Postgres Vector Store

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Node Details

• Name: Postgres_VectorStores
• Type: Postgres
• Version: 6.0
• Category: Vector Stores

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Base Classes

• Postgres
• VectorStoreRetriever
• BaseRetriever

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Credential

• Type: PostgresApi
• Required Fields:
  • User
  • Password

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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 to use for vectorizing documents
3. Record Manager (optional)
   • Type: RecordManager
   • Description: Keeps track of records to prevent duplication
4. Host
   • Type: string
   • Description: PostgreSQL server host
5. Database
   • Type: string
   • Description: Name of the PostgreSQL database
6. Port (optional)
   • Type: number
   • Default: 6432
   • Description: PostgreSQL server port
7. Table Name (optional)
   • Type: string
   • Default: “documents”
   • Description: Name of the table to store vectors
8. File Upload (optional)
   • Type: boolean
   • Description: Enables file upload functionality in the chat
9. Additional Configuration (optional)
   • Type: JSON
   • Description: Additional PostgreSQL connection options
10. Top K (optional)
    • Type: number
    • Default: 4
    • Description: Number of top results to fetch in similarity search
11. Postgres Metadata Filter (optional)
    • Type: JSON
    • Description: Filter to apply on metadata during similarity search

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Outputs

1. Postgres Retriever
   • Type: VectorStoreRetriever
   • Description: A retriever object for similarity search operations
2. Postgres Vector Store
   • Type: TypeORMVectorStore
   • Description: The vector store object for direct interactions

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Functionality

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Initialization

• Establishes a connection to the PostgreSQL database
• Sets up the vector store with the specified table and embeddings
• Configures similarity search function to use pg pool for better performance

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Upsert Method

• Allows adding new documents to the vector store
• Handles file upload scenarios by adding chat ID to metadata
• Supports using a record manager to prevent duplicates

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Delete Method

• Enables deletion of documents from the vector store
• Supports deletion using record manager or direct ID-based deletion

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Similarity Search

• Performs similarity search on stored vectors
• Applies metadata filters if specified
• Returns top K results based on cosine similarity

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Use Cases

• Document storage and retrieval in AI-powered applications
• Semantic search functionality in large document collections
• Building knowledge bases for question-answering systems

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Notes

• Requires PostgreSQL with pgvector extension installed
• Optimized for performance with connection pooling
• Supports additional configuration for fine-tuning PostgreSQL connection
• Integrates with file upload functionality for chat-based document ingestion