Search in vector store

Perform semantic search within a vector store.

This endpoint performs vector-based semantic search to find the most relevant content based on the provided query. The search uses embeddings to find semantically similar content.

Search Parameters:

• query: The search query text
• top_k: Number of results to return (ranked by relevance)
• filters: Optional filters to narrow search scope (e.g., by file_id)

Authentication:

• Credentials can be provided either through project-level credentials or request-level credentials
• For SGP Knowledge Base, SGP credentials are required

Returns:

• Ranked list of relevant content chunks
• Each chunk includes content, source blocks, relevance score, and parse result ID
• Results are ordered by relevance score (highest first)

Example Request:

{
    "query": "What are the main features of the product?",
    "top_k": 5,
    "filters": {
        "file_id": "file_123"
    },
    "credentials": {
        "sgp": {
            "api_key": "your-api-key",
            "base_url": "https://api.example.com"
        }
    }
}

Example Response:

{
    "chunks": [
        {
            "content": "The product includes advanced analytics, real-time monitoring, and automated reporting features.",
            "blocks": [
                {
                    "id": "block_123",
                    "content": "The product includes advanced analytics...",
                    "type": "text"
                }
            ],
            "score": 0.95,
            "parse_result_id": "parse_123"
        }
    ]
}