metadata={'score': 0.7595934867858887, 'uuid': '9fa630e6-0b17-4d77-80b0-ba99249850c0', 'created_at': '2023-10-17T22:53:08.630731Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is providing a suggestion or recommendation for the person to read Ursula K. Le Guin or Joanna Russ.'}}, 'token_count': 18}),     Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575579881668091, 'uuid': 'b2dfd1f7-cac6-4e37-94ea-7c15b0a5af2c', 'created_at': '2023-10-17T22:53:08.606283Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'intent': 'The subject is asking about the people who were contemporaries of someone else.'}}, 'token_count': 8})]Previousyou-retrieverNextToolsRetriever Example for Zep - Fast, scalable building blocks for LLM AppsMore on Zep:Retriever ExampleInitialize the Zep Chat Message History Class and add a chat message history to the memory storeUse the Zep Retriever to vector search over the Zep memoryReranking using MMR (Maximal Marginal Relevance)Using metadata filters to refine search resultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Retriever Example for Zep - Fast, scalable building blocks for LLM Apps
Retriever Example for Zep - Fast, scalable building blocks for LLM Apps ->: metadata={'score': 0.7595934867858887, 'uuid': '9fa630e6-0b17-4d77-80b0-ba99249850c0', 'created_at': '2023-10-17T22:53:08.630731Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is providing a suggestion or recommendation for the person to read Ursula K. Le Guin or Joanna Russ.'}}, 'token_count': 18}),     Document(page_content='Who were her contemporaries?', metadata={'score': 0.7575579881668091, 'uuid': 'b2dfd1f7-cac6-4e37-94ea-7c15b0a5af2c', 'created_at': '2023-10-17T22:53:08.606283Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'intent': 'The subject is asking about the people who were contemporaries of someone else.'}}, 'token_count': 8})]Previousyou-retrieverNextToolsRetriever Example for Zep - Fast, scalable building blocks for LLM AppsMore on Zep:Retriever ExampleInitialize the Zep Chat Message History Class and add a chat message history to the memory storeUse the Zep Retriever to vector search over the Zep memoryReranking using MMR (Maximal Marginal Relevance)Using metadata filters to refine search resultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.