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eqa-insert
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95 lines
3.8 KiB
95 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 typing import Dict, Optional, Any
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from pydantic import BaseModel
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from towhee import ops, pipe, AutoPipes, AutoConfig
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@AutoConfig.register
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class EnhancedQAInsertConfig(BaseModel):
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"""
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Config of pipeline
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"""
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# config for text_splitter
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type: Optional[str] = 'RecursiveCharacter'
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chunk_size: Optional[int] = 300
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splitter_kwargs: Optional[Dict[str, Any]] = {}
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# config for sentence_embedding
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model: Optional[str] = 'all-MiniLM-L6-v2'
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openai_api_key: Optional[str] = None
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device: Optional[int] = -1
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# config for insert_milvus
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host: Optional[str] = '127.0.0.1'
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port: Optional[str] = '19530'
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collection_name: Optional[str] = 'chatbot'
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user: Optional[str] = None
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password: Optional[str] = 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.model in _hf_models:
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return True, ops.sentence_embedding.transformers(model_name=config.model, 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, 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, api_key=config.openai_api_key)
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raise RuntimeError('Unknown model: [%s], only support: %s' % (config.model, _hf_models + _sbert_models + _openai_models))
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@AutoPipes.register
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def enhanced_qa_insert_pipe(config):
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text_split_op = ops.text_splitter(type=config.type,
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chunk_size=config.chunk_size,
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**config.splitter_kwargs)
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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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return (
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pipe.input('doc')
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.map('doc', 'text', ops.text_loader())
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.flat_map('text', 'sentence', text_split_op)
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.map('sentence', 'embedding', sentence_embedding_op, config=sentence_embedding_config)
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.map('embedding', 'embedding', ops.towhee.np_normalize())
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.map(('doc', 'sentence', 'embedding'), 'mr', insert_milvus_op)
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.output('mr')
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)
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