In-Memory Vector Store

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Node Details

• Name: memoryVectorStore
• Label: In-Memory Vector Store
• Version: 1.0
• Type: Memory
• Category: Vector Stores

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Base Classes

• Memory
• VectorStoreRetriever
• BaseRetriever

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Input Parameters

1. Document (optional, list)
   • Type: Document
   • Description: A list of documents to be stored in the vector store.
2. Embeddings
   • Type: Embeddings
   • Description: The embeddings model used to convert documents into vector representations.
3. Top K (optional)
   • Type: number
   • Description: Number of top results to fetch. Defaults to 4 if not specified.
   • Placeholder: 4

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Outputs

1. Memory Retriever
   • Name: retriever
   • Base Classes: [Memory, VectorStoreRetriever, BaseRetriever]
2. Memory Vector Store
   • Name: vectorStore
   • Base Classes: [Memory, …BaseClasses of MemoryVectorStore]

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Functionality

1. Initialization:
   • Creates a MemoryVectorStore from the provided documents and embeddings.
   • Can return either a retriever or the vector store itself based on the specified output.
2. Upsert Method:
   • Allows adding new documents to the vector store.
   • Returns the number of added documents and the added documents themselves.

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Usage

• You need a quick, in-memory solution for storing and retrieving vector embeddings.
• The dataset is small enough to fit in memory.
• You want to perform similarity searches on document embeddings.
• You need a simple integration within a larger language model pipeline.

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Note