Qdrant Vector Store

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Node Details

  • Name: Qdrant_VectorStores
  • Type: Qdrant
  • Version: 5.0
  • Category: Vector Stores

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Base Classes

  • Qdrant
  • VectorStoreRetriever
  • BaseRetriever

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Input Parameters

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Main Parameters

  1. Document (optional, list)
    • Type: Document
    • Description: List of documents to be processed
  2. Embeddings
    • Type: Embeddings
    • Description: Embedding model to use
  3. Record Manager (optional)
    • Type: RecordManager
    • Description: Keeps track of records to prevent duplication
  4. Qdrant Server URL
  5. Qdrant Collection Name
    • Type: string
  6. File Upload (optional)
    • Type: boolean
    • Description: Allows file upload on the chat

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Additional Parameters

  1. Vector Dimension
    • Type: number
    • Default: 1536
  2. Content Key (optional)
    • Type: string
    • Default: ‘content’
    • Description: Key for storing text
  3. Metadata Key (optional)
    • Type: string
    • Default: ‘metadata’
    • Description: Key for storing metadata
  4. Upsert Batch Size (optional)
    • Type: number
    • Description: Upsert in batches of size N
  5. Similarity
    • Type: options
    • Options: Cosine, Euclid, Dot
    • Default: Cosine
  6. Additional Collection Configuration (optional)
    • Type: json
  7. Top K (optional)
    • Type: number
    • Description: Number of top results to fetch (default: 4)
  8. Qdrant Search Filter (optional)
    • Type: json
    • Description: Conditions for returning points

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Outputs

  1. Qdrant Retriever
    • Type: retriever
    • Base Classes: [Qdrant, VectorStoreRetriever, BaseRetriever]
  2. Qdrant Vector Store
    • Type: vectorStore
    • Base Classes: [Qdrant, QdrantVectorStore base classes]

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Credentials

  • Credential Name: qdrantApi
  • Parameters: qdrantApiKey

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Functionality

  • Supports upsert operations for adding or updating documents in the Qdrant collection
  • Enables similarity search and retrieval of documents
  • Allows for batch processing of documents
  • Supports custom configuration for Qdrant collections
  • Integrates with a record manager to prevent duplication
  • Provides options for different similarity measures (Cosine, Euclid, Dot)
  • Supports file upload functionality in chat interfaces

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Use Cases

  • Semantic search applications
  • Document retrieval systems
  • Recommendation engines
  • Any application requiring efficient storage and querying of vector embeddings

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Note

This node is designed to work with the Qdrant vector database and requires proper setup of the Qdrant server. It’s particularly useful for applications dealing with large-scale vector data and requiring fast similarity searches.