How can I overcome the 20-file limit in assistants for large-scale Retrieval-Augmented Generation?

Below is some guidance based on existing strategies and documentation:

Using the File Search Tool vs. Your Own Vector Index

The File Search tool and its associated vector store are designed to cover many common use cases, but they come with practical limits (for example, only one vector store per assistant/thread, and other resource constraints). If your documentation spans thousands of files, the built‐in vector store may not be sufficient.

Recommended Strategy

Many developers facing this challenge build an external retrieval system—often using solutions like LangChain’s retrievers (for example, the ParentDocument or MultiVector retrievers) which allow you to leverage an external vector store built on services like Qdrant, Amazon DocumentDB, or others. In this setup, you perform the semantic retrieval yourself before passing a smaller, highly relevant subset of documents (or file identifiers) on to the assistant. This means you:

1. Maintain Your Own Vector Index:
   Use an external vector database as described in the LangChain documentation. Index your thousands of documents there.

2. Perform a Pre-Retrieval Step:
   When a query is received, run your retrieval query against your vector index. This returns a set of file IDs or document chunks that are most relevant.

3. Dynamically Attach Relevant Files:
   Attach only the subset of files (or their representatives) to the thread/context as needed. You can build caching or use a time window so that you aren’t constantly reattaching files for every message.

4. Optimize for Efficiency:
   Instead of re-running the retrieval for every single user turn, consider retrieving once at the start of the conversation or once per session, then reusing results as long as the conversation context remains valid. This limits the extra round-trips and processing overhead.

Streaming and User Experience

Regarding user experience, streaming results provide a much more responsive interface than waiting for a complete response. This is an area that is actively evolving. Until streaming is supported by the assistant endpoints, you might consider alternative UI elements or caching partial results on the client side to simulate a smoother experience. In the meantime, if partial generation is a priority, external solutions that incorporate advanced streaming mechanisms may be the way forward.

Summary

While the built-in assistant file search tool is useful, its vector store’s limitations mean that for large-scale document retrieval you want to combine your own vector index retrieval with selective file or document attachment. This hybrid approach lets you leverage both the speed of an optimized external retriever (using tools from LangChain) and the convenience of the assistant’s integration, without overwhelming the assistant’s inherent resource limits.

This strategy aligns with current best practices and enables a scalable system without per-message inefficiencies.