Deployment API

Overview

This page explains how to use our Deployment API with several use cases, providing detailed instructions and examples for different scenarios.

Run Deployed LLM Application with Chat Completion

Executes a RAG-enhanced chat completion request against a deployed LLM application.

Endpoint

POST https://deployment.datasaur.ai/api/deployment/:teamId/:deploymentId/chat/completions

Path Parameters

Parameter	Type	Description
teamId	string	Your team identifier
deploymentId	string	The ID of your LLM application deployment

Query Parameters

Parameter	Type	Description
source	string	(Optional) Source identifier
sourceId	string	(Optional) Specific source ID

Request Body

The request body follows the OpenAI-compatible chat completion format, with additional RAG-specific enhancements.

Key Properties

Property	Type	Required	Description
messages	array	Yes	Array of message objects representing the conversation
stream	boolean	No	Enable streaming responses
include_usage	boolean	No
rewrite_query	string	No	Override the behavior of summarizing the messages as query with an arbitrary text.
filter_metadata	object | string	No	Filter data based on the File Properties attached to each file.

Message Types

Messages can be of the following roles:

• user: User inputs

• assistant: Assistant responses

• tool: Tool/function responses

This API doesn’t support the system role and will inherit the System Instruction from the sandbox. See: https://docs.datasaur.ai/llm-projects/deployment

Advanced Features

Multimedia Content Types

The content field in messages supports various types:

type ContentPart =
	| TextContent
	| ImageContent
	| URLContent;

Text Content

{
  "type": "text",
  "text": "Your text here"
}

Image Content

{
  "type": "image",
  "image_url": {
    "url": "https://example.com/image.jpg",
    "detail": "high"
  }
}

URL Content

{
  "type": "url",
  "url": "https://example.com",
  "name": "Optional name",
  "options": {
    "select_pages": "1-3",
    "include_page_screenshot_as_image": false
  }
}

The URL content type supports both standard web URLs and base64-encoded data URLs, allowing you to:

1. Reference external web content: Use standard URLs (https://example.com)

2. Embed file content directly: Use base64 data URLs for PDF, HTML, or other content types

   "url": "data:application/pdf;base64,JVBERi0xLjMKJcTl8uXrp..."

Options:

• select_pages: Specifies which pages to process from multi-page documents (PDFs)

  • Format: "1-5" (range), "1,3,5" (specific pages), or "1-3,7,9-11" (combination)

  • Example: "select_pages": "1-3,5,8-10"

  • Default: All pages if not specified

• include_page_screenshot_as_image: When set to true, includes visual representations of pages. The visual representations are also sent to the model when the model selected in the sandbox application supports visual capability.

  • For PDFs: Renders page as image for visual analysis

  • For websites: Captures screenshot of the rendered page

  • Enables the model to analyze visual layouts, charts, and non-text elements

  • Default: false

Base64 encoding is particularly useful for:

• Embedding content directly without requiring separate file uploads

• Processing temporary or dynamically generated content

• Working with content that doesn't have a public URL

Advanced Retrieval: Query Rewriting

Query rewriting is a technique used in information retrieval and search systems to modify or enhance the original search query to improve search results. There might be cases where you prefer a customized way to summarize the message. You can rewrite the query with your version by specifying the rewrite_query. For example:

{
  "messages": [
	  { "role": "user", "content": "What's the weather in Bali?" },
	  { "role": "assistant", "content": "It's hot and humid." },
	  { "role": "user", "content": "How about in Jakarta?" }
  ],
  "rewrite_query": "What's the weather in Jakarta?"
}

Advanced Retrieval: Metadata Filtering

Another way to improve the accuracy of the retrieval is through additional filtering, to ensure only relevant information is retrieved. Below is an example of filtering search result based on the jurisdiction and the date.

{
  "filter_metadata": {
    "bool" : {
      "must" : [
        {
          "bool" : {
            "should" : [
              { "term" : { "jurisdiction" : "alabama" } },
              { "term" : { "jurisdiction" : "florida" } },
              { "term" : { "jurisdiction" : "nevada" } }
            ]
          }
        },
        {
          "range" : { "date" : {"gt": "2024-10-29" } }
        }
      ]
    }
  }
}

Example Usage

Simple Text Query

{
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ]
}

Multi-turn Conversation

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant who specializes in geography."
    },
    {
      "role": "user",
      "content": "What is the capital of France?"
    },
    {
      "role": "assistant",
      "content": "The capital of France is Paris. It's often called the 'City of Light' (Ville Lumière)."
    },
    {
      "role": "user",
      "content": "Tell me more about its population."
    }
  ]
}

Text with Images

{
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What's in this image?"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://example.com/image.jpg",
            "detail": "high"
          }
        }
      ]
    }
  ]
}

Text with PDF Analysis

{
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Please summarize this research paper"
        },
        {
          "type": "url",
          "url": "https://doompdf.pages.dev/doom.pdf",
          "options": {
            "select_pages": "1-5",
            "include_page_screenshot_as_image": true
          }
        }
      ]
    }
  ]
}

URL with Base64 Screenshot

{
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What does this webpage contain?"
        },
        {
          "type": "url",
          "url": "data:text/html;base64,SGVsbG8gV29ybGQ=",
          "name": "Example Page",
          "options": {
            "include_page_screenshot_as_image": true
          }
        }
      ]
    }
  ]
}

Metadata Filtering

{
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What's in pages 2-5 of the technical documentation?"
        }
      ]
    }
  ],
  "filter_metadata": {
    "bool": {
      "must": [
        {
          "bool": {
            "should": [
              {
                "term": {
                  "jurisdiction": "alabama"
                }
              },
              {
                "term": {
                  "jurisdiction": "florida"
                }
              },
              {
                "term": {
                  "jurisdiction": "nevada"
                }
              }
            ]
          }
        },
        {
          "range": {
            "date": {
              "gt": "2024-10-29"
            }
          }
        }
      ]
    }
  }
}

Each example can be enhanced with additional parameters such as:

• stream: true for streaming responses

• tools for function calling capabilities

• rewrite_query for optimized RAG queries

Response

Non-streaming Response

{
  id: string;
  choices: Array<{
    message: {
      role: 'assistant';
      content: string;
      tool_calls?: Array<ToolCall>;
    };
    finish_reason: 'stop' | 'length' | 'tool_calls' | 'content_filter';
    index: number;
  }>;
  usage: {
    prompt_tokens: number;
    completion_tokens: number;
    total_tokens: number;
    prompt_embedding_tokens?: number;
  };
  contexts?: Array<RagContext>;
}

Streaming Response

Sends chunks of the response as Server-Sent Events (SSE) with the following format:

{
  id: string;
  choices: Array<{
    delta: {
      content?: string;
      role?: 'assistant';
      tool_calls?: Array<ToolCall>;
    };
    finish_reason: string | null;
    index: number;
  }>;
  usage: null | UsageInfo;
  contexts?: Array<RagContext> | null;
}

Error Handling

The API uses standard HTTP status codes with specific actions for resolution:

400 Bad Request

Cause: Invalid parameters or malformed request

Recommended resolution:

• Check request body format and required fields

• Validate parameter types and values

• Ensure message array is not empty

• Check if URLs are properly formatted and accessible

401 Unauthorized

Cause: Missing or invalid authentication

Recommended resolution:

• Check if API key is included in the request header

• Verify API key is valid and not expired

• Ensure API key has correct format

• Generate a new API key if necessary

• Contact [e-mail] if API key should be valid

403 Forbidden

Cause: Insufficient permissions for the requested operation

Recommended resolution:

• Verify team membership and permissions

• Check if you have access to the specified LLM application

• Ensure your subscription covers the requested features

• Request necessary permissions from team admin

• Upgrade subscription tier if needed

429 Too Many Requests

Cause: Rate limit exceeded, see Rate Limiting section below for more details

Recommended resolution:

• Implement exponential backoff retry logic

• Check rate limits in response headers

• Reduce request frequency

• Consider upgrading your plan for higher limits

• Optimize batch operations to reduce API calls

500 Internal Server Error

Cause: Server-side error

Recommended resolution:

• Retry request after a brief delay

• Check system status page for outages

• Verify request payload size is within limits

• Contact [e-mail] if error persists

• Save error response for troubleshooting

For all errors, the response will include a detailed error message to help diagnose the issue. If problems persist after taking the recommended actions, please contact [e-mail] with the error details.

Rate Limiting

The API enforces the following rate limits. If any of these limits is reached, subsequent requests will be rejected with a 429 (Too Many Requests) status code until the limit resets:

• Origin IP address-based: 1500 requests per 60 seconds

• Deployment ID-based: 300 requests per 60 seconds

• Team-based: Daily limits apply for free trial accounts

Note: These limits are evaluated independently - hitting any single limit will result in request rejection, regardless of the status of other limits.

Best Practices

1. Set appropriate temperature values in the sandbox:

   1. Lower (0.2) for factual responses

   2. Higher (0.8) for creative responses

2. Enable stream: true for real-time responses

3. Use rewrite_query for optimized RAG queries

4. Include relevant file and URL content for context-aware responses