agentskillexchange Generate and evaluate retrieval embeddings with FlagEmbedding
Use FlagEmbedding to choose BGE embedding or reranking models, encode documents and queries, evaluate retrieval quality, and feed stronger context into RAG workflows.
Prerequisites
Python environment, FlagEmbedding package, selected BGE embedding or reranker model, local corpus, query set, and a downstream vector or RAG pipeline.
Installation
Use the upstream install or setup path that matches your environment:
- pip install -U FlagEmbedding
- pip install -U FlagEmbedding[finetune]
- git clone https://github.com/FlagOpen/FlagEmbedding.git
- pip install .
Basic usage or getting-started notes:
Quick Start |
If you do not want to finetune the models, you can install the package without the finetune dependency:
If you want to finetune the models, you can install the package with the finetune dependency:
Extracted from upstream docs: https://raw.githubusercontent.com/FlagOpen/FlagEmbedding/HEAD/README.md