Releases: explodinggradients/ragas
Releases · explodinggradients/ragas
v0.0.12
What's Changed
- chore: added note about consultation by @jjmachan in #98
- llamaIndex: fix the bugs by @jjmachan in #100
- fix typos by @shahules786 in #102
- Limit context relevancy to 1 by @shahules786 in #105
- Improve context relevancy by @shahules786 in #112
Full Changelog: v0.0.11...v0.0.12
v0.0.11
What's Changed
- docs: correct docs for answer relevancy by @jjmachan in #86
- docs: notebook to showcase langsmith integration by @jjmachan in #84
- Bug fixes and improvements by @shahules786 in #89
- fix(spelling): relavancy -> relevancy by @mikeldking in #93
- feat: add support for langchain by @jjmachan in #94
- feat: column mapping to select columns by @jjmachan in #91
- Context Recall by @shahules786 in #96
New Contributors
- @mikeldking made their first contribution in #93
Full Changelog: v0.0.10...v0.0.11
v0.0.10
Main
- feat: llama_index integration by @jjmachan in #78
- New Answer relevancy metrics by @shahules786 in #77
- feat(context managers): added Context Managers to help with tracing by @jjmachan in #83
What's Changed
- feat: llama_index integration by @jjmachan in #78
- New Answer relevancy metrics by @shahules786 in #77
- added improved NLI prompt by @shahules786 in #81
- Update README.md by @shahules786 in #82
- feat(context managers): added Context Managers to help with tracing by @jjmachan in #83
- docs: notebook for langsmith integration by @jjmachan in #85
Full Changelog: v0.0.9...v0.0.10
v0.0.9
v0.0.8
Main
- Critique metrics by @shahules786 in #70
What's Changed
- fix: created new class for MetricWithLLM by @jjmachan in #71
- chore: support unit testing on python 3.11 by @jjmachan in #69
- Critique metrics by @shahules786 in #70
- feat: add validation step by @jjmachan in #72
- fix: n_swapped check for generate by @jjmachan in #73
Full Changelog: 0.0.7...v0.0.8
0.0.7
0.0.6
Main
- Context Relevancy v2 - measures how relevant is the retrieved context to the prompt. This is done using a combination of OpenAI models and cross-encoder models. To improve the score one can try to optimize the amount of information present in the retrieved context.
What's Changed
- added analytics by @jjmachan in #58
- Context Relevancy v2 by @shahules786 in #59
- doc: added numpy style documentation to
context_relavency
by @jjmachan in #62 - updated docs by @shahules786 in #64
- fix: error in handling device for tensors by @jjmachan in #61
- chore: renamed files and added tqdm by @jjmachan in #65
Full Changelog: 0.0.5...0.0.6
0.0.5
0.0.4
Important feats
- Rename metrics by @shahules786 in #48
- feat: open usage tracking by @jjmachan in #52
What's Changed
- Update README.md by @jjmachan in #42
- Hotfix: Update Readme [ SpellingError ] by @MANISH007700 in #43
- Update metrics.md by @shahules786 in #45
- added discord server by @jjmachan in #47
- Rename metrics by @shahules786 in #48
- feat: open usage tracking by @jjmachan in #52
- docs: moved quickstart by @jjmachan in #54
- docs: fix quickstart and readme by @jjmachan in #55
New Contributors
- @MANISH007700 made their first contribution in #43
Full Changelog: v0.0.3...0.0.4
v0.0.3
v0.0.3 is a major design change
We have added 3 new metrics that help you answer how factually correct is your generated answers, how relevant are the answers to the question and how relevant are the contexts returned form the retriever to the questions. This gives you a sense of the performance of both you generation and retrieval steps. We also have a "ragas_score" which is unified score to give a single metric about your pipelines.
checkout the quickstart to see how it works: https://github.com/explodinggradients/ragas/blob/main/examples/quickstart.ipynb