Strategies for Managing Long Conversation History in a Legal RAG Application

[masif1212]

Hey everyone,

We're developing an AI-based legal platform, using a RAG (Retrieval-Augmented Generation) architecture. Our stack includes LangChain, an OpenAI model, and FAISS for vector retrieval from a large, static database of legal documents.

The platform is working well for single-turn or short conversations. However, we're hitting a significant challenge with maintaining context in long-running user chats.

The Core Challenge: Context Window vs. Complex Legal Dialogue 🤔

Legal conversations are rarely simple. A user might start with a general question about contract law, then ask about a specific clause, then ask how a recent court precedent affects that clause. Each new question builds upon the previous context.

Our main problems are:

• Context Window Limitations: As the conversation grows, the chat history quickly exceeds the token limit of the LLM.

• Loss of Nuance: Simply truncating the history (e.g., keeping only the last 4 turns) is not viable. A critical detail mentioned in the first message could be essential for answering the tenth.

• Inefficiency and Cost: Passing an ever-growing chat history with every API call is inefficient and dramatically increases operational costs.

• RAG Pollution: The user's entire chat history can "pollute" the query sent to our legal vector database, leading to less relevant document retrievals.

Initial Approaches & Their Limitations

We've explored some standard LangChain memory types, but they fall short for our specific legal use case:

1. Standard Buffer Memory (ConversationBufferMemory): This is the root of the problem. It works great until it hits the token limit.

2. Windowed Memory (ConversationBufferWindowMemory): Better, but still risky. We might cut off the crucial, foundational part of the legal query.

3. Summarization Memory (ConversationSummaryMemory): This seems promising, but automatic summarization can be a double-edged sword in law. A summary like "user asked about inheritance" loses the critical context that "the user is an undeclared heir and the will is being contested." Precision is everything in law.

🚀 Seeking Community Wisdom: How Do We Solve This?
We believe this is a common challenge for anyone building sophisticated, stateful AI agents. We're opening this discussion to ask for your insights, experiences, and suggestions. How are you tackling long-term memory in your RAG applications?