text: Here are the installation instructions.#!pip install pinecone-client pinecone-textimport osimport getpassos.environ["PINECONE_API_KEY"] = getpass.getpass("Pinecone API Key:")from langchain.retrievers import PineconeHybridSearchRetrieveros.environ["PINECONE_ENVIRONMENT"] = getpass.getpass("Pinecone Environment:")We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Setup Pineconeâ€‹You should only have to do this part once.Note: it's important to make sure that the "context" field that holds the document text in the metadata is not indexed. Currently you need to specify explicitly the fields you do want to index. For more information checkout Pinecone's docs.import osimport pineconeapi_key = os.getenv("PINECONE_API_KEY") or "PINECONE_API_KEY"# find environment next to your API key in the Pinecone consoleenv = os.getenv("PINECONE_ENVIRONMENT") or "PINECONE_ENVIRONMENT"index_name = "langchain-pinecone-hybrid-search"pinecone.init(api_key=api_key, environment=env)pinecone.whoami()    WhoAmIResponse(username='load', user_label='label', projectname='load-test')# create the indexpinecone.create_index(    name=index_name,    dimension=1536,  # dimensionality of dense model    metric="dotproduct",  # sparse values supported only for dotproduct    pod_type="s1",    metadata_config={"indexed": []},  # see explaination above)Now that its created, we can use itindex = pinecone.Index(index_name)Get embeddings and sparse encodersâ€‹Embeddings are used for