ChromaDB with LangChain

graph TD;
    A(Home) --> B(OpenAI);
    A --> C(LangChain);
    A --> D(LlamaIndex);
    A --> E(Hire me);
    A --> F(Sign in);
    A --> G(Subscribe);
    C --> H(Chroma: One of the best vector databases to use with LangChain for storing embeddings);
    C --> I(jeff);
    C --> J(Dec 11, 2023);
    H --> K(Introduction);
    H --> L(What is LangChain?);
    H --> M(What are vector embeddings?);
    H --> N(What is Chroma?);
    K --> O(Building our app);
    L --> P(LangChain);
    L --> Q(Convert text to SQL queries);
    L --> R(Summarization of videos);
    M --> S(Similarity Search);
    M --> T(Anomaly Detection);
    M --> U(Natural Language Processing Tasks);
    N --> V(Chroma DB Logo);
    V --> W(Chroma official website);
    O --> X(Environment setup);
    O --> Y(Install Chroma & LangChain);
    O --> Z(Create a vector store from chunks);
    O --> AA(Perform a similarity search locally);
    O --> AB(Query the model);
    X --> AC(Grab your OpenAI API Key);
    X --> AD(Set up Python application);
    AD --> AE(Install OpenAI Python SDK);
    AD --> AF(Install Chroma);
    AD --> AG(Install LangChain, PyPDF, and tiktoken);
    Z --> AH(Download and place the file);
    Z --> AI(Use the LangChain PDF loader);
    AI --> AJ(PyPDFLoader);
    AI --> AK(Use the LangChain RetrievalQA chain);
    AK --> AL(Prepare query);
    AB --> AM(Success!);
    AM --> AN(Bonus: Common methods);
    AN --> AO(Get a collection of documents);
    AN --> AP(Update a document in the collection);
    AN --> AQ(Delete a collection);
    AP --> AR(Final thoughts);
    AR --> AS(More from Getting Started with AI);
    AR --> AT(More from the Web);

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