transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersProvidersMoreVectaraChat Over Documents with VectaraOn this pageChat Over Documents with VectaraThis notebook is based on the chat_vector_db notebook, but using Vectara as the vector database.import osfrom langchain.vectorstores import Vectarafrom langchain.vectorstores.vectara import VectaraRetrieverfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainLoad in documents. You can replace this with a loader for whatever type of data you wantfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../modules/state_of_the_union.txt")documents = loader.load()We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them.vectorstore = Vectara.from_documents(documents, embedding=None)We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)We now initialize the ConversationalRetrievalChainopenai_api_key = os.environ["OPENAI_API_KEY"]llm = OpenAI(openai_api_key=openai_api_key, temperature=0)retriever = vectorstore.as_retriever(lambda_val=0.025, k=5, filter=None)d = retriever.get_relevant_documents(    "What did the president say about Ketanji Brown Jackson")qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})result["answer"]    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence."query = "Did he mention who she succeeded"result = qa({"question": query})result["answer"]    ' Ketanji Brown Jackson
This notebook is based on the chatvectordb notebook, but using Vectara as the vector database.
This notebook is based on the chatvectordb notebook, but using Vectara as the vector database. ->: transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersProvidersMoreVectaraChat Over Documents with VectaraOn this pageChat Over Documents with VectaraThis notebook is based on the chat_vector_db notebook, but using Vectara as the vector database.import osfrom langchain.vectorstores import Vectarafrom langchain.vectorstores.vectara import VectaraRetrieverfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainLoad in documents. You can replace this with a loader for whatever type of data you wantfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../modules/state_of_the_union.txt")documents = loader.load()We now split the documents, create embeddings for them, and put them in a vectorstore. This allows us to do semantic search over them.vectorstore = Vectara.from_documents(documents, embedding=None)We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)We now initialize the ConversationalRetrievalChainopenai_api_key = os.environ["OPENAI_API_KEY"]llm = OpenAI(openai_api_key=openai_api_key, temperature=0)retriever = vectorstore.as_retriever(lambda_val=0.025, k=5, filter=None)d = retriever.get_relevant_documents(    "What did the president say about Ketanji Brown Jackson")qa = ConversationalRetrievalChain.from_llm(llm, retriever, memory=memory)query = "What did the president say about Ketanji Brown Jackson"result = qa({"question": query})result["answer"]    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds and a former top litigator in private practice, and that she will continue Justice Breyer's legacy of excellence."query = "Did he mention who she succeeded"result = qa({"question": query})result["answer"]    ' Ketanji Brown Jackson