mirror of
https://github.com/MODSetter/SurfSense.git
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feat: Added content based hashing to prevent duplicates and fix resync issues
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
38516e74f9
commit
5411bac8e0
17 changed files with 297 additions and 334 deletions
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@ -2,7 +2,6 @@
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Revision ID: 1
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Revises:
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Create Date: 2023-10-27 10:00:00.000000
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"""
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from typing import Sequence, Union
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Revision ID: 2
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Revises: e55302644c51
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Create Date: 2025-04-16 10:00:00.000000
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"""
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from typing import Sequence, Union
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Revision ID: 3
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Revises: 2
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Create Date: 2025-04-16 10:05:00.059921
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"""
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from typing import Sequence, Union
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Revision ID: 4
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Revises: 3
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Create Date: 2025-04-18 10:00:00.000000
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"""
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from typing import Sequence, Union
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Revision ID: 5
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Revises: 4
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Create Date: 2023-06-10 00:00:00.000000
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"""
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from typing import Sequence, Union
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Revision ID: 6
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Revises: 5
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Create Date: 2023-08-15 00:00:00.000000
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"""
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from typing import Sequence, Union
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@ -2,7 +2,6 @@
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Revision ID: 7
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Revises: 6
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Create Date: 2023-08-15 01:00:00.000000
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"""
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from typing import Sequence, Union
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@ -0,0 +1,56 @@
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"""Add content_hash column to documents table
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Revision ID: 8
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Revises: 7
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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 = '8'
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down_revision: Union[str, None] = '7'
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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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# Add content_hash column as nullable first to handle existing data
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op.add_column('documents', sa.Column('content_hash', sa.String(), nullable=True))
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# Update existing documents to generate content hashes
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# Using SHA-256 hash of the content column with proper UTF-8 encoding
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op.execute("""
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UPDATE documents
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SET content_hash = encode(sha256(convert_to(content, 'UTF8')), 'hex')
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WHERE content_hash IS NULL
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""")
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# Handle duplicate content hashes by keeping only the oldest document for each hash
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# Delete newer documents with duplicate content hashes
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op.execute("""
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DELETE FROM documents
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WHERE id NOT IN (
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SELECT MIN(id)
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FROM documents
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GROUP BY content_hash
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)
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""")
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# Now alter the column to match the model: nullable=False, index=True, unique=True
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op.alter_column('documents', 'content_hash',
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existing_type=sa.String(),
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nullable=False)
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op.create_index(op.f('ix_documents_content_hash'), 'documents', ['content_hash'], unique=False)
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op.create_unique_constraint(op.f('uq_documents_content_hash'), 'documents', ['content_hash'])
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def downgrade() -> None:
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# Remove constraints and index first
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op.drop_constraint(op.f('uq_documents_content_hash'), 'documents', type_='unique')
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op.drop_index(op.f('ix_documents_content_hash'), table_name='documents')
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# Remove content_hash column from documents table
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op.drop_column('documents', 'content_hash')
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@ -2,7 +2,6 @@
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Revision ID: e55302644c51
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Revises: 1
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Create Date: 2025-04-13 19:56:00.059921
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"""
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from typing import Sequence, Union
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@ -6,7 +6,7 @@ Allows fetching issue lists and their comments with date range filtering.
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"""
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import requests
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from datetime import datetime, timedelta
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple, Any, Union
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@ -8,7 +8,7 @@ Allows fetching channel lists and message history with date range filtering.
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import os
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from slack_sdk import WebClient
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from slack_sdk.errors import SlackApiError
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from datetime import datetime, timedelta
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from datetime import datetime
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from typing import Dict, List, Optional, Tuple, Any, Union
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@ -99,6 +99,7 @@ class Document(BaseModel, TimestampMixin):
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document_metadata = Column(JSON, nullable=True)
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content = Column(Text, nullable=False)
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content_hash = Column(String, nullable=False, index=True, unique=True)
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embedding = Column(Vector(config.embedding_model_instance.dimension))
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search_space_id = Column(Integer, ForeignKey("searchspaces.id", ondelete='CASCADE'), nullable=False)
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@ -21,7 +21,7 @@ from app.utils.check_ownership import check_ownership
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from pydantic import BaseModel, Field, ValidationError
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from app.tasks.connectors_indexing_tasks import index_slack_messages, index_notion_pages, index_github_repos, index_linear_issues
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from app.connectors.github_connector import GitHubConnector
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from datetime import datetime, timezone, timedelta
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from datetime import datetime, timedelta
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import logging
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# Set up logging
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@ -1,12 +1,13 @@
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from typing import Optional, List
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.exc import SQLAlchemyError
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from sqlalchemy.future import select
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from app.db import Document, DocumentType, Chunk
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from app.schemas import ExtensionDocumentContent
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from app.config import config
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from app.prompts import SUMMARY_PROMPT_TEMPLATE
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from datetime import datetime
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from app.utils.document_converters import convert_document_to_markdown
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from app.utils.document_converters import convert_document_to_markdown, generate_content_hash
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from langchain_core.documents import Document as LangChainDocument
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from langchain_community.document_loaders import FireCrawlLoader, AsyncChromiumLoader
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from langchain_community.document_transformers import MarkdownifyTransformer
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@ -14,7 +15,6 @@ import validators
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from youtube_transcript_api import YouTubeTranscriptApi
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from urllib.parse import urlparse, parse_qs
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import aiohttp
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from app.db import Document as DB_Document, DocumentType as DB_DocumentType
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import logging
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md = MarkdownifyTransformer()
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@ -73,6 +73,17 @@ async def add_crawled_url_document(
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document_parts.append("</DOCUMENT>")
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combined_document_string = "\n".join(document_parts)
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content_hash = generate_content_hash(combined_document_string)
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# Check if document with this content hash already exists
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existing_doc_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document = existing_doc_result.scalars().first()
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if existing_document:
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logging.info(f"Document with content hash {content_hash} already exists. Skipping processing.")
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return existing_document
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# Generate summary
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summary_chain = SUMMARY_PROMPT_TEMPLATE | config.long_context_llm_instance
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@ -102,6 +113,7 @@ async def add_crawled_url_document(
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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session.add(document)
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document_parts.append("</DOCUMENT>")
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combined_document_string = "\n".join(document_parts)
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content_hash = generate_content_hash(combined_document_string)
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# Check if document with this content hash already exists
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existing_doc_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document = existing_doc_result.scalars().first()
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if existing_document:
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logging.info(f"Document with content hash {content_hash} already exists. Skipping processing.")
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return existing_document
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# Generate summary
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summary_chain = SUMMARY_PROMPT_TEMPLATE | config.long_context_llm_instance
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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session.add(document)
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@ -210,6 +234,18 @@ async def add_received_markdown_file_document(
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session: AsyncSession, file_name: str, file_in_markdown: str, search_space_id: int
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) -> Optional[Document]:
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try:
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content_hash = generate_content_hash(file_in_markdown)
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# Check if document with this content hash already exists
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existing_doc_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document = existing_doc_result.scalars().first()
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if existing_document:
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logging.info(f"Document with content hash {content_hash} already exists. Skipping processing.")
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return existing_document
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# Generate summary
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summary_chain = SUMMARY_PROMPT_TEMPLATE | config.long_context_llm_instance
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summary_result = await summary_chain.ainvoke({"document": file_in_markdown})
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@ -237,6 +273,7 @@ async def add_received_markdown_file_document(
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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session.add(document)
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unstructured_processed_elements
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)
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content_hash = generate_content_hash(file_in_markdown)
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# Check if document with this content hash already exists
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existing_doc_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document = existing_doc_result.scalars().first()
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if existing_document:
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logging.info(f"Document with content hash {content_hash} already exists. Skipping processing.")
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return existing_document
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# TODO: Check if file_markdown exceeds token limit of embedding model
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# Generate summary
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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)
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session.add(document)
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document_parts.append("</DOCUMENT>")
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combined_document_string = "\n".join(document_parts)
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content_hash = generate_content_hash(combined_document_string)
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# Check if document with this content hash already exists
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existing_doc_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document = existing_doc_result.scalars().first()
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if existing_document:
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logging.info(f"Document with content hash {content_hash} already exists. Skipping processing.")
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return existing_document
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# Generate summary
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summary_chain = SUMMARY_PROMPT_TEMPLATE | config.long_context_llm_instance
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# Create document
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document = DB_Document(
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document = Document(
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title=video_data.get("title", "YouTube Video"),
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document_type=DB_DocumentType.YOUTUBE_VIDEO,
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document_type=DocumentType.YOUTUBE_VIDEO,
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document_metadata={
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"url": url,
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"video_id": video_id,
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embedding=summary_embedding,
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chunks=chunks,
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search_space_id=search_space_id,
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content_hash=content_hash,
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)
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session.add(document)
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@ -14,6 +14,8 @@ from app.connectors.linear_connector import LinearConnector
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from slack_sdk.errors import SlackApiError
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import logging
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from app.utils.document_converters import generate_content_hash
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# Set up logging
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logger = logging.getLogger(__name__)
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@ -67,13 +69,13 @@ async def index_slack_messages(
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# Check if last_indexed_at is in the future or after end_date
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if last_indexed_naive > end_date:
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logger.warning(f"Last indexed date ({last_indexed_naive.strftime('%Y-%m-%d')}) is in the future. Using 30 days ago instead.")
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start_date = end_date - timedelta(days=30)
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logger.warning(f"Last indexed date ({last_indexed_naive.strftime('%Y-%m-%d')}) is in the future. Using 365 days ago instead.")
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start_date = end_date - timedelta(days=365)
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else:
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start_date = last_indexed_naive
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logger.info(f"Using last_indexed_at ({start_date.strftime('%Y-%m-%d')}) as start date")
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else:
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start_date = end_date - timedelta(days=30) # Use 30 days instead of 365 to catch recent issues
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start_date = end_date - timedelta(days=365) # Use 365 days as default
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logger.info(f"No last_indexed_at found, using {start_date.strftime('%Y-%m-%d')} (30 days ago) as start date")
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# Format dates for Slack API
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@ -89,27 +91,8 @@ async def index_slack_messages(
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if not channels:
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return 0, "No Slack channels found"
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# Get existing documents for this search space and connector type to prevent duplicates
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existing_docs_result = await session.execute(
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select(Document)
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.filter(
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Document.search_space_id == search_space_id,
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Document.document_type == DocumentType.SLACK_CONNECTOR
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)
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)
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existing_docs = existing_docs_result.scalars().all()
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# Create a lookup dictionary of existing documents by channel_id
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existing_docs_by_channel_id = {}
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for doc in existing_docs:
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if "channel_id" in doc.document_metadata:
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existing_docs_by_channel_id[doc.document_metadata["channel_id"]] = doc
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logger.info(f"Found {len(existing_docs_by_channel_id)} existing Slack documents in database")
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# Track the number of documents indexed
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documents_indexed = 0
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documents_updated = 0
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documents_skipped = 0
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skipped_channels = []
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@ -189,10 +172,9 @@ async def index_slack_messages(
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("METADATA", [
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f"CHANNEL_NAME: {channel_name}",
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f"CHANNEL_ID: {channel_id}",
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f"START_DATE: {start_date_str}",
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f"END_DATE: {end_date_str}",
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f"MESSAGE_COUNT: {len(formatted_messages)}",
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f"INDEXED_AT: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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# f"START_DATE: {start_date_str}",
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# f"END_DATE: {end_date_str}",
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f"MESSAGE_COUNT: {len(formatted_messages)}"
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]),
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("CONTENT", [
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"FORMAT: markdown",
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@ -213,6 +195,18 @@ async def index_slack_messages(
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document_parts.append("</DOCUMENT>")
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combined_document_string = '\n'.join(document_parts)
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content_hash = generate_content_hash(combined_document_string)
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# Check if document with this content hash already exists
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existing_doc_by_hash_result = await session.execute(
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select(Document).where(Document.content_hash == content_hash)
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)
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existing_document_by_hash = existing_doc_by_hash_result.scalars().first()
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if existing_document_by_hash:
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logger.info(f"Document with content hash {content_hash} already exists for channel {channel_name}. Skipping processing.")
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documents_skipped += 1
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continue
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# Generate summary
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summary_chain = SUMMARY_PROMPT_TEMPLATE | config.long_context_llm_instance
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@ -226,61 +220,28 @@ async def index_slack_messages(
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for chunk in config.chunker_instance.chunk(channel_content)
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]
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# Check if this channel already exists in our database
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existing_document = existing_docs_by_channel_id.get(channel_id)
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if existing_document:
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# Update existing document instead of creating a new one
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logger.info(f"Updating existing document for channel {channel_name}")
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# Update document fields
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existing_document.title = f"Slack - {channel_name}"
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existing_document.document_metadata = {
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# Create and store new document
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document = Document(
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search_space_id=search_space_id,
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title=f"Slack - {channel_name}",
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document_type=DocumentType.SLACK_CONNECTOR,
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document_metadata={
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"channel_name": channel_name,
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"channel_id": channel_id,
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"start_date": start_date_str,
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"end_date": end_date_str,
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"message_count": len(formatted_messages),
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"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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existing_document.content = summary_content
|
||||
existing_document.embedding = summary_embedding
|
||||
|
||||
# Delete existing chunks and add new ones
|
||||
await session.execute(
|
||||
delete(Chunk)
|
||||
.where(Chunk.document_id == existing_document.id)
|
||||
)
|
||||
|
||||
# Assign new chunks to existing document
|
||||
for chunk in chunks:
|
||||
chunk.document_id = existing_document.id
|
||||
session.add(chunk)
|
||||
|
||||
documents_updated += 1
|
||||
else:
|
||||
# Create and store new document
|
||||
document = Document(
|
||||
search_space_id=search_space_id,
|
||||
title=f"Slack - {channel_name}",
|
||||
document_type=DocumentType.SLACK_CONNECTOR,
|
||||
document_metadata={
|
||||
"channel_name": channel_name,
|
||||
"channel_id": channel_id,
|
||||
"start_date": start_date_str,
|
||||
"end_date": end_date_str,
|
||||
"message_count": len(formatted_messages),
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new channel {channel_name} with {len(formatted_messages)} messages")
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks,
|
||||
content_hash=content_hash,
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new channel {channel_name} with {len(formatted_messages)} messages")
|
||||
|
||||
except SlackApiError as slack_error:
|
||||
logger.error(f"Slack API error for channel {channel_name}: {str(slack_error)}")
|
||||
|
|
@ -295,7 +256,7 @@ async def index_slack_messages(
|
|||
|
||||
# Update the last_indexed_at timestamp for the connector only if requested
|
||||
# and if we successfully indexed at least one channel
|
||||
total_processed = documents_indexed + documents_updated
|
||||
total_processed = documents_indexed
|
||||
if update_last_indexed and total_processed > 0:
|
||||
connector.last_indexed_at = datetime.now()
|
||||
|
||||
|
|
@ -305,11 +266,11 @@ async def index_slack_messages(
|
|||
# Prepare result message
|
||||
result_message = None
|
||||
if skipped_channels:
|
||||
result_message = f"Processed {total_processed} channels ({documents_indexed} new, {documents_updated} updated). Skipped {len(skipped_channels)} channels: {', '.join(skipped_channels)}"
|
||||
result_message = f"Processed {total_processed} channels. Skipped {len(skipped_channels)} channels: {', '.join(skipped_channels)}"
|
||||
else:
|
||||
result_message = f"Processed {total_processed} channels ({documents_indexed} new, {documents_updated} updated)."
|
||||
result_message = f"Processed {total_processed} channels."
|
||||
|
||||
logger.info(f"Slack indexing completed: {documents_indexed} new channels, {documents_updated} updated, {documents_skipped} skipped")
|
||||
logger.info(f"Slack indexing completed: {documents_indexed} new channels, {documents_skipped} skipped")
|
||||
return total_processed, result_message
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
|
|
@ -386,27 +347,8 @@ async def index_notion_pages(
|
|||
logger.info("No Notion pages found to index")
|
||||
return 0, "No Notion pages found"
|
||||
|
||||
# Get existing documents for this search space and connector type to prevent duplicates
|
||||
existing_docs_result = await session.execute(
|
||||
select(Document)
|
||||
.filter(
|
||||
Document.search_space_id == search_space_id,
|
||||
Document.document_type == DocumentType.NOTION_CONNECTOR
|
||||
)
|
||||
)
|
||||
existing_docs = existing_docs_result.scalars().all()
|
||||
|
||||
# Create a lookup dictionary of existing documents by page_id
|
||||
existing_docs_by_page_id = {}
|
||||
for doc in existing_docs:
|
||||
if "page_id" in doc.document_metadata:
|
||||
existing_docs_by_page_id[doc.document_metadata["page_id"]] = doc
|
||||
|
||||
logger.info(f"Found {len(existing_docs_by_page_id)} existing Notion documents in database")
|
||||
|
||||
# Track the number of documents indexed
|
||||
documents_indexed = 0
|
||||
documents_updated = 0
|
||||
documents_skipped = 0
|
||||
skipped_pages = []
|
||||
|
||||
|
|
@ -482,8 +424,7 @@ async def index_notion_pages(
|
|||
metadata_sections = [
|
||||
("METADATA", [
|
||||
f"PAGE_TITLE: {page_title}",
|
||||
f"PAGE_ID: {page_id}",
|
||||
f"INDEXED_AT: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
f"PAGE_ID: {page_id}"
|
||||
]),
|
||||
("CONTENT", [
|
||||
"FORMAT: markdown",
|
||||
|
|
@ -504,6 +445,18 @@ async def index_notion_pages(
|
|||
|
||||
document_parts.append("</DOCUMENT>")
|
||||
combined_document_string = '\n'.join(document_parts)
|
||||
content_hash = generate_content_hash(combined_document_string)
|
||||
|
||||
# Check if document with this content hash already exists
|
||||
existing_doc_by_hash_result = await session.execute(
|
||||
select(Document).where(Document.content_hash == content_hash)
|
||||
)
|
||||
existing_document_by_hash = existing_doc_by_hash_result.scalars().first()
|
||||
|
||||
if existing_document_by_hash:
|
||||
logger.info(f"Document with content hash {content_hash} already exists for page {page_title}. Skipping processing.")
|
||||
documents_skipped += 1
|
||||
continue
|
||||
|
||||
# Generate summary
|
||||
logger.debug(f"Generating summary for page {page_title}")
|
||||
|
|
@ -519,55 +472,25 @@ async def index_notion_pages(
|
|||
for chunk in config.chunker_instance.chunk(markdown_content)
|
||||
]
|
||||
|
||||
# Check if this page already exists in our database
|
||||
existing_document = existing_docs_by_page_id.get(page_id)
|
||||
|
||||
if existing_document:
|
||||
# Update existing document instead of creating a new one
|
||||
logger.info(f"Updating existing document for page {page_title}")
|
||||
|
||||
# Update document fields
|
||||
existing_document.title = f"Notion - {page_title}"
|
||||
existing_document.document_metadata = {
|
||||
# Create and store new document
|
||||
document = Document(
|
||||
search_space_id=search_space_id,
|
||||
title=f"Notion - {page_title}",
|
||||
document_type=DocumentType.NOTION_CONNECTOR,
|
||||
document_metadata={
|
||||
"page_title": page_title,
|
||||
"page_id": page_id,
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
}
|
||||
existing_document.content = summary_content
|
||||
existing_document.embedding = summary_embedding
|
||||
|
||||
# Delete existing chunks and add new ones
|
||||
await session.execute(
|
||||
delete(Chunk)
|
||||
.where(Chunk.document_id == existing_document.id)
|
||||
)
|
||||
|
||||
# Assign new chunks to existing document
|
||||
for chunk in chunks:
|
||||
chunk.document_id = existing_document.id
|
||||
session.add(chunk)
|
||||
|
||||
documents_updated += 1
|
||||
else:
|
||||
# Create and store new document
|
||||
document = Document(
|
||||
search_space_id=search_space_id,
|
||||
title=f"Notion - {page_title}",
|
||||
document_type=DocumentType.NOTION_CONNECTOR,
|
||||
document_metadata={
|
||||
"page_title": page_title,
|
||||
"page_id": page_id,
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new Notion page: {page_title}")
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
content_hash=content_hash,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new Notion page: {page_title}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing Notion page {page.get('title', 'Unknown')}: {str(e)}", exc_info=True)
|
||||
|
|
@ -577,7 +500,7 @@ async def index_notion_pages(
|
|||
|
||||
# Update the last_indexed_at timestamp for the connector only if requested
|
||||
# and if we successfully indexed at least one page
|
||||
total_processed = documents_indexed + documents_updated
|
||||
total_processed = documents_indexed
|
||||
if update_last_indexed and total_processed > 0:
|
||||
connector.last_indexed_at = datetime.now()
|
||||
logger.info(f"Updated last_indexed_at for connector {connector_id}")
|
||||
|
|
@ -588,11 +511,11 @@ async def index_notion_pages(
|
|||
# Prepare result message
|
||||
result_message = None
|
||||
if skipped_pages:
|
||||
result_message = f"Processed {total_processed} pages ({documents_indexed} new, {documents_updated} updated). Skipped {len(skipped_pages)} pages: {', '.join(skipped_pages)}"
|
||||
result_message = f"Processed {total_processed} pages. Skipped {len(skipped_pages)} pages: {', '.join(skipped_pages)}"
|
||||
else:
|
||||
result_message = f"Processed {total_processed} pages ({documents_indexed} new, {documents_updated} updated)."
|
||||
result_message = f"Processed {total_processed} pages."
|
||||
|
||||
logger.info(f"Notion indexing completed: {documents_indexed} new pages, {documents_updated} updated, {documents_skipped} skipped")
|
||||
logger.info(f"Notion indexing completed: {documents_indexed} new pages, {documents_skipped} skipped")
|
||||
return total_processed, result_message
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
|
|
@ -660,19 +583,6 @@ async def index_github_repos(
|
|||
# If a repo is inaccessible, get_repository_files will likely fail gracefully later.
|
||||
logger.info(f"Starting indexing for {len(repo_full_names_to_index)} selected repositories.")
|
||||
|
||||
# 5. Get existing documents for this search space and connector type to prevent duplicates
|
||||
existing_docs_result = await session.execute(
|
||||
select(Document)
|
||||
.filter(
|
||||
Document.search_space_id == search_space_id,
|
||||
Document.document_type == DocumentType.GITHUB_CONNECTOR
|
||||
)
|
||||
)
|
||||
existing_docs = existing_docs_result.scalars().all()
|
||||
# Create a lookup dict: key=repo_fullname/file_path, value=Document object
|
||||
existing_docs_lookup = {doc.document_metadata.get("full_path"): doc for doc in existing_docs if doc.document_metadata.get("full_path")}
|
||||
logger.info(f"Found {len(existing_docs_lookup)} existing GitHub documents in database for search space {search_space_id}")
|
||||
|
||||
# 6. Iterate through selected repositories and index files
|
||||
for repo_full_name in repo_full_names_to_index:
|
||||
if not repo_full_name or not isinstance(repo_full_name, str):
|
||||
|
|
@ -699,12 +609,6 @@ async def index_github_repos(
|
|||
logger.warning(f"Skipping file with missing info in {repo_full_name}: {file_info}")
|
||||
continue
|
||||
|
||||
# Check if document already exists and if content hash matches
|
||||
existing_doc = existing_docs_lookup.get(full_path_key)
|
||||
if existing_doc and existing_doc.document_metadata.get("sha") == file_sha:
|
||||
logger.debug(f"Skipping unchanged file: {full_path_key}")
|
||||
continue # Skip if SHA matches (content hasn't changed)
|
||||
|
||||
# Get file content
|
||||
file_content = github_client.get_file_content(repo_full_name, file_path)
|
||||
|
||||
|
|
@ -712,6 +616,18 @@ async def index_github_repos(
|
|||
logger.warning(f"Could not retrieve content for {full_path_key}. Skipping.")
|
||||
continue # Skip if content fetch failed
|
||||
|
||||
content_hash = generate_content_hash(file_content)
|
||||
|
||||
# Check if document with this content hash already exists
|
||||
existing_doc_by_hash_result = await session.execute(
|
||||
select(Document).where(Document.content_hash == content_hash)
|
||||
)
|
||||
existing_document_by_hash = existing_doc_by_hash_result.scalars().first()
|
||||
|
||||
if existing_document_by_hash:
|
||||
logger.info(f"Document with content hash {content_hash} already exists for file {full_path_key}. Skipping processing.")
|
||||
continue
|
||||
|
||||
# Use file_content directly for chunking, maybe summary for main content?
|
||||
# For now, let's use the full content for both, might need refinement
|
||||
summary_content = f"GitHub file: {full_path_key}\n\n{file_content[:1000]}..." # Simple summary
|
||||
|
|
@ -738,42 +654,20 @@ async def index_github_repos(
|
|||
"indexed_at": datetime.now(timezone.utc).isoformat()
|
||||
}
|
||||
|
||||
if existing_doc:
|
||||
# Update existing document
|
||||
logger.info(f"Updating document for file: {full_path_key}")
|
||||
existing_doc.title = f"GitHub - {file_path}"
|
||||
existing_doc.document_metadata = doc_metadata
|
||||
existing_doc.content = summary_content # Update summary
|
||||
existing_doc.embedding = summary_embedding # Update embedding
|
||||
|
||||
# Delete old chunks
|
||||
await session.execute(
|
||||
delete(Chunk)
|
||||
.where(Chunk.document_id == existing_doc.id)
|
||||
)
|
||||
# Add new chunks
|
||||
for chunk_obj in chunks_data:
|
||||
chunk_obj.document_id = existing_doc.id
|
||||
session.add(chunk_obj)
|
||||
|
||||
documents_processed += 1
|
||||
else:
|
||||
# Create new document
|
||||
logger.info(f"Creating new document for file: {full_path_key}")
|
||||
document = Document(
|
||||
title=f"GitHub - {file_path}",
|
||||
document_type=DocumentType.GITHUB_CONNECTOR,
|
||||
document_metadata=doc_metadata,
|
||||
content=summary_content, # Store summary
|
||||
embedding=summary_embedding,
|
||||
search_space_id=search_space_id,
|
||||
chunks=chunks_data # Associate chunks directly
|
||||
)
|
||||
session.add(document)
|
||||
documents_processed += 1
|
||||
|
||||
# Commit periodically or at the end? For now, commit per repo
|
||||
# await session.commit()
|
||||
# Create new document
|
||||
logger.info(f"Creating new document for file: {full_path_key}")
|
||||
document = Document(
|
||||
title=f"GitHub - {file_path}",
|
||||
document_type=DocumentType.GITHUB_CONNECTOR,
|
||||
document_metadata=doc_metadata,
|
||||
content=summary_content, # Store summary
|
||||
content_hash=content_hash,
|
||||
embedding=summary_embedding,
|
||||
search_space_id=search_space_id,
|
||||
chunks=chunks_data # Associate chunks directly
|
||||
)
|
||||
session.add(document)
|
||||
documents_processed += 1
|
||||
|
||||
except Exception as repo_err:
|
||||
logger.error(f"Failed to process repository {repo_full_name}: {repo_err}")
|
||||
|
|
@ -847,14 +741,14 @@ async def index_linear_issues(
|
|||
|
||||
# Check if last_indexed_at is in the future or after end_date
|
||||
if last_indexed_naive > end_date:
|
||||
logger.warning(f"Last indexed date ({last_indexed_naive.strftime('%Y-%m-%d')}) is in the future. Using 30 days ago instead.")
|
||||
start_date = end_date - timedelta(days=30)
|
||||
logger.warning(f"Last indexed date ({last_indexed_naive.strftime('%Y-%m-%d')}) is in the future. Using 365 days ago instead.")
|
||||
start_date = end_date - timedelta(days=365)
|
||||
else:
|
||||
start_date = last_indexed_naive
|
||||
logger.info(f"Using last_indexed_at ({start_date.strftime('%Y-%m-%d')}) as start date")
|
||||
else:
|
||||
start_date = end_date - timedelta(days=30) # Use 30 days instead of 365 to catch recent issues
|
||||
logger.info(f"No last_indexed_at found, using {start_date.strftime('%Y-%m-%d')} (30 days ago) as start date")
|
||||
start_date = end_date - timedelta(days=365) # Use 365 days as default
|
||||
logger.info(f"No last_indexed_at found, using {start_date.strftime('%Y-%m-%d')} (365 days ago) as start date")
|
||||
|
||||
# Format dates for Linear API
|
||||
start_date_str = start_date.strftime("%Y-%m-%d")
|
||||
|
|
@ -905,35 +799,8 @@ async def index_linear_issues(
|
|||
if len(issues) > 10:
|
||||
logger.info(f" ...and {len(issues) - 10} more issues")
|
||||
|
||||
# Get existing documents for this search space and connector type to prevent duplicates
|
||||
existing_docs_result = await session.execute(
|
||||
select(Document)
|
||||
.filter(
|
||||
Document.search_space_id == search_space_id,
|
||||
Document.document_type == DocumentType.LINEAR_CONNECTOR
|
||||
)
|
||||
)
|
||||
existing_docs = existing_docs_result.scalars().all()
|
||||
|
||||
# Create a lookup dictionary of existing documents by issue_id
|
||||
existing_docs_by_issue_id = {}
|
||||
for doc in existing_docs:
|
||||
if "issue_id" in doc.document_metadata:
|
||||
existing_docs_by_issue_id[doc.document_metadata["issue_id"]] = doc
|
||||
|
||||
logger.info(f"Found {len(existing_docs_by_issue_id)} existing Linear documents in database")
|
||||
|
||||
# Log existing document IDs for debugging
|
||||
if existing_docs_by_issue_id:
|
||||
logger.info("Existing Linear document issue IDs in database:")
|
||||
for idx, (issue_id, doc) in enumerate(list(existing_docs_by_issue_id.items())[:10]): # Log first 10
|
||||
logger.info(f" {idx+1}. {issue_id} - {doc.document_metadata.get('issue_identifier', 'Unknown')} - {doc.document_metadata.get('issue_title', 'Unknown')}")
|
||||
if len(existing_docs_by_issue_id) > 10:
|
||||
logger.info(f" ...and {len(existing_docs_by_issue_id) - 10} more existing documents")
|
||||
|
||||
# Track the number of documents indexed
|
||||
documents_indexed = 0
|
||||
documents_updated = 0
|
||||
documents_skipped = 0
|
||||
skipped_issues = []
|
||||
|
||||
|
|
@ -979,6 +846,19 @@ async def index_linear_issues(
|
|||
comment_count = len(formatted_issue.get("comments", []))
|
||||
summary_content += f"Comments: {comment_count}"
|
||||
|
||||
content_hash = generate_content_hash(issue_content)
|
||||
|
||||
# Check if document with this content hash already exists
|
||||
existing_doc_by_hash_result = await session.execute(
|
||||
select(Document).where(Document.content_hash == content_hash)
|
||||
)
|
||||
existing_document_by_hash = existing_doc_by_hash_result.scalars().first()
|
||||
|
||||
if existing_document_by_hash:
|
||||
logger.info(f"Document with content hash {content_hash} already exists for issue {issue_identifier}. Skipping processing.")
|
||||
documents_skipped += 1
|
||||
continue
|
||||
|
||||
# Generate embedding for the summary
|
||||
summary_embedding = config.embedding_model_instance.embed(summary_content)
|
||||
|
||||
|
|
@ -988,62 +868,29 @@ async def index_linear_issues(
|
|||
for chunk in config.chunker_instance.chunk(issue_content)
|
||||
]
|
||||
|
||||
# Check if this issue already exists in our database
|
||||
existing_document = existing_docs_by_issue_id.get(issue_id)
|
||||
|
||||
if existing_document:
|
||||
# Update existing document instead of creating a new one
|
||||
logger.info(f"Updating existing document for issue {issue_identifier} - {issue_title}")
|
||||
|
||||
# Update document fields
|
||||
existing_document.title = f"Linear - {issue_identifier}: {issue_title}"
|
||||
existing_document.document_metadata = {
|
||||
# Create and store new document
|
||||
logger.info(f"Creating new document for issue {issue_identifier} - {issue_title}")
|
||||
document = Document(
|
||||
search_space_id=search_space_id,
|
||||
title=f"Linear - {issue_identifier}: {issue_title}",
|
||||
document_type=DocumentType.LINEAR_CONNECTOR,
|
||||
document_metadata={
|
||||
"issue_id": issue_id,
|
||||
"issue_identifier": issue_identifier,
|
||||
"issue_title": issue_title,
|
||||
"state": state,
|
||||
"comment_count": comment_count,
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"last_updated": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
}
|
||||
existing_document.content = summary_content
|
||||
existing_document.embedding = summary_embedding
|
||||
|
||||
# Delete existing chunks and add new ones
|
||||
await session.execute(
|
||||
delete(Chunk)
|
||||
.where(Chunk.document_id == existing_document.id)
|
||||
)
|
||||
|
||||
# Assign new chunks to existing document
|
||||
for chunk in chunks:
|
||||
chunk.document_id = existing_document.id
|
||||
session.add(chunk)
|
||||
|
||||
documents_updated += 1
|
||||
else:
|
||||
# Create and store new document
|
||||
logger.info(f"Creating new document for issue {issue_identifier} - {issue_title}")
|
||||
document = Document(
|
||||
search_space_id=search_space_id,
|
||||
title=f"Linear - {issue_identifier}: {issue_title}",
|
||||
document_type=DocumentType.LINEAR_CONNECTOR,
|
||||
document_metadata={
|
||||
"issue_id": issue_id,
|
||||
"issue_identifier": issue_identifier,
|
||||
"issue_title": issue_title,
|
||||
"state": state,
|
||||
"comment_count": comment_count,
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new issue {issue_identifier} - {issue_title}")
|
||||
"indexed_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
},
|
||||
content=summary_content,
|
||||
content_hash=content_hash,
|
||||
embedding=summary_embedding,
|
||||
chunks=chunks
|
||||
)
|
||||
|
||||
session.add(document)
|
||||
documents_indexed += 1
|
||||
logger.info(f"Successfully indexed new issue {issue_identifier} - {issue_title}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing issue {issue.get('identifier', 'Unknown')}: {str(e)}", exc_info=True)
|
||||
|
|
@ -1052,7 +899,7 @@ async def index_linear_issues(
|
|||
continue # Skip this issue and continue with others
|
||||
|
||||
# Update the last_indexed_at timestamp for the connector only if requested
|
||||
total_processed = documents_indexed + documents_updated
|
||||
total_processed = documents_indexed
|
||||
if update_last_indexed:
|
||||
connector.last_indexed_at = datetime.now()
|
||||
logger.info(f"Updated last_indexed_at to {connector.last_indexed_at}")
|
||||
|
|
@ -1062,7 +909,7 @@ async def index_linear_issues(
|
|||
logger.info(f"Successfully committed all Linear document changes to database")
|
||||
|
||||
|
||||
logger.info(f"Linear indexing completed: {documents_indexed} new issues, {documents_updated} updated, {documents_skipped} skipped")
|
||||
logger.info(f"Linear indexing completed: {documents_indexed} new issues, {documents_skipped} skipped")
|
||||
return total_processed, None # Return None as the error message to indicate success
|
||||
|
||||
except SQLAlchemyError as db_error:
|
||||
|
|
|
|||
|
|
@ -1,19 +1,22 @@
|
|||
import hashlib
|
||||
|
||||
|
||||
async def convert_element_to_markdown(element) -> str:
|
||||
"""
|
||||
Convert an Unstructured element to markdown format based on its category.
|
||||
|
||||
|
||||
Args:
|
||||
element: The Unstructured API element object
|
||||
|
||||
|
||||
Returns:
|
||||
str: Markdown formatted string
|
||||
"""
|
||||
element_category = element.metadata["category"]
|
||||
content = element.page_content
|
||||
|
||||
|
||||
if not content:
|
||||
return ""
|
||||
|
||||
|
||||
markdown_mapping = {
|
||||
"Formula": lambda x: f"```math\n{x}\n```",
|
||||
"FigureCaption": lambda x: f"*Figure: {x}*",
|
||||
|
|
@ -31,7 +34,7 @@ async def convert_element_to_markdown(element) -> str:
|
|||
"PageNumber": lambda x: f"*Page {x}*\n\n",
|
||||
"UncategorizedText": lambda x: f"{x}\n\n"
|
||||
}
|
||||
|
||||
|
||||
converter = markdown_mapping.get(element_category, lambda x: x)
|
||||
return converter(content)
|
||||
|
||||
|
|
@ -39,29 +42,30 @@ async def convert_element_to_markdown(element) -> str:
|
|||
async def convert_document_to_markdown(elements):
|
||||
"""
|
||||
Convert all document elements to markdown.
|
||||
|
||||
|
||||
Args:
|
||||
elements: List of Unstructured API elements
|
||||
|
||||
|
||||
Returns:
|
||||
str: Complete markdown document
|
||||
"""
|
||||
markdown_parts = []
|
||||
|
||||
|
||||
for element in elements:
|
||||
markdown_text = await convert_element_to_markdown(element)
|
||||
if markdown_text:
|
||||
markdown_parts.append(markdown_text)
|
||||
|
||||
|
||||
return "".join(markdown_parts)
|
||||
|
||||
|
||||
def convert_chunks_to_langchain_documents(chunks):
|
||||
"""
|
||||
Convert chunks from hybrid search results to LangChain Document objects.
|
||||
|
||||
|
||||
Args:
|
||||
chunks: List of chunk dictionaries from hybrid search results
|
||||
|
||||
|
||||
Returns:
|
||||
List of LangChain Document objects
|
||||
"""
|
||||
|
|
@ -71,20 +75,20 @@ def convert_chunks_to_langchain_documents(chunks):
|
|||
raise ImportError(
|
||||
"LangChain is not installed. Please install it with `pip install langchain langchain-core`"
|
||||
)
|
||||
|
||||
|
||||
langchain_docs = []
|
||||
|
||||
|
||||
for chunk in chunks:
|
||||
# Extract content from the chunk
|
||||
content = chunk.get("content", "")
|
||||
|
||||
|
||||
# Create metadata dictionary
|
||||
metadata = {
|
||||
"chunk_id": chunk.get("chunk_id"),
|
||||
"score": chunk.get("score"),
|
||||
"rank": chunk.get("rank") if "rank" in chunk else None,
|
||||
}
|
||||
|
||||
|
||||
# Add document information to metadata
|
||||
if "document" in chunk:
|
||||
doc = chunk["document"]
|
||||
|
|
@ -93,24 +97,25 @@ def convert_chunks_to_langchain_documents(chunks):
|
|||
"document_title": doc.get("title"),
|
||||
"document_type": doc.get("document_type"),
|
||||
})
|
||||
|
||||
|
||||
# Add document metadata if available
|
||||
if "metadata" in doc:
|
||||
# Prefix document metadata keys to avoid conflicts
|
||||
doc_metadata = {f"doc_meta_{k}": v for k, v in doc.get("metadata", {}).items()}
|
||||
doc_metadata = {f"doc_meta_{k}": v for k,
|
||||
v in doc.get("metadata", {}).items()}
|
||||
metadata.update(doc_metadata)
|
||||
|
||||
|
||||
# Add source URL if available in metadata
|
||||
if "url" in doc.get("metadata", {}):
|
||||
metadata["source"] = doc["metadata"]["url"]
|
||||
elif "sourceURL" in doc.get("metadata", {}):
|
||||
metadata["source"] = doc["metadata"]["sourceURL"]
|
||||
|
||||
|
||||
# Ensure source_id is set for citation purposes
|
||||
# Use document_id as the source_id if available
|
||||
if "document_id" in metadata:
|
||||
metadata["source_id"] = metadata["document_id"]
|
||||
|
||||
|
||||
# Update content for citation mode - format as XML with explicit source_id
|
||||
new_content = f"""
|
||||
<document>
|
||||
|
|
@ -124,13 +129,18 @@ def convert_chunks_to_langchain_documents(chunks):
|
|||
</content>
|
||||
</document>
|
||||
"""
|
||||
|
||||
|
||||
# Create LangChain Document
|
||||
langchain_doc = LangChainDocument(
|
||||
page_content=new_content,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
|
||||
langchain_docs.append(langchain_doc)
|
||||
|
||||
|
||||
return langchain_docs
|
||||
|
||||
|
||||
def generate_content_hash(content: str) -> str:
|
||||
"""Generate SHA-256 hash for the given content."""
|
||||
return hashlib.sha256(content.encode('utf-8')).hexdigest()
|
||||
|
|
|
|||
|
|
@ -1,5 +0,0 @@
|
|||
from app.agents.researcher.graph import graph as researcher_graph
|
||||
from app.agents.researcher.sub_section_writer.graph import graph as sub_section_writer_graph
|
||||
|
||||
print(researcher_graph.get_graph().draw_mermaid())
|
||||
print(sub_section_writer_graph.get_graph().draw_mermaid())
|
||||
Loading…
Add table
Reference in a new issue