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Vector Database Connectors

Vector Database Connectors enable seamless integration with various vector storage solutions, supporting efficient similarity search and vector operations for AI applications.

1.1 Milvus

Milvus Configuration Interface

1.1.1 Input Configuration

  • Collection Name: Name of the vector collection
  • Connection URI: Milvus server address
  • Token: Authentication token
  • Primary Field Name: Primary key field (e.g., "pk")
  • Text Field Name: Field for text data
  • Vector Field Name: Field for vector data
  • Consistency Level: Session/Strong/Bounded

1.2 FAISS

FAISS Configuration Interface

1.2.1 Input Configuration

  • Index Name: Name of the FAISS index
  • Persist Directory: Storage location for index
  • Allow Dangerous Deserialization: Safety flag

1.3 Astra DB

Astra DB Configuration Interface

1.3.1 Input Configuration

  • Application Token: Astra DB auth token
  • API Endpoint: Astra DB API endpoint
  • Collection: Vector collection name
  • Keyspace: Database keyspace
  • Embedding Model: Vector embedding model

1.4 Chroma DB

Chroma DB Configuration Interface

1.4.1 Input Configuration

  • Collection Name: Name of collection
  • Persist Directory: Storage location
  • Server CORS Allow Origins: CORS settings
  • Server Host: Host address
  • Server HTTP Port: HTTP port number
  • Server gRPC Port: gRPC port number
  • Allow Duplicates: Duplicate handling flag

1.5 Weaviate

Weaviate Configuration Interface

1.5.1 Input Configuration

  • Weaviate URL: Server endpoint
  • API Key: Authentication key
  • Index Name: Name of the index
  • Text Key: Text field identifier

Common Operations

  • Search Query
  • Ingest Data
  • Embedding Generation
  • Number of Results Configuration
  • Search Results Retrieval

Note: Each vector database has specific optimization parameters and performance characteristics. Choose based on your scaling needs and use case requirements.