eqa-insert/eqa_insert.py

# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Dict, Optional, Any
from pydantic import BaseModel

from towhee import ops, pipe, AutoPipes, AutoConfig


@AutoConfig.register
class EnhancedQAInsertConfig(BaseModel):
    """
    Config of pipeline
    """
    # config for text_splitter
    type: Optional[str] = 'RecursiveCharacter'
    chunk_size: Optional[int] = 300
    splitter_kwargs: Optional[Dict[str, Any]] = {}
    # config for sentence_embedding
    embedding_model: Optional[str] = 'all-MiniLM-L6-v2'
    openai_api_key: Optional[str] = None
    embedding_device: Optional[int] = -1
    # config for insert_milvus
    host: Optional[str] = '127.0.0.1'
    port: Optional[str] = '19530'
    collection_name: Optional[str] = 'chatbot'
    user: Optional[str] = None
    password: Optional[str] = None


_hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names()
_sbert_models = ops.sentence_embedding.sbert().get_op().supported_model_names()
_openai_models = ['text-embedding-ada-002', 'text-similarity-davinci-001',
                  'text-similarity-curie-001', 'text-similarity-babbage-001',
                  'text-similarity-ada-001']


def _get_embedding_op(config):
    if config.embedding_device == -1:
        device = 'cpu'
    else:
        device = config.embedding_device

    if config.embedding_model in _hf_models:
        return True, ops.sentence_embedding.transformers(model_name=config.embedding_model, device=device)
    if config.embedding_model in _sbert_models:
        return True, ops.sentence_embedding.sbert(model_name=config.embedding_model, device=device)
    if config.embedding_model in _openai_models:
        return False, ops.sentence_embedding.openai(model_name=config.embedding_model, api_key=config.openai_api_key)
    raise RuntimeError('Unknown model: [%s], only support: %s' % (config.embedding_model, _hf_models + _sbert_models + _openai_models))



@AutoPipes.register
def enhanced_qa_insert_pipe(config):
    text_split_op = ops.text_splitter(type=config.type, 
                                      chunk_size=config.chunk_size, 
                                      **config.splitter_kwargs)
    
    
    allow_triton, sentence_embedding_op = _get_embedding_op(config)
    sentence_embedding_config = {}
    if allow_triton:
        if config.embedding_device >= 0:
            sentence_embedding_config = AutoConfig.TritonGPUConfig(device_ids=[config.embedding_device], max_batch_size=128)
        else:
            sentence_embedding_config = AutoConfig.TritonCPUConfig()

    insert_milvus_op = ops.ann_insert.milvus_client(host=config.host,
                                                    port=config.port,
                                                    collection_name=config.collection_name,
                                                    user=config.user,
                                                    password=config.password,
                                                    )

    return (
        pipe.input('doc')
            .map('doc', 'text', ops.text_loader())
            .flat_map('text', 'sentence', text_split_op)
            .map('sentence', 'embedding', sentence_embedding_op, config=sentence_embedding_config)
            .map('embedding', 'embedding', ops.towhee.np_normalize())
            .map(('doc', 'sentence', 'embedding'), 'mr', insert_milvus_op)
            .output('mr')
    )