# Install text-embeddings-inference with Homebrew Blazing fast inference solution for text embeddings models. Version 1.9.3 via Homebrew; verified from local package data. ## Install ```sh sudo av install brew:text-embeddings-inference ``` Additional install commands: ### macOS - Homebrew (100%): ```sh brew install text-embeddings-inference ``` Evidence: local Homebrew formula metadata ## Package facts - **Package key:** brew:text-embeddings-inference - **Package manager:** Homebrew - **Package manager page:** - **Version:** 1.9.3 - **Source summary:** Blazing fast inference solution for text embeddings models - **Homepage:** - **Repository:** - **Upstream docs:** - **License:** Apache-2.0 - **Source archive:** - **Generated:** 2026-07-08T07:18:31+00:00 ## Executables - text-embeddings-router (cli) - text-embeddings-router (alias) ## Dependencies - openssl@3 ## Build dependencies - pkgconf - rust ## Install behavior - Post-install hook: not defined - Bottle: available on arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux ## Freshness - Page generated: 2026-07-08 - Package-manager version: 1.9.3 - Local data: ok - Upstream repository: https://github.com/huggingface/text-embeddings-inference - Upstream latest detected: v1.9.3 (current) - info: No package-manager update timestamp was available. ## Project history and usage 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. ### Project history 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. ### Adoption history 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. ### How it is used 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. ### Why package nerds care 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. ### Timeline - 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. ### Sources - - - - - - ## Security Notes narrow executable package without higher-risk signals. - **Geiger risk:** green / low - 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 ## Source Database Details - **Source Database:** Homebrew formula API - **Tap:** homebrew/core - **Full Name:** text-embeddings-inference - **Version Scheme:** 0 - **Revision:** 0 - **Bottle Stable Root URL:** - **Deprecated:** no - **Disabled:** no - **Keg Only:** no - **URL Keys:** stable ## Related links - [Terminal utility packages](https://www.automicvault.com/pkg/terminal-utilities/) - Matched terminal and command-line workflow metadata. - [Text processing packages](https://www.automicvault.com/pkg/text-processing-tools/) - Matched text, document, or structured-data processing metadata. - [Developer build packages](https://www.automicvault.com/pkg/developer-build-tools/) - Matched build, compiler, generator, or developer workflow metadata. - [Networking and protocol packages](https://www.automicvault.com/pkg/networking-protocol-tools/) - Matched network, protocol, or remote-service metadata. - [openssl@3](https://www.automicvault.com/pkg/brew/openssl-3/) - Runtime dependency declared by Homebrew. - [pkgconf](https://www.automicvault.com/pkg/brew/pkgconf/) - Build dependency declared by Homebrew. - [rust](https://www.automicvault.com/pkg/brew/rust/) - Build dependency declared by Homebrew. - [hf-mcp-server](https://www.automicvault.com/pkg/brew/hf-mcp-server/) - Shares av.db curated category or tags: cli, developer-tools, hugging-face, machine-learning, server. - [crf++](https://www.automicvault.com/pkg/brew/crf/) - Shares av.db curated category or tags: cli, developer-tools, machine-learning, nlp. - [hf](https://www.automicvault.com/pkg/brew/hf/) - Shares av.db curated category or tags: cli, developer-tools, hugging-face, machine-learning. - [mlx-lm](https://www.automicvault.com/pkg/brew/mlx-lm/) - Shares av.db curated category or tags: cli, developer-tools, inference, machine-learning. - [dvc](https://www.automicvault.com/pkg/brew/dvc/) - Shares av.db curated category or tags: cli, developer-tools, machine-learning. - [llama.cpp](https://www.automicvault.com/pkg/brew/llama-cpp/) - Shares av.db curated category or tags: cli, developer-tools, machine-learning. - [mlx](https://www.automicvault.com/pkg/brew/mlx/) - Shares av.db curated category or tags: cli, developer-tools, machine-learning. - [pytorch](https://www.automicvault.com/pkg/brew/pytorch/) - Shares av.db curated category or tags: cli, developer-tools, machine-learning. ## Combined YAML source View the package source record on GitHub. [combined/text-embeddings-inference.yml](https://github.com/automic-vault/db/blob/main/combined/text-embeddings-inference.yml) ## Sources - Nucleus package database - Geiger risk classifier - package-page enrichment - curated configuration and credential file locations - curated package history - package version freshness - av.db category and tag curation - package relationship graph - cross-ecosystem install command graph