text: Query Understanding: GPT-4 processes user queries, grasping the context and extracting relevant details.Construct the Retriever: Conversational RetrieverChain searches the VectorStore to identify the most relevant code snippets for a given query.Build the Conversational Chain: Customize the retriever settings and define any user-defined filters as needed. Ask questions: Define a list of questions to ask about the codebase, and then use the ConversationalRetrievalChain to generate context-aware answers. The LLM (GPT-4) generates comprehensive, context-aware answers based on retrieved code snippets and conversation history.The full tutorial is available below.Twitter the-algorithm codebase analysis with Deep Lake: A notebook walking through how to parse github source code and run queries conversation.LangChain codebase analysis with Deep Lake: A notebook walking through how to analyze and do question answering over THIS code base.PreviousSummarizationNextUse LangChain, GPT and Activeloop's Deep Lake to work with code baseConversational Retriever ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.