8.8 KiB
API Endpoints
This document describes the HTTP endpoints provided by the XControl platform. Each entry lists the request method and path, required parameters, and a sample curl command for verification.
Account Service(MFA/TLS 支持)
The standalone account service exposes user registration, MFA provisioning, and login endpoints on its configured host (default http://localhost:8080).
POST /api/auth/register
- Description: Create a new local user with email/password credentials.
- Body Parameters (JSON):
name– Display name.email– Unique email address.password– Password with at least 8 characters.
- Test:
curl -X POST http://localhost:8080/api/auth/register \ -H "Content-Type: application/json" \ -d '{"name":"demo","email":"demo@example.com","password":"Secret123"}'
POST /api/auth/mfa/totp/provision
- Description: Issue a temporary TOTP secret (and QR code) for Google Authenticator binding. Requires an MFA challenge token returned by the login flow.
- Body Parameters (JSON):
token– MFA challenge token obtained from a prior/api/auth/loginattempt.issuer– Optional override for the TOTP issuer label.account– Optional override for the account label in authenticator apps.
- Notes: Challenge tokens expire after 10 minutes. If the token is lost or expired, re-run the login flow to obtain a fresh value.
- Test:
curl -X POST http://localhost:8080/api/auth/mfa/totp/provision \ -H "Content-Type: application/json" \ -d '{"token":"<MFA_TOKEN_FROM_LOGIN>"}'
POST /api/auth/mfa/totp/verify
- Description: Confirm the generated one-time passcode and activate MFA for the user.
- Body Parameters (JSON):
token– MFA challenge token used during provisioning.code– 6-digit TOTP from Google Authenticator/oathtool.
- Notes: Codes are calculated in 30-second windows with ±1 window skew. Ensure server and client clocks stay synchronized (e.g., via NTP) to prevent false negatives.
- Test:
curl -X POST http://localhost:8080/api/auth/mfa/totp/verify \ -H "Content-Type: application/json" \ -d '{"token":"<MFA_TOKEN_FROM_LOGIN>","code":"123456"}'
POST /api/auth/login
- Description: Issue a session cookie after validating credentials and MFA. The first request after registration returns
401 mfa_setup_requiredwith anmfaTokenused for provisioning. Once MFA is enabled, supports both password+TOTP and email+TOTP-only flows. - Body Parameters (JSON):
identifier– Email or username.password– Optional when performing email+TOTP-only login.totpCode– Required once MFA is enabled.
- Test:
curl -X POST http://localhost:8080/api/auth/login \ -H "Content-Type: application/json" \ -c cookies.txt \ -d '{"identifier":"demo@example.com","password":"Secret123","totpCode":"123456"}'
GET /api/auth/mfa/status
- Description: Inspect MFA status for a user using either a session token or the pending
mfaToken. - Parameters:
- Query
tokenor headerX-MFA-Tokenwhen checking a pending MFA challenge.
- Query
- Test:
curl "http://localhost:8080/api/auth/mfa/status?token=<MFA_TOKEN_FROM_LOGIN>"
GET /api/auth/session
- Description: Return sanitized user information for the active session, including MFA status.
- Headers:
Cookieheader withaccount_sessionvalue. - Test:
curl -b cookies.txt http://localhost:8080/api/auth/session
TLS note: When
accountsvcis started with certificates, replacehttp://withhttps://and add-kfor curl if using self-signed certificates during development.
GET /api/users
- Description: Return all users.
- Parameters: None.
- Test:
curl -s http://localhost:8080/api/users
GET /api/nodes
- Description: Return all nodes.
- Parameters: None.
- Test:
curl -s http://localhost:8080/api/nodes
POST /api/sync
- Description: Clone or update a knowledge repository.
- Body Parameters (JSON):
repo_url– Git repository URL.local_path– Destination directory on the server.
- Test:
curl -X POST http://localhost:8080/api/sync \ -H "Content-Type: application/json" \ -d '{"repo_url": "https://github.com/example/repo.git", "local_path": "/tmp/repo"}'
POST /api/rag/sync
- Description: Trigger RAG background synchronization. The endpoint streams plain-text progress logs during the sync.
- Parameters: None.
- Test:
curl -N -X POST http://localhost:8080/api/rag/sync - Notes: A future evolution could expose this operation via a gRPC streaming RPC. That approach would allow high-speed synchronization, rate limiting, and resumable transfers over long-lived connections while supporting dynamic, lossless queues for weak networks.
POST /api/rag/upsert
- Description: Upsert pre-embedded document chunks into the RAG database.
- Body Parameters (JSON):
docs– Array of documents each containingrepo,path,chunk_id,content,embedding,metadata, andcontent_sha.
- Test:
curl -X POST http://localhost:8080/api/rag/upsert
-H "Content-Type: application/json" --data-binary @/Users/shenlan/workspaces/XControl/docs/upsert_1024.json
Expected response on success: `{"rows":1}`. If the vector database is unavailable, the endpoint returns `{"rows":0,"error":"..."}`.
## POST /api/rag/query
- **Description:** Query the RAG service.
- **Body Parameters (JSON):**
- `question` – Query text.
- **Test:**
```bash
curl -X POST http://localhost:8080/api/rag/query \
-H "Content-Type: application/json" \
-d '{"question": "What is XControl?"}'
When copying the multi-line example above, ensure your shell treats the trailing
\ characters as line continuations. Copying literal \n sequences will cause
curl: (3) URL rejected: Bad hostname errors. You can also run the command on a
single line without the backslashes:
curl -X POST http://localhost:8080/api/rag/query -H "Content-Type: application/json" -d '{"question": "What is XControl?"}'
POST /api/askai
- Description: Ask the AI service for an answer. The endpoint uses LangChainGo to communicate with the configured model provider (e.g., OpenAI-compatible services or a local Ollama instance). Ensure the server configuration includes the proper token or local server URL.
- Body Parameters (JSON):
question– Question text. Configuration: Inserver/config/server.yamlthemodelssection selects the LLM and embedding providers. For local debugging with HuggingFace and Ollama:
models:
embedder:
models: "bge-m3"
endpoint: "http://127.0.0.1:9000/v1/embeddings"
generator:
models:
- 'llama2:13b'
endpoint: "http://127.0.0.1:11434"
For online services using Chutes:
#models:
# embedder:
# models: "bge-m3"
# endpoint: "https://chutes-baai-bge-m3.chutes.ai/embed"
# token: "cpk_xxxx"
# generator:
# models:
# - 'moonshotai/Kimi-K2-Instruct'
# endpoint: "https://llm.chutes.ai/v1"
# token: "cpk_xxxx"
The api.askai section controls request behaviour:
api:
askai:
timeout: 60 # seconds
retries: 3 # retry attempts
- Test:
curl -X POST http://localhost:8080/api/askai \ -H "Content-Type: application/json" \ -d '{"question": "Hello"}'
GET Localhost embeddings API
- 运行(首次会自动下载模型) python offline_embed_server.py
- 测试接口
- 健康检查(端口就绪即返回 ok) curl -v http://127.0.0.1:9000/healthz
- 就绪检查(模型加载完成后返回 ready) curl -v http://127.0.0.1:9000/readyz
- 调用 embeddings
curl http://127.0.0.1:9000/v1/embeddings
-H "Content-Type: application/json"
-d '{"model":"BAAI/bge-m3","input":["你好","PGVector 怎么建 HNSW?"]}'
如果你要把 DEVICE 固定为 mps 并行内核,保留默认即可;如需落回 CPU:DEVICE=cpu python docs/offline_embed_server.py。
GET Localhost Ollama API
用流式接收(推荐):
curl http://127.0.0.1:11434/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model": "gpt-oss:20b",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Tell me three tips for optimizing HNSW in PostgreSQL."}
],
"max_tokens": 512,
"stream": true
}'
这样会实时输出分块数据
curl http://127.0.0.1:11434/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model": "llama3:latest",
"messages": [{"role":"user","content":"你好,简要介绍一下自己"}],
"max_tokens": 200,
"temperature": 0.7
}'