ragas
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Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
[ ] I have checked the [documentation](https://docs.ragas.io/) and related resources and couldn't resolve my bug. **Describe the bug** A clear and concise description of what the bug is. Ragas version:...
[ ] I checked the [documentation](https://docs.ragas.io/) and related resources and couldn't find an answer to my question. How to integrate Ragas with Ragflow for evaluation? Is there any documentation I...
[ ] I checked the [documentation](https://docs.ragas.io/) and related resources and couldn't find an answer to my question. **Your Question** The code is identical only difference is the packages version ex)...
https://docs.ragas.io/en/latest/howtos/applications/compare_embeddings.html shows 404
[ ] I have checked the [documentation](https://docs.ragas.io/) and related resources and couldn't resolve my bug. **Describe the bug** I set up a defination to split the input Dataset into several...
[ ] I have checked the [documentation](https://docs.ragas.io/) and related resources and couldn't resolve my bug. **Describe the bug** Error message is "Invalid n value (currently only n = 1 is...
WARNING:ragas.testset.docstore:Filename and doc_id are the same for all nodes. --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [](https://localhost:8080/#) in () 9 10 # generate testset ---> 11 testset1 = generator.generate_with_langchain_docs(documents, test_size=10,...
[x] I have checked the [documentation](https://docs.ragas.io/) and related resources and couldn't resolve my bug. **Describe the bug** LLM is started by ollama, so there's no connection issue and it is...
----> 1 testset = generator.generate_with_langchain_docs(documents, test_size=10, distributions={simple: 0.5, reasoning: 0.25, multi_context: 0.25},raise_exceptions=False) 2 frames /usr/local/lib/python3.10/dist-packages/ragas/testset/docstore.py in add_nodes(self, nodes, show_progress) 251 results = executor.results() 252 if not results: --> 253 raise...
I checked the evaluate function and deep inside there is a function called 'embedding_factory'. by default it use openai I put the whole function here. def embedding_factory( model: str =...