Generate AI Answer

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Overview

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Authentication

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Body Parameters

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Available Models

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Field Restrictions

curl -X POST "https://api.moorcheh.ai/v1/answer" \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key-here" \
  -d '{
    "namespace": "my-document-collection",
    "query": "What are the main benefits of Moorcheh?",
    "type": "text",
    "top_k": 5
  }'

{
  "answer": "Serverless architecture offers several benefits, including reduced operational costs as you only pay for what you use, automatic scaling to handle workload changes, and faster time-to-market since developers can focus on code instead of infrastructure management.",
  "model": "deepseek.r1-v1:0",
  "context_count": 3,
  "query": "What are the main benefits of using serverless architecture?"
}

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Response Fields

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Structured Output

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Enabling

{
  "namespace": "my-namespace",
  "query": "What are the main benefits of product X?",
  "structured_response": { "enabled": true }
}

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structured_response fields

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Default schema

{
  "type": "object",
  "properties": {
    "answer": {
      "type": "string",
      "description": "The main answer to the user's query"
    },
    "confidence": {
      "type": "number",
      "description": "Confidence score from 0 to 1 indicating how confident the model is in the answer",
      "minimum": 0,
      "maximum": 1
    },
    "sources": {
      "type": "array",
      "description": "List of source references used to generate the answer",
      "items": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "The identifier of the source document/chunk"
          },
          "relevance": {
            "type": "string",
            "description": "How relevant this source was to the answer",
            "enum": ["high", "medium", "low"]
          }
        },
        "required": ["id"]
      }
    },
    "summary": {
      "type": "string",
      "description": "A brief summary of the answer (max 200 characters)",
      "maxLength": 200
    },
    "topics": {
      "type": "array",
      "description": "Key topics or themes identified in the query/answer",
      "items": { "type": "string" }
    },
    "follow_up_questions": {
      "type": "array",
      "description": "Suggested follow-up questions the user might want to ask",
      "items": { "type": "string" }
    }
  },
  "required": ["answer", "confidence"]
}

• Required: answer, confidence
• Optional: sources, summary, topics, follow_up_questions

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Custom schema

{
  "namespace": "docs",
  "query": "What is the return policy?",
  "structured_response": {
    "enabled": true,
    "schema": {
      "type": "object",
      "properties": {
        "answer": { "type": "string" },
        "risk_level": { "type": "string", "enum": ["low", "medium", "high"] }
      },
      "required": ["answer", "risk_level"]
    }
  }
}

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Structured output errors

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API Modes

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Search Mode (with namespace)

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Direct AI Mode (empty namespace)

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Temperature Guide

• 0.0-0.5: Conservative, factual responses - best for technical documentation
• 0.5-1.0: Balanced creativity - good for general Q&A
• 1.0-2.0: More creative and varied responses - use carefully for factual content

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Relevance Score Threshold

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Important Notes

• Search Mode: The namespace must exist and contain indexed data for meaningful results
• Search Mode: Higher top_k values provide more context but may increase response time
• Search Mode: The threshold parameter can be used to filter low-relevance results
• Direct AI Mode: Use empty string "" as namespace for direct AI model calls
• Field Restrictions: Empty namespace mode only allows basic AI fields, not search-specific fields
• Field Restrictions: Provided namespace mode allows all fields including search parameters
• Chat history enables conversational context across multiple queries
• Custom prompts allow fine-tuning of AI behavior and response format
• Temperature controls creativity: 0.0 for deterministic, 2.0 for highly creative responses
• Failed requests still count towards usage limits and are tracked in statistics
• Some models may have different token limits and capabilities

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Use Cases

• Customer Support: Answer customer questions using your documentation
• Internal Q&A: Help employees find answers in company knowledge bases
• Educational Tools: Create AI tutors using educational content
• Research Assistance: Get insights from research papers and publications
• Technical Support: Provide technical answers based on documentation
• Content Creation: Generate content based on existing materials

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Related Endpoints

• Search - Search for relevant context documents
• Upload Text Data - Add documents for AI context
• List Namespaces - View available namespaces for generation