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Node Details

  • Name: zep

  • Type: Zep

  • Version: 2.0

  • Category: Vector Stores

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Base Classes

  • Zep

  • VectorStoreRetriever

  • BaseRetriever

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Parameters

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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

  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)

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Credential Parameters

  • API Key (optional)

    • Type: credential

    • Description: JWT authentication for the Zep instance.

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Outputs

  1. Zep Retriever

    • Type: retriever

    • Base Classes: [Zep, VectorStoreRetriever, BaseRetriever]

  2. Zep Vector Store

    • Type: vectorStore

    • Base Classes: [Zep, ZepVectorStore]

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Functionality

The Zep_VectorStores node provides two main functionalities:

  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.

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Usage

This node is particularly useful for:

  • Building knowledge bases or document retrieval systems

  • Implementing semantic search functionality in applications

  • Creating question-answering systems with context retrieval

The node can be integrated into larger workflows where document storage, retrieval, and similarity search are required, making it a versatile component for various LLM-powered applications.

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Zep Collection - CloudThe Zep Collection - Cloud node is a vector store component that integrates with Zep, a fast and scalable building block for LLM applications. It allows for upsert of embedded data and performs similarity or MMR (Maximal Marginal Relevance) search upon query.