Discussion: Improving RAG Systems with LangChain

[BennisonDevadoss]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

python ...

Description

I'm building a Retrieval-Augmented Generation (RAG) system using LangChain. In this setup, users upload documents to a vector database (DB), which the system uses to answer questions.

Scenario Overview

1. Document Upload: Users upload documents that are stored in the vector DB as embeddings.
2. User Queries:
   • When a user asks, "What is [a topic]?" (where the topic is from the uploaded documents), the RAG system retrieves relevant information.
   • However, if the user asks, "Explain in detail," the system may struggle to provide the right context because it relies on the previous question.

Key Challenges

• Contextual Dependency: The system needs to understand that "Explain in detail" refers to the topic from the earlier question.
• Handling General Queries: The RAG may not accurately retrieve information for broad questions without specific context.

Proposed Solutions

1. Context Tracking: Maintain a record of user interactions to connect follow-up queries to previous topics.
2. Enhanced Retrieval: Use strategies that can retrieve both specific and general information based on past interactions.

Questions for the Community

• How do you manage context in RAG systems?
• What methods do you use to ensure accurate retrieval for vague queries?
• Any tips for maintaining context in user interactions?

I’d love to hear your thoughts on enhancing the RAG capabilities in LangChain, especially for handling user queries that may lack context.

System Info

python 3.12