towhee
/
eqa-insert
copied
shiyu22
2 years ago
1 changed files with 104 additions and 0 deletions
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# 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 EnhancedQAInsertConfig: |
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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 text_loader |
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self.chunk_size = 300 |
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self.source_type = 'file' |
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# config for sentence_embedding |
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self.model = 'all-MiniLM-L6-v2' |
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self.dim = None |
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self.openai_api_key = None |
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self.customize_embedding_op = None |
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self.normalize_vec = True |
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self.device = -1 |
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# config for insert_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.user = None |
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self.password = None |
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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.device == -1: |
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device = 'cpu' |
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else: |
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device = config.device |
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if config.customize_embedding_op is not None: |
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return True, config.customize_embedding_op |
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if config.model in _hf_models: |
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return True, ops.sentence_embedding.transformers(model_name=config.model, |
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device=device) |
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if config.model in _sbert_models: |
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return True, ops.sentence_embedding.sbert(model_name=config.model, |
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device=device) |
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if config.model in _openai_models: |
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return False, ops.sentence_embedding.openai(model_name=config.model, |
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api_key=config.openai_api_key) |
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raise RuntimeError('Unknown model: [%s], only support: %s' % (config.model, _hf_models + _openai_models)) |
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@AutoPipes.register |
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def enhanced_qa_insert_pipe(config): |
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if not config: |
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config = EnhancedQAInsertConfig() |
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text_load_op = ops.text_loader() |
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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.device >= 0: |
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sentence_embedding_config = AutoConfig.TritonGPUConfig(device_ids=[config.device], max_batch_size=128) |
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else: |
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sentence_embedding_config = AutoConfig.TritonCPUConfig() |
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insert_milvus_op = ops.ann_insert.milvus_client(host=config.host, |
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port=config.port, |
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collection_name=config.collection_name, |
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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('doc') |
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.flat_map('doc', 'sentence', text_load_op) |
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.map('sentence', 'embedding', sentence_embedding_op, config=sentence_embedding_config) |
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) |
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if config.normalize_vec: |
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p = p.map('vec', 'vec', ops.towhee.np_normalize()) |
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return (p.map(('doc', 'sentence', 'embedding'), 'mr', insert_milvus_op) |
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.output('mr') |
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) |
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