docs: update README with API and deployment guide
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# CLIP Embedding Service
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# CLIP Embedding — 多模态向量化服务
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CLIP-ViT-H/14 多模态 Embedding 服务,支持文本和图片向量化。
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## 概述
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## Overview
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基于 OpenAI CLIP ViT-H-14 的多模态嵌入服务,将文本和图像编码为统一的 1024 维向量空间,实现图文跨模态检索。
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- **Model**: laion/CLIP-ViT-H-14-laion2B-s32B-b79K
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## 模型
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- **Dimension**: 1024
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- **Precision**: float16
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- BAAI/CLIP-ViT-H-14 (OpenCLIP)
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- **Port**: 9086
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- 向量维度: 1024
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- **GPU**: 2 (default)
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- GPU: 部署于 GPU 服务器
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## API
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## API
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### GET /api/status
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| 端点 | 方法 | 说明 |
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Service health and GPU info.
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|------|------|------|
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| /api/status | GET | 服务状态和模型信息 |
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| /api/text | POST | 文本 → 向量 |
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| /api/image | POST | 图片 → 向量 |
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| /api/similarity | POST | 文本-图片相似度计算 |
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## 请求示例
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### POST /api/text
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Text embedding.
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```json
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```json
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{"texts": ["hello world", "a cat"]}
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POST /api/text
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{"texts": ["一只猫坐在沙发上", "日落海滩"]}
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POST /api/image
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{"images": ["base64图片数据"]}
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```
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```
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### POST /api/image
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## 部署
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Image embedding (file path, URL, or base64 data URI).
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```json
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{"images": ["/path/to/img.jpg", "https://example.com/img.png"]}
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```
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### POST /api/embed
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Combined text + image embedding.
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```json
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{"texts": ["a cat"], "images": ["/path/to/cat.jpg"]}
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```
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## Model Download (Offline Deploy)
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```bash
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```bash
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pip install huggingface_hub
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cd /data/ymq/clip_embedding
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huggingface-cli download laion/CLIP-ViT-H-14-laion2B-s32B-b79K \
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bash build.sh
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--local-dir /data/ymq/models/laion/CLIP-ViT-H-14-laion2B-s32B-b79K \
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sudo systemctl restart clip
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--local-dir-use-symlinks False
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```
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```
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Size: ~15GB
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## Deploy
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## 端口
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```bash
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9086
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bash build.sh deploy # start
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bash build.sh stop # stop
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bash build.sh status # check
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```
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