text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKUse casesQA and Chat over DocumentsAnalyzing structured dataExtractionInteracting with APIsChatbotsSummarizationCode UnderstandingUse LangChain, GPT and Activeloop's Deep Lake to work with code baseAnalysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep LakeAgent simulationsAgentsAutonomous (long-running) agentsMulti-modalUse casesCode UnderstandingOn this pageCode UnderstandingOverviewLangChain is a useful tool designed to parse GitHub code repositories. By leveraging VectorStores, Conversational RetrieverChain, and GPT-4, it can answer questions in the context of an entire GitHub repository or generate new code. This documentation page outlines the essential components of the system and guides using LangChain for better code comprehension, contextual question answering, and code generation in GitHub repositories.Conversational Retriever Chainâ€‹Conversational RetrieverChain is a retrieval-focused system that interacts with the data stored in a VectorStore. Utilizing advanced techniques, like context-aware filtering and ranking, it retrieves the most relevant code snippets and information for a given user query. Conversational RetrieverChain is engineered to deliver high-quality, pertinent results while considering conversation history and context.LangChain Workflow for Code Understanding and GenerationIndex the code base: Clone the target repository, load all files within, chunk the files, and execute the indexing process. Optionally, you can skip this step and use an already indexed dataset.Embedding and Code Store: Code snippets are embedded using a code-aware embedding model and stored in a VectorStore.