FlagEmbedding

// Overview

FlagEmbedding is an open-source retrieval toolkit from BAAI (Beijing Academy of Artificial Intelligence). It packages the widely used BGE family of models, including dense text embedding models and cross-encoder rerankers, behind a single Python library so you can turn text into vectors and score query-document pairs.

It is aimed at developers building search and RAG (retrieval-augmented generation) pipelines. You load a model from Hugging Face by name, call encode to get embeddings, and compute similarity to find relevant passages. The library also covers the multilingual BGE-M3 model, which supports dense, lexical, and multi-vector retrieval in one model.

Within the rerankers and hybrid-search category, FlagEmbedding gives you both halves of a retrieval stack: embedding models for the first-stage recall and reranker models to reorder the top candidates before they reach your LLM.

// What it does

• BGE embedding models for dense text retrieval, loaded by name from Hugging Face
• Cross-encoder reranker models (e.g. bge-reranker-v2-m3) for reordering retrieved passages
• BGE-M3 supports 100+ languages, inputs up to 8192 tokens, and dense, lexical, and multi-vector retrieval in one model
• Simple Python API: from_finetuned to load a model, encode to embed, then matrix-multiply for similarity
• Optional fp16 inference (use_fp16=True) to reduce memory and speed up encoding
• Optional finetuning extras installable via the FlagEmbedding[finetune] package

// Getting started

Install the library from PyPI, then load a BGE model and encode a couple of sentences to measure their similarity.

pip install -U FlagEmbedding

from FlagEmbedding import FlagAutoModel

model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5',
                                      query_instruction_for_retrieval="Represent this sentence for searching relevant passages:",
                                      use_fp16=True)

sentences_1 = ["I love NLP", "I love machine learning"]
sentences_2 = ["I love BGE", "I love text retrieval"]
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)

similarity = embeddings_1 @ embeddings_2.T
print(similarity)

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Generate text embeddings for the retrieval step of a RAG pipeline
• Rerank the top candidates from a vector search before passing them to an LLM
• Build multilingual search using BGE-M3 across 100+ languages
• Run semantic similarity or deduplication over a collection of documents

// How FlagEmbedding compares

FlagEmbedding alongside other open-source rerank, search & hybrid tools AI/TLDR tracks, ranked by GitHub stars.