Zep – pgvector-based memory store for LLM apps
Hey HN - we launched Zep's document vector DB today. Zep is an open source memory store for LLM apps, and this builds on existing chat history memory persistence, embedding, and enrichment capabilities. Zep uses Postgres and pgvector for database operations and vector search. Vector search can be complicated on Postgres, with careful configuration required at both index creation and query time. We've focused on significantly improving this developer experience. Zep automatically selects index and query parameters for developers based on best practices and known heuristics. Vector database…
What it does
In the maker’s words, at launch
Hey HN - we launched Zep's document vector DB today. Zep is an open source memory store for LLM apps, and this builds on existing chat history memory persistence, embedding, and enrichment capabilities. Zep uses Postgres and pgvector for database operations and vector search. Vector search can be complicated on Postgres, with careful configuration required at both index creation and query time. We've focused on significantly improving this developer experience. Zep automatically selects index and query parameters for developers based on best practices and known heuristics. Vector database operations are exposed via a simple Python (and LangChain) API for working with document collections, documents, and search results. While we focus on LLM App use cases, you can turn any Postgres instance with pgvector into a vector database with great DX. Our launch announcement: https://blog.getzep.com/introducing-the-zep-document-vector-... -Daniel
Does the same job
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Zep is a long-term memory store designed for conversational AI applications built using modern LLMs. It handles the storage, summarization, embedding, indexing, and enrichment of chat histories, and offers developers a simple, low-latency API to this data. Chat history storage is an infrastructure challenge all developers and enterprises face as they look to move from prototypes to deploying conversational AI applications that provide rich and intimate experiences to users. Key features include long-term memory persistence, auto-summarization, vector search, auto-token counting, and Python…
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I built a super easy to integrate memory storage and retrieval system for NodeJS projects because I saw a need for information to be shared and persisted across LLM chat sessions (and many other LLM feature interactions). I tried to keep the barrier to use as low as possible so I included built-in support for major LLMs (GPT, Gemini, and Claude) as well as major vector store providers (Weaviate and Pinecone). The memory store works by ingesting and automatically extracting “memories” (summarized single bits of information) from LLM interactions and vectorizing those. When you want to provide…
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