# 使用 Homebrew 安装 text-embeddings-inference

查看 text-embeddings-inference 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

## 安装

```sh
sudo av install brew:text-embeddings-inference
```

其他安装命令:

### macOS

- Homebrew (100%):

```sh
brew install text-embeddings-inference
```

  证据: local Homebrew formula metadata

## 软件包事实

- **软件包键:** brew:text-embeddings-inference
- **软件包管理器:** Homebrew
- **版本:** 1.9.3
- **来源摘要:** Blazing fast inference solution for text embeddings models
- **主页:** <https://huggingface.co/docs/text-embeddings-inference/quick_tour>
- **仓库:** <https://github.com/huggingface/text-embeddings-inference>
- **最后更新:** 2026-07-14T17:14:17+09:00
- **已生成:** 2026-08-03T19:37:03+00:00

## 可执行文件

- text-embeddings-router (别名)

## 安装行为

- Bottle: 不可用

## 版本和新鲜度

- 页面生成时间: 2026-08-03
- 管理器版本: 1.9.3
## 项目历史与用法

Text Embeddings Inference, usually abbreviated TEI, is Hugging Face's Rust-oriented serving toolkit for text-embedding, reranking, and sequence-classification models. It emerged from the operational need to serve embedding models efficiently for retrieval-augmented generation, semantic search, and large-scale vector indexing.

### 项目历史

The official repository and documentation describe TEI as a toolkit for deploying and serving open source text embeddings and sequence classification models. Its design emphasizes no model graph compilation step, small Docker images, fast boot times, token-based dynamic batching, optimized inference with Flash Attention, Candle, and cuBLASLt, Safetensors and ONNX weight loading, and production features such as OpenTelemetry tracing and Prometheus metrics.

### 采用历史

Hugging Face's official deployment material places TEI inside the broader Inference Endpoints and embedding-container story. A Hugging Face blog on embedding endpoints presents Text Embedding Inference as the managed solution used to deploy open-source embedding models, and the SageMaker embedding-container announcement says the container is powered by TEI for efficient deployment of embedding models used in RAG applications.

### 使用方式

The normal package-nerd entry point is the text-embeddings-router executable or a ghcr.io/huggingface/text-embeddings-inference Docker image. Users select a Hugging Face model ID or local model directory with --model-id, expose HTTP endpoints such as /embed, /rerank, /predict, or OpenAI-compatible embeddings routes, and tune batch/request limits to match hardware.

Homebrew is explicitly documented for Apple Silicon local installs: the upstream README says users can brew install text-embeddings-inference and launch text-embeddings-router with Metal acceleration. Docker images cover CPU, CUDA architectures, ARM64, Hopper, Blackwell, and related hardware tiers.

### 为什么软件包爱好者会关心

TEI matters to package and infrastructure nerds because it turns a fast-moving ML serving stack into a versioned binary/container artifact. It pulls together model formats, GPU capability constraints, batching limits, metrics, tracing, Hugging Face Hub model IDs, private model tokens, and platform-specific acceleration.

Its Homebrew formula is notable because it gives Mac users a native local embedding server path outside Docker, useful for development, local RAG experiments, and testing Hub-compatible embedding models on Apple Silicon.

### 时间线

- 2023: Hugging Face blog material presents Text Embedding Inference in embedding-model deployment workflows.
- 2024: Hugging Face announces a SageMaker embedding container powered by TEI for embedding models and RAG applications.
- 2025-2026: official docs and repository list expanded model families, hardware images, ONNX loading, OpenAI-compatible routes, Homebrew installation, and continued releases.

### Related projects

- Hugging Face Hub supplies model IDs, revisions, private/gated model access, and compatible model tags.
- Text Generation Inference is the related Hugging Face serving project for generative language models.
- Candle, Safetensors, Flash Attention, ONNX, and cuBLASLt are cited upstream as core performance or loading technologies.
- MTEB and embedding model families such as BGE, E5, GTE, Nomic, Qwen, Jina, and Snowflake Arctic shape the models TEI users package and serve.

### 来源

- <https://github.com/huggingface/text-embeddings-inference - official README documents TEI goals, features, Docker usage, CLI, supported models, and Homebrew install.>
- <https://huggingface.co/blog/inference-endpoints-embeddings - official Hugging Face blog explains embedding deployment with Text Embedding Inference.>
- <https://huggingface.co/blog/sagemaker-huggingface-embedding - official Hugging Face blog announces an embedding container powered by TEI.>
- <https://huggingface.co/docs/text-embeddings-inference/index - official docs summarize TEI features and production deployment focus.>
- <https://huggingface.co/docs/text-embeddings-inference/quick_tour - official quick tour documents Docker deployment, /embed, /rerank, /predict, batching, and air-gapped usage.>
- <https://huggingface.co/docs/text-embeddings-inference/supported_models - official docs list supported model families and hardware images.>


## 安全说明

narrow executable package without higher-risk signals.

- **Geiger 风险:** 绿色 / 低
- narrow executable package without higher-risk signals


## Configuration and credential file locations

These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.


## Credential files

- Unix: $HF_HOME/token

## Combined YAML source

View the package source record on GitHub. [combined/text-embeddings-inference.yml](https://github.com/mxcl/pkgdb/blob/main/combined/text-embeddings-inference.yml)


## 来源

- pkg.so package database
- Geiger risk classifier
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation
- cross-ecosystem install command graph