Implement Context Management and Retrieval-Augmented Generation (RAG) for Enhanced Research Capabilities

[olasunkanmi-SE]

Google Gemini Deepsearch context management strategy

Problem:

Currently, Codebuddy keeps context in memory. For more complex research tasks and follow-up questions, the AI agent needs to maintain context from previous interactions to provide coherent and deeper responses. As users engage in deeper research and ask follow-up questions, the context window can grow rapidly, making effective context management crucial for performance and relevance.

Proposed Solution:

Implement a Context Management and Retrieval-Augmented Generation (RAG) strategy inspired by the DeepMind approach to enhance CodeBuddy's research capabilities, especially for multi-turn conversations and in-depth research scenarios.

This will involve:

1. Conversation History Management (Context Window):

   • Cap Codebudy context window (Size yet to be decided)
   • Maintain a history of user queries and AI responses within the context window..
   • (Optional for future consideration, but good to keep in mind): Implement context window management strategies like truncation or summarization for extremely long conversations.

2. Retrieval-Augmented Generation (RAG) with "Research Notes":

   • "Research Notes" Extraction: After each task/response generation, implement a function to extract key "Research Notes" from the AI's output. These notes should represent the core findings, facts, and insights gathered during the research for that task. Differentiate "Research Notes" from a "Final Report" (which would be a more concise summary). "Research Notes" are the detailed evidence.
   • Knowledge Base (Vector Database): Set up a persistent knowledge base to store these "Research Notes." Consider using a vector database (e.g., Chroma, Pinecone, Weaviate) for efficient semantic search. Embed "Research Notes" using Gemini's embedding models and store them in the SQLite DB.
   • RAG Workflow for Subsequent Tasks:
     • When a new user query is received (for task N+1 and beyond):
       • Retrieve Relevant Notes: Query the knowledge base using the new user query to retrieve the most relevant "Research Notes" from past tasks.
       • Augment Prompt: Construct a prompt for the model by prepending or appending the retrieved "Research Notes" to the current user query. Delineate the notes and the new query in the prompt.
       • Generate Response: Send the augmented prompt to the model to generate a response that leverages both the current query and the retrieved historical knowledge.

3. Technology Stack:

   • As a starter. Utilize the Gemini Generative AI SDK with Gemini Pro 1.5 (gemini-pro-1.5-flash or gemini-pro-1.5-standard). Feature to be implemented across other Models

Benefits:

• Enhanced Contextual Understanding: CodeBuddy will be able to understand and respond more effectively to follow-up questions and build upon previous research, leading to more coherent and in-depth conversations.
• Improved Research Depth: RAG will enable CodeBuddy to leverage knowledge accumulated across multiple tasks, allowing for deeper and more informed research outcomes.
• More Efficient Context Window Usage: RAG helps extend context beyond the immediate conversation window.
• Scalability and Maintainability: Independent storage of "Research Notes" in a knowledge base will improve maintainability and reuseability of the context management system.

Implementation Details (Considerations):

• "Research Notes" Extraction Logic: Define clear criteria and methods for automatically extracting valuable "Research Notes" from model responses. Currently, Codebuddy has a web search tool and a file reader tool that it uses for research.
• Knowledge Base Choice: Codebuddy has an SQLite DB implementation for RAG.
• Prompt Engineering for RAG: Design effective prompts that clearly integrate retrieved "Research Notes" with the user's current query for Gemini Pro 1.5.

Acceptance Criteria:

• CodeBuddy can maintain conversation history and use it to provide contextually relevant responses in multi-turn conversations.
• RAG is implemented, and "Research Notes" from previous tasks are successfully retrieved and used to augment prompts for subsequent tasks.
• The quality of responses for follow-up questions and complex research tasks is demonstrably improved with the implementation of context management and RAG.

This issue provides a comprehensive starting point for implementing context management and RAG in CodeBuddy.