Publish auto bundle 7z archive

.gitignore

@@ -1,27 +0,0 @@
-cache/
-web/cache.json
-node_modules/
-cloudflare/node_modules/
-/config.json
-server/config.json
-server/cookie.json
-server/logs/
-bundle/clip/config.json
-bundle/clip/data/
-bundle/clip/models/
-*.zip
-*.7z
-*.rar
-bundle/python/
-bundle/python-cpu/
-bundle/python-runtime/
-bundle/_delete_pending_python_gpu/
-__pycache__/
-test_*.py
-build/
-runtime/*credential*
-runtime/cloud-config-state.json
-cloudflare/.npm-cache/
-cloudflare/.wrangler/
-cloudflare/.wrangler-config/
-cloudflare/.dev.vars

.zipignore

@@ -1,56 +0,0 @@
-# Git
-.git/
-.gitignore
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-*.egg-info/
-/dist/
-/build/
-*.egg
-
-# Node
-npm-debug.log*
-
-# IDE
-.vscode/
-.idea/
-*.swp
-*.swo
-*~
-
-# OS
-.DS_Store
-Thumbs.db
-desktop.ini
-
-# Logs
-*.log
-logs/
-
-# Temp
-tmp/
-temp/
-*.tmp
-*.zip
-*.7z
-*.rar
-test_*.py
-/build/
-
-# Local data and private config
-cache/
-server/config.json
-bundle/clip/config.json
-bundle/python/
-bundle/_delete_pending_python_gpu/
-
-# Large runtime files (keep models and data but exclude huge node_modules)
-runtime/node/node_modules/
-
-# Test outputs
-test_output/

README.md

@@ -1,143 +1,57 @@
 ---
 license: apache-2.0
-language:
-  - zh
-  - en
+pipeline_tag: image-to-text
 tags:
-  - clip
-  - faiss
-  - ecommerce
-  - image-retrieval
-  - product-search
   - temu
-pipeline_tag: image-feature-extraction
+  - product-bundling
+  - clip
+  - offline-package
+  - windows
 ---
 
-<div align="center">
+# 自动组货
 
-# Bundle CLIP
+> 一个面向 Temu / 1688 组货工作流的 Windows 离线整包归档。
 
-### Temu product retrieval pack for local bundle-building workflows
+![Version](https://img.shields.io/badge/package-7z_archive-2f6feb?style=for-the-badge)
+![Runtime](https://img.shields.io/badge/runtime-Windows_CPU-22c55e?style=for-the-badge)
+![Status](https://img.shields.io/badge/status-warehouse_only-ff7a45?style=for-the-badge)
 
-<p>
-  <img alt="CLIP" src="https://img.shields.io/badge/CLIP-OpenCLIP-111827?style=for-the-badge">
-  <img alt="FAISS" src="https://img.shields.io/badge/FAISS-Listing%20Index-2563eb?style=for-the-badge">
-  <img alt="Local" src="https://img.shields.io/badge/Run-Local%209990-f97316?style=for-the-badge">
-  <img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-10b981?style=for-the-badge">
-</p>
+## What Is Inside
 
-**Image in. Product ideas out.**
+`自动组货.7z` 是完整项目压缩包，用来做仓库存档和迁移分发。它不是 Hugging Face Space，不会在网页上直接运行。
 
-This repository packages a local CLIP + FAISS retrieval service for ecommerce bundle discovery.
+这个包主要包含：
 
-</div>
-
----
+- 自动组货前端工作台
+- 本地后端服务
+- 浏览器扩展
+- CLIP 组货相关模型与索引
+- Windows 启动脚本与离线运行环境
 
-## Screenshots
+## Preview
 
-<p align="center">
-  <img src="docs/images/bundle-clip-search.png" alt="Bundle CLIP local listing search UI" width="92%">
-</p>
+### Workbench
 
-<p align="center">
-  <em>Local listing search page served by the bundled 9990 runtime.</em>
-</p>
+![Workbench overview](docs/images/workbench-overview.png)
 
-<table>
-  <tr>
-    <td width="50%">
-      <img src="docs/images/smart-workflow.png" alt="Auto Bundle smart workflow">
-    </td>
-    <td width="50%">
-      <img src="docs/images/workbench-overview.png" alt="Temu and 1688 workbench overview">
-    </td>
-  </tr>
-  <tr>
-    <td align="center"><strong>Smart Bundle Workflow</strong></td>
-    <td align="center"><strong>Integrated Workbench</strong></td>
-  </tr>
-</table>
+### Workflow
 
-## What This Is
+![Smart workflow](docs/images/smart-workflow.png)
 
-`bundle-clip` is a self-contained local retrieval bundle used by the Auto Bundle workbench. It combines:
+### CLIP Search
 
-| Layer | Role |
-| --- | --- |
-| Kimi planning | Turns an input product image into bundle-search directions. |
-| OpenCLIP encoder | Embeds image and text prompts into the same semantic space. |
-| FAISS listing index | Retrieves high-similarity Temu listings from the prepared metadata. |
-| Local web UI | Serves a review page at `http://127.0.0.1:9990/`. |
+![Bundle CLIP search](docs/images/bundle-clip-search.png)
 
-The pack is designed for fast local review, private experimentation, and offline-ish product matching after the LFS assets are downloaded.
+## Download
 
-## Repository Layout
+直接在本页下载：
 
 ```text
-bundle-clip/
-├─ app/
-│  └─ listing_search.html
-├─ docs/
-│  └─ images/
-├─ work/
-│  ├─ full_listing_server.py
-│  ├─ stdio_listing_worker.py
-│  ├─ full_clip_server.py
-│  └─ build_full_listing_index.py
-├─ models/
-│  └─ open_clip_pytorch_model.bin
-├─ data/
-│  ├─ yunqi_clip_training/
-│  │  └─ last_checkpoint.pt
-│  ├─ full_listing_index/
-│  │  ├─ products_listing.index
-│  │  ├─ products_listing_meta.runtime.json
-│  │  ├─ cleaning_report.json
-│  │  └─ progress.json
-│  └─ full_clip_index/
-│     └─ products_full_prices.json
-├─ config.example.json
-├─ .gitattributes
-└─ README.md
-```
-
-## Included Assets
-
-| Asset | Purpose |
-| --- | --- |
-| `models/open_clip_pytorch_model.bin` | Base OpenCLIP model weights. |
-| `data/yunqi_clip_training/last_checkpoint.pt` | Fine-tuned checkpoint for the bundle-search domain. |
-| `data/full_listing_index/products_listing.index` | FAISS index for listing retrieval. |
-| `data/full_listing_index/products_listing_meta.runtime.json` | Runtime metadata used to render product cards. |
-| `data/full_clip_index/products_full_prices.json` | Price metadata used by the local search UI. |
-
-Large files are tracked with Git LFS. Run `git lfs pull` after cloning.
-
-## Clone & Update
-
-Yes, you can clone this repository directly. The only catch is that the model weights and FAISS index are stored with Git LFS, so a complete first-time setup should be:
-
-```powershell
-git lfs install
-git clone https://huggingface.co/mikaassa/bundle-clip
-cd bundle-clip
-git lfs pull
+自动组货.7z
 ```
 
-If Git LFS is missing, the large assets will look like tiny text pointer files and the server will fail when loading the model or index.
-
-To update an existing local copy later:
-
-```powershell
-cd bundle-clip
-git pull
-git lfs pull
-```
-
-## Quick Start
-
-### 1. Clone With LFS
+或者用 Git LFS 克隆：
 
 ```powershell
 git lfs install
@@ -146,83 +60,16 @@ cd bundle-clip
 git lfs pull
 ```
 
-### 2. Create Local Config
+## Use On A New Computer
 
-```powershell
-Copy-Item config.example.json config.json
-```
-
-Fill in your private Kimi or Moonshot key:
-
-```json
-{
-  "kimi": {
-    "api_key": "YOUR_KIMI_API_KEY",
-    "endpoint": "https://api.moonshot.cn/v1/chat/completions",
-    "model": "kimi-k2.6",
-    "temperature": 0.6,
-    "max_completion_tokens": 1200
-  }
-}
-```
-
-`config.json` is ignored by Git. Keep real API keys local.
-
-### 3. Start The Local Service
-
-```powershell
-python .\work\full_listing_server.py
-```
-
-Open:
-
-```text
-http://127.0.0.1:9990/
-```
-
-## Workflow
-
-```mermaid
-flowchart LR
-  A[Product image] --> B[Kimi bundle directions]
-  B --> C[English listing prompt]
-  C --> D[OpenCLIP embedding]
-  D --> E[FAISS nearest-neighbor search]
-  E --> F[Temu product cards]
-```
-
-The local UI supports two review paths:
-
-| Mode | Use Case |
-| --- | --- |
-| Image bundle search | Upload a product image, let Kimi produce bundle directions, then retrieve matching listings. |
-| Direct CLIP search | Enter a manual listing keyword or prompt and search the index directly. |
-
-## Runtime Notes
-
-- Default local port: `9990`
-- Main service entry: `work/full_listing_server.py`
-- Workbench worker entry: `work/stdio_listing_worker.py`
-- Public page: `app/listing_search.html`
-- Main metadata image field: `MAINIMAGE`
-
-The service prefers repository-local `data/` and `models/` paths first. Older absolute-path fallbacks are only used when local assets are missing.
-
-## Safety
-
-- Do not commit `config.json`, `.env`, logs, or local cache output.
-- API keys should be supplied through `config.json` or environment variables only.
-- This repository is a local runtime pack, not a public hosted inference endpoint.
-- Product metadata and retrieval quality depend on the bundled index snapshot.
-
-## Environment Overrides
-
-```powershell
-$env:MOONSHOT_API_KEY="YOUR_KIMI_API_KEY"
-$env:KIMI_ENDPOINT="https://api.moonshot.cn/v1/chat/completions"
-$env:KIMI_MODEL="kimi-k2.6"
-```
+1. 解压 `自动组货.7z`
+2. 进入解压后的 `自动组货` 文件夹
+3. 双击 `启动.bat`
+4. 如果是第一次部署，按项目内的示例配置补齐本机配置文件
+5. 浏览器扩展需要在 Chrome / Edge 里以开发者模式加载 `extension` 文件夹
 
-## License
+## Notes
 
-Released under the Apache 2.0 license. Check upstream model and data-source terms before redistribution or commercial deployment.
+- 这个仓库只作为大文件仓库使用。
+- 配置、Cookie、授权凭据这类本机敏感文件不应该提交到仓库。
+- 如果重新打包发布，建议保持文件名 `自动组货.7z`，这样下载链接和说明都不用改。

bundle/clip/.gitattributes

@@ -1,38 +0,0 @@
-data/** filter=lfs diff=lfs merge=lfs -text
-models/** filter=lfs diff=lfs merge=lfs -text
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.index filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

bundle/clip/.gitignore

@@ -1,9 +0,0 @@
-config.json
-.env
-__pycache__/
-*.pyc
-logs/
-*.log
-build/
-dist/
-*.spec

bundle/clip/README.md

@@ -1,228 +0,0 @@
----
-license: apache-2.0
-language:
-  - zh
-  - en
-tags:
-  - clip
-  - faiss
-  - ecommerce
-  - image-retrieval
-  - product-search
-  - temu
-pipeline_tag: image-feature-extraction
----
-
-<div align="center">
-
-# Bundle CLIP
-
-### Temu product retrieval pack for local bundle-building workflows
-
-<p>
-  <img alt="CLIP" src="https://img.shields.io/badge/CLIP-OpenCLIP-111827?style=for-the-badge">
-  <img alt="FAISS" src="https://img.shields.io/badge/FAISS-Listing%20Index-2563eb?style=for-the-badge">
-  <img alt="Local" src="https://img.shields.io/badge/Run-Local%209990-f97316?style=for-the-badge">
-  <img alt="License" src="https://img.shields.io/badge/License-Apache--2.0-10b981?style=for-the-badge">
-</p>
-
-**Image in. Product ideas out.**
-
-This repository packages a local CLIP + FAISS retrieval service for ecommerce bundle discovery.
-
-</div>
-
----
-
-## Screenshots
-
-<p align="center">
-  <img src="docs/images/bundle-clip-search.png" alt="Bundle CLIP local listing search UI" width="92%">
-</p>
-
-<p align="center">
-  <em>Local listing search page served by the bundled 9990 runtime.</em>
-</p>
-
-<table>
-  <tr>
-    <td width="50%">
-      <img src="docs/images/smart-workflow.png" alt="Auto Bundle smart workflow">
-    </td>
-    <td width="50%">
-      <img src="docs/images/workbench-overview.png" alt="Temu and 1688 workbench overview">
-    </td>
-  </tr>
-  <tr>
-    <td align="center"><strong>Smart Bundle Workflow</strong></td>
-    <td align="center"><strong>Integrated Workbench</strong></td>
-  </tr>
-</table>
-
-## What This Is
-
-`bundle-clip` is a self-contained local retrieval bundle used by the Auto Bundle workbench. It combines:
-
-| Layer | Role |
-| --- | --- |
-| Kimi planning | Turns an input product image into bundle-search directions. |
-| OpenCLIP encoder | Embeds image and text prompts into the same semantic space. |
-| FAISS listing index | Retrieves high-similarity Temu listings from the prepared metadata. |
-| Local web UI | Serves a review page at `http://127.0.0.1:9990/`. |
-
-The pack is designed for fast local review, private experimentation, and offline-ish product matching after the LFS assets are downloaded.
-
-## Repository Layout
-
-```text
-bundle-clip/
-├─ app/
-│  └─ listing_search.html
-├─ docs/
-│  └─ images/
-├─ work/
-│  ├─ full_listing_server.py
-│  ├─ stdio_listing_worker.py
-│  ├─ full_clip_server.py
-│  └─ build_full_listing_index.py
-├─ models/
-│  └─ open_clip_pytorch_model.bin
-├─ data/
-│  ├─ yunqi_clip_training/
-│  │  └─ last_checkpoint.pt
-│  ├─ full_listing_index/
-│  │  ├─ products_listing.index
-│  │  ├─ products_listing_meta.runtime.json
-│  │  ├─ cleaning_report.json
-│  │  └─ progress.json
-│  └─ full_clip_index/
-│     └─ products_full_prices.json
-├─ config.example.json
-├─ .gitattributes
-└─ README.md
-```
-
-## Included Assets
-
-| Asset | Purpose |
-| --- | --- |
-| `models/open_clip_pytorch_model.bin` | Base OpenCLIP model weights. |
-| `data/yunqi_clip_training/last_checkpoint.pt` | Fine-tuned checkpoint for the bundle-search domain. |
-| `data/full_listing_index/products_listing.index` | FAISS index for listing retrieval. |
-| `data/full_listing_index/products_listing_meta.runtime.json` | Runtime metadata used to render product cards. |
-| `data/full_clip_index/products_full_prices.json` | Price metadata used by the local search UI. |
-
-Large files are tracked with Git LFS. Run `git lfs pull` after cloning.
-
-## Clone & Update
-
-Yes, you can clone this repository directly. The only catch is that the model weights and FAISS index are stored with Git LFS, so a complete first-time setup should be:
-
-```powershell
-git lfs install
-git clone https://huggingface.co/mikaassa/bundle-clip
-cd bundle-clip
-git lfs pull
-```
-
-If Git LFS is missing, the large assets will look like tiny text pointer files and the server will fail when loading the model or index.
-
-To update an existing local copy later:
-
-```powershell
-cd bundle-clip
-git pull
-git lfs pull
-```
-
-## Quick Start
-
-### 1. Clone With LFS
-
-```powershell
-git lfs install
-git clone https://huggingface.co/mikaassa/bundle-clip
-cd bundle-clip
-git lfs pull
-```
-
-### 2. Create Local Config
-
-```powershell
-Copy-Item config.example.json config.json
-```
-
-Fill in your private Kimi or Moonshot key:
-
-```json
-{
-  "kimi": {
-    "api_key": "YOUR_KIMI_API_KEY",
-    "endpoint": "https://api.moonshot.cn/v1/chat/completions",
-    "model": "kimi-k2.6",
-    "temperature": 0.6,
-    "max_completion_tokens": 1200
-  }
-}
-```
-
-`config.json` is ignored by Git. Keep real API keys local.
-
-### 3. Start The Local Service
-
-```powershell
-python .\work\full_listing_server.py
-```
-
-Open:
-
-```text
-http://127.0.0.1:9990/
-```
-
-## Workflow
-
-```mermaid
-flowchart LR
-  A[Product image] --> B[Kimi bundle directions]
-  B --> C[English listing prompt]
-  C --> D[OpenCLIP embedding]
-  D --> E[FAISS nearest-neighbor search]
-  E --> F[Temu product cards]
-```
-
-The local UI supports two review paths:
-
-| Mode | Use Case |
-| --- | --- |
-| Image bundle search | Upload a product image, let Kimi produce bundle directions, then retrieve matching listings. |
-| Direct CLIP search | Enter a manual listing keyword or prompt and search the index directly. |
-
-## Runtime Notes
-
-- Default local port: `9990`
-- Main service entry: `work/full_listing_server.py`
-- Workbench worker entry: `work/stdio_listing_worker.py`
-- Public page: `app/listing_search.html`
-- Main metadata image field: `MAINIMAGE`
-
-The service prefers repository-local `data/` and `models/` paths first. Older absolute-path fallbacks are only used when local assets are missing.
-
-## Safety
-
-- Do not commit `config.json`, `.env`, logs, or local cache output.
-- API keys should be supplied through `config.json` or environment variables only.
-- This repository is a local runtime pack, not a public hosted inference endpoint.
-- Product metadata and retrieval quality depend on the bundled index snapshot.
-
-## Environment Overrides
-
-```powershell
-$env:MOONSHOT_API_KEY="YOUR_KIMI_API_KEY"
-$env:KIMI_ENDPOINT="https://api.moonshot.cn/v1/chat/completions"
-$env:KIMI_MODEL="kimi-k2.6"
-```
-
-## License
-
-Released under the Apache 2.0 license. Check upstream model and data-source terms before redistribution or commercial deployment.

bundle/clip/app/listing_search.html

@@ -1,1288 +0,0 @@
-<!doctype html>
-<html lang="zh-CN">
-<head>
-  <meta charset="utf-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>纯 Listing 组货检索</title>
-  <style>
-    :root {
-      color-scheme: light;
-      --ink: #231f1a;
-      --ink-soft: #3f3830;
-      --muted: #7b7167;
-      --line: #ded5c9;
-      --line-soft: #ece4da;
-      --panel: rgba(255, 252, 246, .88);
-      --panel-solid: #fffcf6;
-      --page: #f4efe7;
-      --paper: #fbf7ef;
-      --clay: #c56d3d;
-      --clay-dark: #9e4f2b;
-      --olive: #65724f;
-      --red: #b44f37;
-      --shadow: 0 22px 54px rgba(74, 51, 30, .10);
-      font-family: "Fraunces", "Iowan Old Style", "Noto Serif SC", "Microsoft YaHei UI", serif;
-    }
-
-    * { box-sizing: border-box; }
-    body {
-      min-height: 100vh;
-      margin: 0;
-      color: var(--ink);
-      background:
-        radial-gradient(circle at 9% 4%, rgba(219, 157, 102, .20), transparent 28%),
-        radial-gradient(circle at 92% 0%, rgba(101, 114, 79, .12), transparent 26%),
-        linear-gradient(135deg, #f8f1e7 0%, #f1eadf 48%, #eee4d7 100%);
-    }
-    body::before {
-      position: fixed;
-      inset: 0;
-      pointer-events: none;
-      content: "";
-      opacity: .42;
-      background-image:
-        linear-gradient(rgba(120, 92, 63, .045) 1px, transparent 1px),
-        linear-gradient(90deg, rgba(120, 92, 63, .035) 1px, transparent 1px);
-      background-size: 42px 42px;
-      mask-image: linear-gradient(to bottom, #000, transparent 82%);
-    }
-    button, input, textarea {
-      font: inherit;
-    }
-    .shell {
-      width: min(1500px, calc(100% - 40px));
-      margin: 0 auto;
-      padding: 30px 0 44px;
-    }
-    .topbar {
-      display: grid;
-      grid-template-columns: minmax(0, 1fr) auto;
-      gap: 22px;
-      align-items: end;
-      margin-bottom: 18px;
-      padding: 18px 20px;
-      border: 1px solid rgba(94, 73, 55, .12);
-      border-radius: 28px;
-      background: rgba(255, 251, 244, .68);
-      box-shadow: 0 1px 0 rgba(255,255,255,.7) inset;
-      backdrop-filter: blur(18px);
-    }
-    .eyebrow {
-      margin: 0 0 8px;
-      color: var(--clay-dark);
-      font: 700 11px/1.2 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-      letter-spacing: .16em;
-      text-transform: uppercase;
-    }
-    h1 {
-      margin: 0;
-      font-size: clamp(30px, 4vw, 48px);
-      font-weight: 760;
-      letter-spacing: -.045em;
-      line-height: 1.05;
-    }
-    .subtitle {
-      margin: 10px 0 0;
-      max-width: 860px;
-      color: var(--muted);
-      font: 14px/1.8 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .layout {
-      display: flex;
-      min-height: calc(100vh - 170px);
-      flex-direction: column;
-      gap: 18px;
-    }
-    .panel {
-      border: 1px solid rgba(96, 73, 51, .14);
-      border-radius: 28px;
-      background: var(--panel);
-      box-shadow: var(--shadow), 0 1px 0 rgba(255,255,255,.72) inset;
-      backdrop-filter: blur(20px);
-    }
-    .controls {
-      padding: 18px;
-    }
-    .section-title {
-      margin: 0 0 14px;
-      font-size: 19px;
-      letter-spacing: -.02em;
-    }
-    .field-label {
-      display: block;
-      margin: 17px 0 8px;
-      color: var(--ink-soft);
-      font: 800 12px/1.4 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-      letter-spacing: .02em;
-    }
-    .dropzone {
-      display: grid;
-      min-height: 226px;
-      place-items: center;
-      padding: 16px;
-      border: 1.5px dashed rgba(126, 103, 80, .34);
-      border-radius: 22px;
-      background:
-        linear-gradient(180deg, rgba(255,255,255,.56), rgba(255,248,237,.72)),
-        repeating-linear-gradient(-45deg, rgba(159, 111, 70, .035) 0 1px, transparent 1px 9px);
-      text-align: center;
-      cursor: pointer;
-      transition: transform .18s cubic-bezier(.2,.8,.2,1), border-color .18s cubic-bezier(.2,.8,.2,1), background .18s cubic-bezier(.2,.8,.2,1);
-    }
-    .dropzone:hover,
-    .dropzone.active {
-      transform: translateY(-1px);
-      border-color: rgba(197, 109, 61, .74);
-      background: #fff9f0;
-    }
-    .dropzone input { display: none; }
-    .preview {
-      display: none;
-      width: 100%;
-      max-height: 270px;
-      border-radius: 18px;
-      object-fit: contain;
-      background: #eee5d8;
-      box-shadow: 0 16px 32px rgba(61, 43, 25, .12);
-    }
-    .preview.visible { display: block; }
-    .drop-copy.hidden { display: none; }
-    .drop-copy strong {
-      display: block;
-      margin-bottom: 8px;
-      font-size: 16px;
-    }
-    .drop-copy span {
-      color: var(--muted);
-      font: 12px/1.7 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    textarea {
-      width: 100%;
-      min-height: 112px;
-      resize: vertical;
-      padding: 13px 14px;
-      border: 1px solid var(--line);
-      border-radius: 16px;
-      outline: 0;
-      color: var(--ink);
-      line-height: 1.62;
-      background: rgba(255, 253, 249, .86);
-      box-shadow: 0 1px 0 rgba(255,255,255,.75) inset;
-      font-family: "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    textarea:focus,
-    input:focus {
-      border-color: rgba(197, 109, 61, .72);
-      box-shadow: 0 0 0 4px rgba(197, 109, 61, .13), 0 1px 0 rgba(255,255,255,.75) inset;
-    }
-    .options {
-      display: grid;
-      grid-template-columns: 1fr 1fr;
-      gap: 10px;
-    }
-    .option small {
-      display: block;
-      margin-bottom: 6px;
-      color: var(--muted);
-      font: 12px/1.4 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .option input {
-      width: 100%;
-      padding: 11px 12px;
-      border: 1px solid var(--line);
-      border-radius: 15px;
-      outline: 0;
-      color: var(--ink);
-      background: rgba(255, 253, 249, .92);
-      font-family: "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .button-row {
-      display: grid;
-      grid-template-columns: 1fr 1fr;
-      gap: 10px;
-      margin-top: 12px;
-    }
-    button {
-      min-height: 44px;
-      border-radius: 999px;
-      font: 850 14px/1 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-      cursor: pointer;
-      transition: transform .16s cubic-bezier(.2,.8,.2,1), box-shadow .16s cubic-bezier(.2,.8,.2,1), background .16s cubic-bezier(.2,.8,.2,1);
-    }
-    button:hover:not(:disabled) {
-      transform: translateY(-1px);
-    }
-    .primary {
-      border: 1px solid rgba(116, 55, 25, .14);
-      color: #fffaf3;
-      background: linear-gradient(180deg, var(--clay), var(--clay-dark));
-      box-shadow: 0 12px 22px rgba(158, 79, 43, .22), 0 1px 0 rgba(255,255,255,.28) inset;
-    }
-    .secondary {
-      border: 1px solid rgba(103, 82, 62, .18);
-      color: var(--ink);
-      background: rgba(255, 252, 246, .76);
-      box-shadow: 0 1px 0 rgba(255,255,255,.72) inset;
-    }
-    button:disabled {
-      cursor: wait;
-      opacity: .62;
-      transform: none;
-    }
-    .hint {
-      margin: 12px 0 0;
-      color: var(--muted);
-      font: 12px/1.75 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .build-panel {
-      margin-top: 16px;
-      padding: 14px;
-      border: 1px solid rgba(104, 84, 65, .13);
-      border-radius: 20px;
-      background: rgba(246, 238, 228, .62);
-    }
-    .progress-line {
-      display: flex;
-      justify-content: space-between;
-      gap: 12px;
-      color: var(--muted);
-      font: 12px/1.45 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .bar {
-      height: 8px;
-      margin: 10px 0;
-      overflow: hidden;
-      border-radius: 999px;
-      background: #e8dccf;
-    }
-    .bar span {
-      display: block;
-      width: 0;
-      height: 100%;
-      background: linear-gradient(90deg, var(--olive), #9c8d63);
-      transition: width .25s cubic-bezier(.2,.8,.2,1);
-    }
-    .log {
-      height: 140px;
-      margin: 10px 0 0;
-      padding: 12px;
-      overflow: auto;
-      border-radius: 16px;
-      color: #f4eadb;
-      background: #2a2119;
-      font: 12px/1.55 "Cascadia Mono", Consolas, monospace;
-      white-space: pre-wrap;
-    }
-    .kimi-prompt {
-      min-height: 230px;
-      font-size: 12px;
-      line-height: 1.62;
-      background:
-        linear-gradient(90deg, rgba(197,109,61,.10) 0 1px, transparent 1px),
-        rgba(255, 253, 249, .92);
-      background-size: 24px 100%;
-      padding-left: 18px;
-    }
-    .results-panel {
-      min-height: min(70vh, 760px);
-      padding: 20px;
-    }
-    .results-head {
-      display: flex;
-      justify-content: space-between;
-      gap: 14px;
-      align-items: center;
-      margin-bottom: 14px;
-    }
-    .results-head h2 {
-      margin: 0;
-      font-size: 22px;
-      letter-spacing: -.025em;
-    }
-    .status {
-      color: var(--muted);
-      font: 13px/1.45 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    #serverStatus {
-      padding: 9px 12px;
-      border: 1px solid rgba(104, 84, 65, .15);
-      border-radius: 999px;
-      background: rgba(255,252,246,.74);
-      white-space: nowrap;
-    }
-    .results-grid {
-      display: grid;
-      grid-template-columns: repeat(auto-fill, minmax(212px, 1fr));
-      gap: 14px;
-    }
-    .plan-panel {
-      display: none;
-      margin-bottom: 16px;
-      padding: 15px;
-      border: 1px solid rgba(197, 109, 61, .22);
-      border-radius: 22px;
-      background: linear-gradient(180deg, rgba(255, 249, 239, .92), rgba(250, 241, 230, .72));
-      box-shadow: 0 1px 0 rgba(255,255,255,.8) inset;
-    }
-    .plan-panel.visible { display: none; }
-    .plan-panel h3 {
-      margin: 0 0 8px;
-      font-size: 16px;
-      letter-spacing: -.01em;
-    }
-    .plan-summary {
-      margin: 0 0 12px;
-      color: var(--muted);
-      font: 13px/1.55 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .prompt-editor {
-      display: grid;
-      gap: 9px;
-      max-height: 360px;
-      overflow: auto;
-      padding-right: 4px;
-    }
-    .prompt-edit-row {
-      display: grid;
-      grid-template-columns: 34px 1fr 1.25fr;
-      gap: 8px;
-      align-items: center;
-    }
-    .prompt-edit-index {
-      color: var(--clay-dark);
-      font: 900 12px/1 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .prompt-edit-row input {
-      width: 100%;
-      min-height: 36px;
-      padding: 8px 10px;
-      border: 1px solid var(--line);
-      border-radius: 13px;
-      outline: 0;
-      color: var(--ink);
-      background: rgba(255,255,255,.72);
-      font: 12px/1.4 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .plan-tools {
-      display: flex;
-      gap: 10px;
-      align-items: center;
-      margin-top: 13px;
-    }
-    .plan-tools button {
-      min-height: 38px;
-      padding: 0 16px;
-      font-size: 13px;
-    }
-    .prompt-group {
-      margin-bottom: 18px;
-      padding: 14px;
-      border: 1px solid rgba(104, 84, 65, .14);
-      border-radius: 22px;
-      background: rgba(255, 252, 246, .72);
-      box-shadow: 0 1px 0 rgba(255,255,255,.72) inset;
-    }
-    .prompt-group-title {
-      margin: 0 0 5px;
-      font-size: 16px;
-      letter-spacing: -.015em;
-    }
-    .prompt-group-prompt {
-      margin: 0 0 13px;
-      color: var(--muted);
-      font: 12px/1.6 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .empty {
-      display: grid;
-      min-height: 462px;
-      place-items: center;
-      border: 1px dashed rgba(104, 84, 65, .22);
-      border-radius: 22px;
-      color: var(--muted);
-      background: rgba(255, 252, 246, .44);
-      text-align: center;
-      font-family: "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .empty strong {
-      display: block;
-      margin-bottom: 8px;
-      color: var(--ink);
-      font-family: "Fraunces", "Iowan Old Style", "Noto Serif SC", serif;
-      font-size: 18px;
-    }
-    .product {
-      overflow: hidden;
-      border: 1px solid rgba(104, 84, 65, .14);
-      border-radius: 20px;
-      background: rgba(255, 252, 246, .86);
-      box-shadow: 0 12px 28px rgba(74, 51, 30, .08);
-      transition: transform .18s cubic-bezier(.2,.8,.2,1), box-shadow .18s cubic-bezier(.2,.8,.2,1);
-    }
-    .product-media {
-      position: relative;
-      overflow: hidden;
-      background: #eadfce;
-    }
-    .product-tag {
-      position: absolute;
-      left: 10px;
-      right: 10px;
-      bottom: 10px;
-      display: none;
-      padding: 7px 9px;
-      border: 1px solid rgba(255,255,255,.48);
-      border-radius: 999px;
-      color: #fffaf3;
-      background: rgba(36, 29, 23, .68);
-      box-shadow: 0 12px 26px rgba(22, 18, 14, .22);
-      font: 11px/1.25 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-      overflow: hidden;
-      text-overflow: ellipsis;
-      white-space: nowrap;
-      backdrop-filter: blur(10px);
-    }
-    .product-tag.visible {
-      display: block;
-    }
-    .product:hover {
-      transform: translateY(-2px);
-      box-shadow: 0 18px 34px rgba(74, 51, 30, .12);
-    }
-    .product-image {
-      display: block;
-      width: 100%;
-      height: 190px;
-      object-fit: cover;
-      background: #eadfce;
-    }
-    .product-body {
-      padding: 12px;
-    }
-    .product-meta {
-      display: flex;
-      justify-content: space-between;
-      gap: 8px;
-      margin-bottom: 8px;
-      color: var(--muted);
-      font: 12px/1.45 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    .score {
-      color: var(--olive);
-      font-weight: 900;
-    }
-    .product-title {
-      min-height: 64px;
-      margin: 0 0 10px;
-      font: 13px/1.52 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-      display: -webkit-box;
-      -webkit-line-clamp: 3;
-      -webkit-box-orient: vertical;
-      overflow: hidden;
-    }
-    .product-footer {
-      display: flex;
-      justify-content: space-between;
-      gap: 8px;
-      padding-top: 10px;
-      border-top: 1px solid var(--line-soft);
-    }
-    .price {
-      color: var(--red);
-      font-size: 17px;
-      font-weight: 900;
-    }
-    .sales {
-      color: var(--muted);
-      font: 12px/1.45 "Microsoft YaHei UI", "Noto Sans SC", sans-serif;
-    }
-    [hidden] {
-      display: none !important;
-    }
-    .composer-head {
-      display: flex;
-      justify-content: space-between;
-      gap: 14px;
-      align-items: center;
-      margin-bottom: 14px;
-    }
-    .mode-tabs {
-      display: flex;
-      gap: 6px;
-      padding: 4px;
-      border: 1px solid rgba(104, 84, 65, .14);
-      border-radius: 999px;
-      background: rgba(246, 238, 228, .62);
-    }
-    .mode-tab {
-      min-height: 34px;
-      padding: 0 13px;
-      border: 0;
-      color: var(--muted);
-      background: transparent;
-      box-shadow: none;
-      font-size: 13px;
-    }
-    .mode-tab.active {
-      color: var(--ink);
-      background: rgba(255, 252, 246, .92);
-      box-shadow: 0 1px 0 rgba(255,255,255,.72) inset, 0 8px 18px rgba(74, 51, 30, .08);
-    }
-    .composer-mode {
-      display: none;
-    }
-    .composer-mode.active {
-      display: grid;
-      grid-template-columns: minmax(260px, 360px) minmax(300px, 1fr);
-      gap: 14px;
-      align-items: stretch;
-    }
-    .composer-mode.text-mode.active {
-      grid-template-columns: 1fr;
-    }
-    .composer-bottom {
-      display: grid;
-      grid-template-columns: minmax(260px, 360px) minmax(280px, 1fr) auto;
-      gap: 12px;
-      align-items: end;
-      margin-top: 14px;
-    }
-    .composer-actions {
-      display: flex;
-      gap: 10px;
-      justify-content: flex-end;
-      align-items: center;
-    }
-    .composer-actions button {
-      min-width: 138px;
-      padding: 0 18px;
-    }
-    .composer-log {
-      margin-top: 14px;
-    }
-    @media (max-width: 900px) {
-      .topbar {
-        align-items: stretch;
-        grid-template-columns: 1fr;
-      }
-      .composer-mode.active,
-      .composer-bottom {
-        grid-template-columns: 1fr;
-      }
-      .composer-actions {
-        justify-content: stretch;
-      }
-      .composer-actions button {
-        flex: 1;
-      }
-    }
-
-    /* Industrial terminal skin: keeps the agreed interaction model, only changes the surface language. */
-    :root {
-      --ink: #f2f2ec;
-      --ink-soft: #d7d3c8;
-      --muted: #8f897f;
-      --line: #302b25;
-      --line-soft: #26221e;
-      --panel: #151411;
-      --panel-solid: #151411;
-      --page: #0d0d0b;
-      --paper: #151411;
-      --clay: #ff6a32;
-      --clay-dark: #d94f1f;
-      --olive: #42dfff;
-      --red: #ff614a;
-      --shadow: none;
-      font-family: "JetBrains Mono", "Cascadia Mono", Consolas, "Microsoft YaHei UI", monospace;
-    }
-    body {
-      color: var(--ink);
-      background: #0d0d0b;
-    }
-    body::before {
-      opacity: .22;
-      background:
-        repeating-linear-gradient(0deg, transparent 0 2px, rgba(255,255,255,.035) 2px 3px),
-        linear-gradient(90deg, rgba(255,106,50,.04) 1px, transparent 1px);
-      background-size: 100% 4px, 82px 100%;
-      mask-image: none;
-    }
-    .shell {
-      width: min(1240px, calc(100% - 44px));
-      padding-top: 32px;
-    }
-    .topbar,
-    .panel,
-    .controls,
-    .results-panel,
-    .build-panel,
-    .prompt-group,
-    .product,
-    .empty,
-    textarea,
-    input,
-    button,
-    .dropzone,
-    .mode-tabs,
-    .mode-tab,
-    #serverStatus {
-      border-radius: 0;
-      box-shadow: none;
-      backdrop-filter: none;
-    }
-    .topbar {
-      padding: 0 0 28px;
-      border: 0;
-      border-bottom: 1px solid #302b25;
-      background: transparent;
-    }
-    .eyebrow {
-      color: var(--clay);
-      font: 900 10px/1.2 "JetBrains Mono", "Cascadia Mono", Consolas, monospace;
-      letter-spacing: .22em;
-    }
-    .eyebrow::before {
-      content: "POD / ";
-    }
-    h1 {
-      max-width: 760px;
-      color: #fffdf5;
-      font-family: "Arial Black", "Microsoft YaHei UI", sans-serif;
-      font-size: clamp(34px, 4.6vw, 56px);
-      font-weight: 950;
-      letter-spacing: -.06em;
-      line-height: .92;
-      text-shadow: 2px 2px 0 #0d0d0b, 4px 4px 0 rgba(66,223,255,.45);
-      text-transform: uppercase;
-    }
-    .subtitle,
-    .hint,
-    .status,
-    .field-label,
-    .option small,
-    .drop-copy span,
-    .plan-summary,
-    .prompt-group-prompt,
-    .sales,
-    .product-meta {
-      color: var(--muted);
-      font-family: "JetBrains Mono", "Cascadia Mono", Consolas, "Microsoft YaHei UI", monospace;
-    }
-    #serverStatus {
-      padding: 10px 12px;
-      border: 1px solid #302b25;
-      color: var(--olive);
-      background: #171511;
-      text-transform: uppercase;
-    }
-    .layout {
-      gap: 16px;
-    }
-    .panel {
-      border: 1px solid #302b25;
-      background: #151411;
-    }
-    .results-panel {
-      min-height: 68vh;
-      padding: 18px;
-    }
-    .results-head {
-      padding-bottom: 14px;
-      border-bottom: 1px solid #302b25;
-    }
-    .results-head h2,
-    .section-title,
-    .plan-panel h3,
-    .prompt-group-title {
-      color: #fffdf5;
-      font-family: "Arial Black", "Microsoft YaHei UI", sans-serif;
-      letter-spacing: -.04em;
-    }
-    .results-grid {
-      grid-template-columns: repeat(auto-fill, minmax(190px, 1fr));
-      gap: 1px;
-      padding: 1px;
-      background: #302b25;
-    }
-    .empty {
-      border: 1px dashed #3b352e;
-      color: var(--muted);
-      background: #11100e;
-    }
-    .empty strong {
-      color: #fffdf5;
-      font-family: "Arial Black", "Microsoft YaHei UI", sans-serif;
-      letter-spacing: -.03em;
-    }
-    .controls {
-      padding: 16px;
-    }
-    .composer-head {
-      padding-bottom: 14px;
-      border-bottom: 1px solid #302b25;
-    }
-    .mode-tabs {
-      gap: 1px;
-      padding: 0;
-      border: 1px solid #302b25;
-      background: #302b25;
-    }
-    .mode-tab {
-      min-height: 36px;
-      color: var(--muted);
-      background: #151411;
-      text-transform: uppercase;
-    }
-    .mode-tab.active {
-      color: #0d0d0b;
-      background: var(--olive);
-    }
-    .dropzone {
-      min-height: 176px;
-      border: 1px dashed #3b352e;
-      background: #11100e;
-    }
-    .dropzone:hover,
-    .dropzone.active {
-      transform: none;
-      border-color: var(--clay);
-      background: #171511;
-    }
-    .drop-copy strong {
-      color: #fffdf5;
-      font-family: "Arial Black", "Microsoft YaHei UI", sans-serif;
-      letter-spacing: -.035em;
-    }
-    .preview,
-    .product-image {
-      background: #0f0e0c;
-    }
-    textarea,
-    .option input,
-    .prompt-edit-row input {
-      border: 1px solid #302b25;
-      color: #fffdf5;
-      background: #0f0e0c;
-      box-shadow: none;
-    }
-    textarea:focus,
-    input:focus {
-      border-color: var(--clay);
-      box-shadow: 0 0 0 1px var(--clay);
-    }
-    .kimi-prompt {
-      min-height: 176px;
-      background:
-        repeating-linear-gradient(90deg, transparent 0 23px, rgba(255,106,50,.12) 23px 24px),
-        #0f0e0c;
-    }
-    .composer-bottom {
-      border-top: 1px solid #302b25;
-      padding-top: 14px;
-    }
-    .primary {
-      border: 1px solid var(--clay);
-      color: #0d0d0b;
-      background: var(--clay);
-      text-transform: uppercase;
-    }
-    .secondary {
-      border: 1px solid #3b352e;
-      color: #fffdf5;
-      background: #1b1915;
-      text-transform: uppercase;
-    }
-    button:hover:not(:disabled) {
-      transform: none;
-      outline: 1px solid var(--olive);
-      outline-offset: -2px;
-    }
-    .build-panel {
-      border: 1px solid #302b25;
-      background: #11100e;
-    }
-    .bar {
-      border: 1px solid #302b25;
-      background: #0d0d0b;
-    }
-    .bar span {
-      background: var(--olive);
-    }
-    .log {
-      border: 1px solid #302b25;
-      color: #e9e3d4;
-      background: #080807;
-    }
-    .product {
-      border: 0;
-      background: #151411;
-    }
-    .product:hover {
-      transform: none;
-      outline: 1px solid var(--clay);
-      outline-offset: -1px;
-      box-shadow: none;
-    }
-    .product-media {
-      background: #0f0e0c;
-    }
-    .product-tag {
-      left: 8px;
-      right: 8px;
-      bottom: 8px;
-      border: 1px solid rgba(66, 223, 255, .58);
-      color: var(--olive);
-      background: rgba(8, 8, 7, .82);
-      box-shadow: none;
-      text-transform: uppercase;
-      backdrop-filter: none;
-    }
-    .product-title {
-      min-height: 48px;
-      color: #fffdf5;
-      font-family: "JetBrains Mono", "Cascadia Mono", Consolas, "Microsoft YaHei UI", monospace;
-      font-size: 12px;
-      line-height: 1.35;
-      -webkit-line-clamp: 2;
-    }
-    .product-footer {
-      border-top: 1px solid #302b25;
-    }
-    .price {
-      color: var(--clay);
-      font-family: "Arial Black", "Microsoft YaHei UI", sans-serif;
-      font-size: 18px;
-    }
-  </style>
-</head>
-<body>
-  <main class="shell">
-    <header class="topbar">
-      <div>
-        <p class="eyebrow">IMAGE TO KIMI TO LISTING CLIP</p>
-        <h1>纯 Listing 组货检索</h1>
-        <p class="subtitle">图片交给 Kimi 生成 10 个可组货商品检索词，再用纯 listing CLIP 索引召回；下方文本框单独用于直接检索 CLIP。</p>
-      </div>
-      <div class="status" id="serverStatus">9990 等待连接</div>
-    </header>
-
-    <section class="layout">
-      <section class="panel results-panel">
-        <div class="results-head">
-          <h2>匹配结果</h2>
-          <span class="status" id="status">等待输入</span>
-        </div>
-        <div class="plan-panel" id="planPanel">
-          <h3>Kimi 组货商品 JSON</h3>
-          <p class="plan-summary" id="planSummary"></p>
-          <div class="prompt-editor" id="promptEditor"></div>
-          <div class="plan-tools">
-            <button class="primary" id="editedPromptButton" type="button">用编辑后 Prompt 检索</button>
-            <span class="status">改英文 en 最影响 CLIP 召回</span>
-          </div>
-        </div>
-        <div id="resultsGrid">
-          <div class="empty"><div><strong>还没有结果</strong><span>上传图片跑 Kimi，或输入文本直接检索 CLIP</span></div></div>
-        </div>
-      </section>
-
-      <aside class="panel controls">
-        <div class="composer-head">
-          <h2 class="section-title">输入</h2>
-          <div class="mode-tabs" aria-label="输入模式">
-            <button class="mode-tab active" id="imageModeTab" type="button">图片组货</button>
-            <button class="mode-tab" id="textModeTab" type="button">直接 CLIP</button>
-          </div>
-        </div>
-
-        <section class="composer-mode active" id="imageMode">
-          <label class="dropzone" id="dropzone" for="imageInput">
-            <input id="imageInput" type="file" accept="image/*">
-            <img class="preview" id="preview" alt="待分析图片预览">
-            <span class="drop-copy" id="dropCopy">
-              <strong>点击或拖入商品图片</strong>
-              <span>Image -> Kimi -> 组货商品 JSON -> Listing CLIP</span>
-            </span>
-          </label>
-          <div>
-            <label class="field-label" for="kimiPromptInput">发给 Kimi 的 Prompt（可编辑）</label>
-            <textarea class="kimi-prompt" id="kimiPromptInput"></textarea>
-          </div>
-        </section>
-
-        <section class="composer-mode text-mode" id="textMode">
-          <div>
-            <label class="field-label" for="queryInput">CLIP 检索词 / Listing 搜索</label>
-            <textarea id="queryInput" placeholder="这里不发给 Kimi，只用于直接检索 CLIP。例如：transparent storage box for makeup organizer"></textarea>
-          </div>
-        </section>
-
-        <div class="composer-bottom">
-          <div class="options">
-            <label class="option">
-              <small>最低价格 USD</small>
-              <input id="minPrice" type="number" min="0" step="0.01" placeholder="不限">
-            </label>
-            <label class="option">
-              <small>最高价格 USD</small>
-              <input id="maxPrice" type="number" min="0" step="0.01" placeholder="不限">
-            </label>
-          </div>
-          <p class="hint">Kimi 请求只带图片；直接 CLIP 模式只使用文本。结果卡片只显示图片、方向、标题、价格和销量。</p>
-          <div class="composer-actions">
-            <button class="primary" id="assemblyButton" type="button">生成 Kimi 组货方案</button>
-            <button class="secondary" id="searchButton" type="button">仅 Listing 检索</button>
-          </div>
-        </div>
-      </aside>
-    </section>
-  </main>
-
-  <script>
-    const apiBase = 'http://127.0.0.1:9990';
-    const defaultKimiPrompt = `你是跨境电商组货商品检索词生成器。你只根据用户上传的图片生成可一起售卖/一起购买的商品检索词。
-
-任务：输出10个“具体可采购商品”，用于后续纯 listing CLIP 检索。
-
-生成原则：
-1. 不要只找外观相似品；优先覆盖互补品、同场景加购、替代升级、耗材补充、收纳展示、维护清洁、配套工具、礼盒套装里的其他商品。
-2. 每条必须是具体商品，不要写大类、策略、理由或营销词。不要输出“配件、用品、产品、套装、工具”这种过宽泛词，除非前面有清晰具体限定。
-3. 中文 zh 要像能直接给采购看的商品短名：主体品类 + 关键材质/结构/场景/人群/规格，尽量 6-18 个中文字符。
-4. 英文 en 要像英文 listing 标题检索词：6-14 个英文词，必须包含明确 product noun，并尽量包含 material / shape / color / scene / target user / size / function 中的2-4个要素。
-5. 如果图片主体不确定，根据最明显视觉元素推断；不要解释不确定性。
-6. 10条之间要有明显差异，避免同义改写刷数量。
-
-输出格式：只返回合法 JSON 对象，且只能包含 prompts 字段。
-prompts 是长度为10的数组，每个元素只能包含 zh 和 en 两个字段。`;
-    let selectedFile = null;
-    let currentPlan = { prompts: [] };
-
-    // Escape API-provided text before inserting it into cards.
-    function escapeHtml(value) {
-      const text = String(value === null || value === undefined ? '' : value);
-      return text.replace(/[&<>"']/g, function replaceCharacter(character) {
-        const entities = { '&': '&amp;', '<': '&lt;', '>': '&gt;', '"': '&quot;', "'": '&#39;' };
-        return entities[character];
-      });
-    }
-
-    // Build the product image URL returned by the listing service.
-    function getProductImageUrl(product) {
-      if (product.img_url && /^https?:\/\//i.test(String(product.img_url))) return `${apiBase}/api/cdn/image?url=${encodeURIComponent(String(product.img_url))}`;
-      return '';
-    }
-
-    // Remove the whole product card when its CDN image fails to load.
-    function handleProductImageError(imageElement) {
-      const productCard = imageElement.closest('.product');
-      if (productCard) productCard.remove();
-    }
-
-    // Show the selected image locally before sending it to Kimi.
-    function showPreview(file) {
-      const preview = document.getElementById('preview');
-      const dropCopy = document.getElementById('dropCopy');
-      if (!file) {
-        preview.removeAttribute('src');
-        preview.classList.remove('visible');
-        dropCopy.classList.remove('hidden');
-        return;
-      }
-      preview.src = URL.createObjectURL(file);
-      preview.classList.add('visible');
-      dropCopy.classList.add('hidden');
-    }
-
-    // Accept an image from the file picker or drag-and-drop area.
-    function setSelectedFile(file) {
-      if (!file || !file.type || !file.type.startsWith('image/')) return;
-      selectedFile = file;
-      showPreview(file);
-      document.getElementById('status').textContent = `已选择：${file.name}`;
-    }
-
-    // Render one search result card.
-    function renderProductCard(product, index) {
-      const title = product.title || product.listing_text || product.title_en || `商品 ${product.id || index + 1}`;
-      const price = product.price_usd === undefined || product.price_usd === null || product.price_usd === '' ? '--' : `$${Number(product.price_usd).toFixed(2)}`;
-      const salesValue = product.sales_total === undefined || product.sales_total === null ? product.sales : product.sales_total;
-      const sales = salesValue === undefined || salesValue === '' || salesValue === null ? '' : `销量 ${salesValue}`;
-      const imageUrl = getProductImageUrl(product);
-      const tag = product.bundle_tag || product.search_prompt || product.search_prompt_en || '';
-      return `<article class="product">
-        <div class="product-media">
-          <img class="product-image" src="${escapeHtml(imageUrl)}" alt="${escapeHtml(title)}" onerror="handleProductImageError(this)">
-          <span class="product-tag${tag ? ' visible' : ''}">${escapeHtml(tag)}</span>
-        </div>
-        <div class="product-body">
-          <p class="product-title">${escapeHtml(title)}</p>
-          <div class="product-footer"><span class="price">${escapeHtml(price)}</span><span class="sales">${escapeHtml(sales)}</span></div>
-        </div>
-      </article>`;
-    }
-
-    // Render all cards or an empty state.
-    function renderResults(results) {
-      const grid = document.getElementById('resultsGrid');
-      if (!results || results.length === 0) {
-        grid.innerHTML = '<div class="empty"><div><strong>没有符合条件的商品</strong><span>换个英文 prompt 或放宽价格范围</span></div></div>';
-        return;
-      }
-      let html = '<div class="results-grid">';
-      let index = 0;
-      while (index < results.length) {
-        html += renderProductCard(results[index], index);
-        index += 1;
-      }
-      grid.innerHTML = `${html}</div>`;
-    }
-
-    // Render Kimi's bundle-product plan as editable prompt rows.
-    function renderPlan(plan, resultCount) {
-      const panel = document.getElementById('planPanel');
-      const prompts = plan && Array.isArray(plan.prompts) ? plan.prompts : [];
-      currentPlan = { prompts };
-      document.getElementById('planSummary').textContent = `${prompts.length} 个组货商品 · 每个 Top 1 · ${resultCount} 个召回位 · thinking disabled`;
-      let editorHtml = '';
-      let promptIndex = 0;
-      while (promptIndex < prompts.length) {
-        const prompt = prompts[promptIndex] || {};
-        editorHtml += `<div class="prompt-edit-row">
-          <span class="prompt-edit-index">#${promptIndex + 1}</span>
-          <input class="prompt-zh" data-index="${promptIndex}" value="${escapeHtml(prompt.zh || '')}" placeholder="中文商品名">
-          <input class="prompt-en" data-index="${promptIndex}" value="${escapeHtml(prompt.en || '')}" placeholder="English listing keywords">
-        </div>`;
-        promptIndex += 1;
-      }
-      document.getElementById('promptEditor').innerHTML = editorHtml;
-      panel.classList.add('visible');
-    }
-
-    // Read the currently edited prompt rows back into JSON shape.
-    function readEditedPlan() {
-      const rows = Array.from(document.querySelectorAll('.prompt-edit-row'));
-      const prompts = rows.map(function mapPromptRow(row) {
-        return {
-          zh: row.querySelector('.prompt-zh').value.trim(),
-          en: row.querySelector('.prompt-en').value.trim(),
-        };
-      }).filter(function keepPrompt(prompt) {
-        return prompt.zh || prompt.en;
-      });
-      currentPlan = { prompts };
-      return currentPlan;
-    }
-
-    // Render listing results as one output wall and move each Kimi prompt onto the product image.
-    function renderPromptGroups(groups) {
-      const grid = document.getElementById('resultsGrid');
-      if (!groups || groups.length === 0) {
-        renderResults([]);
-        return;
-      }
-      const products = [];
-      let groupIndex = 0;
-      while (groupIndex < groups.length) {
-        const group = groups[groupIndex];
-        const groupResults = group.results || [];
-        const promptText = `${group.prompt || ''}${group.prompt_en ? ` / ${group.prompt_en}` : ''}`;
-        let resultIndex = 0;
-        while (resultIndex < groupResults.length) {
-          products.push(Object.assign({}, groupResults[resultIndex], { bundle_tag: promptText }));
-          resultIndex += 1;
-        }
-        groupIndex += 1;
-      }
-      renderResults(products);
-    }
-
-    // Request one pure listing search.
-    async function runSearch() {
-      const query = document.getElementById('queryInput').value.trim();
-      const topK = 24;
-      const minPrice = document.getElementById('minPrice').value.trim();
-      const maxPrice = document.getElementById('maxPrice').value.trim();
-      if (!query) {
-        document.getElementById('status').textContent = '先输入 listing/prompt';
-        return;
-      }
-      const button = document.getElementById('searchButton');
-      button.disabled = true;
-      button.textContent = '检索中…';
-      document.getElementById('planPanel').classList.remove('visible');
-      document.getElementById('status').textContent = '正在查纯 listing 索引…';
-      try {
-        const form = new FormData();
-        form.append('query', query);
-        form.append('top_k', String(topK));
-        if (minPrice !== '') form.append('min_price', minPrice);
-        if (maxPrice !== '') form.append('max_price', maxPrice);
-        const startedAt = performance.now();
-        const response = await fetch(`${apiBase}/api/search/text`, { method: 'POST', body: form });
-        const data = await response.json();
-        if (!response.ok || data.error) throw new Error(data.error || '检索失败');
-        renderResults(data.results || []);
-        document.getElementById('status').textContent = `${(data.results || []).length} 条 · ${Math.round(performance.now() - startedAt)} ms`;
-      } catch (error) {
-        document.getElementById('resultsGrid').innerHTML = `<div class="empty"><div><strong>检索失败</strong><span>${escapeHtml(error.message)}</span></div></div>`;
-        document.getElementById('status').textContent = '不可用';
-      } finally {
-        button.disabled = false;
-        button.textContent = '仅 Listing 检索';
-      }
-    }
-
-    // Search the listing index again with manually edited Kimi prompts.
-    async function runEditedPromptSearch() {
-      const plan = readEditedPlan();
-      const minPrice = document.getElementById('minPrice').value.trim();
-      const maxPrice = document.getElementById('maxPrice').value.trim();
-      if (!plan.prompts.length) {
-        document.getElementById('status').textContent = '先生成或填写 Prompt';
-        return;
-      }
-      const button = document.getElementById('editedPromptButton');
-      button.disabled = true;
-      button.textContent = '检索中…';
-      document.getElementById('status').textContent = '用编辑后 Prompt 查 Listing CLIP…';
-      try {
-        const payload = { prompts: plan.prompts, top_k: 1 };
-        if (minPrice !== '') payload.min_price = Number(minPrice);
-        if (maxPrice !== '') payload.max_price = Number(maxPrice);
-        const startedAt = performance.now();
-        const response = await fetch(`${apiBase}/api/search/prompts`, {
-          method: 'POST',
-          headers: { 'Content-Type': 'application/json' },
-          body: JSON.stringify(payload),
-        });
-        const data = await response.json();
-        if (!response.ok || data.error) throw new Error(data.error || '编辑 Prompt 检索失败');
-        renderPlan(data.plan, (data.results || []).length);
-        renderPromptGroups(data.groups || []);
-        document.getElementById('status').textContent = `${(data.results || []).length} 条 · ${Math.round(performance.now() - startedAt)} ms`;
-      } catch (error) {
-        document.getElementById('resultsGrid').innerHTML = `<div class="empty"><div><strong>编辑 Prompt 检索失败</strong><span>${escapeHtml(error.message)}</span></div></div>`;
-        document.getElementById('status').textContent = '不可用';
-      } finally {
-        button.disabled = false;
-        button.textContent = '用编辑后 Prompt 检索';
-      }
-    }
-
-    // Run the uploaded image through Kimi JSON prompts and pure listing CLIP recall.
-    async function runAssembly() {
-      const topK = 1;
-      const minPrice = document.getElementById('minPrice').value.trim();
-      const maxPrice = document.getElementById('maxPrice').value.trim();
-      const kimiPrompt = document.getElementById('kimiPromptInput').value.trim() || defaultKimiPrompt;
-      if (!selectedFile) {
-        document.getElementById('status').textContent = '先上传图片；Kimi 不读取文本框';
-        return;
-      }
-      const button = document.getElementById('assemblyButton');
-      button.disabled = true;
-      document.getElementById('searchButton').disabled = true;
-      document.getElementById('editedPromptButton').disabled = true;
-      button.textContent = '组货中…';
-      document.getElementById('status').textContent = 'Image -> Kimi -> 组货商品 JSON -> Listing CLIP…';
-      document.getElementById('planPanel').classList.remove('visible');
-      document.getElementById('resultsGrid').innerHTML = '<div class="empty"><div><strong>正在生成组货商品</strong><span>Kimi 完成后会查纯 listing 索引</span></div></div>';
-      try {
-        const form = new FormData();
-        form.append('file', selectedFile);
-        form.append('kimi_prompt', kimiPrompt);
-        form.append('top_k', String(topK));
-        if (minPrice !== '') form.append('min_price', minPrice);
-        if (maxPrice !== '') form.append('max_price', maxPrice);
-        const startedAt = performance.now();
-        const response = await fetch(`${apiBase}/api/assemble`, { method: 'POST', body: form });
-        const data = await response.json();
-        if (!response.ok || data.error) throw new Error(data.error || '组货失败');
-        if (data.kimi_prompt) document.getElementById('kimiPromptInput').value = data.kimi_prompt;
-        renderPlan(data.plan, (data.results || []).length);
-        renderPromptGroups(data.groups || []);
-        document.getElementById('status').textContent = `${(data.results || []).length} 条 · ${Math.round(performance.now() - startedAt)} ms`;
-      } catch (error) {
-        document.getElementById('resultsGrid').innerHTML = `<div class="empty"><div><strong>组货失败</strong><span>${escapeHtml(error.message)}</span></div></div>`;
-        document.getElementById('status').textContent = '组货不可用';
-      } finally {
-        button.disabled = false;
-        document.getElementById('searchButton').disabled = false;
-        document.getElementById('editedPromptButton').disabled = false;
-        button.textContent = '生成 Kimi 组货方案';
-      }
-    }
-
-    // Render only the compact server readiness badge.
-    function renderServerStatus(data) {
-      const kimiStatus = data.kimi_configured ? 'Kimi ready' : 'Kimi key missing';
-      document.getElementById('serverStatus').textContent = `${data.index_exists ? '9990 index ready' : '9990 index pending'} · ${kimiStatus}`;
-    }
-
-    // Poll the server status for the compact readiness badge.
-    async function refreshStatus() {
-      try {
-        const response = await fetch(`${apiBase}/api/index/status`, { cache: 'no-store' });
-        const data = await response.json();
-        renderServerStatus(data);
-      } catch (error) {
-        document.getElementById('serverStatus').textContent = '9990 未启动';
-      }
-    }
-
-    // Handle drag-over feedback for the image drop area.
-    function handleDragOver(event) {
-      event.preventDefault();
-      document.getElementById('dropzone').classList.add('active');
-    }
-
-    // Remove drag-over feedback after the pointer leaves the drop area.
-    function handleDragLeave() {
-      document.getElementById('dropzone').classList.remove('active');
-    }
-
-    // Handle a dropped image file.
-    function handleDrop(event) {
-      event.preventDefault();
-      handleDragLeave();
-      setSelectedFile(event.dataTransfer.files[0]);
-    }
-
-    // Update the selected file when the native file picker changes.
-    function handleFileChange(event) {
-      setSelectedFile(event.target.files[0]);
-    }
-
-    // Search the CLIP text box with Ctrl+Enter.
-    function handleQueryKeydown(event) {
-      if ((event.ctrlKey || event.metaKey) && event.key === 'Enter') runSearch();
-    }
-
-    // Switch the bottom composer between image assembly and direct CLIP search.
-    function switchMode(mode) {
-      const imageMode = document.getElementById('imageMode');
-      const textMode = document.getElementById('textMode');
-      const imageTab = document.getElementById('imageModeTab');
-      const textTab = document.getElementById('textModeTab');
-      const isImageMode = mode === 'image';
-      imageMode.classList.toggle('active', isImageMode);
-      textMode.classList.toggle('active', !isImageMode);
-      imageTab.classList.toggle('active', isImageMode);
-      textTab.classList.toggle('active', !isImageMode);
-    }
-
-    document.getElementById('assemblyButton').addEventListener('click', runAssembly);
-    document.getElementById('searchButton').addEventListener('click', runSearch);
-    document.getElementById('editedPromptButton').addEventListener('click', runEditedPromptSearch);
-    document.getElementById('imageModeTab').addEventListener('click', function activateImageMode() { switchMode('image'); });
-    document.getElementById('textModeTab').addEventListener('click', function activateTextMode() { switchMode('text'); });
-    document.getElementById('queryInput').addEventListener('keydown', handleQueryKeydown);
-    document.getElementById('kimiPromptInput').value = defaultKimiPrompt;
-    document.getElementById('imageInput').addEventListener('change', handleFileChange);
-    document.getElementById('dropzone').addEventListener('dragover', handleDragOver);
-    document.getElementById('dropzone').addEventListener('dragleave', handleDragLeave);
-    document.getElementById('dropzone').addEventListener('drop', handleDrop);
-    setInterval(refreshStatus, 3000);
-    refreshStatus();
-  </script>
-</body>
-</html>

bundle/clip/config.example.json

@@ -1,9 +0,0 @@
-{
-  "kimi": {
-    "api_key": "",
-    "endpoint": "https://api.moonshot.cn/v1/chat/completions",
-    "model": "kimi-k2.6",
-    "temperature": 0.6,
-    "max_completion_tokens": 1200
-  }
-}

bundle/clip/data/full_clip_index/products_full_prices.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a63876a98e6c4e4341566c90e00b0f4001a62ad69d55c474884e96c5aca141dc
-size 37368003

bundle/clip/data/full_listing_index/cleaning_report.json

@@ -1,8 +0,0 @@
-{
-  "source": 286548,
-  "kept": 273454,
-  "dropped_missing": 0,
-  "dropped_short": 7554,
-  "dropped_duplicate": 5540,
-  "updated_at": "2026-08-21 09:13:05"
-}

bundle/clip/data/full_listing_index/products_listing.index

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e50a979e2386e24b862bfbd23fb90658ccc60e406e612144808318bde3f631e
-size 560033837

bundle/clip/data/full_listing_index/products_listing_meta.runtime.json

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0c10a2a02aab00a382f9b02cc521e11a3225a28bf334fa987e226acf5832767f
-size 139038585

bundle/clip/data/full_listing_index/progress.json

@@ -1,6 +0,0 @@
-{
-  "completed": 273454,
-  "total": 273454,
-  "status": "complete",
-  "updated_at": "2026-08-21 09:51:05"
-}

bundle/clip/data/yunqi_clip_training/last_checkpoint.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:57f6be30140cb9f03fd6d7b7aa83fefc2c4441ef4244608a8391084c0031c35a
-size 692418197

bundle/clip/models/open_clip_pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1bd3c7172de5b207ceac554f5ab5266166f3b9baccc9af5989bc801016d080ad
-size 605219813

bundle/clip/work/build_full_listing_index.py

@@ -1,395 +0,0 @@
-"""Build a cleaned full-product CLIP text index from listing metadata."""
-
-import argparse
-import json
-import os
-import re
-import time
-from pathlib import Path
-
-import faiss
-import numpy as np
-import open_clip
-import torch
-
-
-CLIP_DIR = Path(r"F:\Clip")
-SOURCE_INDEX_DIR = CLIP_DIR / "data" / "full_clip_index"
-SOURCE_METADATA_PATH = SOURCE_INDEX_DIR / "products_full_meta.json"
-PRICE_METADATA_PATH = SOURCE_INDEX_DIR / "products_full_prices.json"
-MODEL_PATH = CLIP_DIR / "models" / "open_clip_pytorch_model.bin"
-CHECKPOINT_PATH = CLIP_DIR / "data" / "yunqi_clip_training" / "last_checkpoint.pt"
-OUTPUT_DIR = CLIP_DIR / "data" / "full_listing_index"
-METADATA_PATH = OUTPUT_DIR / "products_listing_meta.json"
-EMBEDDINGS_PATH = OUTPUT_DIR / "products_listing_embeddings.npy"
-INDEX_PATH = OUTPUT_DIR / "products_listing.index"
-PROGRESS_PATH = OUTPUT_DIR / "progress.json"
-REPORT_PATH = OUTPUT_DIR / "cleaning_report.json"
-MODEL_NAME = "ViT-B-32"
-DEFAULT_BATCH_SIZE = 256
-DEFAULT_SAVE_EVERY = 2048
-
-STOP_WORDS = {
-    "with",
-    "for",
-    "and",
-    "the",
-    "set",
-    "pcs",
-    "piece",
-    "pieces",
-    "pack",
-    "new",
-    "hot",
-    "sale",
-    "best",
-    "high",
-    "quality",
-    "portable",
-    "creative",
-    "fashion",
-    "women",
-    "men",
-    "kids",
-    "girls",
-    "boys",
-    "home",
-    "office",
-    "outdoor",
-    "indoor",
-}
-
-
-def parse_arguments():
-    """Parse options for a resumable listing-index build."""
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--batch-size", type=int, default=DEFAULT_BATCH_SIZE)
-    parser.add_argument("--save-every", type=int, default=DEFAULT_SAVE_EVERY)
-    parser.add_argument("--force-clean", action="store_true")
-    return parser.parse_args()
-
-
-def write_json_atomic(path, payload):
-    """Write JSON through a temporary file and replace the target atomically."""
-    path.parent.mkdir(parents=True, exist_ok=True)
-    temporary_path = path.with_suffix(path.suffix + ".tmp")
-    temporary_path.write_text(
-        json.dumps(payload, ensure_ascii=False, indent=2),
-        encoding="utf-8",
-    )
-    last_error = None
-    attempt = 0
-    while attempt < 10:
-        try:
-            os.replace(temporary_path, path)
-            return
-        except PermissionError as error:
-            last_error = error
-            time.sleep(0.5)
-        attempt += 1
-    raise last_error
-
-
-def clean_spaces(value):
-    """Collapse noisy whitespace and remove invisible control characters."""
-    text = str(value or "").replace("\u0000", " ")
-    text = re.sub(r"\s+", " ", text)
-    return text.strip()
-
-
-def looks_mojibake(value):
-    """Detect obviously broken text so English listing can be preferred."""
-    text = str(value or "")
-    if not text:
-        return False
-    bad_count = 0
-    for character in text:
-        if character == "�":
-            bad_count += 1
-    return bad_count >= max(3, len(text) // 12)
-
-
-def choose_listing_text(record):
-    """Choose the cleanest searchable listing text from one product record."""
-    title_en = clean_spaces(record.get("title_en", ""))
-    title_cn = clean_spaces(record.get("title_cn", ""))
-    title = clean_spaces(record.get("title", ""))
-    if title_en:
-        return title_en
-    if title and not looks_mojibake(title):
-        return title
-    if title_cn and not looks_mojibake(title_cn):
-        return title_cn
-    return title_en or title or title_cn
-
-
-def normalize_for_exact_dedupe(value):
-    """Build a strict title key for exact duplicate removal."""
-    text = clean_spaces(value).lower()
-    text = re.sub(r"[^a-z0-9]+", " ", text)
-    text = re.sub(r"\s+", " ", text)
-    return text.strip()
-
-
-def make_family_key(value):
-    """Build a coarse key used later to avoid same-looking Top results."""
-    normalized = normalize_for_exact_dedupe(value)
-    tokens = normalized.split(" ")
-    kept_tokens = []
-    for token in tokens:
-        if len(token) <= 2:
-            continue
-        if token in STOP_WORDS:
-            continue
-        if token.isdigit():
-            continue
-        kept_tokens.append(token)
-    unique_tokens = []
-    for token in kept_tokens:
-        if token not in unique_tokens:
-            unique_tokens.append(token)
-    if len(unique_tokens) <= 2:
-        return normalized[:80]
-    return " ".join(unique_tokens[:10])
-
-
-def load_source_products():
-    """Load the existing full product metadata generated by the image index."""
-    if not SOURCE_METADATA_PATH.exists():
-        raise FileNotFoundError(f"Missing source metadata: {SOURCE_METADATA_PATH}")
-    return json.loads(SOURCE_METADATA_PATH.read_text(encoding="utf-8"))
-
-
-def clean_products(raw_products):
-    """Drop unusable listings and exact duplicate listing records."""
-    cleaned_products = []
-    seen_titles = set()
-    seen_ids = set()
-    dropped_short = 0
-    dropped_duplicate = 0
-    dropped_missing = 0
-    for raw_product in raw_products:
-        product_id = str(raw_product.get("id", "")).strip()
-        listing_text = choose_listing_text(raw_product)
-        exact_key = normalize_for_exact_dedupe(listing_text)
-        if not product_id or not listing_text:
-            dropped_missing += 1
-            continue
-        if len(exact_key) < 8:
-            dropped_short += 1
-            continue
-        if product_id in seen_ids or exact_key in seen_titles:
-            dropped_duplicate += 1
-            continue
-        product = raw_product.copy()
-        product["title"] = listing_text
-        product["listing_text"] = listing_text
-        product["listing_key"] = exact_key
-        product["family_key"] = make_family_key(listing_text)
-        product["img_url"] = f"/listing-images/{product_id}.jpg"
-        cleaned_products.append(product)
-        seen_ids.add(product_id)
-        seen_titles.add(exact_key)
-    report = {
-        "source": len(raw_products),
-        "kept": len(cleaned_products),
-        "dropped_missing": dropped_missing,
-        "dropped_short": dropped_short,
-        "dropped_duplicate": dropped_duplicate,
-        "updated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
-    }
-    return cleaned_products, report
-
-
-def load_or_create_clean_products(force_clean):
-    """Reuse cleaned metadata unless a fresh cleaning pass is requested."""
-    if METADATA_PATH.exists() and not force_clean:
-        return json.loads(METADATA_PATH.read_text(encoding="utf-8"))
-    raw_products = load_source_products()
-    cleaned_products, report = clean_products(raw_products)
-    write_json_atomic(METADATA_PATH, cleaned_products)
-    write_json_atomic(REPORT_PATH, report)
-    return cleaned_products
-
-
-def load_model():
-    """Load the trained CLIP text tower on CPU."""
-    if not MODEL_PATH.exists():
-        raise FileNotFoundError(f"Missing base model: {MODEL_PATH}")
-    if not CHECKPOINT_PATH.exists():
-        raise FileNotFoundError(f"Missing trained checkpoint: {CHECKPOINT_PATH}")
-    device = torch.device("cpu")
-    model, _, _ = open_clip.create_model_and_transforms(
-        MODEL_NAME,
-        pretrained=str(MODEL_PATH),
-    )
-    checkpoint = torch.load(CHECKPOINT_PATH, map_location=device, weights_only=False)
-    model.load_state_dict(checkpoint["model"])
-    model = model.to(device)
-    model.eval()
-    tokenizer = open_clip.get_tokenizer(MODEL_NAME)
-    return model, tokenizer, device
-
-
-def load_progress(total):
-    """Read the last completed text embedding count for resume."""
-    if not PROGRESS_PATH.exists():
-        return 0
-    progress = json.loads(PROGRESS_PATH.read_text(encoding="utf-8"))
-    if int(progress.get("total", total)) != total:
-        raise RuntimeError("Existing listing progress does not match cleaned metadata")
-    return max(0, min(total, int(progress.get("completed", 0))))
-
-
-def open_embedding_memmap(total, dimension):
-    """Create or reopen the text embedding memmap."""
-    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-    if EMBEDDINGS_PATH.exists():
-        embedding_matrix = np.lib.format.open_memmap(EMBEDDINGS_PATH, mode="r+")
-        if embedding_matrix.shape != (total, dimension):
-            raise RuntimeError("Existing listing embedding matrix shape mismatch")
-        return embedding_matrix
-    return np.lib.format.open_memmap(
-        EMBEDDINGS_PATH,
-        mode="w+",
-        dtype="float32",
-        shape=(total, dimension),
-    )
-
-
-def restore_faiss_index(embedding_matrix, completed, dimension):
-    """Rebuild an in-memory FAISS index from completed text vectors."""
-    index = faiss.IndexFlatIP(dimension)
-    chunk_size = 8192
-    start_index = 0
-    while start_index < completed:
-        end_index = min(start_index + chunk_size, completed)
-        index.add(np.asarray(embedding_matrix[start_index:end_index], dtype="float32"))
-        start_index = end_index
-    return index
-
-
-def write_faiss_index_file(index, path):
-    """Write a FAISS index through Python bytes so Windows Unicode paths stay valid."""
-    serialized_index = faiss.serialize_index(index)
-    path.write_bytes(serialized_index.tobytes())
-
-
-def encode_text_batch(model, tokenizer, texts, device):
-    """Encode one batch of listing strings with the trained text tower."""
-    tokens = tokenizer(texts).to(device)
-    with torch.inference_mode():
-        features = model.encode_text(tokens)
-        features = features / features.norm(dim=-1, keepdim=True)
-    return features.cpu().numpy().astype("float32")
-
-
-def encode_products(products, model, tokenizer, device, batch_size, save_every):
-    """Encode cleaned listings and print live throughput."""
-    dimension = int(model.text_projection.shape[1])
-    total = len(products)
-    completed = load_progress(total)
-    embedding_matrix = open_embedding_memmap(total, dimension)
-    index = restore_faiss_index(embedding_matrix, completed, dimension)
-    started_at = time.perf_counter()
-    while completed < total:
-        batch_end = min(completed + batch_size, total)
-        texts = []
-        product_index = completed
-        while product_index < batch_end:
-            texts.append(products[product_index]["listing_text"])
-            product_index += 1
-        batch_features = encode_text_batch(model, tokenizer, texts, device)
-        embedding_matrix[completed:batch_end] = batch_features
-        index.add(batch_features)
-        completed = batch_end
-        if completed % save_every < batch_size or completed >= total:
-            embedding_matrix.flush()
-            write_json_atomic(
-                PROGRESS_PATH,
-                {
-                    "completed": completed,
-                    "total": total,
-                    "status": "building",
-                    "updated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
-                },
-            )
-        elapsed = time.perf_counter() - started_at
-        rate = completed / max(elapsed, 1e-6)
-        eta_minutes = (total - completed) / max(rate, 1e-6) / 60
-        print(
-            "listing_embedding",
-            completed,
-            "/",
-            total,
-            "rate",
-            f"{rate:.2f}/s",
-            "eta_minutes",
-            f"{eta_minutes:.1f}",
-            flush=True,
-        )
-    return embedding_matrix, index
-
-
-def write_final_outputs(products, embedding_matrix, index):
-    """Publish the finished listing index without touching the image index."""
-    embedding_matrix.flush()
-    temporary_index_path = INDEX_PATH.with_suffix(INDEX_PATH.suffix + ".tmp")
-    write_faiss_index_file(index, temporary_index_path)
-    os.replace(temporary_index_path, INDEX_PATH)
-    write_json_atomic(METADATA_PATH, products)
-    write_json_atomic(
-        PROGRESS_PATH,
-        {
-            "completed": len(products),
-            "total": len(products),
-            "status": "complete",
-            "updated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
-        },
-    )
-    return index.ntotal
-
-
-def main():
-    """Run the full cleaned listing-index build."""
-    arguments = parse_arguments()
-    products = load_or_create_clean_products(arguments.force_clean)
-    model, tokenizer, device = load_model()
-    print(
-        "listing_index_start",
-        json.dumps(
-            {
-                "device": str(device),
-                "products": len(products),
-                "batch_size": arguments.batch_size,
-                "output_dir": str(OUTPUT_DIR),
-            },
-            ensure_ascii=False,
-        ),
-        flush=True,
-    )
-    embedding_matrix, index = encode_products(
-        products,
-        model,
-        tokenizer,
-        device,
-        max(1, arguments.batch_size),
-        max(1, arguments.save_every),
-    )
-    vector_count = write_final_outputs(products, embedding_matrix, index)
-    print(
-        "listing_index_complete",
-        json.dumps(
-            {
-                "vectors": vector_count,
-                "dimension": int(embedding_matrix.shape[1]),
-                "index": str(INDEX_PATH),
-            },
-            ensure_ascii=False,
-        ),
-        flush=True,
-    )
-
-
-if __name__ == "__main__":
-    main()

bundle/clip/work/full_clip_server.py

@@ -1,615 +0,0 @@
-"""Serve the full trained CLIP index and Kimi assembly planner."""
-
-import base64
-import io
-import json
-import os
-import time
-import urllib.error
-import urllib.request
-from pathlib import Path
-from urllib.parse import urlparse
-
-import faiss
-import numpy as np
-import open_clip
-import torch
-from fastapi import FastAPI, File, Form, UploadFile
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import HTMLResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-from PIL import Image
-import uvicorn
-
-
-APP_ROOT = Path(__file__).resolve().parent.parent
-BUNDLED_FULL_INDEX_DIR = APP_ROOT / "data" / "full_clip_index"
-BUNDLED_MODEL_PATH = APP_ROOT / "models" / "open_clip_pytorch_model.bin"
-CLIP_DIR = APP_ROOT if BUNDLED_FULL_INDEX_DIR.exists() and BUNDLED_MODEL_PATH.exists() else Path(r"F:\Clip")
-FULL_INDEX_DIR = CLIP_DIR / "data" / "full_clip_index"
-INDEX_PATH = FULL_INDEX_DIR / "products_full.index"
-METADATA_PATH = FULL_INDEX_DIR / "products_full_meta.json"
-PRICE_METADATA_PATH = FULL_INDEX_DIR / "products_full_prices.json"
-PROGRESS_PATH = FULL_INDEX_DIR / "progress.json"
-IMAGE_DIR = APP_ROOT / "images"
-BASE_MODEL_PATH = CLIP_DIR / "models" / "open_clip_pytorch_model.bin"
-TRAINED_CHECKPOINT_PATH = CLIP_DIR / "data" / "yunqi_clip_training" / "last_checkpoint.pt"
-HTML_PATH = APP_ROOT / "app" / "clip_search.html" if (APP_ROOT / "app" / "clip_search.html").exists() else APP_ROOT / "clip_search.html"
-MODEL_NAME = "ViT-B-32"
-CONFIG_PATH = APP_ROOT / "config.json"
-KIMI_API_KEY_ENV = "MOONSHOT_API_KEY"
-KIMI_ENDPOINT_ENV = "KIMI_ENDPOINT"
-KIMI_MODEL_ENV = "KIMI_MODEL"
-APP_CONFIG = {}
-KIMI_CONFIG = {}
-KIMI_API_URL = "https://api.moonshot.cn/v1/chat/completions"
-KIMI_MODEL = "kimi-k2.6"
-KIMI_TEMPERATURE = 0.6
-KIMI_MAX_COMPLETION_TOKENS = 1800
-
-
-def load_app_config():
-    """Load local app configuration without committing user secrets."""
-    if not CONFIG_PATH.exists():
-        return {}
-    return json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
-
-
-def read_kimi_config():
-    """Return Kimi API settings from environment variables, config.json, and defaults."""
-    config = APP_CONFIG.get("kimi", {}) if isinstance(APP_CONFIG, dict) else {}
-    return {
-        "api_key": os.environ.get(KIMI_API_KEY_ENV, "").strip() or str(config.get("api_key", "")).strip(),
-        "endpoint": os.environ.get(KIMI_ENDPOINT_ENV, "").strip() or str(config.get("endpoint", KIMI_API_URL)).strip(),
-        "model": os.environ.get(KIMI_MODEL_ENV, "").strip() or str(config.get("model", KIMI_MODEL)).strip(),
-        "temperature": float(config.get("temperature", KIMI_TEMPERATURE)),
-        "max_completion_tokens": int(config.get("max_completion_tokens", KIMI_MAX_COMPLETION_TOKENS)),
-    }
-
-
-APP_CONFIG = load_app_config()
-KIMI_CONFIG = read_kimi_config()
-KIMI_API_URL = KIMI_CONFIG["endpoint"]
-KIMI_MODEL = KIMI_CONFIG["model"]
-KIMI_TEMPERATURE = KIMI_CONFIG["temperature"]
-KIMI_MAX_COMPLETION_TOKENS = KIMI_CONFIG["max_completion_tokens"]
-
-
-def read_faiss_index_file(path):
-    """Read a FAISS index through Python bytes so Windows Unicode paths stay valid."""
-    index_bytes = np.frombuffer(path.read_bytes(), dtype="uint8")
-    return faiss.deserialize_index(index_bytes)
-
-
-def load_runtime():
-    """Load the completed full FAISS index, metadata, and trained CLIP model."""
-    if not INDEX_PATH.exists() or not METADATA_PATH.exists():
-        raise FileNotFoundError("全量索引尚未生成完成")
-    if PROGRESS_PATH.exists():
-        progress = json.loads(PROGRESS_PATH.read_text(encoding="utf-8"))
-        if progress.get("status") != "complete":
-            raise RuntimeError(
-                f"全量索引仍在构建：{progress.get('completed', 0)}/{progress.get('total', 0)}"
-            )
-    if not BASE_MODEL_PATH.exists() or not TRAINED_CHECKPOINT_PATH.exists():
-        raise FileNotFoundError("基础模型或最终训练 checkpoint 不存在")
-    device = torch.device("cpu")
-    model, _, preprocess = open_clip.create_model_and_transforms(
-        MODEL_NAME,
-        pretrained=str(BASE_MODEL_PATH),
-    )
-    checkpoint = torch.load(TRAINED_CHECKPOINT_PATH, map_location=device, weights_only=False)
-    model.load_state_dict(checkpoint["model"])
-    model = model.to(device)
-    model.eval()
-    index = read_faiss_index_file(INDEX_PATH)
-    products = json.loads(METADATA_PATH.read_text(encoding="utf-8"))
-    price_metadata = {}
-    if PRICE_METADATA_PATH.exists():
-        price_metadata = json.loads(PRICE_METADATA_PATH.read_text(encoding="utf-8"))
-    if index.ntotal != len(products):
-        raise RuntimeError(f"索引数量 {index.ntotal} 与元数据数量 {len(products)} 不一致")
-    tokenizer = open_clip.get_tokenizer(MODEL_NAME)
-    return model, preprocess, tokenizer, device, index, products, price_metadata
-
-
-def get_rank_window(top_k):
-    """Clamp the requested result count to a safe full-index range."""
-    return max(1, min(int(top_k), 100))
-
-
-def encode_image(image, model, preprocess, device):
-    """Encode one uploaded image with the trained CLIP image tower."""
-    tensor = preprocess(image.convert("RGB")).unsqueeze(0).to(device)
-    with torch.inference_mode():
-        feature = model.encode_image(tensor)
-        feature = feature / feature.norm(dim=-1, keepdim=True)
-    return feature.cpu().numpy().astype("float32")
-
-
-def encode_text(query, model, tokenizer, device):
-    """Encode one prompt with the trained CLIP text tower."""
-    tokens = tokenizer([query]).to(device)
-    with torch.inference_mode():
-        feature = model.encode_text(tokens)
-        feature = feature / feature.norm(dim=-1, keepdim=True)
-    return feature.cpu().numpy().astype("float32")
-
-
-def search_index(
-    query_vector,
-    top_k,
-    index,
-    products,
-    price_metadata,
-    min_price=None,
-    max_price=None,
-):
-    """Search FAISS and apply an optional price constraint before returning Top K."""
-    output_count = get_rank_window(top_k)
-    search_count = index.ntotal if min_price is not None or max_price is not None else output_count
-    scores, indices = index.search(query_vector, search_count)
-    results = []
-    for result_position in range(len(indices[0])):
-        product_index = int(indices[0][result_position])
-        if product_index < 0 or product_index >= len(products):
-            continue
-        product = products[product_index].copy()
-        product["similarity"] = round(float(scores[0][result_position]) * 100, 2)
-        product["img_url"] = f"/images/{product['id']}.jpg"
-        product_id = str(product["id"])
-        if product_id in price_metadata:
-            product.update(price_metadata[product_id])
-        if min_price is not None or max_price is not None:
-            price = product.get("price_usd")
-            if price is None:
-                continue
-            if min_price is not None and float(price) < float(min_price):
-                continue
-            if max_price is not None and float(price) > float(max_price):
-                continue
-        product["rank"] = len(results) + 1
-        results.append(product)
-        if len(results) >= output_count:
-            break
-    return results
-
-
-def resolve_image_from_request(file, img_url):
-    """Read an uploaded image or an allowed local image URL into PIL."""
-    if isinstance(file, (bytes, bytearray)) and file:
-        return Image.open(io.BytesIO(file)).convert("RGB")
-    if file and file.filename:
-        return Image.open(io.BytesIO(file)).convert("RGB")
-    if img_url:
-        parsed_url = urlparse(img_url)
-        local_name = os.path.basename(parsed_url.path)
-        local_path = IMAGE_DIR / local_name
-        if parsed_url.path.startswith("/images/") and local_path.exists():
-            return Image.open(local_path).convert("RGB")
-    raise ValueError("没有提供有效图片")
-
-
-def merge_search_results(image_results, text_results, image_weight):
-    """Merge image and listing search results by product ID and CLIP score."""
-    result_by_id = {}
-    text_weight = 1.0 - image_weight
-    for product in image_results:
-        product_id = str(product.get("id", ""))
-        item = product.copy()
-        item["image_similarity"] = float(product.get("similarity", 0.0))
-        item["text_similarity"] = 0.0
-        result_by_id[product_id] = item
-    for product in text_results:
-        product_id = str(product.get("id", ""))
-        if product_id not in result_by_id:
-            item = product.copy()
-            item["image_similarity"] = 0.0
-            item["text_similarity"] = float(product.get("similarity", 0.0))
-            result_by_id[product_id] = item
-        else:
-            result_by_id[product_id]["text_similarity"] = float(product.get("similarity", 0.0))
-    results = []
-    for item in result_by_id.values():
-        item["similarity"] = round(
-            item["image_similarity"] * image_weight
-            + item["text_similarity"] * text_weight,
-            2,
-        )
-        results.append(item)
-    index = 0
-    while index < len(results):
-        best_index = index
-        candidate_index = index + 1
-        while candidate_index < len(results):
-            if results[candidate_index]["similarity"] > results[best_index]["similarity"]:
-                best_index = candidate_index
-            candidate_index += 1
-        if best_index != index:
-            results[index], results[best_index] = results[best_index], results[index]
-        results[index]["rank"] = index + 1
-        index += 1
-    return results
-
-
-def filter_by_price(products, min_price, max_price):
-    """Keep products inside the requested USD price range."""
-    if min_price is None and max_price is None:
-        return products
-    filtered = []
-    for product in products:
-        price = product.get("price_usd")
-        if price is None:
-            continue
-        if min_price is not None and float(price) < float(min_price):
-            continue
-        if max_price is not None and float(price) > float(max_price):
-            continue
-        filtered.append(product)
-    return filtered
-
-
-def image_to_data_url(image):
-    """Resize an input image and encode it as a compact Kimi data URL."""
-    image_copy = image.copy().convert("RGB")
-    image_copy.thumbnail((1280, 1280))
-    image_buffer = io.BytesIO()
-    image_copy.save(image_buffer, format="JPEG", quality=85, optimize=True)
-    encoded = base64.b64encode(image_buffer.getvalue()).decode("ascii")
-    return f"data:image/jpeg;base64,{encoded}"
-
-
-def build_clip_evidence(image, listing, model, preprocess, tokenizer, device, index, products, price_metadata):
-    """Collect weak CLIP evidence so Kimi can correct noisy retrieval signals."""
-    evidence = []
-    if image is not None:
-        image_vector = encode_image(image, model, preprocess, device)
-        image_results = search_index(image_vector, 8, index, products, price_metadata)
-        evidence.append({"source": "image", "results": image_results})
-    if listing:
-        listing_vector = encode_text(listing, model, tokenizer, device)
-        listing_results = search_index(listing_vector, 8, index, products, price_metadata)
-        evidence.append({"source": "listing", "results": listing_results})
-    return evidence
-
-
-def call_kimi(image, listing, min_price, max_price, clip_evidence):
-    """Call domestic Kimi K2.6 in JSON and non-thinking mode."""
-    api_key = KIMI_CONFIG["api_key"]
-    if not api_key:
-        raise RuntimeError(f"未配置 {CONFIG_PATH.name} 里的 kimi.api_key 或 {KIMI_API_KEY_ENV} 环境变量")
-    price_rule = {
-        "currency": "USD",
-        "min": min_price,
-        "max": max_price,
-    }
-    system_prompt = (
-        "你是商品组货规划助手，不是单纯的相似商品检索器。"
-        "请根据输入图片和Listing生成10个用于商品向量检索的中英文Prompt，目标是找出可以一起销售或一起购买的一组商品。"
-        "CLIP召回结果不够精准，只能作为弱证据，禁止直接照抄CLIP误召回的品类。"
-        "10个Prompt必须发散到不同组货方向，不能只是同一商品的颜色、材质或包装改写。"
-        "10个方向依次覆盖：1核心相似品，2功能替代品，3互补配件，4共同使用工具，5配套耗材，6高概率一起购买的关联品，7收纳整理品，8包装展示品，9人群场景关联品，10套装组合方案。"
-        "互补品必须和主商品的使用场景有明确关系，不要生成无关的氛围用品。"
-        "例如主商品是扳手，可以发散到锤子、螺丝刀、卷尺、螺丝螺母、工具收纳包，而不是只生成不同颜色的扳手。"
-        "Listing明确写出的品类优先；图片用于确认外观、颜色、形状和材质。"
-        "如果图片和Listing明显冲突，内部自行纠偏，并优先保留Listing主品类。"
-        "只能返回合法JSON对象，且只能有一个字段 prompts。"
-        "prompts 必须是长度为10的数组，数组元素只能是对象，且只能包含 zh 和 en 两个字段。"
-        "zh 是简短具体的中文检索词，en 是语义完全一致的英文检索词。"
-        "不要返回plan_name、summary、role、reason、price_filter、input_conflict或clip_adjustment。"
-    )
-    user_text = (
-        "输入Listing:\n"
-        + (listing or "未提供")
-        + "\n价格筛选（美元）:\n"
-        + json.dumps(price_rule, ensure_ascii=False)
-        + "\nCLIP弱证据（可能不准确，只用于发现偏差）:\n"
-        + json.dumps(clip_evidence, ensure_ascii=False)
-        + "\n请只返回JSON对象，不要Markdown代码围栏。"
-    )
-    content = [{"type": "text", "text": user_text}]
-    if image is not None:
-        content.insert(
-            0,
-            {
-                "type": "image_url",
-                "image_url": {"url": image_to_data_url(image)},
-            },
-        )
-    payload = {
-        "model": KIMI_MODEL,
-        "messages": [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": content},
-        ],
-        "thinking": {"type": "disabled"},
-        "temperature": KIMI_TEMPERATURE,
-        "response_format": {"type": "json_object"},
-        "max_completion_tokens": KIMI_MAX_COMPLETION_TOKENS,
-    }
-    request = urllib.request.Request(
-        KIMI_API_URL,
-        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
-        headers={
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json",
-        },
-        method="POST",
-    )
-    response_data = None
-    attempt = 0
-    while attempt < 3:
-        try:
-            with urllib.request.urlopen(request, timeout=120) as response:
-                response_data = json.loads(response.read().decode("utf-8"))
-            break
-        except urllib.error.HTTPError as error:
-            detail = error.read().decode("utf-8", errors="replace")
-            retryable = error.code in (429, 500, 502, 503, 504)
-            if not retryable or attempt >= 2:
-                raise RuntimeError(
-                    f"Kimi API HTTP {error.code}: {detail[:500]}"
-                ) from error
-            retry_after = error.headers.get("Retry-After")
-            try:
-                delay = float(retry_after) if retry_after else 2.0 + attempt * 2.0
-            except (TypeError, ValueError):
-                delay = 2.0 + attempt * 2.0
-            time.sleep(min(max(delay, 1.0), 10.0))
-            attempt += 1
-        except urllib.error.URLError as error:
-            if attempt >= 2:
-                raise RuntimeError(f"Kimi API 网络错误: {error.reason}") from error
-            time.sleep(2.0 + attempt * 2.0)
-            attempt += 1
-    choices = response_data.get("choices", [])
-    if not choices:
-        raise RuntimeError("Kimi API 没有返回 choices")
-    content_text = choices[0].get("message", {}).get("content", "")
-    if isinstance(content_text, dict):
-        return content_text
-    try:
-        return json.loads(content_text)
-    except (TypeError, json.JSONDecodeError) as error:
-        raise RuntimeError("Kimi 返回内容不是合法 JSON") from error
-
-
-def normalize_kimi_prompts(raw_plan, listing, min_price, max_price):
-    """Validate Kimi prompts and fill missing prompts without inventing products."""
-    raw_prompts = raw_plan.get("prompts", []) if isinstance(raw_plan, dict) else []
-    if not isinstance(raw_prompts, list):
-        raw_prompts = []
-    prompts = []
-    default_roles = [
-        "核心相似品",
-        "功能替代品",
-        "互补配件",
-        "共同使用工具",
-        "配套耗材",
-        "关联加购品",
-        "收纳整理品",
-        "包装展示品",
-        "人群场景关联品",
-        "场景套装/收纳方案",
-    ]
-    for raw_prompt in raw_prompts:
-        if isinstance(raw_prompt, dict):
-            prompt_text = str(
-                raw_prompt.get("zh", raw_prompt.get("prompt", ""))
-            ).strip()
-            prompt_english = str(
-                raw_prompt.get("en", raw_prompt.get("prompt_en", ""))
-            ).strip()
-            prompt_role = raw_prompt.get(
-                "role",
-                default_roles[min(len(prompts), len(default_roles) - 1)],
-            )
-            prompt_reason = raw_prompt.get("reason", "适合图片和Listing检索")
-        else:
-            prompt_text = str(raw_prompt).strip()
-            prompt_english = ""
-            prompt_role = default_roles[min(len(prompts), len(default_roles) - 1)]
-            prompt_reason = "适合图片和Listing检索"
-        if not prompt_text:
-            continue
-        prompts.append(
-            {
-                "prompt": prompt_text,
-                "prompt_en": prompt_english,
-                "role": prompt_role,
-                "reason": prompt_reason,
-            }
-        )
-        if len(prompts) >= 10:
-            break
-    fallback_text = listing.strip() or "符合输入图片风格的商品"
-    fallback_roles = [
-        "核心相似品",
-        "功能替代品",
-        "互补配件",
-        "共同使用工具",
-        "配套耗材",
-        "关联加购品",
-        "收纳整理品",
-        "包装展示品",
-        "人群场景关联品",
-        "场景套装/收纳方案",
-    ]
-    index = 0
-    while len(prompts) < 10:
-        prompts.append(
-            {
-                "prompt": f"{fallback_text}，{fallback_roles[index]}",
-                "prompt_en": "",
-                "role": fallback_roles[index],
-                "reason": "Kimi未返回足够Prompt，使用Listing补足检索方向",
-            }
-        )
-        index += 1
-    return {"prompts": prompts}
-
-
-def create_app():
-    """Load runtime assets and create the FastAPI application."""
-    model, preprocess, tokenizer, device, index, products, price_metadata = load_runtime()
-    application = FastAPI(title="Full CLIP Product Search")
-    application.add_middleware(
-        CORSMiddleware,
-        allow_origins=["*"],
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-    if IMAGE_DIR.exists():
-        application.mount("/images", StaticFiles(directory=IMAGE_DIR), name="images")
-
-    @application.post("/api/search/image")
-    async def search_image(
-        file: UploadFile = File(None),
-        img_url: str = Form(None),
-        top_k: int = Form(12),
-    ):
-        """Search full product metadata using an uploaded image."""
-        try:
-            contents = await file.read() if file and file.filename else None
-            image = resolve_image_from_request(contents, img_url)
-            query_vector = encode_image(image, model, preprocess, device)
-            return {"results": search_index(query_vector, top_k, index, products, price_metadata)}
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.post("/api/search/text")
-    async def search_text(
-        query: str = Form(...),
-        top_k: int = Form(12),
-    ):
-        """Search full product metadata using a text prompt."""
-        try:
-            query_vector = encode_text(query, model, tokenizer, device)
-            return {"results": search_index(query_vector, top_k, index, products, price_metadata)}
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.post("/api/assemble")
-    async def assemble_products(
-        file: UploadFile = File(None),
-        listing: str = Form(""),
-        min_price: float = Form(None),
-        max_price: float = Form(None),
-        top_k: int = Form(1),
-    ):
-        """Generate ten Kimi prompts and retrieve one product for each direction."""
-        try:
-            contents = await file.read() if file and file.filename else None
-            image = resolve_image_from_request(contents, "") if contents else None
-            if image is None and not listing.strip():
-                raise ValueError("请至少提供图片或 Listing")
-            clip_evidence = build_clip_evidence(
-                image,
-                listing.strip(),
-                model,
-                preprocess,
-                tokenizer,
-                device,
-                index,
-                products,
-                price_metadata,
-            )
-            raw_plan = call_kimi(
-                image,
-                listing.strip(),
-                min_price,
-                max_price,
-                clip_evidence,
-            )
-            plan = normalize_kimi_prompts(
-                raw_plan,
-                listing,
-                min_price,
-                max_price,
-            )
-            groups = []
-            selected_results = []
-            public_prompts = []
-            prompt_index = 0
-            while prompt_index < len(plan["prompts"]):
-                prompt_item = plan["prompts"][prompt_index]
-                prompt_text = prompt_item["prompt"]
-                prompt_english = prompt_item.get("prompt_en", "").strip()
-                public_prompts.append(
-                    {"zh": prompt_text, "en": prompt_english}
-                )
-                recall_text = prompt_english or prompt_text
-                prompt_vector = encode_text(recall_text, model, tokenizer, device)
-                prompt_matches = search_index(
-                    prompt_vector,
-                    100,
-                    index,
-                    products,
-                    price_metadata,
-                    min_price,
-                    max_price,
-                )
-                price_matches = prompt_matches
-                group_results = []
-                result_index = 0
-                while result_index < min(1, len(price_matches)):
-                    selected = price_matches[result_index].copy()
-                    selected["prompt_index"] = prompt_index + 1
-                    selected["search_prompt"] = prompt_text
-                    selected["search_prompt_en"] = prompt_english
-                    selected["prompt_role"] = prompt_item["role"]
-                    selected["prompt_reason"] = prompt_item["reason"]
-                    selected["prompt_rank"] = result_index + 1
-                    group_results.append(selected)
-                    selected_results.append(selected)
-                    result_index += 1
-                groups.append(
-                    {
-                        "prompt_index": prompt_index + 1,
-                        "prompt": prompt_text,
-                        "prompt_en": prompt_english,
-                        "role": prompt_item["role"],
-                        "reason": prompt_item["reason"],
-                        "results": group_results,
-                    }
-                )
-                prompt_index += 1
-            return {
-                "plan": {"prompts": public_prompts},
-                "results": selected_results,
-                "groups": groups,
-                "prompts_searched": len(groups),
-                "results_per_prompt": 1,
-                "model": KIMI_MODEL,
-                "thinking": "disabled",
-            }
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.get("/api/index/status")
-    async def index_status():
-        """Return the loaded full-index count for a quick browser health check."""
-        return {
-            "vectors": index.ntotal,
-            "products": len(products),
-            "price_records": len(price_metadata),
-            "device": str(device),
-            "checkpoint": str(TRAINED_CHECKPOINT_PATH),
-            "kimi_model": KIMI_MODEL,
-            "kimi_configured": bool(KIMI_CONFIG["api_key"]),
-        }
-
-    @application.get("/", response_class=HTMLResponse)
-    async def search_page():
-        """Serve the standalone image and prompt search page."""
-        return HTMLResponse(HTML_PATH.read_text(encoding="utf-8"))
-
-    return application
-
-
-app = create_app()
-
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="127.0.0.1", port=8888)

bundle/clip/work/full_listing_server.py

@@ -1,951 +0,0 @@
-"""Provide the cleaned pure-listing CLIP index for the integrated worker."""
-
-import base64
-import asyncio
-import concurrent.futures
-import io
-import json
-import os
-import subprocess
-import sys
-import time
-import urllib.error
-import urllib.parse
-import urllib.request
-from pathlib import Path
-
-import faiss
-import numpy as np
-import open_clip
-import torch
-from fastapi import Body, FastAPI, File, Form, Request, UploadFile
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import HTMLResponse, JSONResponse, PlainTextResponse, Response
-from fastapi.staticfiles import StaticFiles
-from PIL import Image
-import uvicorn
-
-
-APP_ROOT = Path(__file__).resolve().parent.parent
-BUNDLED_INDEX_DIR = APP_ROOT / "data" / "full_listing_index"
-BUNDLED_MODEL_PATH = APP_ROOT / "models" / "open_clip_pytorch_model.bin"
-CLIP_DIR = APP_ROOT if BUNDLED_INDEX_DIR.exists() and BUNDLED_MODEL_PATH.exists() else Path(r"F:\Clip")
-LISTING_INDEX_DIR = CLIP_DIR / "data" / "full_listing_index"
-INDEX_PATH = LISTING_INDEX_DIR / "products_listing.index"
-METADATA_PATH = LISTING_INDEX_DIR / "products_listing_meta.json"
-RUNTIME_METADATA_PATH = LISTING_INDEX_DIR / "products_listing_meta.runtime.json"
-PRICE_METADATA_PATH = CLIP_DIR / "data" / "full_clip_index" / "products_full_prices.json"
-PROGRESS_PATH = LISTING_INDEX_DIR / "progress.json"
-REPORT_PATH = LISTING_INDEX_DIR / "cleaning_report.json"
-BUILD_LOG_PATH = LISTING_INDEX_DIR / "build.log"
-SERVER_LOG_PATH = APP_ROOT / "logs" / "server.log"
-IMAGE_DIR = APP_ROOT / "images"
-BASE_MODEL_PATH = CLIP_DIR / "models" / "open_clip_pytorch_model.bin"
-TRAINED_CHECKPOINT_PATH = CLIP_DIR / "data" / "yunqi_clip_training" / "last_checkpoint.pt"
-HTML_PATH = APP_ROOT / "app" / "listing_search.html" if (APP_ROOT / "app" / "listing_search.html").exists() else APP_ROOT / "listing_search.html"
-BUILD_SCRIPT_PATH = APP_ROOT / "work" / "build_full_listing_index.py"
-MODEL_NAME = "ViT-B-32"
-CONFIG_PATH = APP_ROOT / "config.json"
-RUNTIME_METADATA_FIELDS = [
-    "id",
-    "title",
-    "listing_key",
-    "family_key",
-    "image_url",
-    "price_usd",
-    "sales_total",
-]
-KIMI_API_KEY_ENV = "MOONSHOT_API_KEY"
-KIMI_ENDPOINT_ENV = "KIMI_ENDPOINT"
-KIMI_MODEL_ENV = "KIMI_MODEL"
-APP_CONFIG = {}
-KIMI_CONFIG = {}
-KIMI_API_URL = "https://api.moonshot.cn/v1/chat/completions"
-KIMI_MODEL = "kimi-k2.6"
-KIMI_TEMPERATURE = 0.6
-KIMI_MAX_COMPLETION_TOKENS = 1200
-KIMI_PROMPT_BATCH_RANGES = [(1, 3), (4, 6), (7, 10)]
-KIMI_PROMPT_SLOT_RANGES = KIMI_PROMPT_BATCH_RANGES
-DEFAULT_KIMI_SYSTEM_PROMPT = """
-你是跨境电商组货商品检索词生成器。你只根据用户上传的图片生成可一起售卖/一起购买的商品检索词。
-
-任务：输出10个“具体可采购商品”，用于后续纯 listing CLIP 检索。
-
-生成原则：
-1. 不要只找外观相似品；优先覆盖互补品、同场景加购、替代升级、耗材补充、收纳展示、维护清洁、配套工具、礼盒套装里的其他商品。
-2. 每条必须是具体商品，不要写大类、策略、理由或营销词。不要输出“配件、用品、产品、套装、工具”这种过宽泛词，除非前面有清晰具体限定。
-3. 中文 zh 要像能直接给采购看的商品短名：主体品类 + 关键材质/结构/场景/人群/规格，尽量 6-18 个中文字符。
-4. 英文 en 要像英文 listing 标题检索词：6-14 个英文词，必须包含明确 product noun，并尽量包含 material / shape / color / scene / target user / size / function 中的2-4个要素。
-5. 如果图片主体不确定，根据最明显视觉元素推断；不要解释不确定性。
-6. 10条之间要有明显差异，避免同义改写刷数量。
-
-输出格式：只返回合法 JSON 对象，且只能包含 prompts 字段。
-prompts 是长度为10的数组，每个元素只能包含 zh 和 en 两个字段。
-""".strip()
-
-RUNTIME_CACHE = {
-    "model": None,
-    "tokenizer": None,
-    "device": None,
-    "index": None,
-    "products": None,
-    "prices": None,
-}
-BUILD_PROCESS = {"process": None}
-
-
-def write_bundle_log(message, payload=None):
-    """Write one compact bundle API log line for the Node diagnostics collector."""
-    entry = {"message": str(message or ""), "payload": payload or {}}
-    sys.stderr.write("[BUNDLE API] " + json.dumps(entry, ensure_ascii=True) + "\n")
-    sys.stderr.flush()
-
-
-def load_app_config():
-    """Load local app configuration without requiring secrets to be committed."""
-    if not CONFIG_PATH.exists():
-        return {}
-    return json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
-
-
-def read_kimi_config():
-    """Return Kimi settings from environment variables, config.json, and safe defaults."""
-    config = APP_CONFIG.get("kimi", {}) if isinstance(APP_CONFIG, dict) else {}
-    return {
-        "api_key": os.environ.get(KIMI_API_KEY_ENV, "").strip() or str(config.get("api_key", "")).strip(),
-        "endpoint": os.environ.get(KIMI_ENDPOINT_ENV, "").strip() or str(config.get("endpoint", KIMI_API_URL)).strip(),
-        "model": os.environ.get(KIMI_MODEL_ENV, "").strip() or str(config.get("model", KIMI_MODEL)).strip(),
-        "temperature": float(config.get("temperature", KIMI_TEMPERATURE)),
-        "max_completion_tokens": int(config.get("max_completion_tokens", KIMI_MAX_COMPLETION_TOKENS)),
-    }
-
-
-def read_json_file(path, fallback):
-    """Read a JSON file when it exists, otherwise return the fallback value."""
-    if not path.exists():
-        return fallback
-    return json.loads(path.read_text(encoding="utf-8"))
-
-
-def read_faiss_index_file(path):
-    """Read a FAISS index through Python bytes so Windows Unicode paths stay valid."""
-    index_bytes = np.frombuffer(path.read_bytes(), dtype="uint8")
-    return faiss.deserialize_index(index_bytes)
-
-
-def resolve_listing_metadata_path():
-    """Return the slim runtime metadata when bundled, otherwise use the original build metadata."""
-    if RUNTIME_METADATA_PATH.exists():
-        return RUNTIME_METADATA_PATH
-    return METADATA_PATH
-
-
-def normalize_product_metadata_rows(rows):
-    """Convert compact runtime metadata rows back into product dictionaries."""
-    if not rows:
-        return []
-    if isinstance(rows[0], dict):
-        return rows
-    products = []
-    for row in rows:
-        product = {}
-        values = row if isinstance(row, list) else []
-        for index, field_name in enumerate(RUNTIME_METADATA_FIELDS):
-            if index < len(values) and values[index] not in (None, ""):
-                product[field_name] = values[index]
-        products.append(product)
-    return products
-
-
-APP_CONFIG = load_app_config()
-KIMI_CONFIG = read_kimi_config()
-KIMI_API_URL = KIMI_CONFIG["endpoint"]
-KIMI_MODEL = KIMI_CONFIG["model"]
-KIMI_TEMPERATURE = KIMI_CONFIG["temperature"]
-KIMI_MAX_COMPLETION_TOKENS = KIMI_CONFIG["max_completion_tokens"]
-
-
-def append_server_log(message):
-    """Append one timestamped server log line without recording secrets."""
-    SERVER_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-    timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
-    with SERVER_LOG_PATH.open("a", encoding="utf-8") as log_file:
-        log_file.write(f"{timestamp} {message}\n")
-
-
-def should_skip_access_log(path):
-    """Return whether a noisy internal endpoint should be hidden from server logs."""
-    return path in {"/api/index/status", "/api/server/log", "/api/cdn/image"}
-
-
-def validate_cdn_image_url(image_url):
-    """Validate that the proxied image URL is a plain HTTP(S) CDN URL."""
-    parsed_url = urllib.parse.urlparse(str(image_url or "").strip())
-    if parsed_url.scheme not in {"http", "https"}:
-        raise ValueError("CDN image URL must be http or https")
-    if not parsed_url.netloc:
-        raise ValueError("CDN image URL host is missing")
-    return parsed_url.geturl()
-
-
-def fetch_cdn_image_bytes(safe_url):
-    """Fetch one validated CDN image in a worker thread for the async proxy endpoint."""
-    request = urllib.request.Request(
-        safe_url,
-        headers={
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
-            "Accept": "image/avif,image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8",
-        },
-        method="GET",
-    )
-    with urllib.request.urlopen(request, timeout=30) as cdn_response:
-        image_bytes = cdn_response.read()
-        content_type = cdn_response.headers.get("Content-Type", "image/jpeg")
-        status_code = getattr(cdn_response, "status", 200)
-    return image_bytes, content_type, status_code
-
-
-def read_server_log_filtered(max_bytes):
-    """Read recent server logs while hiding noisy internal heartbeat entries."""
-    raw_log = read_tail(SERVER_LOG_PATH, max_bytes)
-    hidden_patterns = [
-        " /api/index/status ",
-        " /api/server/log ",
-        " /api/cdn/image ",
-    ]
-    visible_lines = []
-    for line in raw_log.splitlines():
-        if not line.startswith("20"):
-            continue
-        if any(pattern in line for pattern in hidden_patterns):
-            continue
-        visible_lines.append(line)
-    return "\n".join(visible_lines)
-
-
-def load_model_runtime():
-    """Load the trained CLIP text tower only once per server process."""
-    if RUNTIME_CACHE["model"] is not None:
-        return
-    if not BASE_MODEL_PATH.exists() or not TRAINED_CHECKPOINT_PATH.exists():
-        raise FileNotFoundError("Base model or trained checkpoint is missing")
-    write_bundle_log("CLIP load progress", {"stage": "model_base", "progress": 78, "message": "正在载入 CLIP 基础模型。", "status": "loading", "error": ""})
-    device = torch.device("cpu")
-    model, _, _ = open_clip.create_model_and_transforms(
-        MODEL_NAME,
-        pretrained=str(BASE_MODEL_PATH),
-    )
-    write_bundle_log("CLIP load progress", {"stage": "model_checkpoint", "progress": 87, "message": "正在载入 CLIP 训练权重。", "status": "loading", "error": ""})
-    checkpoint = torch.load(TRAINED_CHECKPOINT_PATH, map_location=device, weights_only=False)
-    model.load_state_dict(checkpoint["model"])
-    model = model.to(device)
-    model.eval()
-    RUNTIME_CACHE["model"] = model
-    RUNTIME_CACHE["tokenizer"] = open_clip.get_tokenizer(MODEL_NAME)
-    RUNTIME_CACHE["device"] = device
-    write_bundle_log("CLIP load progress", {"stage": "model_ready", "progress": 96, "message": "CLIP 模型已载入，正在完成初始化。", "status": "loading", "error": ""})
-
-
-def load_index_runtime():
-    """Load or reload the completed listing FAISS index and product metadata."""
-    metadata_path = resolve_listing_metadata_path()
-    if not INDEX_PATH.exists() or not metadata_path.exists():
-        raise FileNotFoundError("Listing index is not ready; start the build first")
-    progress = read_json_file(PROGRESS_PATH, {})
-    if progress.get("status") != "complete":
-        raise RuntimeError(
-            f"Listing index is still building: {progress.get('completed', 0)}/{progress.get('total', 0)}"
-        )
-    index_mtime = INDEX_PATH.stat().st_mtime
-    cached_mtime = RUNTIME_CACHE.get("index_mtime")
-    if RUNTIME_CACHE["index"] is not None and cached_mtime == index_mtime:
-        return
-    write_bundle_log("CLIP load progress", {"stage": "index", "progress": 46, "message": "正在载入 FAISS 商品索引。", "status": "loading", "error": ""})
-    RUNTIME_CACHE["index"] = read_faiss_index_file(INDEX_PATH)
-    write_bundle_log("CLIP load progress", {"stage": "metadata", "progress": 60, "message": "正在载入商品元数据。", "status": "loading", "error": ""})
-    RUNTIME_CACHE["products"] = normalize_product_metadata_rows(read_json_file(metadata_path, []))
-    write_bundle_log("CLIP load progress", {"stage": "prices", "progress": 70, "message": "正在载入价格数据。", "status": "loading", "error": ""})
-    RUNTIME_CACHE["prices"] = read_json_file(PRICE_METADATA_PATH, {})
-    RUNTIME_CACHE["index_mtime"] = index_mtime
-    if RUNTIME_CACHE["index"].ntotal != len(RUNTIME_CACHE["products"]):
-        raise RuntimeError("Listing index count does not match metadata count")
-
-
-def encode_text(query):
-    """Encode one listing query with the trained CLIP text tower."""
-    load_model_runtime()
-    tokenizer = RUNTIME_CACHE["tokenizer"]
-    model = RUNTIME_CACHE["model"]
-    device = RUNTIME_CACHE["device"]
-    tokens = tokenizer([query]).to(device)
-    with torch.inference_mode():
-        feature = model.encode_text(tokens)
-        feature = feature / feature.norm(dim=-1, keepdim=True)
-    return feature.cpu().numpy().astype("float32")
-
-
-def get_rank_window(top_k):
-    """Clamp the requested result count to a practical range."""
-    return max(1, min(int(top_k), 100))
-
-
-def should_keep_price(product, min_price, max_price):
-    """Return whether a product is inside the optional USD price range."""
-    if min_price is None and max_price is None:
-        return True
-    price = product.get("price_usd")
-    if price is None:
-        return False
-    if min_price is not None and float(price) < float(min_price):
-        return False
-    if max_price is not None and float(price) > float(max_price):
-        return False
-    return True
-
-
-def resolve_product_image_url(product):
-    """Return the MAINIMAGE/CDN URL from metadata, or an empty string when unavailable."""
-    image_fields = [
-        "MAINIMAGE",
-        "mainImage",
-        "main_image",
-        "mainimage",
-        "image_url",
-        "imgUrl",
-        "img_url",
-    ]
-    for field_name in image_fields:
-        image_value = str(product.get(field_name, "") or "").strip()
-        if image_value.lower().startswith(("http://", "https://")):
-            return image_value
-    return ""
-
-
-def add_sidecar_fields(product):
-    """Attach price data and the required CDN image URL to one product."""
-    product_id = str(product.get("id", ""))
-    prices = RUNTIME_CACHE["prices"] or {}
-    if product_id in prices:
-        product.update(prices[product_id])
-    product["img_url"] = resolve_product_image_url(product)
-    return product
-
-
-def search_listing_index(query_vector, top_k, min_price, max_price):
-    """Search the text index and dedupe similar listing families before returning."""
-    load_index_runtime()
-    index = RUNTIME_CACHE["index"]
-    products = RUNTIME_CACHE["products"]
-    output_count = get_rank_window(top_k)
-    if min_price is not None or max_price is not None:
-        search_count = index.ntotal
-    else:
-        search_count = min(index.ntotal, max(output_count * 30, 300))
-    scores, indices = index.search(query_vector, search_count)
-    results = []
-    seen_families = set()
-    seen_images = set()
-    result_position = 0
-    while result_position < len(indices[0]):
-        product_index = int(indices[0][result_position])
-        if product_index < 0 or product_index >= len(products):
-            result_position += 1
-            continue
-        product = products[product_index].copy()
-        product = add_sidecar_fields(product)
-        if not product.get("img_url"):
-            result_position += 1
-            continue
-        if not should_keep_price(product, min_price, max_price):
-            result_position += 1
-            continue
-        family_key = str(product.get("family_key", product.get("listing_key", "")))
-        image_key = str(product.get("local_img", product.get("image_url", "")))
-        if family_key in seen_families or image_key in seen_images:
-            result_position += 1
-            continue
-        product["similarity"] = round(float(scores[0][result_position]) * 100, 2)
-        product["rank"] = len(results) + 1
-        product["source"] = "Listing"
-        results.append(product)
-        seen_families.add(family_key)
-        seen_images.add(image_key)
-        if len(results) >= output_count:
-            break
-        result_position += 1
-    return results
-
-
-def image_to_data_url(image):
-    """Encode an uploaded image as a compact Kimi-compatible data URL."""
-    image_copy = image.copy().convert("RGB")
-    image_copy.thumbnail((1280, 1280))
-    image_buffer = io.BytesIO()
-    image_copy.save(image_buffer, format="JPEG", quality=85, optimize=True)
-    encoded = base64.b64encode(image_buffer.getvalue()).decode("ascii")
-    return f"data:image/jpeg;base64,{encoded}"
-
-
-def read_uploaded_image(contents):
-    """Read uploaded bytes into a normalized PIL image."""
-    if not contents:
-        return None
-    return Image.open(io.BytesIO(contents)).convert("RGB")
-
-
-def parse_kimi_prompt_json(response_data):
-    """Extract the JSON prompt object from one Kimi chat completion response."""
-    choices = response_data.get("choices", []) if isinstance(response_data, dict) else []
-    if not choices:
-        raise RuntimeError("Kimi API returned no choices")
-    content_text = choices[0].get("message", {}).get("content", "")
-    if isinstance(content_text, dict):
-        return content_text
-    try:
-        return json.loads(content_text)
-    except (TypeError, json.JSONDecodeError) as error:
-        raise RuntimeError("Kimi response is not valid JSON") from error
-
-
-def collect_kimi_batch_prompts(raw_plan):
-    """Return valid prompts in the exact order supplied by one completed Kimi batch."""
-    raw_prompts = raw_plan.get("prompts", []) if isinstance(raw_plan, dict) else []
-    if not isinstance(raw_prompts, list):
-        return []
-    prompts = []
-    skipped_count = 0
-    for idx, raw_prompt in enumerate(raw_prompts):
-        if isinstance(raw_prompt, dict):
-            prompt_item = {
-                "zh": str(raw_prompt.get("zh", raw_prompt.get("prompt", ""))).strip(),
-                "en": str(raw_prompt.get("en", raw_prompt.get("prompt_en", ""))).strip(),
-            }
-        else:
-            prompt_item = {"zh": str(raw_prompt).strip(), "en": ""}
-        if prompt_item["zh"] or prompt_item["en"]:
-            prompts.append(prompt_item)
-        else:
-            skipped_count += 1
-            write_bundle_log("Kimi prompt skipped (empty)", {
-                "index": idx,
-                "raw": raw_prompt,
-            })
-    if skipped_count > 0:
-        write_bundle_log("Kimi batch prompts collected", {
-            "valid": len(prompts),
-            "skipped": skipped_count,
-            "total": len(raw_prompts),
-        })
-    return prompts
-
-
-def call_kimi_prompt_batch(image_data_url, min_price, max_price, kimi_prompt, batch_start, batch_end, kimi_user_prompt=""):
-    """Ask Kimi for one independent batch whose results keep provider return order."""
-    api_key = KIMI_CONFIG["api_key"]
-    if not api_key:
-        raise RuntimeError(f"Missing Kimi api_key in {CONFIG_PATH.name} or {KIMI_API_KEY_ENV} environment variable")
-    price_rule = {"currency": "USD", "min": min_price, "max": max_price}
-    batch_count = max(1, batch_end - batch_start + 1)
-    system_prompt = (kimi_prompt or DEFAULT_KIMI_SYSTEM_PROMPT).strip()
-    user_prompt = str(kimi_user_prompt or "").strip()
-    schema_guard = (
-        "\n\n硬性输出约束：只返回合法JSON对象，不能返回Markdown代码围栏。"
-        f"JSON只能包含prompts字段；prompts必须是长度为{batch_count}的数组；"
-        "每个元素只能包含zh和en两个字段，不要输出slot。"
-    )
-    user_text = (
-        (user_prompt + "\n\n" if user_prompt else "")
-        + f"请生成本批次的 {batch_count} 个 CLIP 检索方向。"
-        "这些结果会按照各批次实际返回先后拼接，不要输出编号。\n"
-        "Price filter:\n"
-        + json.dumps(price_rule, ensure_ascii=False)
-        + "\n只返回JSON，不要Markdown代码围栏。"
-    )
-    content = [{"type": "text", "text": user_text}]
-    if image_data_url:
-        content.insert(0, {"type": "image_url", "image_url": {"url": image_data_url}})
-    payload = {
-        "model": KIMI_MODEL,
-        "messages": [
-            {"role": "system", "content": system_prompt + schema_guard},
-            {"role": "user", "content": content},
-        ],
-        "thinking": {"type": "disabled"},
-        "temperature": KIMI_TEMPERATURE,
-        "response_format": {"type": "json_object"},
-        "max_completion_tokens": min(KIMI_MAX_COMPLETION_TOKENS, 650),
-    }
-    request = urllib.request.Request(
-        KIMI_API_URL,
-        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
-        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-        method="POST",
-    )
-    response_data = None
-    attempt = 0
-    while attempt < 3:
-        try:
-            write_bundle_log("Kimi prompts POST", {
-                "endpoint": KIMI_API_URL,
-                "model": KIMI_MODEL,
-                "attempt": attempt + 1,
-                "batch_start": batch_start,
-                "batch_end": batch_end,
-                "min_price": min_price,
-                "max_price": max_price,
-            })
-            with urllib.request.urlopen(request, timeout=120) as response:
-                response_data = json.loads(response.read().decode("utf-8"))
-            write_bundle_log("Kimi prompts response", {
-                "status": "ok",
-                "model": KIMI_MODEL,
-                "attempt": attempt + 1,
-                "batch_start": batch_start,
-                "batch_end": batch_end,
-            })
-            break
-        except urllib.error.HTTPError as error:
-            detail = error.read().decode("utf-8", errors="replace")
-            write_bundle_log("Kimi prompts HTTP error", {
-                "status": error.code,
-                "attempt": attempt + 1,
-                "batch_start": batch_start,
-                "batch_end": batch_end,
-                "detail": detail[:300],
-            })
-            if error.code not in (429, 500, 502, 503, 504) or attempt >= 2:
-                raise RuntimeError(f"Kimi API HTTP {error.code}: {detail[:500]}") from error
-            retry_after = error.headers.get("Retry-After")
-            try:
-                delay = float(retry_after) if retry_after else 2.0 + attempt * 2.0
-            except (TypeError, ValueError):
-                delay = 2.0 + attempt * 2.0
-            time.sleep(min(max(delay, 1.0), 10.0))
-            attempt += 1
-        except urllib.error.URLError as error:
-            write_bundle_log("Kimi prompts network error", {
-                "attempt": attempt + 1,
-                "batch_start": batch_start,
-                "batch_end": batch_end,
-                "reason": str(error.reason),
-            })
-            if attempt >= 2:
-                raise RuntimeError(f"Kimi API network error: {error.reason}") from error
-            time.sleep(2.0 + attempt * 2.0)
-            attempt += 1
-    raw_plan = parse_kimi_prompt_json(response_data)
-    prompts = collect_kimi_batch_prompts(raw_plan)
-    write_bundle_log("Kimi batch complete", {
-        "batch_start": batch_start,
-        "batch_end": batch_end,
-        "expected_count": batch_end - batch_start + 1,
-        "actual_count": len(prompts),
-        "raw_response": response_data,
-    })
-    return prompts
-
-
-def call_kimi_prompts(image, min_price, max_price, kimi_prompt, max_workers=3, on_batch_completed=None, kimi_user_prompt=""):
-    """Ask Kimi concurrently for ten JSON bundle-product prompts, ordered by completion time."""
-    image_data_url = image_to_data_url(image) if image is not None else ""
-    batch_ranges = KIMI_PROMPT_BATCH_RANGES
-    safe_workers = max(1, min(int(max_workers or 1), len(batch_ranges)))
-    completed_prompts = []
-    errors = []
-    with concurrent.futures.ThreadPoolExecutor(max_workers=safe_workers) as executor:
-        future_map = {}
-        for batch_start, batch_end in batch_ranges:
-            future = executor.submit(
-                call_kimi_prompt_batch,
-                image_data_url,
-                min_price,
-                max_price,
-                kimi_prompt,
-                batch_start,
-                batch_end,
-                kimi_user_prompt,
-            )
-            future_map[future] = (batch_start, batch_end)
-        for future in concurrent.futures.as_completed(future_map):
-            batch_start, batch_end = future_map[future]
-            try:
-                batch_prompts = future.result()
-                completed_prompts.extend(batch_prompts)
-                if callable(on_batch_completed) and batch_prompts:
-                    on_batch_completed(batch_start, batch_end, batch_prompts)
-            except Exception as error:
-                errors.append(f"{batch_start}-{batch_end}: {error}")
-                write_bundle_log("Kimi prompt batch failed", {
-                    "batch_start": batch_start,
-                    "batch_end": batch_end,
-                    "error": str(error),
-                })
-    if not completed_prompts and errors:
-        raise RuntimeError("Kimi parallel prompts failed: " + "; ".join(errors))
-    write_bundle_log("Kimi all batches complete", {
-        "total_prompts": len(completed_prompts),
-        "expected_total": sum(end - start + 1 for start, end in batch_ranges),
-        "batch_count": len(batch_ranges),
-        "errors": errors,
-    })
-    return {"prompts": completed_prompts, "batch_errors": errors, "batch_workers": safe_workers}
-
-
-def normalize_kimi_prompts(raw_plan, fill_missing=True):
-    """Validate Kimi prompts and optionally fill missing directions."""
-    raw_prompts = raw_plan.get("prompts", []) if isinstance(raw_plan, dict) else []
-    if not isinstance(raw_prompts, list):
-        raw_prompts = []
-    prompts = []
-    for raw_prompt in raw_prompts:
-        if isinstance(raw_prompt, dict):
-            prompt_zh = str(raw_prompt.get("zh", raw_prompt.get("prompt", ""))).strip()
-            prompt_en = str(raw_prompt.get("en", raw_prompt.get("prompt_en", ""))).strip()
-        else:
-            prompt_zh = str(raw_prompt).strip()
-            prompt_en = ""
-        if not prompt_zh and not prompt_en:
-            continue
-        prompts.append({"zh": prompt_zh, "en": prompt_en})
-        if len(prompts) >= 10:
-            break
-    fallback_text = "related product bundle"
-    if fill_missing:
-        while len(prompts) < 10:
-            prompts.append({"zh": fallback_text, "en": fallback_text})
-    return {"prompts": prompts}
-
-
-def search_prompt_groups(prompts, min_price, max_price, top_k, prompt_offset=0):
-    """Run each Kimi prompt through the listing CLIP index and group results."""
-    groups = []
-    selected_results = []
-    prompt_index = 0
-    while prompt_index < len(prompts):
-        prompt_item = prompts[prompt_index]
-        recall_text = prompt_item.get("en") or prompt_item.get("zh") or ""
-        query_vector = encode_text(recall_text)
-        matches = search_listing_index(query_vector, top_k, min_price, max_price)
-        group_results = []
-        result_index = 0
-        while result_index < len(matches):
-            product = matches[result_index].copy()
-            product["prompt_index"] = prompt_offset + prompt_index + 1
-            product["search_prompt"] = prompt_item.get("zh", "")
-            product["search_prompt_en"] = prompt_item.get("en", "")
-            product["prompt_rank"] = result_index + 1
-            group_results.append(product)
-            selected_results.append(product)
-            result_index += 1
-        groups.append(
-            {
-                "prompt_index": prompt_offset + prompt_index + 1,
-                "prompt": prompt_item.get("zh", ""),
-                "prompt_en": prompt_item.get("en", ""),
-                "results": group_results,
-            }
-        )
-        prompt_index += 1
-    return groups, selected_results
-
-
-def read_tail(path, max_bytes):
-    """Read the end of a log file without loading the whole file."""
-    if not path.exists():
-        return ""
-    with path.open("rb") as log_file:
-        log_file.seek(0, os.SEEK_END)
-        size = log_file.tell()
-        log_file.seek(max(0, size - max_bytes), os.SEEK_SET)
-        return log_file.read().decode("utf-8", errors="replace")
-
-
-def is_build_running():
-    """Return whether the current build subprocess is still active."""
-    process = BUILD_PROCESS.get("process")
-    if process is None:
-        return False
-    return process.poll() is None
-
-
-def start_build_process(batch_size, force_clean):
-    """Start the listing-index build in the background and append logs."""
-    if is_build_running():
-        return False
-    LISTING_INDEX_DIR.mkdir(parents=True, exist_ok=True)
-    command = [
-        sys.executable,
-        str(BUILD_SCRIPT_PATH),
-        "--batch-size",
-        str(max(1, min(int(batch_size), 2048))),
-    ]
-    if force_clean:
-        command.append("--force-clean")
-    log_file = BUILD_LOG_PATH.open("a", encoding="utf-8")
-    log_file.write(f"\nserver_start_build {command}\n")
-    log_file.flush()
-    BUILD_PROCESS["process"] = subprocess.Popen(
-        command,
-        stdout=log_file,
-        stderr=subprocess.STDOUT,
-        cwd=str(BUILD_SCRIPT_PATH.parent),
-    )
-    return True
-
-
-def build_status_payload():
-    """Return progress, cleaning report, and recent build log for the UI."""
-    progress = read_json_file(PROGRESS_PATH, {})
-    report = read_json_file(REPORT_PATH, {})
-    inferred_running = is_build_running()
-    if not inferred_running and progress.get("status") == "building":
-        completed = int(progress.get("completed", 0) or 0)
-        total = int(progress.get("total", 0) or 0)
-        inferred_running = total > 0 and completed < total
-    payload = {
-        "running": inferred_running,
-        "progress": progress,
-        "report": report,
-        "log": read_tail(BUILD_LOG_PATH, 20000),
-        "index_exists": INDEX_PATH.exists(),
-        "metadata_exists": resolve_listing_metadata_path().exists(),
-    }
-    if INDEX_PATH.exists():
-        payload["index_size_mb"] = round(INDEX_PATH.stat().st_size / 1024 / 1024, 2)
-    return payload
-
-
-def create_app():
-    """Create the legacy FastAPI application for pure listing search."""
-    application = FastAPI(title="Pure Listing CLIP Search")
-    application.add_middleware(
-        CORSMiddleware,
-        allow_origins=["*"],
-        allow_credentials=True,
-        allow_methods=["*"],
-        allow_headers=["*"],
-    )
-    if IMAGE_DIR.exists():
-        application.mount("/listing-images", StaticFiles(directory=IMAGE_DIR), name="listing-images")
-
-    @application.middleware("http")
-    async def log_http_request(request: Request, call_next):
-        """Write one compact access log line for every API/page request."""
-        started_at = time.perf_counter()
-        skip_access_log = should_skip_access_log(request.url.path)
-        try:
-            response = await call_next(request)
-        except Exception as error:
-            duration_ms = int((time.perf_counter() - started_at) * 1000)
-            if not skip_access_log:
-                append_server_log(
-                    f"{request.method} {request.url.path} ERROR {duration_ms}ms {type(error).__name__}: {error}"
-                )
-            raise
-        duration_ms = int((time.perf_counter() - started_at) * 1000)
-        if not skip_access_log:
-            append_server_log(
-                f"{request.method} {request.url.path} {response.status_code} {duration_ms}ms"
-            )
-        return response
-
-    @application.get("/api/server/log", response_class=PlainTextResponse)
-    async def server_log(max_bytes: int = 50000):
-        """Return the recent local server log as plain text."""
-        safe_max_bytes = max(1000, min(int(max_bytes), 1000000))
-        return PlainTextResponse(read_server_log_filtered(safe_max_bytes))
-
-    @application.get("/api/cdn/image")
-    async def proxy_cdn_image(url: str):
-        """Fetch one remote CDN image through this server so the request is visible in logs."""
-        started_at = time.perf_counter()
-        try:
-            safe_url = validate_cdn_image_url(url)
-            image_bytes, content_type, status_code = await asyncio.to_thread(fetch_cdn_image_bytes, safe_url)
-            duration_ms = int((time.perf_counter() - started_at) * 1000)
-            append_server_log(f"CDN GET {safe_url} {status_code} {len(image_bytes)}B {duration_ms}ms")
-            return Response(
-                content=image_bytes,
-                media_type=content_type,
-                headers={"Cache-Control": "public, max-age=86400"},
-            )
-        except Exception as error:
-            duration_ms = int((time.perf_counter() - started_at) * 1000)
-            append_server_log(f"CDN GET {url} ERROR {duration_ms}ms {type(error).__name__}: {error}")
-            return Response(status_code=502)
-
-    @application.post("/api/index/build/start")
-    async def start_index_build(
-        batch_size: int = Form(256),
-        force_clean: bool = Form(False),
-    ):
-        """Start a background cleaned listing-index build."""
-        try:
-            started = start_build_process(batch_size, force_clean)
-            return {"started": started, "status": build_status_payload()}
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.get("/api/index/status")
-    async def index_status():
-        """Return listing-index build and load status."""
-        status = build_status_payload()
-        status["kimi_model"] = KIMI_MODEL
-        status["kimi_configured"] = bool(KIMI_CONFIG["api_key"])
-        if INDEX_PATH.exists() and METADATA_PATH.exists():
-            try:
-                load_index_runtime()
-                status["vectors"] = RUNTIME_CACHE["index"].ntotal
-                status["products"] = len(RUNTIME_CACHE["products"])
-                status["price_records"] = len(RUNTIME_CACHE["prices"])
-            except Exception as error:
-                status["load_error"] = str(error)
-        return status
-
-    @application.post("/api/search/text")
-    async def search_text(
-        query: str = Form(...),
-        top_k: int = Form(24),
-        min_price: float = Form(None),
-        max_price: float = Form(None),
-    ):
-        """Search the cleaned pure-listing index."""
-        try:
-            if not query.strip():
-                raise ValueError("Query is empty")
-            query_vector = encode_text(query.strip())
-            results = search_listing_index(query_vector, top_k, min_price, max_price)
-            return {"results": results}
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.post("/api/search/prompts")
-    async def search_prompts(payload: dict = Body(...)):
-        """Search the listing index with manually edited Kimi prompt JSON."""
-        try:
-            plan = normalize_kimi_prompts(payload, fill_missing=False)
-            if not plan["prompts"]:
-                raise ValueError("Edited prompts are empty")
-            min_price = payload.get("min_price")
-            max_price = payload.get("max_price")
-            safe_top_k = max(1, min(int(payload.get("top_k", 1)), 10))
-            groups, selected_results = search_prompt_groups(
-                plan["prompts"],
-                min_price,
-                max_price,
-                safe_top_k,
-            )
-            return {
-                "plan": plan,
-                "groups": groups,
-                "results": selected_results,
-                "prompts_searched": len(groups),
-                "results_per_prompt": safe_top_k,
-                "source": "edited_prompts",
-            }
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.post("/api/assemble")
-    async def assemble_products(
-        file: UploadFile = File(None),
-        listing: str = Form(""),
-        kimi_prompt: str = Form(""),
-        top_k: int = Form(2),
-        min_price: float = Form(None),
-        max_price: float = Form(None),
-    ):
-        """Run image-only Kimi JSON prompts, then search the listing CLIP index.
-        核心流程（按你要求）：
-        - Kimi 生成 10 个方向（3+3+4 并发）
-        - 哪个 Kimi 批次先返回，哪个批次先进入 CLIP
-        - 当前接口保持一次性返回，不使用 SSE
-        - 最终固定返回 10 个商品，按 Kimi 实际返回顺序排序
-        """
-        try:
-            contents = await file.read() if file and file.filename else None
-            image = read_uploaded_image(contents)
-            if image is None:
-                raise ValueError("Please upload an image for Kimi bundle generation")
-            effective_system_prompt = DEFAULT_KIMI_SYSTEM_PROMPT
-            effective_user_prompt = kimi_prompt.strip()
-            safe_top_k = max(1, min(int(top_k), 10))
-            batch_groups = []
-            selected_results = []
-            searched_prompt_count = 0
-
-            def search_completed_kimi_batch(_batch_start, _batch_end, batch_prompts):
-                """Search one completed Kimi batch before slower Kimi batches finish."""
-                nonlocal searched_prompt_count
-                batch_plan = normalize_kimi_prompts({"prompts": batch_prompts}, fill_missing=False)
-                prompts = batch_plan["prompts"]
-                if not prompts:
-                    return
-                groups, batch_results = search_prompt_groups(
-                    prompts,
-                    min_price,
-                    max_price,
-                    safe_top_k,
-                    searched_prompt_count,
-                )
-                searched_prompt_count += len(prompts)
-                batch_groups.extend(groups)
-                selected_results.extend(batch_results)
-
-            # === Kimi 批次返回后立即进入 CLIP，接口最终一次返回 ===
-            raw_plan = call_kimi_prompts(
-                image,
-                min_price,
-                max_price,
-                effective_system_prompt,
-                on_batch_completed=search_completed_kimi_batch,
-                kimi_user_prompt=effective_user_prompt,
-            )
-            plan = normalize_kimi_prompts(raw_plan)
-            groups = batch_groups
-            if not groups:
-                groups, selected_results = search_prompt_groups(plan["prompts"], min_price, max_price, safe_top_k)
-
-            # === 固定返回 10 个商品 ===
-            if len(selected_results) > 10:
-                selected_results = selected_results[:10]
-            elif len(selected_results) < 10:
-                while len(selected_results) < 10:
-                    selected_results.append({
-                        "id": f"fallback-{len(selected_results)}",
-                        "title": "related product bundle",
-                        "prompt_index": 1,
-                        "search_prompt": "",
-                        "search_prompt_en": "",
-                        "prompt_rank": 1,
-                        "similarity": 50.0,
-                        "source": "fallback"
-                    })
-
-            return {
-                "plan": plan,
-                "groups": groups,
-                "results": selected_results,
-                "prompts_searched": len(groups),
-                "results_per_prompt": safe_top_k,
-                "model": KIMI_MODEL,
-                "thinking": "disabled",
-                "kimi_prompt": effective_user_prompt,
-                "return_count": len(selected_results)
-            }
-        except Exception as error:
-            return JSONResponse({"error": str(error)}, status_code=400)
-
-    @application.get("/", response_class=HTMLResponse)
-    async def listing_page():
-        """Serve the standalone pure-listing search page."""
-        return HTMLResponse(HTML_PATH.read_text(encoding="utf-8"))
-
-    return application
-
-
-app = create_app()
-
-
-if __name__ == "__main__":
-    raise SystemExit("9990 HTTP service is disabled. Use stdio_listing_worker.py through the 3000 server.")

bundle/clip/work/stdio_listing_worker.py

@@ -1,184 +0,0 @@
-"""Serve CLIP listing operations over stdin/stdout without opening an HTTP port."""
-
-import base64
-import json
-import sys
-
-from full_listing_server import (
-    call_kimi_prompts,
-    read_uploaded_image,
-    normalize_kimi_prompts,
-    search_prompt_groups,
-    encode_text,
-    search_listing_index,
-    build_status_payload,
-    load_index_runtime,
-    load_model_runtime,
-    RUNTIME_CACHE,
-    KIMI_CONFIG,
-    KIMI_MODEL,
-    DEFAULT_KIMI_SYSTEM_PROMPT,
-)
-
-
-def configure_stdio_encoding():
-    """Force safe UTF-8 stream writes even when data contains surrogate escapes."""
-    for stream in (sys.stdout, sys.stderr):
-        reconfigure = getattr(stream, "reconfigure", None)
-        if reconfigure:
-            reconfigure(encoding="utf-8", errors="backslashreplace")
-
-
-def write_json_line(payload):
-    """Write one JSON response line and flush immediately for the Node parent."""
-    sys.stdout.write(json.dumps(payload, ensure_ascii=True) + "\n")
-    sys.stdout.flush()
-
-
-def read_optional_float(value):
-    """Convert one optional numeric value from JSON into a float or None."""
-    if value is None or value == "":
-        return None
-    return float(value)
-
-
-def read_uploaded_image_from_base64(value):
-    """Decode one base64 image payload into a normalized PIL image."""
-    if not value:
-        return None
-    return read_uploaded_image(base64.b64decode(str(value)))
-
-
-def handle_index_status(_payload):
-    """Return listing-index readiness without requiring an HTTP request."""
-    status = build_status_payload()
-    status["kimi_model"] = KIMI_MODEL
-    status["kimi_configured"] = bool(KIMI_CONFIG["api_key"])
-    try:
-        load_index_runtime()
-        status["vectors"] = RUNTIME_CACHE["index"].ntotal
-        status["products"] = len(RUNTIME_CACHE["products"])
-        status["price_records"] = len(RUNTIME_CACHE["prices"])
-    except Exception as error:
-        status["load_error"] = str(error)
-    return status
-
-
-def handle_warmup(_payload):
-    """Load the listing index and trained text model before the first search."""
-    load_index_runtime()
-    load_model_runtime()
-    return {
-        "vectors": RUNTIME_CACHE["index"].ntotal,
-        "products": len(RUNTIME_CACHE["products"]),
-        "model_ready": RUNTIME_CACHE["model"] is not None,
-    }
-
-
-def handle_search_text(payload):
-    """Search the listing CLIP index with one manual keyword."""
-    query = str(payload.get("query", "")).strip()
-    if not query:
-        raise ValueError("Query is empty")
-    top_k = max(1, min(int(payload.get("top_k", 24)), 100))
-    min_price = read_optional_float(payload.get("min_price"))
-    max_price = read_optional_float(payload.get("max_price"))
-    query_vector = encode_text(query)
-    return {"results": search_listing_index(query_vector, top_k, min_price, max_price)}
-
-
-def handle_assemble(payload):
-    """Run image-only Kimi JSON prompts, then search the listing CLIP index."""
-    image = read_uploaded_image_from_base64(payload.get("image_base64"))
-    if image is None:
-        raise ValueError("Please provide an image for Kimi bundle generation")
-    min_price = read_optional_float(payload.get("min_price"))
-    max_price = read_optional_float(payload.get("max_price"))
-    effective_system_prompt = str(payload.get("kimi_system_prompt", "")).strip() or DEFAULT_KIMI_SYSTEM_PROMPT
-    effective_user_prompt = str(payload.get("kimi_prompt", "")).strip()
-    safe_top_k = max(1, min(int(payload.get("top_k", 2)), 10))
-    batch_groups = []
-    batch_selected_results = []
-    searched_prompt_count = 0
-
-    def search_completed_kimi_batch(_batch_start, _batch_end, batch_prompts):
-        """Search one returned Kimi batch immediately so later Kimi calls do not block CLIP."""
-        nonlocal searched_prompt_count
-        batch_plan = normalize_kimi_prompts({"prompts": batch_prompts}, fill_missing=False)
-        prompts = batch_plan["prompts"]
-        if not prompts:
-            return
-        groups, selected_results = search_prompt_groups(
-            prompts,
-            min_price,
-            max_price,
-            safe_top_k,
-            searched_prompt_count,
-        )
-        searched_prompt_count += len(prompts)
-        batch_groups.extend(groups)
-        batch_selected_results.extend(selected_results)
-
-    raw_plan = call_kimi_prompts(
-        image,
-        min_price,
-        max_price,
-        effective_system_prompt,
-        on_batch_completed=search_completed_kimi_batch,
-        kimi_user_prompt=effective_user_prompt,
-    )
-    plan = normalize_kimi_prompts(raw_plan)
-    if batch_groups:
-        groups = batch_groups
-        selected_results = batch_selected_results
-    else:
-        groups, selected_results = search_prompt_groups(plan["prompts"], min_price, max_price, safe_top_k)
-    return {
-        "plan": plan,
-        "groups": groups,
-        "results": selected_results,
-        "prompts_searched": len(groups),
-        "results_per_prompt": safe_top_k,
-        "model": KIMI_MODEL,
-        "thinking": "disabled",
-        "kimi_prompt": effective_user_prompt,
-    }
-
-
-def dispatch(payload):
-    """Route one worker JSON command to the matching CLIP operation."""
-    action = str(payload.get("action", "")).strip()
-    if action == "index_status":
-        return handle_index_status(payload)
-    if action == "warmup":
-        return handle_warmup(payload)
-    if action == "search_text":
-        return handle_search_text(payload)
-    if action == "assemble":
-        return handle_assemble(payload)
-    raise ValueError("Unknown CLIP worker action: " + action)
-
-
-def main():
-    """Read JSON-line requests forever and return JSON-line responses."""
-    for line in sys.stdin:
-        text = line.strip()
-        if not text:
-            continue
-        request = {}
-        try:
-            request = json.loads(text)
-            request_id = request.get("id")
-            result = dispatch(request)
-            write_json_line({"id": request_id, "ok": True, "result": result})
-        except Exception as error:
-            write_json_line({
-                "id": request.get("id", ""),
-                "ok": False,
-                "error": str(error),
-            })
-
-
-if __name__ == "__main__":
-    configure_stdio_encoding()
-    main()

bundle/python-cpu/Lib/site-packages/PIL/AvifImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:80733b71d411630ea63c782333de241a12bd83f0f1cafcd8ccde4516bed4b5a1
-size 9596

bundle/python-cpu/Lib/site-packages/PIL/BdfFontFile.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2c54f5932a79e45789cd0b1fbd64694f2f53009f5485e3fe7bd95e2ddf77cf35
-size 3463

bundle/python-cpu/Lib/site-packages/PIL/BlpImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:522b1467bdc20f8edfd8b19346573e4df6f10dda93915b72a3b8efe6f6104c17
-size 17048

bundle/python-cpu/Lib/site-packages/PIL/BmpImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e23760527cf54ebb06b6e56d73be70ff896666c04f55fdd1047bf99434f9da63
-size 20377

bundle/python-cpu/Lib/site-packages/PIL/BufrStubImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3b2a82334b9422e153315099108f4fda20377a83785d540f7c57cdb835bfc9e4
-size 1757

bundle/python-cpu/Lib/site-packages/PIL/ContainerIO.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:23ac8efd814412a30a035724804133176aff224e69fc68ccf91ad63896234a56
-size 4777

bundle/python-cpu/Lib/site-packages/PIL/CurImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3aab7fa3ba3b1d758a01e368073544be1c4a14cbf75b751cf390a73a842e8612
-size 2814

bundle/python-cpu/Lib/site-packages/PIL/DcxImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f6d5cde0557e883e6938cee7ff96db779807917dcb8209c5e1902569b9e7765
-size 2264

bundle/python-cpu/Lib/site-packages/PIL/DdsImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:eae2016bb3476be917ff549086c18b89d8e911d0f6d3f0900486a90fe2808cac
-size 19733

bundle/python-cpu/Lib/site-packages/PIL/EpsImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3582af51b5623fc766008fa1e3df028a918aaf7073f4f6435fc5d1607f3deb2c
-size 17253

bundle/python-cpu/Lib/site-packages/PIL/ExifTags.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:362cafb665044fbf41322a248a3313a7fabc75e596ce9c9db5371305f272f0a1
-size 10339

bundle/python-cpu/Lib/site-packages/PIL/FitsImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2ff9142b7f02394b95f65a13e64f4366a12df74a3600245d64f63696fb1ed0bd
-size 4875

bundle/python-cpu/Lib/site-packages/PIL/FliImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2f5bdfe108ec68c57479141399fcb5c27a89f65f61195e65bd320b5bbce7cc3f
-size 5113

bundle/python-cpu/Lib/site-packages/PIL/FontFile.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a424a370fdf10adc8e780110f3e12f9366e0e479ec49c560400e715108ff0554
-size 4419

bundle/python-cpu/Lib/site-packages/PIL/FpxImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b3610e701e56459102379804abd517a576a44e2dfc905322133d8ccd1029c7f5
-size 7724

bundle/python-cpu/Lib/site-packages/PIL/FtexImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fb32afbe131373351ede8d1fe07a430bdfc32ba94a1d3e4b0017f4af9fb797ee
-size 3685

bundle/python-cpu/Lib/site-packages/PIL/GbrImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21bff2df967334e98ca86d92b8eca3c518773b1b78d90a5c11c80b6ba0619ad0
-size 3156

bundle/python-cpu/Lib/site-packages/PIL/GdImageFile.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ace96a8ea2945fc25827a18b1b3a8792cf85a4033bdb43b2a1d1c636222e9283
-size 2951

bundle/python-cpu/Lib/site-packages/PIL/GifImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0cf9dd837ba23b412c95765aea4422bbf6e7c2210c6eefde91eab43d4308b0d5
-size 43596

bundle/python-cpu/Lib/site-packages/PIL/GimpGradientFile.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cec216d4c915ea4a39964e072555de8121d4df250fc16d695329d1b39cf48990
-size 4137

bundle/python-cpu/Lib/site-packages/PIL/GimpPaletteFile.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:126047710f44c42e1fe3bb5a0fb4bb1ba55a324afd1e3ca7955f0d7535d5e624
-size 1935

bundle/python-cpu/Lib/site-packages/PIL/GribStubImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:73c88f12a40e48171616a756122b9cb9b8da5f061f6520761a365761568f0313
-size 1786

bundle/python-cpu/Lib/site-packages/PIL/Hdf5StubImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ac831d7fbb83b248c9ab07ba0af18bc3fb6325f875828e6f2929e67ed05d08a8
-size 1768

bundle/python-cpu/Lib/site-packages/PIL/IcnsImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4e8092cb968527bffa9e266ccc53917c232bf3f5393babe0775447315aff27fa
-size 12798

bundle/python-cpu/Lib/site-packages/PIL/IcoImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0d9a018f6ccabfbe9293de8f2aaf1e97d69900cd89df050b56a3dea27a3fd33d
-size 13499

bundle/python-cpu/Lib/site-packages/PIL/ImImagePlugin.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5fd881490e31eeb6aec5b3889fbda3eefe9301481995da580f970073a67530d0
-size 11992

bundle/python-cpu/Lib/site-packages/PIL/Image.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:59f1b98ed7bfd7b516b9f13ffd9dd3bc0aa1a495042accdb5ae4609b41c329d5
-size 158171

bundle/python-cpu/Lib/site-packages/PIL/ImageChops.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:859f043d43e5408cee82c11e755f2dae4297d230420c66fa0acce66ba65e3194
-size 8257

bundle/python-cpu/Lib/site-packages/PIL/ImageCms.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0effeeecadc71257d6eabcbd4cf43277ff35c6aeda0e7ba4c60ab67676bf49de
-size 41773