chatdoc-studio-api

name	chatdoc-studio-api
description	ChatDOC Studio API usage guide - complete documentation and examples for PDF parsing, chat applications, agent applications, content retrieval, and data extraction APIs

Overview

ChatDOC Studio is an AI-powered document processing and conversation platform providing multiple API capabilities:

• PDF Parser - Parse PDF documents into structured data (JSON, Markdown, Excel)
• Chat App - Create document-based Q&A chat applications
• Agent App - Run task-based document analysis with published Agent Apps
• RAG App - Content retrieval applications based on documents
• Extract App - Extract structured data from documents

API Basics

Base URL

https://api.chatdoc.studio/v1

Authentication

All API requests require a JWT Token in the HTTP Header:

Authorization: Bearer YOUR_API_KEY

Environment Variables

Manage API configuration through environment variables:

Environment Variable	Description	Default Value
CHATDOC_STUDIO_BASE_URL	API Base URL	https://api.chatdoc.studio/v1
CHATDOC_STUDIO_API_KEY	API authentication key	-

Supported File Types

API	PDF	DOC	DOCX	MD	TXT
PDF Parser	✓	✗	✗	✗	✗
Chat App	✓	✓	✓	✓	✓
Agent App	✓	✓	✓	✗	✗
RAG App	✓	✓	✓	✗	✗
Extract App	✓	✓	✓	✗	✗

API Module Documentation

Uploads API

Required for all apps except PDF Parser. Upload documents to your team before using them in Chat Apps, Agent Apps, RAG Apps, or Extract Apps.

Documentation: uploads/uploads_api.md Code Examples: uploads/uploads_api_examples.md

PDF Parser API

Parse PDF documents into structured data, supporting JSON, Markdown, and Excel exports.

Documentation: parsers/pdf_parser.md Code Examples: parsers/pdf_parser_examples.md

Chat App API

Create document-based Q&A chat applications with multi-turn conversations and source tracing.

Documentation: chat/chat_app.md Code Examples: chat/chat_app_examples.md

Agent App API

Submit uploaded files to published Agent Apps, poll task status, and fetch final task results.

Documentation: agent/agent_app.md Code Examples: agent/agent_app_examples.md

RAG App API

Perform semantic retrieval based on document content to retrieve relevant document fragments.

Documentation: retrieval/rag_app.md Code Examples: retrieval/rag_app_examples.md

Extract App API

Extract structured data from documents based on JSON Schema definitions.

Documentation: extraction/extract_app.md Code Examples: extraction/extract_app_examples.md

Apps API

Manage all types of applications (Chat, Agent, Extract, RAG) in your team - list and delete apps.

Documentation: apps/apps.md Code Examples: apps/apps_examples.md

Document Status (DocumentStatus)

All uploaded documents go through a processing status flow. Understanding document status is crucial for proper API usage.

Documentation: docs/document_status.md

Common Response Format

All API responses follow a unified format:

Success Response:

{
  "type": "System",
  "code": "success",
  "data": { ... },
  "detail": null
}

Error Response:

{
  "type": "...",
  "code": "...",
  "data": ...,
  "detail": "...."
}

Common Error Codes

In addition to API-specific error codes, the following error codes may be returned by any API endpoint:

Plan Error Codes (PlanErrorEnum)

These errors are related to your subscription plan's credit and capacity limits. Your API usage consumes credits and counts against your plan's capacity.

Error Code	Description
credit_not_enough	Insufficient credits to perform the operation. Top up your credits or upgrade your plan.
capacity_not_enough	Storage capacity exceeded. Delete unused documents or upgrade your plan.
app_count_not_enough	Maximum number of apps allowed by your plan has been reached.
member_count_not_enough	Maximum number of team members allowed by your plan has been reached.
upgrade_plan_error	Error occurred during plan upgrade process.
not_found	Plan not found. Contact support.

System Error Codes (SystemErrorEnum)

These are general system-level errors that may occur during API operations.

Error Code	Description
unknown_error	An unexpected error occurred. Try again or contact support if it persists.
validation_error	Request validation failed. Check your request parameters.
project_expired	The project or subscription has expired. Renew your subscription to continue.
handshake_error	Authentication handshake failed. Check your API key.

Rate Limiting

API calls are subject to rate limits based on your subscription plan. HTTP 429 status code will be returned when limits are exceeded.

Getting Started

Basic Workflow

1. Obtain an API Key from the ChatDOC Studio console
2. Configure environment variables
3. Review the module's documentation and examples
4. Upload documents (for Chat/Agent/RAG/Extract Apps) using the Uploads API
5. Immediately create your app or task using the upload IDs (processing is auto-triggered when referenced)
6. Wait for the app or task to become ready before using downstream features
7. Integrate into your application

Quick Start Examples

PDF Parser: Upload and parse → Get JSON/Markdown/Excel

Chat App: Upload documents → Create Chat App → Send messages

Agent App: Upload required documents → Create Agent task with documents grouped by slot label → Poll status → Get final result

RAG App: Upload documents → Create RAG App → Query content

Extract App: Create Extract App with schema → Upload document → Get extracted data

Additional Resources

• See docs/document_status.md for document processing status details