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# Loading from LangChainHub

This notebook covers how to load chains from LangChainHub.

    from langchain.chains import load_chain

    chain = load_chain("lc://chains/llm-math/chain.json")

    chain.run("whats 2 raised to .12")

    > Entering new LLMMathChain chain...
    whats 2 raised to .12
    Answer: 1.0791812460476249
    > Finished chain.

    'Answer: 1.0791812460476249'

Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require a vector database.

    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.vectorstores import Chroma
    from langchain.text_splitter import CharacterTextSplitter
    from langchain import OpenAI, VectorDBQA

    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)
    texts = text_splitter.split_documents(documents)

    embeddings = OpenAIEmbeddings()
    vectorstore = Chroma.from_documents(texts, embeddings)

    Running Chroma using direct local API.
    Using DuckDB in-memory for database. Data will be transient.

    chain = load_chain("lc://chains/vector-db-qa/stuff/chain.json", vectorstore=vectorstore)

    query = "What did the president say about Ketanji Brown Jackson"
    chain.run(query)

    " The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence."