129 lines
4.3 KiB
Markdown
129 lines
4.3 KiB
Markdown
# API Endpoints
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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.
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## GET /api/users
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- **Description:** Return all users.
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- **Parameters:** None.
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- **Test:**
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```bash
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curl -s http://localhost:8080/api/users
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```
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## GET /api/nodes
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- **Description:** Return all nodes.
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- **Parameters:** None.
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- **Test:**
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```bash
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curl -s http://localhost:8080/api/nodes
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```
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## POST /api/sync
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- **Description:** Clone or update a knowledge repository.
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- **Body Parameters (JSON):**
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- `repo_url` – Git repository URL.
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- `local_path` – Destination directory on the server.
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- **Test:**
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```bash
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curl -X POST http://localhost:8080/api/sync \
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-H "Content-Type: application/json" \
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-d '{"repo_url": "https://github.com/example/repo.git", "local_path": "/tmp/repo"}'
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```
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## POST /api/rag/sync
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- **Description:** Trigger RAG background synchronization. The endpoint streams
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plain-text progress logs during the sync.
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- **Parameters:** None.
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- **Test:**
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```bash
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curl -N -X POST http://localhost:8080/api/rag/sync
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```
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- **Notes:** A future evolution could expose this operation via a gRPC
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streaming RPC. That approach would allow high-speed synchronization, rate
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limiting, and resumable transfers over long-lived connections while
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supporting dynamic, lossless queues for weak networks.
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## POST /api/rag/upsert
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- **Description:** Upsert pre-embedded document chunks into the RAG database.
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- **Body Parameters (JSON):**
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- `docs` – Array of documents each containing `repo`, `path`, `chunk_id`, `content`, `embedding`, `metadata`, and `content_sha`.
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- **Test:**
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curl -X POST http://localhost:8080/api/rag/upsert \
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-H "Content-Type: application/json" --data-binary @/Users/shenlan/workspaces/XControl/docs/upsert_1024.json
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```bash
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Expected response on success: `{"rows":1}`. If the vector database is unavailable, the endpoint returns `{"rows":0,"error":"..."}`.
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## POST /api/rag/query
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- **Description:** Query the RAG service.
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- **Body Parameters (JSON):**
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- `question` – Query text.
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- **Test:**
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```bash
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curl -X POST http://localhost:8080/api/rag/query \
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-H "Content-Type: application/json" \
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-d '{"question": "What is XControl?"}'
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```
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When copying the multi-line example above, ensure your shell treats the trailing
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`\` characters as line continuations. Copying literal `\n` sequences will cause
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`curl: (3) URL rejected: Bad hostname` errors. You can also run the command on a
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single line without the backslashes:
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```bash
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curl -X POST http://localhost:8080/api/rag/query -H "Content-Type: application/json" -d '{"question": "What is XControl?"}'
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```
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## POST /api/askai
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- **Description:** Ask the AI service for an answer. Requires a valid Chutes token in the server configuration.
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- **Body Parameters (JSON):**
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- `question` – Question text.
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- **Test:**
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```bash
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curl -X POST http://localhost:8080/api/askai \
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-H "Content-Type: application/json" \
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-d '{"question": "Hello"}'
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```
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## GET Localhost embeddings API
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1. 运行（首次会自动下载模型）
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python offline_embed_server.py
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2. 测试接口
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1) 健康检查（端口就绪即返回 ok） curl -v http://127.0.0.1:9000/healthz
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2) 就绪检查（模型加载完成后返回 ready） curl -v http://127.0.0.1:9000/readyz
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3) 调用 embeddings
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curl http://127.0.0.1:9000/v1/embeddings \
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-H "Content-Type: application/json" \
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-d '{"model":"BAAI/bge-m3","input":["你好","PGVector 怎么建 HNSW？"]}'
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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。
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## GET Localhost Ollama API
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用流式接收（推荐）：
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curl http://127.0.0.1:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-oss:20b",
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Tell me three tips for optimizing HNSW in PostgreSQL."}
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],
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"max_tokens": 512,
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"stream": true
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}'
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这样会实时输出分块数据
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curl http://127.0.0.1:11434/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "llama3:latest",
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"messages": [{"role":"user","content":"你好，简要介绍一下自己"}],
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"max_tokens": 200,
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"temperature": 0.7
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}'
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