API

Welcome to the Neura AI API documentation! This guide provides detailed information about how to interact with the Neura AI endpoints to integrate AI-powered capabilities into your applications.

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Table of Contents

1. JSON Endpoint

2. Omni Multi-Part Endpoint

3. Research and Discovery Endpoint

4. Endpoints for History and Logging

5. NLP Specific Endpoint

6. Proxy Interaction Endpoint

7. Error Handling

8. Common Error Codes

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1. JSON Endpoint

Overview

The JSON Endpoint offers a comprehensive way to interact with the Neura AI capabilities. It accepts a JSON payload containing parameters for authentication, messages, file data, user ID, and session ID, allowing for versatile and context-aware interactions.

HTTP Method & Path

• HTTP Method: POST

• Path: /v1/chat/completions

Request Body Format

The request body should be in JSON format, containing the following parameters:

{
  "auth": {
    "token": "Bearer YOUR_TOKEN"
  },
  "messages": "string",
  "file_data": "optional_byte_array_or_file_reference",
  "user_id": "optional_string",
  "session_id": "optional_string",
  "stream": "optional_string",
  "reasoning_format": "optional_string"
}

Parameters

Parameter	Type	Required	Description
auth.token	String	Yes	Bearer token for authentication.
messages	String	Yes	The message array or prompt for the AI.
file_data	Vec
<u8>	No	Byte array representing file data in various formats (e.g., PDF, DOCX, JPG).
user_id	String	No	User ID for personalization or tracking.
session_id	String	No	UUIDv4 - Session ID for maintaining context across multiple interactions.
stream	String	No	To stream response in real time.
reasoning_format	String	No	This optional parameter is available for O3 and DeepSeek R1 models.

Example Request

curl -X POST "https://api.meetneura.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
  "messages": "What is the sentiment of this text?",
  "file_data": "optional_byte_array_data_here",
  "user_id": "user123",
  "session_id": "8939e6af-a041-4d6b-a847-b366fb5ed828",
  "stream": "true",
  "reasoning_format": "raw"
}'

Example Response

The response will depend on the request and the AI model's processing. Here’s a general structure of the response:

{
  "request_id": "uuid-v4-string",
  "response": "The sentiment of the text is positive.",
  "context": "optional_context_or_state_for_future_interactions",
  "error": "optional_error_message",
  "processing_time_ms": 1234
}

Authentication

All requests must include a valid Bearer token in the auth.token field. The token should be provided in the JSON payload as shown in the example above.

Additional Considerations

• File Formats: Supported file formats include PDF, DOCX, TXT, JPG, and others. Ensure that file_data is provided as a byte array or file reference.

• Session Management: Use session_id to maintain context across multiple interactions, enhancing the conversational experience.

• User ID: Include user_id for personalization or tracking purposes.

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2. Omni Multi-Part Endpoint

Overview

The Omni Multi-Part Endpoint is designed to handle multi-modal interactions, supporting both text and file inputs through a single endpoint. It provides a flexible way to integrate AI capabilities into your applications, allowing for a combination of text prompts and file uploads.

HTTP Method & Path

• HTTP Method: POST

• Path: /v1/chat/completions/multipart

Request Format

The request should be sent as multipart/form-data, allowing for both text fields and file uploads. The supported fields are:

Field Name	Description
messages	The text input or prompt for the AI.
session_id	UUIDv4 for maintaining context across multiple interactions.
user_id	User ID for personalization or tracking.
file	File data for processing (e.g., PDF, DOCX, JPG).
stream	Boolean indicating whether to stream the response in real-time.
reasoning_format	Optional parameter for models that support different reasoning formats.

Example Request

curl -X POST "https://api.meetneura.ai/v1/chat/completions/multipart" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: multipart/form-data" \
  -F "messages=What is the sentiment of this text?" \
  -F "session_id=8939e6af-a041-4d6b-a847-b366fb5ed828" \
  -F "user_id=user123" \
  -F "file=@example.pdf" \
  -F "stream=true" \
  -F "reasoning_format=raw"

Example Response

The response will depend on the request and the AI model's processing. Here’s a general structure of the response:

{
  "request_id": "uuid-v4-string",
  "response": "The sentiment of the text is positive.",
  "processing_time_ms": 1234
}

Streamed Response Format

If streaming is enabled, the response will be sent in chunks:

data: {"chunk": "First part of the response...", "request_id": "uuid-v4-string"}
data: {"chunk": "Next part of the response...", "request_id": "uuid-v4-string"}
...

Parameters

Parameter	Type	Required	Description
messages	String	Yes	The message array or prompt for the AI.
session_id	String	No	UUIDv4 - Session ID for maintaining context across multiple interactions.
user_id	String	No	User ID for personalization or tracking.
file	File	No	File data for processing (e.g., PDF, DOCX, JPG).
stream	Boolean	No	To stream the response in real time.
reasoning_format	String	No	This optional parameter is available for models that support different reasoning formats.

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3. Research and Discovery Endpoint

Overview

The Research and Discovery Endpoint is designed to fetch relevant information from the web based on user queries. It utilizes the Gemini API to provide accurate and up-to-date results.

HTTP Method & Path

• HTTP Method: POST

• Path: /v1/research/web

Request Format

The request body should be in JSON format, containing the following parameters:

{
  "query": "string",
  "num_results": "optional_number",
  "session_id": "optional_string",
  "user_id": "optional_string"
}

Parameters

Parameter	Type	Required	Description
query	String	Yes	The search query to be processed.
num_results	Number	No	The number of results to return.
session_id	String	No	UUIDv4 - Session ID for maintaining context across multiple interactions.
user_id	String	No	User ID for personalization or tracking.

Example Request

curl -X POST "https://api.meetneura.ai/v1/research/web" \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
  "query": "latest advancements in quantum computing",
  "num_results": 10,
  "session_id": "8939e6af-a041-4d6b-a847-b366fb5ed828",
  "user_id": "user123"
}'

Example Response

{
  "request_id": "uuid-v4-string",
  "results": [
    {
      "source": "Source Name",
      "url": "https://example.com/article",
      "content": "Content of the article.",
      "published_date": "2023-10-01"
    },
    // ... more results
  ],
  "processing_time_ms": 1234
}

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4. Endpoints for History and Logging

These endpoints allow you to fetch chat history, user IDs, logs, and clear logs.

Fetch Chat History

• HTTP Method: GET

• Path: /v1/history/fetch

Get User IDs

• HTTP Method: GET

• Path: /v1/history/users/ids

Fetch Logs

• HTTP Method: GET

• Path: /v1/logs/fetch

Clear Logs

• HTTP Method: POST

• Path: /v1/logs/clear

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5. NLP Specific Endpoint

Lexicon Access

• HTTP Method: POST

• Path: /v1/lexicon

This endpoint provides access to the Lexicon NLP capabilities for advanced text processing.

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6. Proxy Interaction Endpoint

Overview

This endpoint is used for load-balanced request handling and follows the same format as the main interaction endpoint.

• HTTP Method: POST

• Path: /v1/proxy/chat/completions

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7. Error Handling

All errors follow this format:

{
  "error": "detailed error message",
  "request_id": "associated-request-id",
  "code": "ERROR_CODE",
  "processing_time_ms": 1234
}

Common Error Codes

• PROCESSING_ERROR: General processing failure.

• TIMEOUT_ERROR: Request exceeded the time limit.

• VALIDATION_ERROR: Invalid input.

• AUTH_ERROR: Authentication failure.

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This documentation is regularly updated to reflect new features and improvements. For any questions or feedback, please get in touch with us at [e-mail].