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eqa-search
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113 lines
3.8 KiB
113 lines
3.8 KiB
# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from towhee import ops, pipe, AutoPipes, AutoConfig
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@AutoConfig.register
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class EnhancedQASearchConfig:
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"""
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Config of pipeline
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"""
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def __init__(self):
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# config for sentence_embedding
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self.model = 'all-MiniLM-L6-v2'
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self.openai_api_key = None
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self.embedding_device = -1
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# config for search_milvus
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self.host = '127.0.0.1'
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self.port = '19530'
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self.collection_name = 'chatbot'
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self.top_k = 5
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self.user = None
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self.password = None
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# config for similarity evaluation
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self.threshold = 0.6
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# config for llm
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self.llm_device = -1
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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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_openai_models = ['text-embedding-ada-002', 'text-similarity-davinci-001',
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'text-similarity-curie-001', 'text-similarity-babbage-001',
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'text-similarity-ada-001']
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def _get_embedding_op(config):
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if config.embedding_device == -1:
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device = 'cpu'
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else:
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device = config.embedding_device
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if config.model in _hf_models:
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return True, ops.sentence_embedding.transformers(
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model_name=config.model, device=device
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)
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if config.model in _sbert_models:
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return True, ops.sentence_embedding.sbert(
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model_name=config.model, device=device
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)
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if config.model in _openai_models:
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return False, ops.sentence_embedding.openai(
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model_name=config.model, api_key=config.openai_api_key
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)
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raise RuntimeError('Unknown model: [%s], only support: %s' % (config.model, _hf_models + _openai_models))
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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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@AutoPipes.register
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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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sentence_embedding_config = {}
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if allow_triton:
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if config.embedding_device >= 0:
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sentence_embedding_config = AutoConfig.TritonGPUConfig(device_ids=[config.embedding_device], max_batch_size=128)
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else:
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sentence_embedding_config = AutoConfig.TritonCPUConfig()
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search_milvus_op = ops.ann_search.milvus_client(
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host=config.host,
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port=config.port,
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collection_name=config.collection_name,
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limit=config.top_k,
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output_fields=['text'],
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metric_type='IP',
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user=config.user,
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password=config.password,
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)
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p = (
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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('embedding', 'embedding', ops.towhee.np_normalize())
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.map('embedding', 'result', search_milvus_op)
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)
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# if config.similarity_evaluation:
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if config.threshold:
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sim_eval_op = _get_similarity_evaluation_op(config)
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p = p.map('result', 'result', sim_eval_op)
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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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.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer())
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)
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return p.output('question', 'history', 'docs', 'prompt')
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