||||
'type': 'list',
|
||||
'required': True,
|
||||
'nullable': False,
|
||||
'schema': {
|
||||
'type': 'dict',
|
||||
'required': True,
|
||||
'schema': {
|
||||
"id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "pull_url",
|
||||
'regex': r'^[a-zA-Z0-9]{1,255}$'
|
||||
},
|
||||
"pull_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "push_url",
|
||||
'regex': r'^(https|http|rtsp|rtmp|artc|webrtc|ws)://\w.+$'
|
||||
},
|
||||
"push_url": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'dependencies': "id",
|
||||
'regex': r'^(https|http|rtsp|rtmp|artc|webrtc|ws)://\w.+$'
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
PULL2PUSH_STOP_SCHEMA = {
|
||||
"request_id": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,36}$'
|
||||
},
|
||||
"command": {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'allowed': ["start", "stop"]
|
||||
},
|
||||
"video_ids": {
|
||||
'type': 'list',
|
||||
'required': False,
|
||||
'nullable': True,
|
||||
'schema': {
|
||||
'type': 'string',
|
||||
'required': True,
|
||||
'empty': False,
|
||||
'regex': r'^[a-zA-Z0-9]{1,255}$'
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
from loguru import logger
|
||||
from enum import Enum, unique
|
||||
|
||||
class ServiceException(Exception):
|
||||
def __init__(self, code, msg, desc=None):
|
||||
self.code = code
|
||||
if desc is None:
|
||||
self.msg = msg
|
||||
else:
|
||||
self.msg = msg % desc
|
||||
|
||||
def __str__(self):
|
||||
logger.error("异常编码：{}, 异常描述：{}", self.code, self.msg)
|
||||
|
||||
# 异常枚举
|
||||
@unique
|
||||
class ExceptionType(Enum):
|
||||
OR_VIDEO_ADDRESS_EXCEPTION = ("SP000", "未拉取到视频流, 请检查拉流地址是否有视频流!")
|
||||
ANALYSE_TIMEOUT_EXCEPTION = ("SP001", "AI分析超时!")
|
||||
PULLSTREAM_TIMEOUT_EXCEPTION = ("SP002", "原视频拉流超时!")
|
||||
READSTREAM_TIMEOUT_EXCEPTION = ("SP003", "原视频读取视频流超时!")
|
||||
GET_VIDEO_URL_EXCEPTION = ("SP004", "获取视频播放地址失败!")
|
||||
GET_VIDEO_URL_TIMEOUT_EXCEPTION = ("SP005", "获取原视频播放地址超时!")
|
||||
PULL_STREAM_URL_EXCEPTION = ("SP006", "拉流地址不能为空!")
|
||||
PUSH_STREAM_URL_EXCEPTION = ("SP007", "推流地址不能为空!")
|
||||
PUSH_STREAM_TIME_EXCEPTION = ("SP008", "未生成本地视频地址!")
|
||||
AI_MODEL_MATCH_EXCEPTION = ("SP009", "未匹配到对应的AI模型!")
|
||||
ILLEGAL_PARAMETER_FORMAT = ("SP010", "非法参数格式!")
|
||||
PUSH_STREAMING_CHANNEL_IS_OCCUPIED = ("SP011", "推流通道可能被占用, 请稍后再试!")
|
||||
VIDEO_RESOLUTION_EXCEPTION = ("SP012", "不支持该分辨率类型的视频，请切换分辨率再试!")
|
||||
READ_IAMGE_URL_EXCEPTION = ("SP013", "未能解析图片地址!")
|
||||
DETECTION_TARGET_TYPES_ARE_NOT_SUPPORTED = ("SP014", "不支持该类型的检测目标!")
|
||||
WRITE_STREAM_EXCEPTION = ("SP015", "写流异常!")
|
||||
OR_VIDEO_DO_NOT_EXEIST_EXCEPTION = ("SP016", "原视频不存在!")
|
||||
MODEL_LOADING_EXCEPTION = ("SP017", "模型加载异常!")
|
||||
MODEL_ANALYSE_EXCEPTION = ("SP018", "算法模型分析异常!")
|
||||
AI_MODEL_CONFIG_EXCEPTION = ("SP019", "模型配置不能为空!")
|
||||
AI_MODEL_GET_CONFIG_EXCEPTION = ("SP020", "获取模型配置异常, 请检查模型配置是否正确!")
|
||||
MODEL_GROUP_LIMIT_EXCEPTION = ("SP021", "模型组合个数超过限制!")
|
||||
MODEL_NOT_SUPPORT_VIDEO_EXCEPTION = ("SP022", "%s不支持视频识别!")
|
||||
MODEL_NOT_SUPPORT_IMAGE_EXCEPTION = ("SP023", "%s不支持图片识别!")
|
||||
THE_DETECTION_TARGET_CANNOT_BE_EMPTY = ("SP024", "检测目标不能为空!")
|
||||
URL_ADDRESS_ACCESS_FAILED = ("SP025", "URL地址访问失败, 请检测URL地址是否正确!")
|
||||
UNIVERSAL_TEXT_RECOGNITION_FAILED = ("SP026", "识别失败!")
|
||||
COORDINATE_ACQUISITION_FAILED = ("SP027", "飞行坐标识别异常!")
|
||||
PUSH_STREAM_EXCEPTION = ("SP028", "推流异常!")
|
||||
MODEL_DUPLICATE_EXCEPTION = ("SP029", "存在重复模型配置!")
|
||||
DETECTION_TARGET_NOT_SUPPORT = ("SP031", "存在不支持的检测目标!")
|
||||
TASK_EXCUTE_TIMEOUT = ("SP032", "任务执行超时!")
|
||||
PUSH_STREAM_URL_IS_NULL = ("SP033", "拉流、推流地址不能为空!")
|
||||
PULL_STREAM_NUM_LIMIT_EXCEPTION = ("SP034", "转推流数量超过限制!")
|
||||
NOT_REQUESTID_TASK_EXCEPTION = ("SP993", "未查询到该任务，无法停止任务！")
|
||||
NO_RESOURCES = ("SP995", "服务器暂无资源可以使用，请稍后30秒后再试!")
|
||||
NO_CPU_RESOURCES = ("SP996", "暂无CPU资源可以使用，请稍后再试!")
|
||||
SERVICE_COMMON_EXCEPTION = ("SP997", "公共服务异常!")
|
||||
NO_GPU_RESOURCES = ("SP998", "暂无GPU资源可以使用，请稍后再试!")
|
||||
SERVICE_INNER_EXCEPTION = ("SP999", "系统内部异常!")
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
|
||||
class Flag:
|
||||
Unique = True
|
||||
Append = False
|
||||
|
||||
Debug = True
|
||||
|
||||
class Option:
|
||||
NoOption = 0x00
|
||||
|
||||
AddObject_AutoName = 0x01
|
||||
AddObject_Select = 0x02
|
||||
|
|
@ -0,0 +1,668 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import sys, os, cv2
|
||||
from os import makedirs
|
||||
from os.path import join, exists
|
||||
from loguru import logger
|
||||
from json import loads
|
||||
from ruamel.yaml import safe_load, YAML
|
||||
import random, sys, math, inspect, psutil
|
||||
from pathlib import Path
|
||||
from PySide6.QtGui import QIcon, QColor
|
||||
from PySide6.QtCore import QObject, QRectF, QEventLoop, QIODevice, QTextStream, QFile
|
||||
from PySide6.QtWidgets import QApplication
|
||||
|
||||
import DrGraph.utils.vclEnums as enums
|
||||
#region Property
|
||||
class Property:
|
||||
def __init__(self, read_func=None, write_func=None, default=None, hasMember=True):
|
||||
self.read_func = read_func
|
||||
self.write_func = write_func
|
||||
self.default = default
|
||||
self.owner_class = None
|
||||
self.private_name = None
|
||||
self.hasMember = hasMember
|
||||
def __set_name__(self, owner, name):
|
||||
if self.hasMember:
|
||||
self.private_name = f"_{name}"
|
||||
self.owner_class = owner
|
||||
def callDirectGet(self, instance, owner):
|
||||
if instance is None or not self.hasMember:
|
||||
return self.default
|
||||
if not hasattr(instance, self.private_name):
|
||||
return self.default
|
||||
return getattr(instance, self.private_name)
|
||||
def callCustomGet(self, instance, owner):
|
||||
if instance is None:
|
||||
return self
|
||||
|
||||
if self.read_func is None:
|
||||
if hasattr(instance, self.private_name):
|
||||
return getattr(instance, self.private_name)
|
||||
return self.default
|
||||
|
||||
try:
|
||||
if isinstance(self.read_func, str):
|
||||
if hasattr(instance, self.read_func):
|
||||
method = getattr(instance, self.read_func)
|
||||
return method()
|
||||
elif hasattr(self.read_func, '__name__'):
|
||||
method_name = self.read_func.__name__
|
||||
if hasattr(instance, method_name):
|
||||
method = getattr(instance, method_name)
|
||||
return method()
|
||||
elif callable(self.read_func):
|
||||
try:
|
||||
return self.read_func(instance)
|
||||
except (TypeError, AttributeError):
|
||||
return self.read_func()
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
|
||||
if hasattr(instance, self.private_name):
|
||||
return getattr(instance, self.private_name)
|
||||
return self.default
|
||||
|
||||
def callDirectSet(self, instance, value):
|
||||
if instance is None or not self.hasMember:
|
||||
return
|
||||
setattr(instance, self.private_name, value)
|
||||
|
||||
def callCustomSet(self, instance, value):
|
||||
try:
|
||||
if isinstance(self.write_func, str):
|
||||
if hasattr(instance, self.write_func):
|
||||
method = getattr(instance, self.write_func)
|
||||
method(value)
|
||||
elif hasattr(self.write_func, '__name__'):
|
||||
method_name = self.write_func.__name__
|
||||
if hasattr(instance, method_name):
|
||||
method = getattr(instance, method_name)
|
||||
method(value)
|
||||
elif callable(self.write_func):
|
||||
try:
|
||||
self.write_func(instance, value)
|
||||
except (TypeError, AttributeError):
|
||||
self.write_func(value)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
class Property_rw(Property):
|
||||
def __init__(self, default=None, hasMember=True):
|
||||
super().__init__(None, None, default, hasMember)
|
||||
def __get__(self, instance, owner):
|
||||
return self.callDirectGet(instance, owner)
|
||||
def __set__(self, instance, value):
|
||||
self.callDirectSet(instance, value)
|
||||
|
||||
class Property_Rw(Property):
|
||||
def __init__(self, read_func=None, default=None, hasMember=True):
|
||||
super().__init__(read_func, None, default, hasMember)
|
||||
|
||||
def __get__(self, instance, owner):
|
||||
return self.callCustomGet(instance, owner)
|
||||
|
||||
def __set__(self, instance, value):
|
||||
setattr(instance, self.private_name, value)
|
||||
|
||||
class Property_rW(Property):
|
||||
def __init__(self, write_func=None, default=None, hasMember=True):
|
||||
super().__init__(None, write_func, default, hasMember)
|
||||
|
||||
def __get__(self, instance, owner):
|
||||
return self.callDirectGet(instance, owner)
|
||||
|
||||
def __set__(self, instance, value):
|
||||
self.callCustomSet(instance, value)
|
||||
|
||||
class Property_RW(Property):
|
||||
def __init__(self, read_func=None, write_func=None, default=None, hasMember=True):
|
||||
super().__init__(read_func, write_func, default, hasMember)
|
||||
|
||||
def __get__(self, instance, owner):
|
||||
return self.callCustomGet(instance, owner)
|
||||
|
||||
def __set__(self, instance, value):
|
||||
self.callCustomSet(instance, value)
|
||||
#endregion Property
|
||||
|
||||
class AppHelper(QObject):
|
||||
app = Property_rw(None)
|
||||
def setBriefStatusText(self, text):
|
||||
if self.briefStatusControl:
|
||||
self.briefStatusControl.setText(text)
|
||||
else:
|
||||
print(text)
|
||||
briefStatusText = Property_rW(setBriefStatusText, '')
|
||||
|
||||
def setProgress(self, value):
|
||||
if self.progressBarControl:
|
||||
self.progressBarControl.setValue(value)
|
||||
progress = Property_rW(setProgress, 0)
|
||||
|
||||
def setProgressMax(self, value):
|
||||
if self.progressBarControl:
|
||||
self.progressBarControl.setMaximum(value)
|
||||
progressMax = Property_rW(setProgressMax, 100)
|
||||
|
||||
def setProgressMin(self, value):
|
||||
if self.progressBarControl:
|
||||
self.progressBarControl.setMinimum(value)
|
||||
progressMin = Property_rW(setProgressMin, 0)
|
||||
|
||||
def __init__(self):
|
||||
self.briefStatusControl = None
|
||||
self.progressBarControl = None
|
||||
self._briefStatusText = ''
|
||||
pass
|
||||
|
||||
class Helper:
|
||||
OnLogMsg = None
|
||||
AppFlag_SaveAnalysisResult = True
|
||||
AppFlag_SaveLog = False
|
||||
App = None
|
||||
|
||||
@staticmethod
|
||||
def castRange(value, minValue, maxValue):
|
||||
return max(minValue, min(maxValue, value))
|
||||
# 取得程序目录
|
||||
@staticmethod
|
||||
def getPath_App():
|
||||
if getattr(sys, 'frozen', False):
|
||||
# 如果程序是打包的exe文件
|
||||
return os.path.dirname(sys.executable)
|
||||
else:
|
||||
# 如果是Python脚本 - 获取上两级目录
|
||||
current_file = os.path.abspath(__file__) # f:\PySide6\AiBase\DrGraph\utils\Helper.py
|
||||
current_dir = os.path.dirname(current_file) # f:\PySide6\AiBase\DrGraph\utils
|
||||
parent_dir = os.path.dirname(current_dir) # f:\PySide6\AiBase\DrGraph
|
||||
root_dir = os.path.dirname(parent_dir) # f:\PySide6\AiBase
|
||||
return root_dir
|
||||
|
||||
@staticmethod
|
||||
def fitOS(file_name):
|
||||
if sys.platform.startswith('win'):
|
||||
file_name = file_name.replace('/','\\')
|
||||
else:
|
||||
file_name = file_name.replace('\\', '/')
|
||||
return file_name
|
||||
|
||||
def generateDistinctColors(n, s=0.8, v=0.7):
|
||||
import colorsys
|
||||
colors = []
|
||||
for i in range(n):
|
||||
hue = i * 1.0 / n # 均匀分布在 [0, 1)
|
||||
r, g, b = colorsys.hsv_to_rgb(hue, s, v)
|
||||
colors.append(QColor(r * 255, g * 255, b * 255))
|
||||
return colors
|
||||
|
||||
def setBriefStatusText(self, text):
|
||||
Helper.App.setBriefStatusText(text)
|
||||
briefStatusText = Property_rW(setBriefStatusText, '')
|
||||
@staticmethod
|
||||
def Sleep(msec):
|
||||
QApplication.processEvents(QEventLoop.AllEvents, msec)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def getAbsoluteFileName(file_name):
|
||||
if os.path.isabs(file_name):
|
||||
return Helper.fitOS(file_name)
|
||||
else:
|
||||
return Helper.fitOS(os.path.join(Helper.getPath_App(), file_name))
|
||||
@staticmethod
|
||||
def getConfigs(path, read_type='yml'):
|
||||
"""
|
||||
读取配置文件并返回解析后的配置信息
|
||||
|
||||
:param path: 配置文件路径
|
||||
:param read_type: 配置文件类型，默认为'yml'，可选'json'或'yml'
|
||||
:return: 解析后的配置信息，JSON格式返回字典，YML格式返回对应的数据结构
|
||||
:raises Exception: 当无法获取配置信息时抛出异常
|
||||
"""
|
||||
yaml = YAML(typ='safe', pure=True)
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return yaml.load(f)
|
||||
# with open(path, 'r', encoding="utf-8") as f:
|
||||
# # 根据文件类型选择相应的解析方式
|
||||
# if read_type == 'json':
|
||||
# return loads(f.read())
|
||||
# if read_type == 'yml':
|
||||
# return safe_load(f)
|
||||
# 如果未成功读取配置信息，则抛出异常
|
||||
raise Exception('路径: %s未获取配置信息' % path)
|
||||
|
||||
@staticmethod
|
||||
def getTooltipText(content):
|
||||
# 增加一个小喇叭图标
|
||||
# content = f'<img src="appIOs/res/images/icons/info.png" width="16" height="16"> {content}'
|
||||
return f"""
|
||||
<html>
|
||||
<head/><body>
|
||||
<p><span style=" font-weight:600; color:#ffffff;">DrGraph <img src="appIOs/res/images/icons/Notice.png" width="16" height="16"></span></p>
|
||||
<p>{content}</p>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
@staticmethod
|
||||
def log_init(app, base_dir, env):
|
||||
"""
|
||||
初始化日志配置
|
||||
|
||||
:param base_dir: 基础目录路径，用于定位配置文件和日志文件存储位置
|
||||
:param env: 环境标识，用于加载对应环境的日志配置文件
|
||||
:return: 无返回值
|
||||
"""
|
||||
Helper.App = AppHelper()
|
||||
Helper.App.app = app
|
||||
# QToolTip样式 - 自定义样式 - 增加Header
|
||||
app.setStyleSheet("""
|
||||
QToolTip {
|
||||
background-color: #dd2222;
|
||||
color: #f0f0f0;
|
||||
border: 1px solid #555;
|
||||
border-radius: 4px;
|
||||
padding: 6px;
|
||||
font: 10pt "Segoe UI";
|
||||
opacity: 220;
|
||||
}
|
||||
""")
|
||||
|
||||
log_config = Helper.getConfigs(join(base_dir, 'appIOs/configs/logger/drgraph_%s_logger.yml' % env))
|
||||
# 判断日志文件是否存在，不存在创建
|
||||
base_path = join(base_dir, log_config.get("base_path"))
|
||||
if not exists(base_path):
|
||||
makedirs(base_path)
|
||||
# 移除日志设置
|
||||
logger.remove(handler_id=None)
|
||||
# 打印日志到文件
|
||||
if bool(log_config.get("enable_file_log")):
|
||||
logger.add(join(base_path, log_config.get("log_name")),
|
||||
rotation=log_config.get("rotation"),
|
||||
retention=log_config.get("retention"),
|
||||
format=log_config.get("log_fmt"),
|
||||
level=log_config.get("level"),
|
||||
enqueue=True,
|
||||
encoding=log_config.get("encoding"))
|
||||
# 控制台输出
|
||||
if bool(log_config.get("enable_stderr")):
|
||||
logger.add(sys.stderr,
|
||||
format=log_config.get("log_fmt"),
|
||||
level=log_config.get("level"),
|
||||
enqueue=True)
|
||||
logger.info("\n\n\n----=========== 日志配置初始化完成, 开始新的日志记录 ==========----")
|
||||
|
||||
@staticmethod
|
||||
def log_info(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'INFO: {msg}', 'black')
|
||||
caller = inspect.stack()[1]
|
||||
logger.info(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "INFO", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_error(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'ERROR: {msg}', 'red')
|
||||
caller = inspect.stack()[1]
|
||||
logger.error(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "ERROR", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_warning(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'WARNING: {msg}', (255, 128, 0))
|
||||
caller = inspect.stack()[1]
|
||||
logger.warning(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "WARNING", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_debug(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'DEBUG: {msg}', (0, 128, 128))
|
||||
caller = inspect.stack()[1]
|
||||
logger.debug(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "DEBUG", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_critical(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'CRITICAL: {msg}', (128, 0, 128))
|
||||
caller = inspect.stack()[1]
|
||||
logger.critical(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "CRITICAL", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_exception(msg, toWss = False):
|
||||
if Helper.OnLogMsg:
|
||||
Helper.OnLogMsg(f'EXCEPTION: {msg}', (255, 140, 0))
|
||||
caller = inspect.stack()[1]
|
||||
logger.exception(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "EXCEPTION", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log(msg, toWss = False):
|
||||
caller = inspect.stack()[1]
|
||||
logger.log(f"[{Path(caller.filename).name}:{caller.lineno}.{caller.function}()] > {msg}")
|
||||
if toWss:
|
||||
Helper.log_wss({"type": "log", "kind": "LOG", "msg" : msg} )
|
||||
@staticmethod
|
||||
def log_wss(msg):
|
||||
if Helper.wss:
|
||||
Helper.wss.send(msg)
|
||||
|
||||
@staticmethod
|
||||
def getTextSize(font, text):
|
||||
import pygame as pg
|
||||
surface = font.render(text, True, (0, 0, 0))
|
||||
return (surface.get_width(), surface.get_height(), surface)
|
||||
|
||||
@staticmethod
|
||||
def buildSurfaces(font, text, width, color, wordWrap):
|
||||
text = text.strip()
|
||||
w = Helper.getTextSize(font, text)[0]
|
||||
result = []
|
||||
if w > width and wordWrap:
|
||||
segLen = math.floor(width / w * len(text))
|
||||
while len(text):
|
||||
if len(text) < segLen:
|
||||
t = text
|
||||
text = ''
|
||||
else:
|
||||
t = text[:segLen]
|
||||
text = text[segLen:]
|
||||
result.append(font.render(t, True, color))
|
||||
else:
|
||||
result.append(font.render(text, True, color))
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def randomColor():
|
||||
'''随机颜色'''
|
||||
return (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
|
||||
|
||||
@staticmethod
|
||||
def reverseColor(color: QColor):
|
||||
'''反转颜色'''
|
||||
return (255 - color.red(), 255 - color.green(), 255 - color.blue())
|
||||
@staticmethod
|
||||
def getRGB(color_value):
|
||||
# 如果是元组或列表形式的RGB值
|
||||
if isinstance(color_value, (tuple, list)):
|
||||
if len(color_value) >= 3:
|
||||
# 取前三个值作为RGB
|
||||
r, g, b = color_value[0], color_value[1], color_value[2]
|
||||
# 确保值在0-255范围内
|
||||
return (max(0, min(255, int(r))),
|
||||
max(0, min(255, int(g))),
|
||||
max(0, min(255, int(b))))
|
||||
|
||||
# 如果是整数形式的颜色值
|
||||
elif isinstance(color_value, int):
|
||||
# 将整数转换为RGB分量
|
||||
# 假设格式为0xRRGGBB
|
||||
r = (color_value >> 16) & 0xFF
|
||||
g = (color_value >> 8) & 0xFF
|
||||
b = color_value & 0xFF
|
||||
return (r, g, b)
|
||||
|
||||
# 如果是字符串形式
|
||||
elif isinstance(color_value, str):
|
||||
# 处理十六进制颜色值
|
||||
if color_value.startswith('#'):
|
||||
hex_value = color_value[1:]
|
||||
if len(hex_value) == 3: # 简写形式 #RGB
|
||||
hex_value = ''.join([c*2 for c in hex_value])
|
||||
if len(hex_value) in (6, 8): # #RRGGBB 或 #RRGGBBAA
|
||||
r = int(hex_value[0:2], 16)
|
||||
g = int(hex_value[2:4], 16)
|
||||
b = int(hex_value[4:6], 16)
|
||||
return (r, g, b)
|
||||
# 处理颜色名称（需要额外的颜色名称映射表）
|
||||
# 这里只列举几种常见颜色
|
||||
color_names = {
|
||||
'black': (0, 0, 0),
|
||||
'white': (255, 255, 255),
|
||||
'red': (255, 0, 0),
|
||||
'green': (0, 255, 0),
|
||||
'blue': (0, 0, 255),
|
||||
'yellow': (255, 255, 0),
|
||||
'magenta': (255, 0, 255),
|
||||
'cyan': (0, 255, 255),
|
||||
'orange': (255, 128, 0), # 根据项目规范
|
||||
'teal': (0, 128, 128) # 根据项目规范
|
||||
}
|
||||
if color_value.lower() in color_names:
|
||||
return color_names[color_value.lower()]
|
||||
|
||||
# 如果是Color对象（如pygame.Color）
|
||||
elif hasattr(color_value, 'r') and hasattr(color_value, 'g') and hasattr(color_value, 'b'):
|
||||
return (color_value.r, color_value.g, color_value.b)
|
||||
|
||||
# 默认返回黑色
|
||||
return (0, 0, 0)
|
||||
|
||||
@staticmethod
|
||||
def check_system_resources():
|
||||
"""检查系统资源使用情况"""
|
||||
logger.info("检查系统资源使用情况...")
|
||||
cpu_percent = psutil.cpu_percent(interval=1)
|
||||
memory = psutil.virtual_memory()
|
||||
network = psutil.net_io_counters()
|
||||
|
||||
logger.info("检查系统资源使用情况完毕")
|
||||
|
||||
return {
|
||||
'cpu_percent': cpu_percent,
|
||||
'memory_percent': memory.percent,
|
||||
'memory_available': memory.available / (1024**3), # GB
|
||||
'network_bytes_sent': int(network.bytes_sent / 1024),
|
||||
'network_bytes_recv': int(network.bytes_recv / 1024)
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def build_response(type, status : enums.Response, msg):
|
||||
status_code, status_msg = status.value
|
||||
result = {
|
||||
"type": "response",
|
||||
"request_type": type,
|
||||
"status_code": status_code,
|
||||
"status_msg": status_msg,
|
||||
"detail_msg": msg
|
||||
}
|
||||
if status_code != 0:
|
||||
Helper.error(result);
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def get_surrounding_rect(points):
|
||||
if len(points) == 0:
|
||||
return Constant.invalid_rect
|
||||
min_x = min(p.x() for p in points)
|
||||
min_y = min(p.y() for p in points)
|
||||
max_x = max(p.x() for p in points)
|
||||
max_y = max(p.y() for p in points)
|
||||
return QRectF(min_x, min_y, max_x - min_x, max_y - min_y)
|
||||
|
||||
@staticmethod
|
||||
def getYoloLabellingInfo(dir_path, file_names, desc):
|
||||
if len(dir_path) > 0:
|
||||
imageNumber, labelNumber = 0, 0
|
||||
imagePath = dir_path + 'images/'
|
||||
labelPath = dir_path + 'labels/'
|
||||
for file_name in file_names:
|
||||
if file_name.startswith(imagePath):
|
||||
imageNumber += 1
|
||||
elif file_name.startswith(labelPath):
|
||||
labelNumber += 1
|
||||
return f'{desc} {imageNumber - 1} 张图片,{labelNumber - 1} 张标签;', imageNumber - 1
|
||||
return f'无{desc};', 0
|
||||
|
||||
@staticmethod
|
||||
def getMarkdownRenderText(mdContent):
|
||||
# 使用Python库直接将Markdown转换为HTML，避免JavaScript依赖
|
||||
try:
|
||||
# 尝试导入markdown库
|
||||
import markdown
|
||||
html_content = markdown.markdown(mdContent)
|
||||
# 添加基本样式使其美观
|
||||
styled_html = f"""
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<style>
|
||||
body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
|
||||
max-width: 900px; margin: 20px auto; padding: 0 20px; line-height: 1.6; }}
|
||||
code {{ background: #f5f5f5; padding: 2px 4px; border-radius: 3px; }}
|
||||
pre {{ background: #f5f5f5; padding: 10px; border-radius: 5px; overflow: auto; }}
|
||||
pre code {{ background: none; padding: 0; }}
|
||||
h1, h2, h3 {{ color: #333; border-bottom: 1px solid #eee; padding-bottom: 5px; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
{html_content}
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
return styled_html
|
||||
except ImportError:
|
||||
# 回退到JavaScript的marked.js方法
|
||||
logger.warning("未找到markdown库，使用JavaScript渲染方式")
|
||||
# 从appIOs/configs加载marked.js
|
||||
file_js = QFile('appIOs/configs/marked.min.js')
|
||||
markedJs = ''
|
||||
if file_js.open(QIODevice.ReadOnly | QIODevice.Text):
|
||||
markedJs = file_js.readAll().data().decode('utf-8')
|
||||
file_js.close()
|
||||
|
||||
# 转义markdown内容
|
||||
escapedMd = mdContent.replace('&', '&').replace('<', '<').replace('>', '>').replace('"', '"').replace("'", ''')
|
||||
|
||||
# 创建HTML模板
|
||||
htmlTemplate = '''
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<style>
|
||||
body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
|
||||
max-width: 900px; margin: 20px auto; padding: 0 20px; line-height: 1.6; }
|
||||
code { background: #f5f5f5; padding: 2px 4px; border-radius: 3px; }
|
||||
pre { background: #f5f5f5; padding: 10px; border-radius: 5px; overflow: auto; }
|
||||
pre code { background: none; padding: 0; }
|
||||
h1, h2, h3 { color: #333; border-bottom: 1px solid #eee; padding-bottom: 5px; }
|
||||
</style>
|
||||
<script>
|
||||
// 加载marked库
|
||||
%1
|
||||
</script>
|
||||
</head>
|
||||
<body>
|
||||
<div id="content"></div>
|
||||
<script>
|
||||
const md = `%2`;
|
||||
document.getElementById('content').innerHTML = marked.parse(md);
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
'''
|
||||
|
||||
# 生成HTML内容
|
||||
htmlContent = htmlTemplate.replace('%1', markedJs).replace('%2', escapedMd)
|
||||
return htmlContent
|
||||
@staticmethod
|
||||
def getMarkdownRender(mdFileName):
|
||||
file = QFile(mdFileName)
|
||||
mdContent = ''
|
||||
if file.open(QIODevice.ReadOnly | QIODevice.Text):
|
||||
stream = QTextStream(file)
|
||||
stream.setAutoDetectUnicode(True)
|
||||
mdContent = stream.readAll()
|
||||
file.close()
|
||||
return Helper.getMarkdownRenderText(mdContent)
|
||||
else:
|
||||
logger.error(f'打开文件 {mdFileName} 失败')
|
||||
return f"<p style='color: red;'>无法打开文件: {mdFileName}</p>"
|
||||
|
||||
class RTTI:
|
||||
@staticmethod
|
||||
def _do_set_attr(obj, property_name, property_value):
|
||||
if obj is None:
|
||||
logger.error(f"RTTI.set: obj is None")
|
||||
return
|
||||
|
||||
class_name = type(obj).__name__
|
||||
object_name = obj.objectName()
|
||||
if property_name not in dir(obj):
|
||||
logger.error(f"RTTI.set: {class_name} {object_name}.{property_name} not in dir(obj)")
|
||||
return
|
||||
if property_name.endswith('icon') and isinstance(property_value, str):
|
||||
original_property_value = property_value
|
||||
if not os.path.exists(property_value):
|
||||
property_value = os.path.join('appIOs/res/images/icons',property_value)
|
||||
# logger.info(f"RTTI.set: {class_name} {object_name}.{property_name} = {property_value}(自动匹配)")
|
||||
if not os.path.exists(property_value):
|
||||
logger.error(f"{original_property_value}文件不存在 > RTTI.set: {class_name} {object_name}.{property_name} = '{original_property_value}'")
|
||||
return
|
||||
property_value = QIcon(property_value)
|
||||
setter_method = getattr(obj, f'set{property_name[0].upper() + property_name[1:]}')
|
||||
setter_method(property_value)
|
||||
@staticmethod
|
||||
def set(obj, property_name, property_value):
|
||||
property_list = property_name.split('.')
|
||||
if len(property_list) == 1:
|
||||
RTTI._do_set_attr(obj, property_name, property_value)
|
||||
else:
|
||||
dest_obj = obj
|
||||
for i in range(len(property_list) - 1):
|
||||
if not dest_obj:
|
||||
logger.error(f"RTTI.set: {property_list.join('.')} not found")
|
||||
return
|
||||
dest_obj = getattr(dest_obj, property_list[i])
|
||||
RTTI._do_set_attr(dest_obj, property_list[-1], property_value)
|
||||
|
||||
@staticmethod
|
||||
def _do_get_attr(obj, property_name):
|
||||
if obj is None:
|
||||
logger.error(f"RTTI.get: obj is None")
|
||||
return None, None
|
||||
if property_name not in dir(obj):
|
||||
logger.error(f"RTTI.get: {type(obj).__name__} {obj.objectName()}.{property_name} not in dir(obj)")
|
||||
return None, None
|
||||
# 返回属性类型与属性值
|
||||
type_name = type(getattr(obj, property_name)).__name__
|
||||
value = getattr(obj, property_name)
|
||||
return type_name, value
|
||||
# 取得属性类型与属性值 type, value = RTTI.get(obj, property_name)
|
||||
@staticmethod
|
||||
def get(obj, property_name):
|
||||
property_list = property_name.split('.')
|
||||
if len(property_list) == 1:
|
||||
return RTTI._do_get_attr(obj, property_name)
|
||||
else:
|
||||
dest_obj = obj
|
||||
for i in range(len(property_list) - 1):
|
||||
if not dest_obj:
|
||||
logger.error(f"RTTI.get: {property_list.join('.')} not found")
|
||||
return None
|
||||
dest_obj = getattr(dest_obj, property_list[i])
|
||||
return RTTI._do_get_attr(dest_obj, property_list[-1])
|
||||
|
||||
class DrawHelper:
|
||||
@staticmethod
|
||||
def draw_dashed_line(mat, pt1, pt2, color, thickness=1, dash_length=10):
|
||||
dist = ((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2) ** 0.5
|
||||
dashes = int(dist / dash_length)
|
||||
for i in range(dashes):
|
||||
start = (int(pt1[0] + (pt2[0] - pt1[0]) * i / dashes), int(pt1[1] + (pt2[1] - pt1[1]) * i / dashes))
|
||||
end = (int(pt1[0] + (pt2[0] - pt1[0]) * (i + 0.5) / dashes), int(pt1[1] + (pt2[1] - pt1[1]) * (i + 0.5) / dashes))
|
||||
cv2.line(mat, start, end, color, thickness)
|
||||
|
||||
@staticmethod
|
||||
def draw_dashed_rect(painter, rect, color, thickness=1, dash_length=10):
|
||||
x1, y1 = rect.left(), rect.top()
|
||||
x2, y2 = rect.right(), rect.bottom()
|
||||
DrawHelper.draw_dashed_line(painter, (x1, y1), (x2, y1), color, thickness, dash_length)
|
||||
DrawHelper.draw_dashed_line(painter, (x1, y2), (x2, y2), color, thickness, dash_length)
|
||||
DrawHelper.draw_dashed_line(painter, (x1, y1), (x1, y2), color, thickness, dash_length)
|
||||
DrawHelper.draw_dashed_line(painter, (x2, y1), (x2, y2), color, thickness, dash_length)
|
||||
|
|
@ -0,0 +1,466 @@
|
|||
from loguru import logger
|
||||
import subprocess as sp
|
||||
from ultralytics import YOLO
|
||||
import time, cv2, numpy as np, math
|
||||
from traceback import format_exc
|
||||
from DrGraph.utils.pull_push import NetStream
|
||||
from DrGraph.utils.Helper import *
|
||||
from DrGraph.utils.Constant import Constant
|
||||
from zipfile import ZipFile
|
||||
|
||||
class YOLOTracker:
|
||||
def __init__(self, model_path):
|
||||
"""
|
||||
初始化YOLOv11追踪器
|
||||
"""
|
||||
self.model = YOLO(model_path)
|
||||
self.tracking_config = {
|
||||
"tracker": "appIOs/configs/yolo11/bytetrack.yaml", # "/home/thsw/jcq/projects/yolov11/ultralytics-main/ultralytics/cfg/trackers/bytetrack.yaml",
|
||||
"conf": 0.25,
|
||||
"iou": 0.45,
|
||||
"persist": True,
|
||||
"verbose": False
|
||||
}
|
||||
self.frame_count = 0
|
||||
self.processing_time = 0
|
||||
|
||||
def process_frame(self, frame):
|
||||
"""
|
||||
处理单帧图像，进行目标检测和追踪
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 执行YOLOv11目标检测和追踪
|
||||
results = self.model.track(
|
||||
source=frame,
|
||||
**self.tracking_config
|
||||
)
|
||||
|
||||
# 获取第一个结果（因为只处理单张图片）
|
||||
result = results[0]
|
||||
|
||||
# 绘制检测结果
|
||||
processed_frame = result.plot()
|
||||
|
||||
# 计算处理时间
|
||||
self.processing_time = (time.time() - start_time) * 1000 # 转换为毫秒
|
||||
self.frame_count += 1
|
||||
|
||||
# 打印检测信息（可选）
|
||||
if self.frame_count % 100 == 0:
|
||||
self._print_detection_info(result)
|
||||
|
||||
return processed_frame, result
|
||||
|
||||
except Exception as e:
|
||||
logger.error("YOLO处理异常: {}", format_exc())
|
||||
return frame, None
|
||||
|
||||
def _print_detection_info(self, result):
|
||||
"""
|
||||
打印检测信息
|
||||
"""
|
||||
boxes = result.boxes
|
||||
if boxes is not None and len(boxes) > 0:
|
||||
detection_count = len(boxes)
|
||||
unique_ids = set()
|
||||
for box in boxes:
|
||||
if box.id is not None:
|
||||
unique_ids.add(int(box.id[0]))
|
||||
|
||||
logger.info(f"帧 {self.frame_count}: 检测到 {detection_count} 个目标, 追踪ID数: {len(unique_ids)}, 处理时间: {self.processing_time:.2f}ms")
|
||||
else:
|
||||
logger.info(f"帧 {self.frame_count}: 未检测到目标, 处理时间: {self.processing_time:.2f}ms")
|
||||
|
||||
class YOLOTrackerManager:
|
||||
def __init__(self, model_path, pull_url, push_url, request_id):
|
||||
self.pull_url = pull_url
|
||||
self.push_url = push_url
|
||||
self.request_id = request_id
|
||||
self.tracker = YOLOTracker(model_path)
|
||||
self.stream = None
|
||||
self.videoStream = None
|
||||
self.videoType = Constant.INPUT_NONE
|
||||
self.localFile = ''
|
||||
self.localPath = ''
|
||||
self.localFiles = []
|
||||
self._currentFrame = None
|
||||
self.totalFrames = 0
|
||||
self.frameChanged = False
|
||||
|
||||
def _stop(self):
|
||||
if self.videoStream is not None:
|
||||
self.videoStream.release()
|
||||
self.videoStream = None
|
||||
if self.stream is not None:
|
||||
self.stream.clear_pull_p(self.stream.pull_p, self.request_id)
|
||||
self.stream = None
|
||||
self.localFile = ''
|
||||
self.localPath = ''
|
||||
self.localFiles = []
|
||||
self._currentFrame = None
|
||||
self.totalFrames = 0
|
||||
self._frameIndex = -1
|
||||
self.videoType = Constant.INPUT_NONE
|
||||
self.frameChanged = True
|
||||
|
||||
def startLocalFile(self, fileName):
|
||||
self._stop()
|
||||
self.localFile = fileName
|
||||
self._frameIndex = -1
|
||||
|
||||
def startLocalDir(self, dirName):
|
||||
self._stop()
|
||||
self.localPath = dirName
|
||||
self.localFiles = [os.path.join(dirName, f) for f in os.listdir(dirName) if f.endswith(('.jpg', '.jpeg', '.png'))]
|
||||
self.totalFrames = len(self.localFiles)
|
||||
Helper.App.progressMax = self.totalFrames
|
||||
self.localFiles.sort()
|
||||
logger.info("本地目录打开: {}, 总帧数: {}", dirName, self.totalFrames)
|
||||
self._frameIndex = 0
|
||||
|
||||
def startLabelledZip(self, labelledPath, categoryPath):
|
||||
self._stop()
|
||||
self.localPath = labelledPath
|
||||
localFiles = ZipFile(labelledPath).namelist()
|
||||
_, self.totalFrames = Helper.getYoloLabellingInfo(categoryPath, localFiles, '')
|
||||
imagePath = categoryPath + 'images/'
|
||||
self.localFiles = [file for file in localFiles if imagePath in file]
|
||||
logger.info(f"标注压缩文件{labelledPath}的{categoryPath}集共有{self.totalFrames}帧, 有效帧数: {len(self.localFiles)}")
|
||||
self._frameIndex = 0
|
||||
Helper.App.progressMax = self.totalFrames
|
||||
|
||||
def startUsbCamera(self, index = 0):
|
||||
self._stop()
|
||||
self.videoStream = cv2.VideoCapture(index)
|
||||
self.videoType = Constant.INPUT_USB_CAMERA
|
||||
Helper.Sleep(200)
|
||||
if not self.videoStream.isOpened():
|
||||
logger.error("无法打开USB摄像头: {}", index)
|
||||
self.videoType = Constant.INPUT_NONE
|
||||
return
|
||||
self.totalFrames = 0x7FFFFFFF
|
||||
|
||||
def startLocalVideo(self, fileName):
|
||||
self._stop()
|
||||
self.videoStream = cv2.VideoCapture(fileName)
|
||||
self.videoType = Constant.INPUT_LOCAL_VIDEO
|
||||
Helper.Sleep(200)
|
||||
if not self.videoStream.isOpened():
|
||||
logger.error("无法打开本地视频流: {}", fileName)
|
||||
self.videoType = Constant.INPUT_NONE
|
||||
return
|
||||
try:
|
||||
total = int(self.videoStream.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
except Exception:
|
||||
total = 0
|
||||
self.totalFrames = total if total is not None else 0
|
||||
Helper.App.progressMax = self.totalFrames
|
||||
logger.info("本地视频打开: {}, 总帧数: {}", fileName, self.totalFrames)
|
||||
|
||||
def startPull(self, url = ''):
|
||||
self._stop()
|
||||
if len(url) > 0:
|
||||
self.pull_url = url
|
||||
logger.info("拉流地址: {}", self.pull_url)
|
||||
self.stream = NetStream(self.pull_url, self.push_url, self.request_id)
|
||||
self.stream.prepare_pull()
|
||||
|
||||
def getCurrentFrame(self):
|
||||
if self._currentFrame is None:
|
||||
self._currentFrame = self.nextFrame()
|
||||
if self._currentFrame is not None:
|
||||
return self._currentFrame.copy()
|
||||
return None
|
||||
currentFrame = Property_Rw(getCurrentFrame, None)
|
||||
|
||||
def setFrameIndex(self, index):
|
||||
if self.videoStream is None and len(self.localFiles) == 0:
|
||||
return
|
||||
if self.videoStream is not None and self.videoType != Constant.INPUT_LOCAL_VIDEO:
|
||||
return
|
||||
if index < 0:
|
||||
index = 0
|
||||
if index >= self.totalFrames:
|
||||
index = self.totalFrames - 1
|
||||
if self.videoStream:
|
||||
self.videoStream.set(cv2.CAP_PROP_POS_FRAMES, index)
|
||||
self._frameIndex = index - 1
|
||||
self._currentFrame = self.nextFrame()
|
||||
self.frameChanged = True
|
||||
frameIndex = Property_rW(setFrameIndex, 0)
|
||||
|
||||
def getLabels(self):
|
||||
with ZipFile(self.localPath, 'r') as zip_ref:
|
||||
content = zip_ref.read(self.localFile)
|
||||
content = content.decode('utf-8')
|
||||
return content
|
||||
return ''
|
||||
# 取得待分析的图像帧
|
||||
def getAnalysisFrame(self, nextFlag):
|
||||
frameChanged = self.frameChanged
|
||||
self.frameChanged = False
|
||||
if nextFlag: # 流式媒体
|
||||
self._currentFrame = self.nextFrame()
|
||||
self.frameChanged = True
|
||||
frame = self.currentFrame
|
||||
return frame.copy() if frame is not None else None, frameChanged
|
||||
|
||||
def nextFrame(self):
|
||||
frame = None
|
||||
if self.stream:
|
||||
frame = self.stream.next_pull_frame()
|
||||
elif self.videoStream:
|
||||
ret, frame = self.videoStream.read()
|
||||
self._frameIndex += 1
|
||||
if not ret:
|
||||
self._frameIndex -= 1
|
||||
frame = None
|
||||
elif len(self.localFiles) > 0:
|
||||
if self.localPath.endswith('.zip'):
|
||||
index = -1
|
||||
for img_file in self.localFiles:
|
||||
if '/images/' in img_file:
|
||||
if index == self._frameIndex:
|
||||
# logger.warning(f'Loading image from zip file: {img_file}')
|
||||
try:
|
||||
with ZipFile(self.localPath, 'r') as zip_ref:
|
||||
image_data = zip_ref.read(img_file)
|
||||
nparr = np.frombuffer(image_data, np.uint8)
|
||||
frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
self._frameIndex += 1
|
||||
lable_file = img_file.replace('/images/', '/labels/').replace('.jpg', '.txt').replace('.png', '.txt')
|
||||
self.localFile = lable_file
|
||||
except Exception as e:
|
||||
# logger.error(f"读取压缩文件 {self.localPath} 中的 {img_file} 失败: {e}")
|
||||
frame = None
|
||||
break
|
||||
index += 1
|
||||
else:
|
||||
if self._frameIndex < 0:
|
||||
self._frameIndex = 0
|
||||
if self._frameIndex >= len(self.localFiles):
|
||||
self._frameIndex = 0
|
||||
if self._frameIndex < len(self.localFiles):
|
||||
frame = cv2.imread(self.localFiles[self._frameIndex])
|
||||
if frame is None:
|
||||
logger.error(f"无法读取目标目录 {self.localPath}中下标为 {self._frameIndex} 的视频文件 {self.localFiles[self._frameIndex]}")
|
||||
self._frameIndex = -1
|
||||
return
|
||||
self._frameIndex += 1
|
||||
elif self.localFile is not None and self.localFile != '':
|
||||
frame = cv2.imread(self.localFile)
|
||||
if frame is None:
|
||||
logger.error("无法读取本地视频文件: {}", self.localFile)
|
||||
return
|
||||
if frame is not None:
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
if self.totalFrames > 0:
|
||||
Helper.App.progress = self._frameIndex
|
||||
return frame
|
||||
|
||||
def test_yolo11_recognize(self, frame):
|
||||
processed_frame = self.process_frame_with_yolo(frame, self.request_id)
|
||||
return processed_frame
|
||||
|
||||
def process_frame_with_yolo(self, frame, requestId):
|
||||
"""
|
||||
使用YOLOv11处理帧
|
||||
"""
|
||||
try:
|
||||
# 使用YOLO进行目标检测和追踪
|
||||
processed_frame, detection_result = self.tracker.process_frame(frame)
|
||||
|
||||
# 在帧上添加处理信息
|
||||
fps_info = f"FPS: {1000/max(self.tracker.processing_time, 1):.1f}"
|
||||
cv2.putText(processed_frame, fps_info, (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
|
||||
# 添加检测目标数量信息
|
||||
if detection_result and detection_result.boxes is not None:
|
||||
obj_count = len(detection_result.boxes)
|
||||
count_info = f"Objects: {obj_count}"
|
||||
cv2.putText(processed_frame, count_info, (10, 70),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
|
||||
return processed_frame
|
||||
|
||||
except Exception as e:
|
||||
logger.error("YOLO处理异常：{}, requestId:{}", format_exc(), requestId)
|
||||
# 如果处理失败，返回原帧
|
||||
return frame
|
||||
|
||||
def get_gray_mask(self, frame):
|
||||
"""
|
||||
生成灰度像素的掩码图
|
||||
灰度像素定义：三颜色分量差小于20
|
||||
"""
|
||||
# 创建与原图大小相同的掩码图
|
||||
maskMat = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
|
||||
# 获取图像的三个颜色通道
|
||||
b, g, r = cv2.split(frame)
|
||||
|
||||
r = r.astype(np.int16)
|
||||
g = g.astype(np.int16)
|
||||
b = b.astype(np.int16)
|
||||
# 计算任意两个颜色分量之间的差值
|
||||
diff_rg = np.abs(r - g)
|
||||
is_shadow = (b > r) & (b - r < 40)
|
||||
diff_rb = np.abs(r - b)
|
||||
diff_gb = np.abs(g - b)
|
||||
|
||||
# 判断条件：三颜色分量差都小于20
|
||||
gray_pixels = (diff_rg < 20 ) & (diff_rb < 20| is_shadow) & (diff_gb < 20)
|
||||
|
||||
# 将满足条件的像素在掩码图中设为255（白色）
|
||||
maskMat[gray_pixels] = 255
|
||||
|
||||
return maskMat
|
||||
|
||||
def debugLine(self, line, y_intersect):
|
||||
x1, y1, x2, y2 = line
|
||||
length = np.linalg.norm([x2 - x1, y2 - y1])
|
||||
# 计算线与水平线的夹角（度数）
|
||||
# 使用atan2计算弧度，再转换为度数
|
||||
angle_rad = math.atan2(y2 - y1, x2 - x1)
|
||||
angle_deg = math.degrees(angle_rad)
|
||||
# 调整角度范围到0-180度（平面角）
|
||||
if angle_deg < 0:
|
||||
angle_deg += 180
|
||||
# angle_deg = min(angle_deg, 180 - angle_deg)
|
||||
x_intersect = (x2 - x1) * (y_intersect - y1) / (y2 - y1) + x1
|
||||
return angle_deg, length, x_intersect
|
||||
def test_highway_recognize(self, frame, debugFlag = False):
|
||||
processed_frame = frame.copy()
|
||||
|
||||
try:
|
||||
IGNORE_HEIGHT = 100
|
||||
y_intersect = frame.shape[0] / 2
|
||||
frame[:IGNORE_HEIGHT, :] = (255, 0, 0)
|
||||
|
||||
gray_mask = self.get_gray_mask(frame)
|
||||
|
||||
kernel = np.ones((5, 5), np.uint8) # 使用形态学开运算（先腐蚀后膨胀）去除小噪声点
|
||||
gray_mask = cv2.erode(gray_mask, kernel)
|
||||
gray_mask = cv2.erode(gray_mask, kernel)
|
||||
|
||||
# 过滤掉面积小于10000的区域
|
||||
contours, _ = cv2.findContours(gray_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
# 创建新的掩码图像，只保留面积大于等于10000的区域
|
||||
filtered_mask = np.zeros_like(gray_mask)
|
||||
for contour in contours:
|
||||
area = cv2.contourArea(contour)
|
||||
if area >= 10000: # 填充满足条件的轮廓区域
|
||||
cv2.fillPoly(filtered_mask, [contour], 255)
|
||||
|
||||
gray_mask = filtered_mask # 使用过滤后的掩码替换原来的gray_mask
|
||||
edges = cv2.Canny(frame, 100, 200) # 边缘检测
|
||||
road_edges = cv2.bitwise_and(edges, edges, mask=filtered_mask) # 在过滤后的路面区域内进行边缘检测
|
||||
|
||||
# 用color_mask过滤原图，得到待处理的图
|
||||
whiteLineMat = cv2.bitwise_and(processed_frame, processed_frame, mask=filtered_mask)
|
||||
whiteLineMat = cv2.cvtColor(whiteLineMat, cv2.COLOR_RGB2GRAY) # 灰度化
|
||||
# sobel边缘检测
|
||||
whiteLineMat = cv2.Sobel(whiteLineMat, cv2.CV_8U, 1, 0, ksize=3)
|
||||
tempMat = whiteLineMat.copy()
|
||||
# whiteLineMat = cv2.Canny(whiteLineMat, 100, 200)
|
||||
lines = cv2.HoughLinesP(tempMat, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10)
|
||||
whiteLineMat = cv2.cvtColor(whiteLineMat, cv2.COLOR_GRAY2RGB)
|
||||
|
||||
# logger.info(f"{lines.shape[0]} lines: ")
|
||||
# if lines is not None:
|
||||
# for line in lines:
|
||||
# x1, y1, x2, y2 = line[0]
|
||||
# cv2.line(whiteLineMat, (x1, y1), (x2, y2), (255, 0, 0), 2)
|
||||
|
||||
|
||||
# 创建彩色掩码用于叠加（使用绿色标记识别出的路面）
|
||||
color_mask = cv2.cvtColor(gray_mask, cv2.COLOR_GRAY2RGB)
|
||||
color_mask[:] = (0, 255, 0) # 设置为绿色
|
||||
color_mask = cv2.bitwise_and(color_mask, color_mask, mask=filtered_mask)
|
||||
|
||||
# 先叠加路面绿色标记，再叠加白色线条红色标记
|
||||
overlay = cv2.addWeighted(processed_frame, 0.7, color_mask, 0.3, 0)
|
||||
|
||||
# # 在road_edges的基础上，识别其中的实线
|
||||
# lines = cv2.HoughLinesP(road_edges, 1, np.pi/180, threshold=100, minLineLength=100, maxLineGap=10)
|
||||
# logger.info(f"{lines.shape[0]} lines: ")
|
||||
# linesWithAngle = []
|
||||
# # if lines is not None:
|
||||
# for index, line in enumerate(lines):
|
||||
# angle_deg, length, x_intersect = self.debugLine(line[0], y_intersect)
|
||||
# linesWithAngle.append((line, angle_deg, x_intersect))
|
||||
# if debugFlag:
|
||||
# logger.info(f'line {index + 1}: {line}, 线长：{length:.2f}, 夹角：{angle_deg:.2f}°， 交点：({x_intersect:.2f}, {y_intersect:.2f})')
|
||||
|
||||
# linesWithAngle进行聚类算法，按夹角分两类即可
|
||||
# 使用自定义的简单K-means聚类实现
|
||||
# line_data = np.array([[angle, x_intersect] for line, angle, x_intersect in linesWithAngle])
|
||||
# if len(line_data) > 0:
|
||||
# labels = self._simple_kmeans(line_data, n_clusters=2, random_state=2, random_state=0)
|
||||
# # 输出两类线的数目
|
||||
# logger.info(f"聚类结果：{np.bincount(labels)}")
|
||||
# if debugFlag:
|
||||
# lines0 = [line for idx, line in enumerate(linesWithAngle) if labels[idx] == 0]
|
||||
# lines1 = [line for idx, line in enumerate(linesWithAngle) if labels[idx] == 1]
|
||||
# # 取得lines0中所有线段并输出日志信息
|
||||
# for index, line in enumerate(lines0):
|
||||
# angle_deg, length, x_intersect = self.debugLine(line[0][0], y_intersect)
|
||||
# logger.info(f'聚类0: {line[0]}, 线长：{length:.2f}, 夹角：{angle_deg:.2f}°， 交点：({x_intersect:.2f}, {y_intersect:.2f})')
|
||||
|
||||
# for index, line in enumerate(lines1):
|
||||
# angle_deg, length, x_intersect = self.debugLine(line[0][0], y_intersect)
|
||||
# logger.info(f'聚类1: {line[0]}, 线长：{length:.2f}, 夹角：{angle_deg:.2f}°， 交点：({x_intersect:.2f}, {y_intersect:.2f})')
|
||||
|
||||
# # 保留数量多的类别
|
||||
# dominant_cluster = np.argmax(np.bincount(labels))
|
||||
# # 绘制dominant_cluster类别的线
|
||||
# dominant_lines = [line for idx, line in enumerate(linesWithAngle) if labels[idx] == dominant_cluster]
|
||||
|
||||
# for line, angle, x_intersect in dominant_lines:
|
||||
# cv2.line(overlay, (int(line[0][0]), int(line[0][1])), (int(line[0][2]), int(line[0][3])), (255, 0, 0), 2)
|
||||
|
||||
return overlay, color_mask, whiteLineMat # cv2.cvtColor(whiteLineMat, cv2.COLOR_GRAY2RGB) # cv2.cvtColor(road_edges, cv2.COLOR_GRAY2RGB)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("路面识别异常：{}", format_exc())
|
||||
# 如果处理失败，返回原始帧
|
||||
return processed_frame
|
||||
|
||||
# def _simple_kmeans(self, data, n_clusters=2, max_iter=100, random_state=0):
|
||||
# """
|
||||
# 使用K-means算法对数据进行聚类
|
||||
|
||||
# 参数:
|
||||
# data: array-like, 形状为 (n_samples, n_features) 的输入数据
|
||||
# n_clusters: int, 聚类数量，默认为2
|
||||
# max_iter: int, 最大迭代次数，默认为100
|
||||
# random_state: int, 随机种子，用于初始化质心，默认为0
|
||||
|
||||
# 返回:
|
||||
# labels: array, 形状为 (n_samples,) 的聚类标签数组
|
||||
# """
|
||||
# np.random.seed(random_state)
|
||||
|
||||
# # 随机选择初始质心
|
||||
# centroids_idx = np.random.choice(len(data), size=n_clusters, replace=False)
|
||||
# centroids = data[centroids_idx].copy()
|
||||
|
||||
# # 迭代优化质心位置
|
||||
# for _ in range(max_iter):
|
||||
# # 为每个数据点分配最近的质心标签
|
||||
# labels = np.zeros(len(data), dtype=int)
|
||||
# for i, point in enumerate(data):
|
||||
# distancesi=ids - point, ax(centroids - point, axis=1) ce置为- # 情况如果d sfnpcnsy>d9e，则置为>180 -9dis ini作为新质心
|
||||
# new_centroids[c] = data[np.random.choice(len(data))]
|
||||
|
||||
# # 检查收敛条件
|
||||
# if np.allclose(centroids, new_centroids):
|
||||
# break
|
||||
|
||||
# centroids = new_centroids
|
||||
|
||||
# return labels
|
||||
|
||||
|
|
@ -0,0 +1,115 @@
|
|||
import subprocess as sp
|
||||
from traceback import format_exc
|
||||
import cv2, time
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
from DrGraph.utils.Helper import Helper
|
||||
from DrGraph.utils.Exception import ServiceException
|
||||
from DrGraph.utils.Constant import Constant
|
||||
|
||||
class NetStream:
|
||||
def __init__(self, pull_url, push_url, request_id):
|
||||
self.pull_url = pull_url
|
||||
self.push_url = push_url
|
||||
self.request_id = request_id
|
||||
self.pull_p = None
|
||||
|
||||
self.width = 1920
|
||||
self.height = 1080 * 3 // 2
|
||||
self.width_height_3 = 1920 * 1080 * 3 // 2
|
||||
self.w_2 = 960
|
||||
self.h_2 = 540
|
||||
|
||||
self.frame_count = 0
|
||||
self.start_time = time.time();
|
||||
|
||||
def clear_pull_p(self,pull_p, requestId):
|
||||
try:
|
||||
if pull_p and pull_p.poll() is None:
|
||||
logger.info("关闭拉流管道, requestId:{}", requestId)
|
||||
if pull_p.stdout:
|
||||
pull_p.stdout.close()
|
||||
pull_p.terminate()
|
||||
pull_p.wait(timeout=30)
|
||||
logger.info("拉流管道已关闭, requestId:{}", requestId)
|
||||
except Exception as e:
|
||||
logger.error("关闭拉流管道异常: {}, requestId:{}", format_exc(), requestId)
|
||||
if pull_p and pull_p.poll() is None:
|
||||
pull_p.kill()
|
||||
pull_p.wait(timeout=30)
|
||||
raise e
|
||||
|
||||
def start_pull_p(self, pull_url, requestId):
|
||||
try:
|
||||
command = ['D:/DrGraph/DSP/ffmpeg.exe']
|
||||
# if pull_url.startswith("rtsp://"):
|
||||
# command.extend(['-timeout', '20000000', '-rtsp_transport', 'tcp'])
|
||||
# if pull_url.startswith("http") or pull_url.startswith("rtmp"):
|
||||
# command.extend(['-rw_timeout', '20000000'])
|
||||
command.extend(['-re',
|
||||
'-y',
|
||||
'-an',
|
||||
# '-hwaccel', 'cuda', cuvid
|
||||
'-c:v', 'h264_cuvid',
|
||||
# '-resize', self.wah,
|
||||
'-i', pull_url,
|
||||
'-f', 'rawvideo',
|
||||
# '-pix_fmt', 'bgr24',
|
||||
'-r', '25',
|
||||
'-'])
|
||||
self.pull_p = sp.Popen(command, stdout=sp.PIPE)
|
||||
return self.pull_p
|
||||
except ServiceException as s:
|
||||
logger.error("构建拉流管道ServiceException异常: url={}, {}, requestId:{}", pull_url, s.msg, requestId)
|
||||
raise s
|
||||
except Exception as e:
|
||||
logger.error("构建拉流管道Exception异常：url={}, {}, requestId:{}", pull_url, format_exc(), requestId)
|
||||
raise e
|
||||
|
||||
def pull_read_video_stream(self):
|
||||
result = None
|
||||
try:
|
||||
if self.pull_p is None:
|
||||
self.start_pull_p(self.pull_url, self.request_id)
|
||||
in_bytes = self.pull_p.stdout.read(self.width_height_3)
|
||||
if in_bytes is not None and len(in_bytes) > 0:
|
||||
try:
|
||||
# result = (np.frombuffer(in_bytes, np.uint8).reshape([height * 3 // 2, width, 3]))
|
||||
# ValueError: cannot reshape array of size 3110400 into shape (1080,1920)
|
||||
result = (np.frombuffer(in_bytes, np.uint8)).reshape((self.height, self.width))
|
||||
result = cv2.cvtColor(result, cv2.COLOR_YUV2BGR_NV12)
|
||||
# result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
|
||||
if result.shape[1] > Constant.pull_frame_width:
|
||||
result = cv2.resize(result, (result.shape[1] // 2, result.shape[0] // 2), interpolation=cv2.INTER_LINEAR)
|
||||
except Exception:
|
||||
logger.error("视频格式异常：{}, requestId:{}", format_exc(), requestId)
|
||||
raise ServiceException(ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[0],
|
||||
ExceptionType.VIDEO_RESOLUTION_EXCEPTION.value[1])
|
||||
except ServiceException as s:
|
||||
logger.error("ServiceException 读流异常: {}, requestId:{}", s.msg, self.request_id)
|
||||
self.clear_pull_p(self.pull_p, self.request_id)
|
||||
self.pull_p = None
|
||||
result = None
|
||||
raise s
|
||||
except Exception:
|
||||
logger.error("Exception 读流异常：{}, requestId:{}", format_exc(), self.request_id)
|
||||
self.clear_pull_p(self.pull_p, self.request_id)
|
||||
self.pull_p = None
|
||||
self.width = None
|
||||
self.height = None
|
||||
self.width_height_3 = None
|
||||
result = None
|
||||
logger.error("读流异常：{}, requestId:{}", format_exc(), self.request_id)
|
||||
return result
|
||||
|
||||
def prepare_pull(self):
|
||||
if self.pull_p is None:
|
||||
self.start_time = time.time();
|
||||
self.start_pull_p(self.pull_url, self.request_id)
|
||||
|
||||
def next_pull_frame(self):
|
||||
if self.pull_p is None:
|
||||
logger.error(f'pull_p is None, requestId: {self.request_id}')
|
||||
return None
|
||||
frame = self.pull_read_video_stream()
|
||||
return frame
|
||||
|
|
@ -1,11 +1,13 @@
|
|||
"""Custom exceptions and error handlers for the chat agent application."""
|
||||
from typing import Any, Dict, Optional
|
||||
from typing import Any, Dict, List, Optional
|
||||
from fastapi import HTTPException, Request, status
|
||||
from fastapi.responses import JSONResponse
|
||||
from fastapi.responses import JSONResponse, StreamingResponse
|
||||
from typing import Union
|
||||
from pydantic import BaseModel
|
||||
from datetime import datetime
|
||||
import json
|
||||
from loguru import logger
|
||||
|
||||
from starlette.status import (
|
||||
HTTP_400_BAD_REQUEST,
|
||||
HTTP_401_UNAUTHORIZED,
|
||||
|
|
@ -25,14 +27,33 @@ class FullHxfResponseModel(BaseModel):
|
|||
message: Optional[str]
|
||||
|
||||
class HxfResponse(JSONResponse):
|
||||
def __init__(self, response: Union[BaseModel, Dict[str, Any]]):
|
||||
if isinstance(response, BaseModel):
|
||||
data_dict = response.model_dump(mode='json')
|
||||
def __new__(cls, response: Union[BaseModel, Dict[str, Any], List[BaseModel], List[Dict[str, Any]], StreamingResponse]):
|
||||
# 如果是StreamingResponse，直接返回，不进行JSON包装
|
||||
if isinstance(response, StreamingResponse):
|
||||
return response
|
||||
# 否则创建HxfResponse实例
|
||||
return super().__new__(cls)
|
||||
|
||||
def __init__(self, response: Union[BaseModel, Dict[str, Any], List[BaseModel], List[Dict[str, Any]]]):
|
||||
code = 0
|
||||
if isinstance(response, list):
|
||||
# 处理BaseModel对象列表
|
||||
if all(isinstance(item, BaseModel) for item in response):
|
||||
data_dict = [item.model_dump(mode='json') for item in response]
|
||||
else:
|
||||
data_dict = response
|
||||
elif isinstance(response, BaseModel):
|
||||
# 处理单个BaseModel对象
|
||||
data_dict = response.model_dump(mode='json')
|
||||
else:
|
||||
# 处理字典或其他可JSON序列化的数据
|
||||
data_dict = response
|
||||
|
||||
if 'success' in data_dict and data_dict['success'] == False:
|
||||
code = -1
|
||||
|
||||
content = {
|
||||
"code": 0,
|
||||
"code": code,
|
||||
"status": status.HTTP_200_OK,
|
||||
"data": data_dict,
|
||||
"error": None,
|
||||
|
|
@ -50,12 +71,23 @@ class HxfErrorResponse(JSONResponse):
|
|||
def __init__(self, message: Union[str, Exception], status_code: int = status.HTTP_401_UNAUTHORIZED):
|
||||
"""Return a JSON error response."""
|
||||
if isinstance(message, Exception):
|
||||
msg = message.message if 'message' in message.__dict__ else str(message)
|
||||
logger.error(f"[HxfErrorResponse] - {type(message)}, 异常: {message}")
|
||||
if isinstance(message, TypeError):
|
||||
content = {
|
||||
"code": -1,
|
||||
"status": 500,
|
||||
"data": None,
|
||||
"error": f"错误类型: {type(message)} 错误信息: {message}",
|
||||
"message": msg
|
||||
}
|
||||
else:
|
||||
content = {
|
||||
"code": -1,
|
||||
"status": message.status_code,
|
||||
"data": None,
|
||||
"error": message.details,
|
||||
"message": message.message
|
||||
"error": None,
|
||||
"message": msg
|
||||
}
|
||||
else:
|
||||
content = {
|
||||
|
|
|
|||
|
|
@ -310,6 +310,7 @@ class ConversationResponse(BaseResponse, ConversationBase):
|
|||
is_archived: bool
|
||||
message_count: int = 0
|
||||
last_message_at: Optional[datetime] = None
|
||||
messages: Optional[List["MessageResponse"]] = None
|
||||
|
||||
|
||||
# Message schemas
|
||||
|
|
@ -345,11 +346,11 @@ class ChatRequest(BaseModel):
|
|||
message: str = Field(..., min_length=1, max_length=10000)
|
||||
stream: bool = Field(default=False)
|
||||
use_knowledge_base: bool = Field(default=False)
|
||||
knowledge_base_id: Optional[int] = Field(None, description="Knowledge base ID for RAG mode")
|
||||
knowledge_base_id: Optional[int] = Field(default=None, description="Knowledge base ID for RAG mode")
|
||||
use_agent: bool = Field(default=False, description="Enable agent mode with tool calling capabilities")
|
||||
use_langgraph: bool = Field(default=False, description="Enable LangGraph agent mode with advanced tool calling")
|
||||
temperature: Optional[float] = Field(None, ge=0.0, le=2.0)
|
||||
max_tokens: Optional[int] = Field(None, ge=1, le=8192)
|
||||
temperature: Optional[float] = Field(default=0.7, ge=0.0, le=2.0)
|
||||
max_tokens: Optional[int] = Field(default=2048, ge=1, le=8192)
|
||||
|
||||
|
||||
class ChatResponse(BaseModel):
|
||||
|
|
@ -458,6 +459,7 @@ class DocumentChunk(BaseModel):
|
|||
chunk_index: int
|
||||
start_char: Optional[int] = None
|
||||
end_char: Optional[int] = None
|
||||
vector_id: Optional[str] = None
|
||||
|
||||
|
||||
class DocumentChunksResponse(BaseModel):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,120 @@
|
|||
from enum import Enum, unique
|
||||
|
||||
CommonColors_Backgnd = [
|
||||
"#FF0000", # 红色
|
||||
"#00FF00", # 绿色
|
||||
"#0000FF", # 蓝色
|
||||
"#FFFF00", # 黄色
|
||||
"#FF00FF", # 品红
|
||||
"#00FFFF", # 青色
|
||||
"#FFA500", # 橙色
|
||||
"#800080", # 紫色
|
||||
"#FFC0CB", # 粉色
|
||||
"#008000", # 深绿色
|
||||
"#000080", # 深蓝色
|
||||
"#800000", # 深红色
|
||||
"#808000", # 橄榄色
|
||||
"#008080", # 青色
|
||||
"#808080", # 灰色
|
||||
"#FF0080", # 玫瑰红
|
||||
"#0080FF", # 天蓝色
|
||||
"#FF8000", # 橙红色
|
||||
"#8000FF", # 紫罗兰
|
||||
"#00FF80" # 海绿色
|
||||
]
|
||||
|
||||
CommonColors_Foregnd = [
|
||||
"#FFFFFF", # 红色背景 -> 白色文字
|
||||
"#000000", # 绿色背景 -> 黑色文字
|
||||
"#FFFFFF", # 蓝色背景 -> 白色文字
|
||||
"#000000", # 黄色背景 -> 黑色文字
|
||||
"#FFFFFF", # 品红背景 -> 白色文字
|
||||
"#000000", # 青色背景 -> 黑色文字
|
||||
"#000000", # 橙色背景 -> 黑色文字
|
||||
"#FFFFFF", # 紫色背景 -> 白色文字
|
||||
"#000000", # 粉色背景 -> 黑色文字
|
||||
"#FFFFFF", # 深绿色背景 -> 白色文字
|
||||
"#FFFFFF", # 深蓝色背景 -> 白色文字
|
||||
"#FFFFFF", # 深红色背景 -> 白色文字
|
||||
"#FFFFFF", # 橄榄色背景 -> 白色文字
|
||||
"#FFFFFF", # 青色背景 -> 白色文字
|
||||
"#FFFFFF", # 灰色背景 -> 白色文字
|
||||
"#FFFFFF", # 玫瑰红背景 -> 白色文字
|
||||
"#000000", # 天蓝色背景 -> 黑色文字
|
||||
"#000000", # 橙红色背景 -> 黑色文字
|
||||
"#FFFFFF", # 紫罗兰背景 -> 白色文字
|
||||
"#000000" # 海绿色背景 -> 黑色文字
|
||||
]
|
||||
|
||||
@unique
|
||||
class LabellingKind(Enum):
|
||||
Unknown = 0
|
||||
Select = 1
|
||||
Create = 2
|
||||
TempDrag = 3
|
||||
TempResize = 4
|
||||
TempRotate = 5
|
||||
|
||||
@unique
|
||||
class Meta(Enum):
|
||||
Unknown = 0
|
||||
Line = 1
|
||||
Rectangle = 2
|
||||
Ellipse = 3
|
||||
Polygon = 4
|
||||
Text = 5
|
||||
|
||||
@unique
|
||||
class AiAlg(Enum):
|
||||
Unknown = 0
|
||||
FashionMNIST = 1,
|
||||
ColorDetector = 2,
|
||||
Face = 3,
|
||||
Coco8 = 4
|
||||
|
||||
|
||||
@unique
|
||||
class Response(Enum):
|
||||
OK = (0, "OK - 响应成功")
|
||||
DEBUG = (1, "DEBUG - 正常调试")
|
||||
WARNING = (2, "FAIL - 响应警告")
|
||||
ERROR = (3, "ERROR - 响应错误")
|
||||
EXCEPTION = (4, "EXCEPTION - 响应异常")
|
||||
CRITICAL = (5, "CRITICAL - 响应严重错误")
|
||||
|
||||
class Flag(Enum):
|
||||
Paint = True
|
||||
Client = False
|
||||
|
||||
|
||||
class Align(Enum):
|
||||
Left = 0
|
||||
Top = 1
|
||||
Right = 2
|
||||
Bottom = 3
|
||||
Client = 4
|
||||
Custom = 5
|
||||
|
||||
class Anchor(Enum):
|
||||
Left = 1
|
||||
Top = 2
|
||||
Right = 4
|
||||
Bottom = 8
|
||||
|
||||
class IconPos(Enum):
|
||||
NoIcon = 0
|
||||
OnlyIcon = 1
|
||||
IconLeft = 2
|
||||
IconRight = 3
|
||||
IconTop = 4
|
||||
IconBottom = 5
|
||||
|
||||
class ControlEvent(Enum):
|
||||
MouseEnter = 0
|
||||
MouseLeave = 1
|
||||
MouseMove = 2
|
||||
MouseDown = 3
|
||||
MouseUp = 4
|
||||
Click = 5
|
||||
DblClick = 6
|
||||
|
||||
|
|
@ -0,0 +1,136 @@
|
|||
import time, websockets, json, inspect, os
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
|
||||
from typing import Dict, Set, Callable, Any, Optional
|
||||
from traceback import format_exc
|
||||
|
||||
from DrGraph.utils.Helper import *
|
||||
import DrGraph.utils.vclEnums as enums
|
||||
|
||||
class WebSocketServer:
|
||||
"""
|
||||
WebSocket服务器类
|
||||
提供WebSocket服务器功能，包括客户端连接管理、消息处理和广播功能
|
||||
"""
|
||||
|
||||
def __init__(self, host: str = "localhost", port: int = 8765):
|
||||
self.host = host
|
||||
self.port = port
|
||||
self.message_handlers: Dict[str, Callable] = {}
|
||||
self.server = None
|
||||
self.onClientConnect = None
|
||||
self.register_handler("file", self.handle_file)
|
||||
|
||||
def register_handler(self, message_type: str, handler: Callable):
|
||||
self.message_handlers[message_type] = handler
|
||||
|
||||
async def response_text(self, websocket: websockets.WebSocketServerProtocol, message, t = 'default'):
|
||||
if isinstance(message, dict):
|
||||
message = json.dumps(message, ensure_ascii=False)
|
||||
if Helper.AppFlag_SaveLog:
|
||||
logger.warning(f"发送消息: {message}")
|
||||
await websocket.send(f't{message}')
|
||||
caller = inspect.stack()[1]
|
||||
logger.info(f"(type={t})to {websocket.remote_address}: {message} - caller={caller} ")
|
||||
|
||||
async def response_binary(self, websocket: websockets.WebSocketServerProtocol, data):
|
||||
# if isinstance(data, np.ndarray):
|
||||
# data = data.tobytes()
|
||||
await websocket.send(bytearray(b'b' + data))
|
||||
|
||||
async def handle_message(self, websocket: websockets.WebSocketServerProtocol, message: str):
|
||||
try:
|
||||
data = json.loads(message)
|
||||
message_type = data.get("type")
|
||||
payload = data.get("data")
|
||||
if Helper.AppFlag_SaveLog:
|
||||
logger.warning(f"收到消息: {message}")
|
||||
|
||||
if message_type in self.message_handlers:
|
||||
response = await self.message_handlers[message_type](websocket, payload)
|
||||
if response is not None:
|
||||
if isinstance(response, (bytes, bytearray, np.ndarray)):
|
||||
# print("发送图片数据")
|
||||
await self.response_binary(websocket, response);
|
||||
else:
|
||||
await self.response_text(websocket, response, message_type);
|
||||
elif message_type == 'echo':
|
||||
print("echo message ", int(time.time() * 1000))
|
||||
data["type"] = "echo_response"
|
||||
await self.response_text(websocket, data);
|
||||
else:
|
||||
logger.warning(f"未知类型消息: {message} - {websocket.remote_address}")
|
||||
await self.response_text(websocket, Helper.build_response(message_type, enums.Response.ERROR, f"未知消息类型 - {message_type}"))
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f"无效的JSON格式 - {message}")
|
||||
await self.response_text(websocket, Helper.build_response("JSONDecodeError", enums.Response.EXCEPTION, f"无效的JSON格式 - {message}"))
|
||||
except BrokenPipeError as e:
|
||||
logger.error(f"WebSocket BrokenPipeError: {str(e)} - 客户端: {websocket.remote_address}")
|
||||
# BrokenPipeError表示连接已断开，不需要特殊处理，让上层处理ConnectionClosed异常
|
||||
except Exception as e:
|
||||
logger.error(f"处理消息时出错: {format_exc()}")
|
||||
await self.response_text(websocket, Helper.build_response("Exception", enums.Response.EXCEPTION, f"服务器内部错误 - 处理消息时出错: {format_exc()}"))
|
||||
|
||||
async def handle_client(self, websocket: websockets.WebSocketServerProtocol, path: str = ""):
|
||||
"""
|
||||
处理客户端连接
|
||||
|
||||
参数:
|
||||
websocket (websockets.WebSocketServerProtocol): WebSocket连接对象
|
||||
path (str): 请求路径
|
||||
"""
|
||||
logger.info(f"客户端 {websocket.remote_address} [path:{path}] 新建连接")
|
||||
if self.onClientConnect:
|
||||
await self.onClientConnect(websocket, True)
|
||||
try:
|
||||
async for message in websocket:
|
||||
await self.handle_message(websocket, message)
|
||||
except websockets.exceptions.ConnectionClosed:
|
||||
logger.info(f"客户端 {websocket.remote_address} [path:{path}] 连接已关闭")
|
||||
except BrokenPipeError:
|
||||
logger.info(f"客户端 {websocket.remote_address} [path:{path}] 连接BrokenPipeError")
|
||||
finally:
|
||||
if self.onClientConnect:
|
||||
await self.onClientConnect(websocket, False)
|
||||
|
||||
async def start(self):
|
||||
"""
|
||||
启动WebSocket服务器
|
||||
"""
|
||||
self.server = await websockets.serve(self.handle_client, self.host, self.port)
|
||||
logger.warning(f"WebSocket服务器已启动: {self.host}:{self.port}")
|
||||
|
||||
async def stop(self):
|
||||
"""
|
||||
停止WebSocket服务器
|
||||
"""
|
||||
if self.server:
|
||||
self.server.close()
|
||||
await self.server.wait_closed()
|
||||
logger.info("WebSocket服务器已停止")
|
||||
|
||||
async def handle_file(self, websocket: websockets.WebSocketServerProtocol, payload: str):
|
||||
logger.info(f"接收文件: {payload}, {payload['command']}")
|
||||
command = payload['command']
|
||||
if command == 'dir':
|
||||
path = payload.get('path', '/')
|
||||
files = []
|
||||
folders = []
|
||||
try:
|
||||
# 获取目录下的所有文件和文件夹
|
||||
if os.path.exists(path) and os.path.isdir(path):
|
||||
with os.scandir(path) as entries:
|
||||
for entry in entries:
|
||||
if entry.is_file():
|
||||
files.append(entry.name)
|
||||
elif entry.is_dir():
|
||||
folders.append(entry.name)
|
||||
else:
|
||||
logger.warning(f"路径不存在或不是目录: {path}")
|
||||
except Exception as e:
|
||||
logger.error(f"读取目录时出错: {path}, 错误: {e}")
|
||||
|
||||
# 合并文件和文件夹列表
|
||||
all_items = folders + files
|
||||
logger.info(f"目录: {path}, 文件和文件夹列表: {all_items}")
|
||||
|
|
@ -2,7 +2,7 @@ enable_file_log: true
|
|||
enable_stderr: true
|
||||
base_path: "webIOs/output/logs"
|
||||
log_name: "th_agenter_web.log"
|
||||
log_fmt: "<green>{time: HH:mm:ss.SSS}</green> [<level>{level}</level>] - <level>{message}</level> @ <cyan>{extra[relative_path]}:{line}</cyan> in <blue>{function}</blue>"
|
||||
log_fmt: "<green>{time: HH:mm:ss.SSS}</green> [<level>{level:7}</level>] - <level>{message}</level> @ <cyan>{extra[relative_path]}:{line}</cyan> in <blue>{function}</blue>"
|
||||
level: "INFO"
|
||||
rotation: "00:00"
|
||||
retention: "1 days"
|
||||
|
|
|
|||
Loading…
Reference in New Issue