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

Enable streaming responses

include_usage

boolean

rewrite_query

string

Override the behavior of summarizing the messages as query with an arbitrary text.

filter_metadata

object | string

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:

Reference external web content: Use standard URLs (https://example.com)
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 [email protected] 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 [email protected] 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 [email protected] 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

Set appropriate temperature values in the sandbox:
1. Lower (0.2) for factual responses
2. Higher (0.8) for creative responses
Enable stream: true for real-time responses
Use rewrite_query for optimized RAG queries
Include relevant file and URL content for context-aware responses

Last updated 4 months ago