Auto-Analyst API Documentation

The core application routes are designed to manage the data and AI analysis capabilities of the Auto-Analyst application.

1. Core Application Routes

Data Management

POST /upload_dataframe

Uploads a CSV dataset for analysis.
Request:

file: CSV file
name: Dataset name
description: Dataset description
Headers:
X-Force-Refresh: "true" (optional) - Forces session reset before upload Response:

{ "message": "Dataframe uploaded successfully", "session_id": "abc123" }

POST /upload_excel

Uploads an Excel file with a specific sheet for analysis.
Request:

file: Excel file
name: Dataset name
description: Dataset description
sheet_name: Name of the Excel sheet to use Headers:
X-Force-Refresh: "true" (optional) - Forces session reset before upload Response:

{ "message": "Excel file processed successfully", "session_id": "abc123", "sheet": "Sheet1" }

POST /api/excel-sheets

Gets the list of sheet names from an Excel file.
Request:

file: Excel file
Response:

{ "sheets": ["Sheet1", "Sheet2", "Data"] }

GET /api/default-dataset

Gets the default dataset.
Response:

{
  "headers": ["column1", "column2", ...],
  "rows": [[val1, val2, ...], ...],
  "name": "Housing Dataset",
  "description": "A comprehensive dataset containing housing information..."
}

POST /reset-session

Resets session to default dataset.
Request Body:

{
  "name": "optional name",
  "description": "optional description",
  "preserveModelSettings": false
}

Response:

{
  "message": "Session reset to default dataset",
  "session_id": "abc123",
  "dataset": "Housing.csv"
}

GET /api/preview-csv / POST /api/preview-csv

Preview the current dataset in the session. Response:

{
  "headers": ["column1", "column2", ...],
  "rows": [[val1, val2, ...], ...],
  "name": "Dataset Name",
  "description": "Dataset description..."
}

2. AI Analysis

POST /chat/{agent_name}

Processes a query using a specific AI agent.
Path Parameters: agent_name
Request Body:

{ "query": "Analyze the relationship between price and size" }

Query Parameters: user_id (optional), chat_id (optional)
Response:

{
  "agent_name": "data_viz_agent",
  "query": "Analyze the relationship between price and size",
  "response": "# Analysis\n\nThere appears to be a strong positive correlation...",
  "session_id": "abc123"
}

POST /chat

Processes a query using multiple AI agents with streaming responses.
Request Body:

{ "query": "Analyze the housing data" }

Query Parameters: user_id (optional), chat_id (optional)
Response: Streaming JSON objects:

{"agent": "data_viz_agent", "content": "# Visualization\n\n...", "status": "success"}
{"agent": "statistical_analytics_agent", "content": "# Statistical Analysis\n\n...", "status": "success"}

POST /chat_history_name

Generates a name for a chat based on the query.
Request Body:

{ "query": "Analyze sales data for Q4" }

Response:

{ "name": "Chat about sales data analysis" }

GET /agents

Lists available AI agents.
Response:

{
  "available_agents": ["data_viz_agent", "sk_learn_agent", "statistical_analytics_agent", "preprocessing_agent"],
  "standard_agents": ["preprocessing_agent", "statistical_analytics_agent", "sk_learn_agent", "data_viz_agent"],
  "template_agents": ["custom_template_1", "custom_template_2"],
  "custom_agents": []
}

3. Deep Analysis

POST /deep_analysis_streaming

Performs comprehensive deep analysis with real-time streaming updates.
Request Body:

{ "goal": "Perform comprehensive analysis of the sales data" }

Query Parameters: user_id (optional), chat_id (optional)
Response: Streaming JSON objects with progress updates

POST /deep_analysis/download_report

Downloads an HTML report from deep analysis results.
Request Body:

{
  "analysis_data": { ... },
  "report_uuid": "optional-uuid"
}

Response: HTML file download

4. Model Settings

GET /api/model-settings

Fetches current model settings.
Response:

{
  "provider": "openai",
  "model": "gpt-4o-mini",
  "hasCustomKey": true,
  "temperature": 1.0,
  "maxTokens": 6000
}

POST /settings/model

Updates model settings.
Request Body:

{
  "provider": "openai",
  "model": "gpt-4",
  "api_key": "sk-...",
  "temperature": 0.7,
  "max_tokens": 8000
}

Response:

{ "message": "Model settings updated successfully" }

5. Session Management

GET /api/session-info

Gets information about the current session.
Response:

{
  "session_id": "abc123",
  "dataset_name": "Housing Dataset",
  "dataset_description": "...",
  "model_config": { ... }
}

POST /set-message-info

Associates message tracking information with the session.
Request Body:

{
  "chat_id": 123,
  "message_id": 456,
  "user_id": 789
}

POST /create-dataset-description

Creates an AI-generated description for a dataset.
Request Body:

{
  "df_preview": "column1,column2\nvalue1,value2\n...",
  "name": "Dataset Name"
}

6. System Endpoints

GET /

Returns API welcome information and feature list.

GET /health

Health check endpoint.
Response:

{ "message": "API is healthy and running" }

7. Debug Endpoints

GET /debug/deep_analysis_agents

Debug information about deep analysis agents for a session.

POST /debug/clear_deep_analyzer

Clears the deep analyzer cache for a session.

8. Authentication & Session Management

Session ID Sources:
- Query parameter: session_id
- Header: X-Session-ID
- Auto-generated if not provided
Session State Includes:
- Current dataset
- AI system instance
- Model configuration
- User and chat associations

9. Error Handling

Comprehensive error handling with appropriate HTTP status codes
Detailed error messages for debugging
Fallback encoding support for CSV files (UTF-8, unicode_escape, ISO-8859-1)
Session state preservation during errors