Index the Codebase: Duplicate the target repository, load all contained files, divide the files, and initiate the indexing procedure. Alternatively, you can bypass this step and use a pre-indexed dataset.
Store Embeddings and the Code: Code segments are embedded using a code-aware embedding model and saved in the Deep Lake VectorStore.
Assemble the Retriever: Conversational Retriever Chain searches the VectorStore to find a specific query's most relevant code segments.
Build the Conversational Chain : Customize retriever settings and define any user-defined filters as necessary.
Pose Questions: Create a list of questions about the codebase, then use the Conversational Retrieval Chain to produce context-sensitive responses. The LLM (GPT-4, in this case) should now generate detailed, context-aware answers based on the retrieved code segments and conversation history.