Zep Collection

​

Node Details

• Name: zep
• Type: Zep
• Version: 2.0
• Category: Vector Stores

​

Base Classes

• Zep
• VectorStoreRetriever
• BaseRetriever

​

Parameters

​

Input Parameters

1. Document (optional)
   • Type: Document
   • List: true
   • Description: The documents to be stored in the vector store.
2. Embeddings
   • Type: Embeddings
   • Description: The embedding model to use for vectorizing the documents.
3. Base URL
   • Type: string
   • Default: “http://127.0.0.1:8000”
   • Description: The base URL of the Zep instance.
4. Zep Collection
   • Type: string
   • Description: The name of the Zep collection to use.
5. Zep Metadata Filter (optional)
   • Type: json
   • Description: A JSON object to filter documents based on metadata.
6. Embedding Dimension
   • Type: number
   • Default: 1536
   • Description: The dimension of the embedding vectors.
7. Top K (optional)
   • Type: number
   • Default: 4
   • Description: The number of top results to fetch.
8. MMR-related parameters (added through addMMRInputParams function)

​

Credential Parameters

• API Key (optional)
  • Type: credential
  • Description: JWT authentication for the Zep instance.

​

Outputs

1. Zep Retriever
   • Type: retriever
   • Base Classes: [Zep, VectorStoreRetriever, BaseRetriever]
2. Zep Vector Store
   • Type: vectorStore
   • Base Classes: [Zep, ZepVectorStore]

​

Functionality

1. Upsert: Allows adding new documents to the Zep collection. It processes the input documents, applies the specified embeddings, and stores them in the Zep vector store.
2. Similarity Search: Enables querying the vector store for similar documents. It supports both standard similarity search and MMR search for diverse results.

​

Usage

• Building knowledge bases or document retrieval systems
• Implementing semantic search functionality in applications
• Creating question-answering systems with context retrieval