Faiss Vector Store

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Node Details

• Name: Faiss
• Type: Faiss
• Version: 1.0
• Category: Vector Stores

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Base Classes

• Faiss
• VectorStoreRetriever
• BaseRetriever

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Input Parameters

1. Document (optional, list)
   • Type: Document
   • Description: List of documents to be stored in the vector store
2. Embeddings
   • Type: Embeddings
   • Description: Embedding model used to convert documents into vector representations
3. Base Path to load
   • Type: string
   • Description: Path to load or save the faiss.index file
   • Placeholder: C:\Users\User\Desktop
4. Top K (optional, additional parameter)
   • Type: number
   • Description: Number of top results to fetch (default: 4)
   • Placeholder: 4

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Outputs

1. Faiss Retriever
   • Name: retriever
   • Base Classes: [Faiss, VectorStoreRetriever, BaseRetriever]
2. Faiss Vector Store
   • Name: vectorStore
   • Base Classes: [Faiss, …FaissStore base classes]

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Functionality

1. Upsert:
   • Processes input documents
   • Creates vector embeddings using the provided embedding model
   • Stores the vectors in a Faiss index
   • Saves the index to the specified base path
2. Init:
   • Loads an existing Faiss index from the specified base path
   • Creates either a retriever or a vector store based on the output selection
   • Configures the similarity search function to avoid illegal invocation errors

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Use Cases

• Efficient similarity search in large document collections
• Building retrieval-augmented generation (RAG) systems
• Creating semantic search engines
• Implementing recommendation systems based on content similarity

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Notes

• The node includes a custom implementation of similaritySearchVectorWithScore to handle potential issues with the number of requested results exceeding the total number of stored vectors.
• It’s designed to work seamlessly within a larger system, likely a node-based workflow for natural language processing tasks.