# fastembed - Doramagic AI Context Pack

> 定位：安装前体验与判断资产。它帮助宿主 AI 有一个好的开始，但不代表已经安装、执行或验证目标项目。

## 充分原则

- **充分原则，不是压缩原则**：AI Context Pack 应该充分到让宿主 AI 在开工前理解项目价值、能力边界、使用入口、风险和证据来源；它可以分层组织，但不以最短摘要为目标。
- **压缩策略**：只压缩噪声和重复内容，不压缩会影响判断和开工质量的上下文。

## 给宿主 AI 的使用方式

你正在读取 Doramagic 为 fastembed 编译的 AI Context Pack。请把它当作开工前上下文：帮助用户理解适合谁、能做什么、如何开始、哪些必须安装后验证、风险在哪里。不要声称你已经安装、运行或执行了目标项目。

## Claim 消费规则

- **事实来源**：Repo Evidence + Claim/Evidence Graph；Human Wiki 只提供显著性、术语和叙事结构。
- **事实最低状态**：`supported`
- `supported`：可以作为项目事实使用，但回答中必须引用 claim_id 和证据路径。
- `weak`：只能作为低置信度线索，必须要求用户继续核实。
- `inferred`：只能用于风险提示或待确认问题，不能包装成项目事实。
- `unverified`：不得作为事实使用，应明确说证据不足。
- `contradicted`：必须展示冲突来源，不得替用户强行选择一个版本。

## 它最适合谁

- **想在安装前理解开源项目价值和边界的用户**：当前证据主要来自项目文档。 证据：`README.md` Claim：`clm_0002` supported 0.86

## 它能做什么

- **命令行启动或安装流程**（需要安装后验证）：项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 证据：`README.md` Claim：`clm_0001` supported 0.86

## 怎么开始

- `pip install fastembed` 证据：`README.md` Claim：`clm_0003` supported 0.86, `clm_0004` supported 0.86
- `pip install fastembed-gpu` 证据：`README.md` Claim：`clm_0004` supported 0.86
- `pip install qdrant-client[fastembed]` 证据：`README.md` Claim：`clm_0005` supported 0.86, `clm_0006` supported 0.86
- `pip install qdrant-client[fastembed-gpu]` 证据：`README.md` Claim：`clm_0006` supported 0.86

## 继续前判断卡

- **当前建议**：仅建议沙盒试装
- **为什么**：项目存在安装命令、宿主配置或本地写入线索，不建议直接进入主力环境，应先在隔离环境试装。

### 30 秒判断

- **现在怎么做**：仅建议沙盒试装
- **最小安全下一步**：先跑 Prompt Preview；若仍要安装，只在隔离环境试装
- **先别相信**：真实输出质量不能在安装前相信。
- **继续会触碰**：命令执行、本地环境或项目文件、宿主 AI 上下文

### 现在可以相信

- **适合人群线索：想在安装前理解开源项目价值和边界的用户**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0002` supported 0.86
- **能力存在：命令行启动或安装流程**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`README.md` Claim：`clm_0001` supported 0.86
- **存在 Quick Start / 安装命令线索**（supported）：可以相信项目文档出现过启动或安装入口；不要因此直接在主力环境运行。 证据：`README.md` Claim：`clm_0003` supported 0.86, `clm_0004` supported 0.86

### 现在还不能相信

- **真实输出质量不能在安装前相信。**（unverified）：Prompt Preview 只能展示引导方式，不能证明真实项目中的结果质量。
- **宿主 AI 版本兼容性不能在安装前相信。**（unverified）：Claude、Cursor、Codex、Gemini 等宿主加载规则和版本差异必须在真实环境验证。
- **不会污染现有宿主 AI 行为，不能直接相信。**（inferred）：Skill、plugin、AGENTS/CLAUDE/GEMINI 指令可能改变宿主 AI 的默认行为。
- **可安全回滚不能默认相信。**（unverified）：除非项目明确提供卸载和恢复说明，否则必须先在隔离环境验证。
- **真实安装后是否与用户当前宿主 AI 版本兼容？**（unverified）：兼容性只能通过实际宿主环境验证。
- **项目输出质量是否满足用户具体任务？**（unverified）：安装前预览只能展示流程和边界，不能替代真实评测。
- **安装命令是否需要网络、权限或全局写入？**（unverified）：这影响企业环境和个人环境的安装风险。 证据：`README.md`

### 继续会触碰什么

- **命令执行**：包管理器、网络下载、本地插件目录、项目配置或用户主目录。 原因：运行第一条命令就可能产生环境改动；必须先判断是否值得跑。 证据：`README.md`
- **本地环境或项目文件**：安装结果、插件缓存、项目配置或本地依赖目录。 原因：安装前无法证明写入范围和回滚方式，需要隔离验证。 证据：`README.md`
- **宿主 AI 上下文**：AI Context Pack、Prompt Preview、Skill 路由、风险规则和项目事实。 原因：导入上下文会影响宿主 AI 后续判断，必须避免把未验证项包装成事实。

### 最小安全下一步

- **先跑 Prompt Preview**：用安装前交互式试用判断工作方式是否匹配，不需要授权或改环境。（适用：任何项目都适用，尤其是输出质量未知时。）
- **只在隔离目录或测试账号试装**：避免安装命令污染主力宿主 AI、真实项目或用户主目录。（适用：存在命令执行、插件配置或本地写入线索时。）
- **安装后只验证一个最小任务**：先验证加载、兼容、输出质量和回滚，再决定是否深用。（适用：准备从试用进入真实工作流时。）

### 退出方式

- **保留安装前状态**：记录原始宿主配置和项目状态，后续才能判断是否可恢复。
- **记录安装命令和写入路径**：没有明确卸载说明时，至少要知道哪些目录或配置需要手动清理。
- **如果没有回滚路径，不进入主力环境**：不可回滚是继续前阻断项，不应靠信任或运气继续。

## 哪些只能预览

- 解释项目适合谁和能做什么
- 基于项目文档演示典型对话流程
- 帮助用户判断是否值得安装或继续研究

## 哪些必须安装后验证

- 真实安装 Skill、插件或 CLI
- 执行脚本、修改本地文件或访问外部服务
- 验证真实输出质量、性能和兼容性

## 边界与风险判断卡

- **把安装前预览误认为真实运行**：用户可能高估项目已经完成的配置、权限和兼容性验证。 处理方式：明确区分 prompt_preview_can_do 与 runtime_required。 Claim：`clm_0007` inferred 0.45
- **命令执行会修改本地环境**：安装命令可能写入用户主目录、宿主插件目录或项目配置。 处理方式：先在隔离环境或测试账号中运行。 证据：`README.md` Claim：`clm_0008` supported 0.86
- **待确认**：真实安装后是否与用户当前宿主 AI 版本兼容？。原因：兼容性只能通过实际宿主环境验证。
- **待确认**：项目输出质量是否满足用户具体任务？。原因：安装前预览只能展示流程和边界，不能替代真实评测。
- **待确认**：安装命令是否需要网络、权限或全局写入？。原因：这影响企业环境和个人环境的安装风险。

## 开工前工作上下文

### 加载顺序

- 先读取 how_to_use.host_ai_instruction，建立安装前判断资产的边界。
- 读取 claim_graph_summary，确认事实来自 Claim/Evidence Graph，而不是 Human Wiki 叙事。
- 再读取 intended_users、capabilities 和 quick_start_candidates，判断用户是否匹配。
- 需要执行具体任务时，优先查 role_skill_index，再查 evidence_index。
- 遇到真实安装、文件修改、网络访问、性能或兼容性问题时，转入 risk_card 和 boundaries.runtime_required。

### 任务路由

- **命令行启动或安装流程**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`README.md` Claim：`clm_0001` supported 0.86

### 上下文规模

- 文件总数：117
- 重要文件覆盖：27/117
- 证据索引条目：27
- 角色 / Skill 条目：5

### 证据不足时的处理

- **missing_evidence**：说明证据不足，要求用户提供目标文件、README 段落或安装后验证记录；不要补全事实。
- **out_of_scope_request**：说明该任务超出当前 AI Context Pack 证据范围，并建议用户先查看 Human Manual 或真实安装后验证。
- **runtime_request**：给出安装前检查清单和命令来源，但不要替用户执行命令或声称已执行。
- **source_conflict**：同时展示冲突来源，标记为待核实，不要强行选择一个版本。

## Prompt Recipes

### 适配判断

- 目标：判断这个项目是否适合用户当前任务。
- 预期输出：适配结论、关键理由、证据引用、安装前可预览内容、必须安装后验证内容、下一步建议。

```text
请基于 fastembed 的 AI Context Pack，先问我 3 个必要问题，然后判断它是否适合我的任务。回答必须包含：适合谁、能做什么、不能做什么、是否值得安装、证据来自哪里。所有项目事实必须引用 evidence_refs、source_paths 或 claim_id。
```

### 安装前体验

- 目标：让用户在安装前感受核心工作流，同时避免把预览包装成真实能力或营销承诺。
- 预期输出：一段带边界标签的体验剧本、安装后验证清单和谨慎建议；不含真实运行承诺或强营销表述。

```text
请把 fastembed 当作安装前体验资产，而不是已安装工具或真实运行环境。

请严格输出四段：
1. 先问我 3 个必要问题。
2. 给出一段“体验剧本”：用 [安装前可预览]、[必须安装后验证]、[证据不足] 三种标签展示它可能如何引导工作流。
3. 给出安装后验证清单：列出哪些能力只有真实安装、真实宿主加载、真实项目运行后才能确认。
4. 给出谨慎建议：只能说“值得继续研究/试装”“先补充信息后再判断”或“不建议继续”，不得替项目背书。

硬性边界：
- 不要声称已经安装、运行、执行测试、修改文件或产生真实结果。
- 不要写“自动适配”“确保通过”“完美适配”“强烈建议安装”等承诺性表达。
- 如果描述安装后的工作方式，必须使用“如果安装成功且宿主正确加载 Skill，它可能会……”这种条件句。
- 体验剧本只能写成“示例台词/假设流程”：使用“可能会询问/可能会建议/可能会展示”，不要写“已写入、已生成、已通过、正在运行、正在生成”。
- Prompt Preview 不负责给安装命令；如用户准备试装，只能提示先阅读 Quick Start 和 Risk Card，并在隔离环境验证。
- 所有项目事实必须来自 supported claim、evidence_refs 或 source_paths；inferred/unverified 只能作风险或待确认项。

```

### 角色 / Skill 选择

- 目标：从项目里的角色或 Skill 中挑选最匹配的资产。
- 预期输出：候选角色或 Skill 列表，每项包含适用场景、证据路径、风险边界和是否需要安装后验证。

```text
请读取 role_skill_index，根据我的目标任务推荐 3-5 个最相关的角色或 Skill。每个推荐都要说明适用场景、可能输出、风险边界和 evidence_refs。
```

### 风险预检

- 目标：安装或引入前识别环境、权限、规则冲突和质量风险。
- 预期输出：环境、权限、依赖、许可、宿主冲突、质量风险和未知项的检查清单。

```text
请基于 risk_card、boundaries 和 quick_start_candidates，给我一份安装前风险预检清单。不要替我执行命令，只说明我应该检查什么、为什么检查、失败会有什么影响。
```

### 宿主 AI 开工指令

- 目标：把项目上下文转成一次对话开始前的宿主 AI 指令。
- 预期输出：一段边界明确、证据引用明确、适合复制给宿主 AI 的开工前指令。

```text
请基于 fastembed 的 AI Context Pack，生成一段我可以粘贴给宿主 AI 的开工前指令。这段指令必须遵守 not_runtime=true，不能声称项目已经安装、运行或产生真实结果。
```


## 角色 / Skill 索引

- 共索引 5 个角色 / Skill / 项目文档条目。

- **⚡️ What is FastEmbed?**（project_doc）：FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models https://qdrant.github.io/fastembed/examples/Supported Models/ . Please open a GitHub issue https://github.com/qdrant/fastembed/issues/new if you want us to add a new model. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`README.md`
- **Contributing to FastEmbed!**（project_doc）：:+1::tada: First off, thanks for taking the time to contribute! :tada::+1: 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`CONTRIBUTING.md`
- **⚡️ What is FastEmbed?**（project_doc）：FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models https://qdrant.github.io/fastembed/examples/Supported Models/ . Please open a Github issue https://github.com/qdrant/fastembed/issues/new if you want us to add a new model. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/index.md`
- **All Submissions:**（project_doc）：Have you followed the guidelines in our Contributing document? Have you checked to ensure there aren't other open Pull Requests ../../../pulls for the same update/change? 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`.github/PULL_REQUEST_TEMPLATE.md`
- **Releasing FastEmbed**（project_doc）：This is a guide how to release fastembed and fastembed-gpu packages. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`RELEASE.md`

## 证据索引

- 共索引 27 条证据。

- **⚡️ What is FastEmbed?**（documentation）：FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models https://qdrant.github.io/fastembed/examples/Supported Models/ . Please open a GitHub issue https://github.com/qdrant/fastembed/issues/new if you want us to add a new model. 证据：`README.md`
- **Contributing to FastEmbed!**（documentation）：:+1::tada: First off, thanks for taking the time to contribute! :tada::+1: 证据：`CONTRIBUTING.md`
- **License**（source_file）：Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ 证据：`LICENSE`
- **⚡️ What is FastEmbed?**（documentation）：FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models https://qdrant.github.io/fastembed/examples/Supported Models/ . Please open a Github issue https://github.com/qdrant/fastembed/issues/new if you want us to add a new model. 证据：`docs/index.md`
- **All Submissions:**（documentation）：Have you followed the guidelines in our Contributing document? Have you checked to ensure there aren't other open Pull Requests ../../../pulls for the same update/change? 证据：`.github/PULL_REQUEST_TEMPLATE.md`
- **Releasing FastEmbed**（documentation）：This is a guide how to release fastembed and fastembed-gpu packages. 证据：`RELEASE.md`
- **Byte-compiled / optimized / DLL files**（source_file）：Byte-compiled / optimized / DLL files pycache / .py cod $py.class 证据：`.gitignore`
- **.Pre Commit Config**（source_file）：repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.3.4 hooks: - id: ruff types or: python, pyi, jupyter args: --fix - id: ruff-format types or: python, pyi, jupyter 证据：`.pre-commit-config.yaml`
- **Notice**（source_file）：This product includes software developed by Qdrant 证据：`NOTICE`
- **Getting Started**（source_file）：{ "cells": { "cell type": "markdown", "id": "3f159fb4", "metadata": {}, "source": " 🚶🏻‍♂️ Getting Started\n", "\n", "Here you will learn how to use the fastembed package to embed your data into a vector space. The package is designed to be easy to use and fast. It is built on top of the ONNX https://onnx.ai/ standard, which allows for fast inference on a variety of hardware called Runtimes in ONNX . \n", "\n", " Quick Start\n", "\n", "The fastembed package is designed to be easy to use. We'll be using TextEmbedding class. It takes a list of strings as input and returns a generator of vectors.\n", "\n", " 💡 You can learn more about generators from Python Wiki https://wiki.python.org/moin/Gen… 证据：`docs/Getting Started.ipynb`
- **01 Onnx Port**（source_file）：{ "cells": { "cell type": "code", "execution count": null, "id": "0e9dbcde", "metadata": {}, "outputs": , "source": "%load ext autoreload\n", "%autoreload 2" }, { "cell type": "code", "execution count": null, "id": "c37e1fda-c7f1-46e7-a5d4-19fa05c36ac1", "metadata": {}, "outputs": , "source": "from pathlib import Path\n", "from typing import Any\n", "\n", "import numpy as np\n", "import time\n", "from torch import Tensor\n", "from transformers import AutoTokenizer, AutoModel\n", "\n", "from optimum.onnxruntime import AutoOptimizationConfig, ORTModelForFeatureExtraction, ORTOptimizer\n", "from optimum.pipelines import pipeline\n", "import torch.nn.functional as F" }, { "cell type": "code", "… 证据：`experiments/01_ONNX_Port.ipynb`
- **02 Splade To Onnx**（source_file）：{ "cells": { "cell type": "code", "execution count": 1, "metadata": {}, "outputs": , "source": "import numpy as np\n", "import torch\n", "from transformers import AutoModelForMaskedLM, AutoTokenizer" }, { "cell type": "markdown", "metadata": {}, "source": " Running the model with Transformers and Torch" }, { "cell type": "code", "execution count": 2, "metadata": {}, "outputs": , "source": "sentences = \n", " \"Hello World\",\n", " \"Built by Nirant Kasliwal\",\n", " " }, { "cell type": "markdown", "metadata": {}, "source": " PyTorch Code from the SPLADERunner https://github.com/PrithivirajDamodaran/SPLADERunner library" }, { "cell type": "code", "execution count": 3, "metadata": {}, "output… 证据：`experiments/02_SPLADE_to_ONNX.ipynb`
- **Example. Convert Resnet50 To Onnx**（source_file）：{ "cells": { "cell type": "code", "execution count": 1, "id": "4bdb2a91-fa2a-4cee-ad5a-176cc957394d", "metadata": { "ExecuteTime": { "end time": "2024-05-23T12:15:28.171586Z", "start time": "2024-05-23T12:15:28.076314Z" } }, "outputs": { "ename": "ModuleNotFoundError", "evalue": "No module named 'torch'", "output type": "error", "traceback": "\u001B 0;31m---------------------------------------------------------------------------\u001B 0m", "\u001B 0;31mModuleNotFoundError\u001B 0m Traceback most recent call last ", "Cell \u001B 0;32mIn 1 , line 1\u001B 0m\n\u001B 0;32m---- 1\u001B 0m \u001B 38;5;28;01mimport\u001B 39;00m \u001B 38;5;21;01mtorch\u001B 39;00m\n\u001B 1;32m 2\u001B 0m \u001B 3… 证据：`experiments/Example. Convert Resnet50 to ONNX.ipynb`
- **Throughput Across Models**（source_file）：{ "cells": { "cell type": "markdown", "metadata": {}, "source": " 🤗 Huggingface vs ⚡ FastEmbed️\n", "\n", "Comparing the performance of Huggingface's 🤗 Transformers and ⚡ FastEmbed️ on a simple task on the following machine: Apple M2 Max, 32 GB RAM\n", "\n", " 📦 Imports\n", "\n", "Importing the necessary libraries for this comparison." }, { "cell type": "code", "execution count": 1, "metadata": { "ExecuteTime": { "end time": "2024-03-30T00:33:35.753669Z", "start time": "2024-03-30T00:33:34.371658Z" } }, "outputs": { "name": "stderr", "output type": "stream", "text": "/Users/joein/work/qdrant/fastembed/venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Pleas… 证据：`experiments/Throughput_Across_Models.ipynb`
- **export if the output model does not exist**（source_file）：from optimum.exporters.onnx import main export from transformers import AutoTokenizer 证据：`experiments/attention_export.py`
- **Prepare the input**（source_file）：import numpy as np import onnx import onnxruntime from transformers import AutoTokenizer 证据：`experiments/try_attention_export.py`
- **Init**（source_file）：from fastembed.image import ImageEmbedding from fastembed.late interaction import LateInteractionTextEmbedding from fastembed.late interaction multimodal import LateInteractionMultimodalEmbedding from fastembed.sparse import SparseEmbedding, SparseTextEmbedding from fastembed.text import TextEmbedding 证据：`fastembed/__init__.py`
- **Embedding**（source_file）：from fastembed import TextEmbedding 证据：`fastembed/embedding.py`
- **Single item should be processed in less than:**（source_file）：import logging import os from collections import defaultdict from copy import deepcopy from enum import Enum from multiprocessing import Queue, get context from multiprocessing.context import BaseContext from multiprocessing.process import BaseProcess from multiprocessing.sharedctypes import Synchronized as BaseValue from queue import Empty from typing import Any, Iterable, Type 证据：`fastembed/parallel_processor.py`
- **Py**（source_file）：partial 证据：`fastembed/py.typed`
- **Primary color**（source_file）：site name: FastEmbed site url: https://qdrant.github.io/fastembed/ site author: Nirant Kasliwal repo url: https://github.com/qdrant/fastembed/ repo name: qdrant/fastembed 证据：`mkdocs.yml`
- **Pyproject**（source_file）：tool.poetry name = "fastembed" version = "0.8.0" description = "Fast, light, accurate library built for retrieval embedding generation" authors = "Qdrant Team ", "NirantK " license = "Apache-2.0" readme = "README.md" packages = {include = "fastembed"} homepage = "https://github.com/qdrant/fastembed" repository = "https://github.com/qdrant/fastembed" keywords = "vector", "embedding", "neural", "search", "qdrant", "sentence-transformers" 证据：`pyproject.toml`
- **disable DeprecationWarning https://github.com/jupyter/jupyter core/issues/398**（source_file）：disable DeprecationWarning https://github.com/jupyter/jupyter core/issues/398 os.environ "JUPYTER PLATFORM DIRS" = "1" 证据：`tests/__init__.py`
- **Config**（source_file）：TEST DIR = Path file .parent TEST MISC DIR = TEST DIR / "misc" 证据：`tests/config.py`
- **%% markdown**（source_file）：%% markdown 🤗 Huggingface vs ⚡ FastEmbed️ Comparing the performance of Huggingface's 🤗 Transformers and ⚡ FastEmbed️ on a simple task on the following machine: Apple M2 Max, 32 GB RAM 📦 Imports Importing the necessary libraries for this comparison. 证据：`tests/profiling.py`
- **Type Stub**（source_file）：from fastembed import TextEmbedding, LateInteractionTextEmbedding, SparseTextEmbedding from fastembed.sparse.bm25 import Bm25 from fastembed.rerank.cross encoder import TextCrossEncoder 证据：`tests/type_stub.py`
- **todo: PermissionDenied is raised on blobs removal in Windows, with blobs 2GB**（source_file）：from pathlib import Path from types import TracebackType from typing import Callable, Any, Type 证据：`tests/utils.py`

## 宿主 AI 必须遵守的规则

- **把本资产当作开工前上下文，而不是运行环境。**：AI Context Pack 只包含证据化项目理解，不包含目标项目的可执行状态。 证据：`README.md`, `CONTRIBUTING.md`, `LICENSE`
- **回答用户时区分可预览内容与必须安装后才能验证的内容。**：安装前体验的消费者价值来自降低误装和误判，而不是伪装成真实运行。 证据：`README.md`, `CONTRIBUTING.md`, `LICENSE`

## 用户开工前应该回答的问题

- 你准备在哪个宿主 AI 或本地环境中使用它？
- 你只是想先体验工作流，还是准备真实安装？
- 你最在意的是安装成本、输出质量、还是和现有规则的冲突？

## 验收标准

- 所有能力声明都能回指到 evidence_refs 中的文件路径。
- AI_CONTEXT_PACK.md 没有把预览包装成真实运行。
- 用户能在 3 分钟内看懂适合谁、能做什么、如何开始和风险边界。

---

## Doramagic Context Augmentation

The following material strengthens the Repomix/AI Context Pack body. Human Manual is only a reading skeleton; pitfall logs become hard operating constraints for the host AI.

## Human Manual Skeleton

Usage rule: this is only a reading path and salience signal, not factual authority. Concrete facts must still come from repo evidence / Claim Graph.

Hard rules for the host AI:
- Do not treat page titles, order, summaries, or importance as project facts.
- When explaining the Human Manual skeleton, state that it is only a reading path / salience signal.
- Capability, installation, compatibility, runtime status, and risk judgments must cite repo evidence, source paths, or Claim Graph.

- **Introduction to FastEmbed**：importance `high`
  - source_paths: README.md, fastembed/__init__.py
- **Installation Guide**：importance `high`
  - source_paths: pyproject.toml, README.md
- **Quick Start Guide**：importance `high`
  - source_paths: README.md, fastembed/text/text_embedding.py, fastembed/image/image_embedding.py
- **System Architecture**：importance `high`
  - source_paths: fastembed/text/text_embedding_base.py, fastembed/image/image_embedding_base.py, fastembed/common/onnx_model.py, fastembed/common/model_management.py, fastembed/parallel_processor.py
- **Text Embedding Module**：importance `high`
  - source_paths: fastembed/text/onnx_embedding.py, fastembed/text/text_embedding.py, fastembed/text/onnx_text_model.py, fastembed/text/pooled_embedding.py, fastembed/text/clip_embedding.py
- **Image Embedding Module**：importance `medium`
  - source_paths: fastembed/image/onnx_embedding.py, fastembed/image/image_embedding.py, fastembed/image/onnx_image_model.py, fastembed/image/transform/operators.py, fastembed/image/transform/functional.py
- **Sparse Embedding Models**：importance `medium`
  - source_paths: fastembed/sparse/splade_pp.py, fastembed/sparse/bm25.py, fastembed/sparse/bm42.py, fastembed/sparse/minicoil.py, fastembed/sparse/sparse_text_embedding.py
- **Late Interaction Models**：importance `medium`
  - source_paths: fastembed/late_interaction/colbert.py, fastembed/late_interaction/jina_colbert.py, fastembed/late_interaction/late_interaction_text_embedding.py, fastembed/late_interaction_multimodal/colpali.py, fastembed/late_interaction_multimodal/colmodernvbert.py

## Repo Inspection Evidence

- repo_clone_verified: true
- repo_inspection_verified: true
- repo_commit: `fde1e0b36138fbea21d36b4b4782695bdf2eeae3`
- inspected_files: `pyproject.toml`, `README.md`, `docs/index.md`

Hard rules for the host AI:
- Without repo_clone_verified=true, do not claim the source code has been read.
- Without repo_inspection_verified=true, do not turn README/docs/package observations into facts.
- Without quick_start_verified=true, do not claim the Quick Start has been successfully run.

## Doramagic Pitfall Constraints

These rules come from Doramagic discovery, validation, or compilation pitfalls. The host AI must treat them as operating constraints, not general background notes.

### Constraint 1: 来源证据：[Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：[Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能增加新用户试用和生产接入成本。
- Evidence: community_evidence:github | cevd_201d4035515846df8830ca0dad6960c5 | https://github.com/qdrant/fastembed/issues/618 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 2: 来源证据：[Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：[Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能阻塞安装或首次运行。
- Evidence: community_evidence:github | cevd_8147621574b345d7955e79ad98f4ba6f | https://github.com/qdrant/fastembed/issues/630 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 3: 失败模式：security_permissions: [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside ca...

- Trigger: Developers should check this security_permissions risk before relying on the project: [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside cache directory
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside cache directory. Context: Observed when using python
- Why it matters: Developers may expose sensitive permissions or credentials: [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside cache directory
- Evidence: failure_mode_cluster:github_issue | fmev_d3890c2b3360ccb937839f70fd4aa584 | https://github.com/qdrant/fastembed/issues/626 | [Bug]: Tar path traversal (Zip Slip) in decompress_to_cache — arbitrary file write outside cache directory
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 4: 失败模式：installation: The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.

- Trigger: Developers should check this installation risk before relying on the project: The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.. Context: Observed when using python, docker
- Why it matters: Developers may fail before the first successful local run: The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.
- Evidence: failure_mode_cluster:github_issue | fmev_16e50a8626aff1576adeb1c0baab4785 | https://github.com/qdrant/fastembed/issues/466 | The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 5: 失败模式：installation: [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2

- Trigger: Developers should check this installation risk before relying on the project: [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2. Context: Observed when using python, windows, linux
- Why it matters: Developers may fail before the first successful local run: [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2
- Evidence: failure_mode_cluster:github_issue | fmev_04529bc774f1c961d4adeb7190edecd7 | https://github.com/qdrant/fastembed/issues/618 | [Bug]: Segmentation Fault or AssertionError during initialization on Python 3.14.2
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 6: 失败模式：installation: [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14

- Trigger: Developers should check this installation risk before relying on the project: [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14. Context: Observed when using python, macos, cuda
- Why it matters: Developers may fail before the first successful local run: [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
- Evidence: failure_mode_cluster:github_issue | fmev_79a43347d96beb6d05eb6bfec2503fb5 | https://github.com/qdrant/fastembed/issues/630 | [Bug]: Unable to load 'Qdrant/bm25' on macOS python3.14
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 7: 失败模式：installation: v0.5.1

- Trigger: Developers should check this installation risk before relying on the project: v0.5.1
- Host AI rule: Before packaging this project, run the relevant install/config/quickstart check for: v0.5.1. Context: Observed when using python
- Why it matters: Upgrade or migration may change expected behavior: v0.5.1
- Evidence: failure_mode_cluster:github_release | fmev_8b37c58c613005c0182d0325aaf032f7 | https://github.com/qdrant/fastembed/releases/tag/v0.5.1 | v0.5.1
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 8: 来源证据：The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：The dependency `py-rust-stemmers` cannot be downloaded in a pure Python environment.
- Host AI rule: 来源显示可能已有修复、规避或版本变化，说明书中必须标注适用版本。
- Why it matters: 可能增加新用户试用和生产接入成本。
- Evidence: community_evidence:github | cevd_0f507b37e33e456ea259e82966cecdc5 | https://github.com/qdrant/fastembed/issues/466 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 9: 来源证据：[Bug]: No timeout on model download — requests.get() can hang indefinitely

- Trigger: GitHub 社区证据显示该项目存在一个配置相关的待验证问题：[Bug]: No timeout on model download — requests.get() can hang indefinitely
- Host AI rule: 来源显示可能已有修复、规避或版本变化，说明书中必须标注适用版本。
- Why it matters: 可能阻塞安装或首次运行。
- Evidence: community_evidence:github | cevd_6d570ba91cfd414f970a3a8da522be04 | https://github.com/qdrant/fastembed/issues/627 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.

### Constraint 10: 能力判断依赖假设

- Trigger: README/documentation is current enough for a first validation pass.
- Host AI rule: 将假设转成下游验证清单。
- Why it matters: 假设不成立时，用户拿不到承诺的能力。
- Evidence: capability.assumptions | github_repo:666260877 | https://github.com/qdrant/fastembed | README/documentation is current enough for a first validation pass.
- Hard boundary: do not present this pitfall as solved, verified, or safe to ignore unless later validation evidence explicitly closes it.