accounts/docs/api-endpoints.md

API Endpoints

This document describes the HTTP endpoints provided by the XControl server. Each entry lists the request method and path, required parameters, and a sample curl command for verification.

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 containing repo, path, chunk_id, content, embedding, metadata, and content_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: In server/config/server.yaml the models section 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

1. 运行（首次会自动下载模型） python offline_embed_server.py
2. 测试接口

1. 健康检查（端口就绪即返回 ok） curl -v http://127.0.0.1:9000/healthz
2. 就绪检查（模型加载完成后返回 ready） curl -v http://127.0.0.1:9000/readyz
3. 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 }'