chatdoc-studio-api

Data & APIs

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

Install

openclaw skills install chatdoc-studio-api

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

Obtain an API Key from the ChatDOC Studio console
Configure environment variables
Review the module's documentation and examples
Upload documents (for Chat/Agent/RAG/Extract Apps) using the Uploads API
Immediately create your app or task using the upload IDs (processing is auto-triggered when referenced)
Wait for the app or task to become ready before using downstream features
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 document → Create Agent task → 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