Personalizing LLM Interactions: Harnessing Generative Feedback Loops
LLM Applications can be personalized using Generative Feedback Loops through advanced memory & personalization.
Focus on business value, not memory persistence, search, and enrichment.
LLM Applications can be personalized using Generative Feedback Loops through advanced memory & personalization.
Zep now supports both vector search over message text and filtering on message metadata, including system metadata such as Named Entities and creation dates.
LangchainJS now supports Zep Memory and Retrievers, allowing developers to take advantage of Zep's long-term memory, auto-summarization, vector search, and named entity extraction.
Today we're launching Zep's EntityExtractor, a Named Entity Recognition tool built using state-of-the-art NLP toolkit, spaCy. With Zep's EntityExtractor, developers can build sophisticated features that: * Trigger the use of custom prompts or agent branching; * Annotate the chat history, enhancing the experience for users with links to additional information, services, or
Chat history storage is an infrastructure challenge all developers and enterprises face as they look to move from prototypes to productionizing LLM/ AI Chat applications that provide rich and intimate experiences to users. Zep allows developers to focus on developing their AI apps, rather than building memory persistence, search, and