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 UnderstandingAnalysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep LakeOn this pageAnalysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep LakeIn this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktokenDefine OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. For full documentation of Deep Lake please follow docs and API reference.Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platformimport osimport getpassfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import DeepLakeos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")activeloop_token = getpass.getpass("Activeloop Token:")os.environ["ACTIVELOOP_TOKEN"] = activeloop_tokenembeddings = OpenAIEmbeddings(disallowed_special=())disallowed_special=() is required to avoid Exception: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte from tiktoken for some repositories1. Index the code base