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
   • Type: string
   • Placeholder: http://localhost:6333
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