Conversation Summary for RAG Context Retrieval

[hectoritr]

Develop a feature that generates a concise summary of entire conversations, optimized for use in Retrieval-Augmented Generation (RAG) workflows. This summary will highlight the key points, user intents, and critical information, enabling the LLM to retrieve context efficiently in future interactions.

• Concise Summarization: Create a token-efficient summary that focuses on the main topics, user objectives, key facts, and any action items discussed during the conversation.
• Prioritize Relevance: Ensure the summary only includes information that is essential for understanding context in future interactions. Avoid unnecessary or repetitive details.
• RAG Integration: Ensure that the summarized data can be easily integrated and retrieved in a RAG pipeline to assist the LLM in providing context-aware responses.
• Token Optimization: Minimize token usage by focusing on high-value information, keeping the summary cost-effective while maintaining clarity.