You can provide those to LangChain in two ways:Include in your environment these three variables: VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY.For example, you can set these variables using os.environ and getpass as follows:import osimport getpassos.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")Provide them as arguments when creating the Vectara vectorstore object:vectorstore = Vectara(                vectara_customer_id=vectara_customer_id,                vectara_corpus_id=vectara_corpus_id,                vectara_api_key=vectara_api_key            )Note: The self-query retriever requires you to have lark installed (pip install lark). Connecting to Vectara from LangChain‚ÄãIn this example, we assume that you've created an account and a corpus, and added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY (created with permissions for both indexing and query) as environment variables.The corpus has 4 fields defined as metadata for filtering: year, director, rating, and genrefrom langchain.embeddings import FakeEmbeddingsfrom langchain.schema import Documentfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Vectarafrom langchain.document_loaders import TextLoaderfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfodocs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher
Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation
Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: You can provide those to LangChain in two ways:Include in your environment these three variables: VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY.For example, you can set these variables using os.environ and getpass as follows:import osimport getpassos.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")Provide them as arguments when creating the Vectara vectorstore object:vectorstore = Vectara(                vectara_customer_id=vectara_customer_id,                vectara_corpus_id=vectara_corpus_id,                vectara_api_key=vectara_api_key            )Note: The self-query retriever requires you to have lark installed (pip install lark). Connecting to Vectara from LangChain‚ÄãIn this example, we assume that you've created an account and a corpus, and added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY (created with permissions for both indexing and query) as environment variables.The corpus has 4 fields defined as metadata for filtering: year, director, rating, and genrefrom langchain.embeddings import FakeEmbeddingsfrom langchain.schema import Documentfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Vectarafrom langchain.document_loaders import TextLoaderfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfodocs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",        metadata={"year": 2010, "director": "Christopher