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Merge pull request chatchat-space#2435 from chatchat-space/reranker
新增特性：使用Reranker模型对召回语句进行重排
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,19 +1,116 @@ | ||
| from langchain.retrievers.document_compressors import CohereRerank | ||
| from llama_index.postprocessor import SentenceTransformerRerank | ||
| from sentence_transformers import SentenceTransformer,CrossEncoder | ||
| import os | ||
| import sys | ||
| sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) | ||
| from typing import Any, List, Optional | ||
| from sentence_transformers import CrossEncoder | ||
| from typing import Optional, Sequence | ||
| from langchain_core.documents import Document | ||
| from langchain.callbacks.manager import Callbacks | ||
| from langchain.retrievers.document_compressors.base import BaseDocumentCompressor | ||
| from llama_index.bridge.pydantic import Field,PrivateAttr | ||
|
|
||
| model_path = "/root/autodl-tmp/models/bge-reranker-large/" | ||
| instruction = "为这个句子生成表示以用于检索相关文章：" | ||
| reranker = SentenceTransformerRerank( | ||
| top_n=5, | ||
| model="local:"+model_path, | ||
| ) | ||
| class LangchainReranker(BaseDocumentCompressor): | ||
| """Document compressor that uses `Cohere Rerank API`.""" | ||
| model_name_or_path:str = Field() | ||
| _model: Any = PrivateAttr() | ||
| top_n:int= Field() | ||
| device:str=Field() | ||
| max_length:int=Field() | ||
| batch_size: int = Field() | ||
| # show_progress_bar: bool = None | ||
| num_workers: int = Field() | ||
| # activation_fct = None | ||
| # apply_softmax = False | ||
|
|
||
| def __init__(self, | ||
| model_name_or_path:str, | ||
| top_n:int=3, | ||
| device:str="cuda", | ||
| max_length:int=1024, | ||
| batch_size: int = 32, | ||
| # show_progress_bar: bool = None, | ||
| num_workers: int = 0, | ||
| # activation_fct = None, | ||
| # apply_softmax = False, | ||
| ): | ||
| # self.top_n=top_n | ||
| # self.model_name_or_path=model_name_or_path | ||
| # self.device=device | ||
| # self.max_length=max_length | ||
| # self.batch_size=batch_size | ||
| # self.show_progress_bar=show_progress_bar | ||
| # self.num_workers=num_workers | ||
| # self.activation_fct=activation_fct | ||
| # self.apply_softmax=apply_softmax | ||
|
|
||
| reranker_model = SentenceTransformer(model_name_or_path=model_path,device="cuda") | ||
| self._model = CrossEncoder(model_name=model_name_or_path,max_length=1024,device=device) | ||
| super().__init__( | ||
| top_n=top_n, | ||
| model_name_or_path=model_name_or_path, | ||
| device=device, | ||
| max_length=max_length, | ||
| batch_size=batch_size, | ||
| # show_progress_bar=show_progress_bar, | ||
| num_workers=num_workers, | ||
| # activation_fct=activation_fct, | ||
| # apply_softmax=apply_softmax | ||
| ) | ||
|
|
||
| reranker_ce = CrossEncoder(model_name=model_path,device="cuda",max_length=1024) | ||
| def compress_documents( | ||
| self, | ||
| documents: Sequence[Document], | ||
| query: str, | ||
| callbacks: Optional[Callbacks] = None, | ||
| ) -> Sequence[Document]: | ||
| """ | ||
| Compress documents using Cohere's rerank API. | ||
| reranker_ce.predict([[],[]]) | ||
|
|
||
| print("Load reranker") | ||
| Args: | ||
| documents: A sequence of documents to compress. | ||
| query: The query to use for compressing the documents. | ||
| callbacks: Callbacks to run during the compression process. | ||
| Returns: | ||
| A sequence of compressed documents. | ||
| """ | ||
| if len(documents) == 0: # to avoid empty api call | ||
| return [] | ||
| doc_list = list(documents) | ||
| _docs = [d.page_content for d in doc_list] | ||
| sentence_pairs = [[query,_doc] for _doc in _docs] | ||
| results = self._model.predict(sentences=sentence_pairs, | ||
| batch_size=self.batch_size, | ||
| # show_progress_bar=self.show_progress_bar, | ||
| num_workers=self.num_workers, | ||
| # activation_fct=self.activation_fct, | ||
| # apply_softmax=self.apply_softmax, | ||
| convert_to_tensor=True | ||
| ) | ||
| top_k = self.top_n if self.top_n < len(results) else len(results) | ||
|
|
||
| values, indices = results.topk(top_k) | ||
| final_results = [] | ||
| for value, index in zip(values,indices): | ||
| doc = doc_list[index] | ||
| doc.metadata["relevance_score"] = value | ||
| final_results.append(doc) | ||
| return final_results | ||
| if __name__ == "__main__": | ||
| from configs import (LLM_MODELS, | ||
| VECTOR_SEARCH_TOP_K, | ||
| SCORE_THRESHOLD, | ||
| TEMPERATURE, | ||
| USE_RERANKER, | ||
| RERANKER_MODEL, | ||
| RERANKER_MAX_LENGTH, | ||
| MODEL_PATH) | ||
| from server.utils import embedding_device | ||
| if USE_RERANKER: | ||
| reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL,"BAAI/bge-reranker-large") | ||
| print("-----------------model path------------------") | ||
| print(reranker_model_path) | ||
| reranker_model = LangchainReranker(top_n=3, | ||
| device=embedding_device(), | ||
| max_length=RERANKER_MAX_LENGTH, | ||
| model_name_or_path=reranker_model_path | ||
| ) |