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# Pinecone

This notebook shows how to use functionality related to the Pinecone vector database.

    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.text_splitter import CharacterTextSplitter
    from langchain.vectorstores import Pinecone
    from langchain.document_loaders import TextLoader

    from langchain.document_loaders import TextLoader
    loader = TextLoader('../../../state_of_the_union.txt')
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    docs = text_splitter.split_documents(documents)

    embeddings = OpenAIEmbeddings()

    import pinecone

    # initialize pinecone
    pinecone.init(
        api_key="YOUR_API_KEY",  # find at app.pinecone.io
        environment="YOUR_ENV"  # next to api key in console
    )

    index_name = "langchain-demo"

    docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)

    # if you already have an index, you can load it like this
    # docsearch = Pinecone.from_existing_index(index_name, embeddings)

    query = "What did the president say about Ketanji Brown Jackson"
    docs = docsearch.similarity_search(query)

    print(docs[0].page_content)