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Search through your uploaded documents using natural language queries. The system uses RAG (Retrieval-Augmented Generation) to find relevant content and generate contextual answers based on your document library.
This endpoint is useful for:
  • Searching content across multiple documents and scopes.
  • Integrating document search into chat-based workflows.
  • Enabling contextual and scoped responses using specific file or workspace data.
Starting from the Victorious Vicuna release (January 2026) and later, using the Agent API is the preferred way of achieving this task.

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Prerequisites

  • A Paradigm API key: if you do not have one, go to your Paradigm profile (/settings/api-key in your instance) and generate a new API key.
  • Uploaded documents: You need documents in your Paradigm instance to search through. See Add a document for upload instructions.

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Example API Request

Example of API request using the requests package: 
import requests
import os

# Get API key and base URL from environment
api_key = os.getenv("PARADIGM_API_KEY")
base_url = os.getenv("PARADIGM_BASE_URL", "https://paradigm.lighton.ai/api/v2")

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

payload = {
    "query": "What is the termination clause in the contract?",
    # Optional parameters (if not filled, same as UI search)
    # "file_ids": ["abc123-file-id"],
    "model": "alfred-4",
    # "workspace_ids": [], # No workspace included
    "company_scope": True, # Documents from the user's company will be added to the scope
    "private_scope": True, # Private documents included
    "tool": "DocumentSearch" # No vision (enter "VisionDocumentSearch" to enable vision)
}

response = requests.post(f"{base_url}/search", headers=headers, json=payload)

if response.status_code == 200:
    data = response.json()
    print("Answer:", data["answer"])
else:
    print("Error:", response.status_code, response.text)