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@ -36,6 +36,8 @@ class EnhancedQASearchConfig: |
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self.threshold = 0.6 |
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self.threshold = 0.6 |
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# config for llm |
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# config for llm |
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self.llm_device = -1 |
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self.llm_device = -1 |
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self.llm_model = 'openai' |
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_hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names() |
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_hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names() |
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_sbert_models = ops.sentence_embedding.sbert().get_op().supported_model_names() |
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_sbert_models = ops.sentence_embedding.sbert().get_op().supported_model_names() |
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@ -72,6 +74,11 @@ def _get_similarity_evaluation_op(config): |
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return lambda x: [i for i in x if i[1] >= config.threshold] |
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return lambda x: [i for i in x if i[1] >= config.threshold] |
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def _get_llm_op(config): |
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if config.llm_model == 'openai': |
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return ops.LLM.OpenAI(api_key=config.openai_api_key) |
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@AutoPipes.register |
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@AutoPipes.register |
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def enhanced_qa_search_pipe(config): |
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def enhanced_qa_search_pipe(config): |
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allow_triton, sentence_embedding_op = _get_embedding_op(config) |
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allow_triton, sentence_embedding_op = _get_embedding_op(config) |
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@ -93,6 +100,8 @@ def enhanced_qa_search_pipe(config): |
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password=config.password, |
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password=config.password, |
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) |
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) |
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llm_op = _get_llm_op(config) |
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p = ( |
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p = ( |
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pipe.input('question', 'history') |
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pipe.input('question', 'history') |
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.map('question', 'embedding', sentence_embedding_op, config=sentence_embedding_config) |
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.map('question', 'embedding', sentence_embedding_op, config=sentence_embedding_config) |
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@ -108,7 +117,7 @@ def enhanced_qa_search_pipe(config): |
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p = ( |
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p = ( |
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p.map('result', 'docs', lambda x:[i[2] for i in x]) |
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p.map('result', 'docs', lambda x:[i[2] for i in x]) |
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.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer()) |
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.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer()) |
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.map('prompt', 'answer', ops.LLM.OpenAI(api_key=config.openai_api_key)) |
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.map('prompt', 'answer', llm_op) |
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) |
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) |
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return p.output('answer') |
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return p.output('answer') |
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