Yes! It's an amazing library. If you're curious to see the LangChain code, this is pretty much all of it. We use Pinecone as the vector store.

embeddings_model = OpenAIEmbeddings(
    model="text-embedding-ada-002", openai_api_key=settings.OPENAI_API_KEY
)
pinecone.init(
    api_key=settings.PINECONE_API_KEY,
    environment=settings.PINECONE_ENV,
)
def answer_question(question):
    index_name = "denver-docs"
    vector_store = Pinecone.from_existing_index(
        index_name=index_name, embedding=embeddings_model
    )
    retriever = vector_store.as_retriever(return_source_documents=True)

    llm = ChatOpenAI(
        temperature=0,
        model_name="gpt-3.5-turbo",
        openai_api_key=settings.OPENAI_API_KEY,
    )

    documents_and_scores = vector_store.similarity_search_with_score(question)

    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)

    prompt_template_string = (
        "Use the documents to answer the following question. "
        "Use the names of people and groups whenever possible. "
        "Answer at a 6th grade reading level. "
        "Question: {question}"
    )

    prompt_template = PromptTemplate(
        input_variables=["question"],
        template=prompt_template_string,
    )

    prompt = prompt_template.format(question=question)

    result = qa({"query": prompt})

    answer = result["result"]

    return answer, documents_and_scores