text: night long. Encased in a silky, removable bamboo cover that's easy to clean and maintain, the Plush Serenity Bamboo Mattress offers a luxurious and eco-friendly sleeping experience.Price: $2,599Sizes available for this product: King"""with open("sample_product_catalog.txt", "w") as f:    f.write(sample_product_catalog)product_catalog = "sample_product_catalog.txt"# Set up a knowledge basedef setup_knowledge_base(product_catalog: str = None):    """    We assume that the product knowledge base is simply a text file.    """    # load product catalog    with open(product_catalog, "r") as f:        product_catalog = f.read()    text_splitter = CharacterTextSplitter(chunk_size=10, chunk_overlap=0)    texts = text_splitter.split_text(product_catalog)    llm = OpenAI(temperature=0)    embeddings = OpenAIEmbeddings()    docsearch = Chroma.from_texts(        texts, embeddings, collection_name="product-knowledge-base"    )    knowledge_base = RetrievalQA.from_chain_type(        llm=llm, chain_type="stuff", retriever=docsearch.as_retriever()    )    return knowledge_basedef get_tools(product_catalog):    # query to get_tools can be used to be embedded and relevant tools found    # see here: https://langchain-langchain.vercel.app/docs/use_cases/agents/custom_agent_with_plugin_retrieval#tool-retriever    # we only use one tool for now, but this is highly extensible!    knowledge_base = setup_knowledge_base(product_catalog)    tools = [