Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost.Note: The
ZepCloudVectorStore
works with Documents
and is intended to be used as a Retriever
.
It offers separate functionality to Zep’s ZepCloudMemory
class, which is designed for persisting, enriching
and searching your user’s chat history.
Why Zep’s VectorStore? 🤖🚀
Zep automatically embeds documents added to the Zep Vector Store using low-latency models local to the Zep server. The Zep TS/JS client can be used in non-Node edge environments. These two together with Zep’s chat memory functionality make Zep ideal for building conversational LLM apps where latency and performance are important.Supported Search Types
Zep supports both similarity search and Maximal Marginal Relevance (MMR) search. MMR search is particularly useful for Retrieval Augmented Generation applications as it re-ranks results to ensure diversity in the returned documents.Installation
Sign up for Zep Cloud and create a project. Follow the Zep Cloud Typescript SDK Installation Guide to install and get started with Zep.Usage
You’ll need your Zep Cloud Project API Key to use the Zep VectorStore. See the Zep Cloud docs for more information. Zep auto embeds all documents by default, and it’s not expecting to receive any embeddings from the user. Since LangChain requires passing in aEmbeddings
instance, we pass in FakeEmbeddings
.
Example: Creating a ZepVectorStore from Documents & Querying
npm
Example: Using ZepCloudVectorStore with Expression Language
Related
- Vector store conceptual guide
- Vector store how-to guides