mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-25 08:46:22 +02:00
d8a05ae4d5
- Added endpoint to list agent tools with metadata, excluding hidden tools. - Updated NewChatRequest and RegenerateRequest schemas to include disabled tools. - Integrated disabled tools management in the NewChatPage and Composer components. - Improved tool instructions and visibility in the system prompt. - Refactored tool registration to support hidden tools and default enabled states. - Enhanced document chunk creation to handle strict zip behavior. - Cleaned up imports and formatting across various files for consistency.
270 lines
9.9 KiB
Python
270 lines
9.9 KiB
Python
import contextlib
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import time
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from datetime import UTC, datetime
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from sqlalchemy import delete, select
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from sqlalchemy.exc import IntegrityError
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.db import Chunk, Document, DocumentStatus
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from app.indexing_pipeline.connector_document import ConnectorDocument
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from app.indexing_pipeline.document_chunker import chunk_text
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from app.indexing_pipeline.document_embedder import embed_texts
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from app.indexing_pipeline.document_hashing import (
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compute_content_hash,
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compute_unique_identifier_hash,
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)
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from app.indexing_pipeline.document_persistence import (
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attach_chunks_to_document,
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rollback_and_persist_failure,
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)
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from app.indexing_pipeline.document_summarizer import summarize_document
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from app.indexing_pipeline.exceptions import (
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EMBEDDING_ERRORS,
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PERMANENT_LLM_ERRORS,
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RETRYABLE_LLM_ERRORS,
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PipelineMessages,
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embedding_message,
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llm_permanent_message,
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llm_retryable_message,
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safe_exception_message,
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)
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from app.indexing_pipeline.pipeline_logger import (
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PipelineLogContext,
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log_batch_aborted,
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log_chunking_overflow,
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log_doc_skipped_unknown,
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log_document_queued,
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log_document_requeued,
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log_document_updated,
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log_embedding_error,
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log_index_started,
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log_index_success,
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log_permanent_llm_error,
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log_race_condition,
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log_retryable_llm_error,
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log_unexpected_error,
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)
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from app.utils.perf import get_perf_logger
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class IndexingPipelineService:
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"""Single pipeline for indexing connector documents. All connectors use this service."""
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def __init__(self, session: AsyncSession) -> None:
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self.session = session
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async def prepare_for_indexing(
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self, connector_docs: list[ConnectorDocument]
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) -> list[Document]:
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"""
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Persist new documents and detect changes, returning only those that need indexing.
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"""
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perf = get_perf_logger()
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t0 = time.perf_counter()
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documents = []
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seen_hashes: set[str] = set()
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batch_ctx = PipelineLogContext(
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connector_id=connector_docs[0].connector_id if connector_docs else 0,
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search_space_id=connector_docs[0].search_space_id if connector_docs else 0,
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unique_id="batch",
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)
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for connector_doc in connector_docs:
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ctx = PipelineLogContext(
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connector_id=connector_doc.connector_id,
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search_space_id=connector_doc.search_space_id,
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unique_id=connector_doc.unique_id,
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)
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try:
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unique_identifier_hash = compute_unique_identifier_hash(connector_doc)
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content_hash = compute_content_hash(connector_doc)
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if unique_identifier_hash in seen_hashes:
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continue
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seen_hashes.add(unique_identifier_hash)
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result = await self.session.execute(
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select(Document).filter(
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Document.unique_identifier_hash == unique_identifier_hash
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)
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)
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existing = result.scalars().first()
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if existing is not None:
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if existing.content_hash == content_hash:
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if existing.title != connector_doc.title:
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existing.title = connector_doc.title
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existing.updated_at = datetime.now(UTC)
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if not DocumentStatus.is_state(
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existing.status, DocumentStatus.READY
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):
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existing.status = DocumentStatus.pending()
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existing.updated_at = datetime.now(UTC)
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documents.append(existing)
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log_document_requeued(ctx)
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continue
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existing.title = connector_doc.title
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existing.content_hash = content_hash
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existing.source_markdown = connector_doc.source_markdown
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existing.document_metadata = connector_doc.metadata
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existing.updated_at = datetime.now(UTC)
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existing.status = DocumentStatus.pending()
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documents.append(existing)
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log_document_updated(ctx)
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continue
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duplicate = await self.session.execute(
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select(Document).filter(Document.content_hash == content_hash)
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)
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if duplicate.scalars().first() is not None:
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continue
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document = Document(
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title=connector_doc.title,
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document_type=connector_doc.document_type,
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content="Pending...",
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content_hash=content_hash,
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unique_identifier_hash=unique_identifier_hash,
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source_markdown=connector_doc.source_markdown,
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document_metadata=connector_doc.metadata,
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search_space_id=connector_doc.search_space_id,
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connector_id=connector_doc.connector_id,
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created_by_id=connector_doc.created_by_id,
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updated_at=datetime.now(UTC),
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status=DocumentStatus.pending(),
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)
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self.session.add(document)
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documents.append(document)
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log_document_queued(ctx)
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except Exception as e:
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log_doc_skipped_unknown(ctx, e)
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try:
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await self.session.commit()
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perf.info(
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"[indexing] prepare_for_indexing in %.3fs input=%d output=%d",
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time.perf_counter() - t0,
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len(connector_docs),
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len(documents),
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)
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return documents
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except IntegrityError:
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log_race_condition(batch_ctx)
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await self.session.rollback()
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return []
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except Exception as e:
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log_batch_aborted(batch_ctx, e)
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await self.session.rollback()
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return []
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async def index(
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self, document: Document, connector_doc: ConnectorDocument, llm
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) -> Document:
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"""
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Run summarization, embedding, and chunking for a document and persist the results.
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"""
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ctx = PipelineLogContext(
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connector_id=connector_doc.connector_id,
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search_space_id=connector_doc.search_space_id,
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unique_id=connector_doc.unique_id,
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doc_id=document.id,
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)
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perf = get_perf_logger()
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t_index = time.perf_counter()
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try:
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log_index_started(ctx)
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document.status = DocumentStatus.processing()
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await self.session.commit()
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t_step = time.perf_counter()
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if connector_doc.should_summarize and llm is not None:
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content = await summarize_document(
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connector_doc.source_markdown, llm, connector_doc.metadata
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)
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perf.info(
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"[indexing] summarize_document doc=%d in %.3fs",
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document.id,
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time.perf_counter() - t_step,
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)
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elif connector_doc.should_summarize and connector_doc.fallback_summary:
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content = connector_doc.fallback_summary
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else:
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content = connector_doc.source_markdown
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await self.session.execute(
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delete(Chunk).where(Chunk.document_id == document.id)
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)
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t_step = time.perf_counter()
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chunk_texts = chunk_text(
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connector_doc.source_markdown,
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use_code_chunker=connector_doc.should_use_code_chunker,
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)
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texts_to_embed = [content, *chunk_texts]
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embeddings = embed_texts(texts_to_embed)
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summary_embedding, *chunk_embeddings = embeddings
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chunks = [
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Chunk(content=text, embedding=emb)
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for text, emb in zip(chunk_texts, chunk_embeddings, strict=False)
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]
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perf.info(
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"[indexing] chunk+embed doc=%d chunks=%d in %.3fs",
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document.id,
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len(chunks),
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time.perf_counter() - t_step,
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)
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document.content = content
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document.embedding = summary_embedding
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attach_chunks_to_document(document, chunks)
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document.updated_at = datetime.now(UTC)
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document.status = DocumentStatus.ready()
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await self.session.commit()
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perf.info(
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"[indexing] index TOTAL doc=%d chunks=%d in %.3fs",
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document.id,
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len(chunks),
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time.perf_counter() - t_index,
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)
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log_index_success(ctx, chunk_count=len(chunks))
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except RETRYABLE_LLM_ERRORS as e:
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log_retryable_llm_error(ctx, e)
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await rollback_and_persist_failure(
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self.session, document, llm_retryable_message(e)
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)
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except PERMANENT_LLM_ERRORS as e:
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log_permanent_llm_error(ctx, e)
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await rollback_and_persist_failure(
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self.session, document, llm_permanent_message(e)
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)
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except RecursionError as e:
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log_chunking_overflow(ctx, e)
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await rollback_and_persist_failure(
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self.session, document, PipelineMessages.CHUNKING_OVERFLOW
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)
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except EMBEDDING_ERRORS as e:
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log_embedding_error(ctx, e)
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await rollback_and_persist_failure(
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self.session, document, embedding_message(e)
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)
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except Exception as e:
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log_unexpected_error(ctx, e)
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await rollback_and_persist_failure(
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self.session, document, safe_exception_message(e)
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)
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with contextlib.suppress(Exception):
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await self.session.refresh(document)
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return document
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