{"data":{"slug":"intellabs-rag-fit","name":"RAG-FiT","tagline":"Framework for enhancing LLMs for RAG tasks using fine-tuning","github_url":"https://github.com/IntelLabs/RAG-FiT","owner":"IntelLabs","repo":"RAG-FiT","owner_avatar_url":"https://avatars.githubusercontent.com/u/1492758?v=4","primary_language":"Python","stars":769,"forks":61,"topics":["evaluation","fine-tuning","information-retrieval","llm","nlp","question-answering","rag","semantic-search"],"archived":false,"github_pushed_at":"2026-06-08T20:39:29+00:00","maintenance_label":"Steady","stars_delta_30d":1,"url":"https://www.graphcanon.com/tools/intellabs-rag-fit","markdown_url":"https://www.graphcanon.com/tools/intellabs-rag-fit.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/intellabs-rag-fit","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=intellabs-rag-fit","description":"Framework for enhancing LLMs for RAG tasks using fine-tuning.","homepage_url":"https://intellabs.github.io/RAG-FiT/","license":"Apache-2.0","open_issues":1,"watchers":7,"ai_summary":"IntelLabs/RAG-FiT is a Python-based repository that provides a framework to enhance large language models (LLMs) specifically for Retriever-Augmented Generation (RAG) tasks through methods of fine-tuning. It caters to areas like evaluation, information retrieval, and semantic search, aiming to improve performance in NLP tasks such as question-answering.","readme_excerpt":"### Installation\nClone and run:\n\n```sh\npip install -e .\n```\n\nOptional packages can be installed:\n```sh\npip install -e .[haystack]\npip install -e .[deepeval]\n```\n\n---\n\n### Quick Start\n\nFor a simple, end-to-end example, see the [PubmedQA Tutorial](./docs/pubmed.md).\n\n---\n\n## License\n\nThe code is licensed under the [Apache 2.0 License](LICENSE).","github_created_at":"2024-07-23T13:20:35+00:00","created_at":"2026-07-11T11:40:29.799382+00:00","updated_at":"2026-08-24T06:01:16.369284+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"evaluation","name":"evaluation"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"information-retrieval","name":"information-retrieval"},{"slug":"llm","name":"llm"},{"slug":"nlp","name":"nlp"},{"slug":"question-answering","name":"question-answering"},{"slug":"rag","name":"rag"},{"slug":"semantic-search","name":"semantic-search"}],"trust":{"provenance":{"is_fork":false,"github_id":832661327,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-24T06:01:15.535Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":76,"last_release_at":"2024-11-12T13:54:34Z","stars_delta_30d":1,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:40:31.091Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-24T06:01:16.024Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-24T06:01:16.024Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-24T06:01:16.024Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized."],"requires_docker":false},"constraints":{"requires_docker":false},"when_to_use":["When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search","For developers who require a solution based on the Apache-2.0 license for flexibility and permissive use conditions"],"when_not_to_use":["If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable","In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers"],"source":"enrich:decision_facts","observed_at":"2026-07-15T08:42:37.640Z"},"constraint_facets":{"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized."},{"label":"Adopt for","value":"RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval."},{"label":"License detail","value":"RAG-FiT operates under the Apache-2.0 license, providing a permissive free software license that permits reuse within proprietary software."}]}}