TXT2KG w/ txtkg_rag.py e2e workflow example
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…eometric into rebase-txt2kg
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…eometric into rebase-txt2kg
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…eometric into rebase-txt2kg
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…eometric into rebase-txt2kg
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253b ultra is not a good enough LLM Judge, looking for better. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ralph Liu <[email protected]>
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(removes retrieved context from question when asking llm judge to eval) --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ralph Liu <[email protected]>
puririshi98
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TXT2KG w/ hotpot_qa.pyGraph Retrieval Example and tech_qa.py HybridRAG e2e workflow exampleTXT2KG w/ txtkg_rag.py e2e workflow example
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TXT2kg docs before their merged into main:
https://pytorch-geometric--9992.org.readthedocs.build/en/9992/modules/nn.html?highlight=txt2kg#torch_geometric.nn.nlp.TXT2KG
example of how to use this in nvidia docker container:
git config --global credential.helper store; huggingface-cli login --token <insert_token>; cd /opt/pyg; pip uninstall -y torch-geometric; rm -rf pytorch_geometric; git clone -b latest-txt2kg https://github.com/pyg-team/pytorch_geometric.git; cd /opt/pyg/pytorch_geometric; pip install .; pip install openaiexample to run:
python3 examples/llm/txt2kg_rag.pythanks to @Kh4L @zaristei @rlratzel @rliu
for their contributions
using syntheticQA: 70%->80% acc over baselines
using TechQA:
Breaking into PRs: