You must also set your document collection to isAutoEmbedded === false. If you set your collection to isAutoEmbedded === false, you must pass in an Embeddings instance.Load or create a Collection from documents‚Äãfrom uuid import uuid4from langchain.document_loaders import WebBaseLoaderfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.vectorstores import ZepVectorStorefrom langchain.vectorstores.zep import CollectionConfigZEP_API_URL = "http://localhost:8000"  # this is the API url of your Zep instanceZEP_API_KEY = "<optional_key>"  # optional API Key for your Zep instancecollection_name = f"babbage{uuid4().hex}"  # a unique collection name. alphanum only# Collection config is needed if we're creating a new Zep Collectionconfig = CollectionConfig(    name=collection_name,    description="<optional description>",    metadata={"optional_metadata": "associated with the collection"},    is_auto_embedded=True,  # we'll have Zep embed our documents using its low-latency embedder    embedding_dimensions=1536  # this should match the model you've configured Zep to use.)# load the documentarticle_url = "https://www.gutenberg.org/cache/epub/71292/pg71292.txt"loader = WebBaseLoader(article_url)documents = loader.load()# split it into chunkstext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)docs = text_splitter.split_documents(documents)# Instantiate the VectorStore. Since the collection does not already exist in Zep,# it will be created and populated with the documents we pass in.vs = ZepVectorStore.from_documents(docs,                                   collection_name=collection_name,                                   config=config,                                   api_url=ZEP_API_URL,                                   api_key=ZEP_API_KEY                                   )# wait for the collection embedding to completeasync def wait_for_ready(collection_name: str) -> None:    from zep_python import ZepClient
Zep is an open-source long-term memory store for LLM applications. Zep makes it easy to add relevant documents,
Zep is an open-source long-term memory store for LLM applications. Zep makes it easy to add relevant documents, ->: You must also set your document collection to isAutoEmbedded === false. If you set your collection to isAutoEmbedded === false, you must pass in an Embeddings instance.Load or create a Collection from documents‚Äãfrom uuid import uuid4from langchain.document_loaders import WebBaseLoaderfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.vectorstores import ZepVectorStorefrom langchain.vectorstores.zep import CollectionConfigZEP_API_URL = "http://localhost:8000"  # this is the API url of your Zep instanceZEP_API_KEY = "<optional_key>"  # optional API Key for your Zep instancecollection_name = f"babbage{uuid4().hex}"  # a unique collection name. alphanum only# Collection config is needed if we're creating a new Zep Collectionconfig = CollectionConfig(    name=collection_name,    description="<optional description>",    metadata={"optional_metadata": "associated with the collection"},    is_auto_embedded=True,  # we'll have Zep embed our documents using its low-latency embedder    embedding_dimensions=1536  # this should match the model you've configured Zep to use.)# load the documentarticle_url = "https://www.gutenberg.org/cache/epub/71292/pg71292.txt"loader = WebBaseLoader(article_url)documents = loader.load()# split it into chunkstext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)docs = text_splitter.split_documents(documents)# Instantiate the VectorStore. Since the collection does not already exist in Zep,# it will be created and populated with the documents we pass in.vs = ZepVectorStore.from_documents(docs,                                   collection_name=collection_name,                                   config=config,                                   api_url=ZEP_API_URL,                                   api_key=ZEP_API_KEY                                   )# wait for the collection embedding to completeasync def wait_for_ready(collection_name: str) -> None:    from zep_python import ZepClient