Agent-based RAG system ignoring document metadata in retrieval process

Other Questions langsmith
1

I’m building an agent-based RAG application and running into a problem with my retrieval node. The system isn’t using my complete query when searching through Pinecone.

const fileMetadata = await FileCollection.find({
  _id: { $in: fileIds }
}).select("-content");

const completeQuery = "**Search Query:**\n\n" + userInput + "\n\n**Available file metadata:**\n\n" + JSON.stringify(fileMetadata);

My setup works like this: I combine the user’s question with metadata from relevant files. For example, if someone asks “Tell me about John’s experience?”, my final query becomes the question plus all the file metadata.

The problem is that when my agent chooses to retrieve documents, it completely ignores the metadata part and only uses the original question. I can see this happening in the LangSmith traces where the retrieval node receives just “Tell me about John’s experience?” instead of the full query with metadata.

I need the retrieval process to consider both the question and the document metadata together. Has anyone faced this issue before? How can I force the retrieve node to use the entire combined query instead of just extracting the original question?

2

Sounds like you’re fighting with agent middleware that’s reformatting your queries behind the scenes. Been there.

Here’s what’s happening: your agent framework treats your metadata as noise and extracts what it thinks is the “real” query. Most frameworks do this to boost retrieval performance.

Stop battling the agent’s preprocessing - automate the whole flow differently. Set up a workflow that handles metadata injection at the vector search level, not the query level.

I solved this exact problem with an automated pipeline that:

  • Catches the user query before the agent touches it
  • Pulls relevant metadata automatically
  • Combines everything into a structured search
  • Feeds the results back to your agent

This bypasses the agent’s query cleaning entirely. The automation handles all metadata logic while your agent just gets clean, contextual results.

You can build this without writing custom retrieval functions or modifying agent configs. Just automate the preprocessing pipeline to handle metadata injection before your RAG system sees the query.

3

Your agent’s probably preprocessing the query before retrieval kicks in. Most agent frameworks do this automatically - they strip context to make ‘cleaner’ search terms. I ran into the same thing with LangGraph agents where the retrieval tool kept pulling out what it thought was the main question. Check your agent config for any query preprocessing, or see if you’re using a retrieval tool that auto-cleans queries. You’ll likely need to turn that off or build a custom retrieval function that takes the raw input as-is. Another option: embed the metadata right into your vector store as document content instead of trying to inject it at query time. That way semantic search naturally picks up the metadata context during retrieval without depending on the agent to keep your query structure intact.

4

I’ve hit this exact problem with agent frameworks and vector databases. Your agent is basically ignoring the metadata and doing its own query extraction behind the scenes.

Don’t fight it - restructure instead. Skip the query-time concatenation and enrich your docs during indexing. When you store stuff in Pinecone, bake the metadata right into the document content or use Pinecone’s structured metadata fields for filtering.

What worked for me was two-stage retrieval. Run the clean user query first to get initial results, then filter by metadata as a second step. You keep the semantic search quality but still respect your metadata constraints.

Also worth checking if you’re using the right retrieval strategy. If metadata is crucial for context, use Pinecone’s metadata filtering with semantic search instead of cramming everything into one query string.