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Address PR feedback: optimize logging and encapsulate document_id access
- Gate debug logging behind isEnabledFor check to avoid unnecessary computation - Add Chunk.document_id property to safely handle metadata/chunk_metadata extraction - Simplify RAG memory code using new property
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3 changed files with 24 additions and 11 deletions
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@ -91,6 +91,22 @@ class Chunk(BaseModel):
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return generate_chunk_id(str(uuid.uuid4()), str(self.content))
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@property
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def document_id(self) -> str | None:
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"""Returns the document_id from either metadata or chunk_metadata, with metadata taking precedence."""
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# Check metadata first (takes precedence)
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doc_id = self.metadata.get("document_id")
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if isinstance(doc_id, str):
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return doc_id
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# Fall back to chunk_metadata if available
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if self.chunk_metadata is not None:
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chunk_doc_id = getattr(self.chunk_metadata, "document_id", None)
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if isinstance(chunk_doc_id, str):
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return chunk_doc_id
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return None
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@json_schema_type
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class QueryChunksResponse(BaseModel):
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@ -5,6 +5,7 @@
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# the root directory of this source tree.
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import asyncio
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import logging
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import uuid
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from typing import Any
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@ -101,13 +102,12 @@ class VectorIORouter(VectorIO):
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chunks: list[Chunk],
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ttl_seconds: int | None = None,
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) -> None:
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doc_ids = [
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getattr(chunk.chunk_metadata, "document_id", None) if chunk.chunk_metadata else None for chunk in chunks[:3]
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]
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logger.debug(
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f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, "
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f"ttl_seconds={ttl_seconds}, chunk_ids={doc_ids}{' and more...' if len(chunks) > 3 else ''}"
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)
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if logger.isEnabledFor(logging.DEBUG):
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doc_ids = [chunk.document_id for chunk in chunks[:3]]
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logger.debug(
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f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, "
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f"ttl_seconds={ttl_seconds}, chunk_ids={doc_ids}{' and more...' if len(chunks) > 3 else ''}"
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)
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provider = await self.routing_table.get_provider_impl(vector_db_id)
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await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
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@ -279,10 +279,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
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return RAGQueryResult(
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content=picked,
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metadata={
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"document_ids": [
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c.metadata.get("document_id") or (c.chunk_metadata.document_id if c.chunk_metadata else None)
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for c in chunks[: len(picked)]
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],
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"document_ids": [c.document_id for c in chunks[: len(picked)]],
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"chunks": [c.content for c in chunks[: len(picked)]],
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"scores": scores[: len(picked)],
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"vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]],
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