FlagOpen/FlagEmbedding

Features

• Embedding Generators - Transforms input text into high-dimensional vector representations for semantic search and retrieval.
• Retrieval Augmented Generation - Grounds language model responses in factual data by fetching relevant information from external documents.
• Embedding Model Fine-Tuning - Provides specialized techniques for training and fine-tuning models that generate vector representations of data.
• Retrieval-Augmented Generation Frameworks - Provides tools and configuration systems for defining and executing retrieval-augmented generation pipelines.
• Model Fine-Tuning - Enables fine-tuning of pre-trained retrieval and reranking models using custom datasets.
• Result Reranking - Evaluates the relevance of retrieved documents against a query to improve search precision.
• Model Evaluation Tools - Assesses the accuracy and effectiveness of embedding and reranking models using standard metrics.
• Vector Indexing - Organizes vector embeddings into searchable structures to facilitate rapid lookup and similarity matching.
• Vector Search - Provides techniques for finding information based on mathematical similarity in high-dimensional vector spaces.
• Model Benchmarking Suites - Evaluates the accuracy and performance of machine learning models against standardized datasets.
• Performance Metrics - Calculates statistical performance indicators like precision and recall for retrieval systems.
• Retrieval Augmented Generation - Toolkit for embedding generation and retrieval-based search.
• Embedding Models - General-purpose vector models and cross-encoders for retrieval tasks.

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