- 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/v1Authentication
All API requests require a JWT Token in the HTTP Header:
Authorization: Bearer YOUR_API_KEYEnvironment 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 | 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