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 UnderstandingUse LangChain, GPT and Activeloop's Deep Lake to work with code baseOn this pageUse LangChain, GPT and Activeloop's Deep Lake to work with code baseIn this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. Designâ€‹Prepare data:Upload all python project files using the langchain.document_loaders.TextLoader. We will call these files the documents.Split all documents to chunks using the langchain.text_splitter.CharacterTextSplitter.Embed chunks and upload them into the DeepLake using langchain.embeddings.openai.OpenAIEmbeddings and langchain.vectorstores.DeepLakeQuestion-Answering:Build a chain from langchain.chat_models.ChatOpenAI and langchain.chains.ConversationalRetrievalChainPrepare questions.Get answers running the chain.Implementationâ€‹Integration preparationsâ€‹We need to set up keys for external services and install necessary python libraries.#!python3 -m pip install --upgrade langchain deeplake openaiSet up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/import osfrom getpass import getpassos.environ["OPENAI_API_KEY"] =