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query_knowledge

Semantic search over a brand's knowledge base — website content, uploaded documents, and training materials.

Semantic search over a brand's knowledge base — website content, uploaded documents, and training materials. Use this to find specific facts, claims, product details, or positioning language when writing content.

Returns the most relevant text chunks ranked by similarity.

Authentication: Required

Parameters

ParameterTypeRequiredDefaultDescription
syntheticIdintegerYesThe agent ID to search knowledge for.
querystringYesNatural language search query. Examples: "pricing tiers and features", "company founding story", "integration with Salesforce".
topKinteger (1–20)No8Number of results to return.

Response

{
  "syntheticId": 1234,
  "brandName": "Example",
  "query": "pricing tiers and features",
  "resultCount": 5,
  "chunks": [
    {
      "score": 0.92,
      "text": "Example offers three pricing tiers: Free (5 scans/month), Starter ($40/month, 50 scans), and Pro ($395/month, unlimited scans with priority support).",
      "sourceType": "webpage",
      "sourceUrl": "https://example.com/pricing"
    },
    {
      "score": 0.87,
      "text": "All paid plans include API access, custom personas, and branded reports...",
      "sourceType": "webpage",
      "sourceUrl": "https://example.com/features"
    }
  ]
}

Key fields

FieldDescription
chunks[].scoreSimilarity score (0–1). Higher is more relevant.
chunks[].textThe text content of the matched chunk.
chunks[].sourceTypeWhere this content came from: "webpage", "document", "training".
chunks[].sourceUrlOriginal URL of the source, if applicable.

Tips

  • Use natural language queries rather than keywords. "How does the product handle enterprise security?" works better than "enterprise security".
  • Increase topK if you want more context or aren't finding what you need.
  • Combine with get_factsheet for a broader overview before diving into specific details.

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