Add text rerank pipeline #2436
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Add text rerank pipeline #2436
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as-suvorov
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| infer_request_guard.get().set_input_tensor(0, ov::Tensor{ov::element::string, {prompts_1.size()}, const_cast<std::string*>(prompts_1.data())}); | ||
| infer_request_guard.get().set_input_tensor(1, ov::Tensor{ov::element::string, {prompts_2.size()}, const_cast<std::string*>(prompts_2.data())}); |
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as-suvorov
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| if MODELS[model_id].get("convert_2_input_tokenizer", False): | ||
| convert_2_input_tokenizer(Path(model_path)) |
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Temporary until 2 inputs tokenizer transformation is implemented
apaniukov
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Jul 18, 2025
rkazants
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Add TextRerankPipeline which reranks a list of candidate documents based on their relevance to a query using a cross-encoder or reranker model.
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.pyi is incorrect |
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**Details**: After #2436 merged, there are two test_rag.py as follow: - [pytests/samples/test_rag.py](https://github.com/openvinotoolkit/openvino.genai/blob/master/tests/python_tests/samples/test_rag.py) - [pytests/test_rag.py](https://github.com/openvinotoolkit/openvino.genai/blob/master/tests/python_tests/test_rag.py) Which introduced following issue in CI precommit test locally: ``` __________________________ERROR collecting test_rag.py__________________________ import file mismatch: imported module 'test_rag' has this __file__ attribute: C:\xsun\openvino.genai\tests\python_tests\samples\test_rag.py which is not the same as the test file we want to collect: C:\xsun\openvino.genai\tests\python_tests\test_rag.py HINT: remove __pycache__ / .pyc files and/or use a unique basename for your test file modules ``` This PR aim to rename `pytests/samples/test_rag.py` to `pytests/samples/test_rag_sample.py` to avoid ambiguous name conflict with [pytests/test_rag.py](https://github.com/openvinotoolkit/openvino.genai/blob/master/tests/python_tests/test_rag.py), therefore fix the pytest import issue in CI precommit test. --------- Co-authored-by: Vladimir Zlobin <[email protected]>
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Add TextRerankPipeline which reranks a list of candidate documents based on their relevance to a query using a cross-encoder or reranker model.