import json import os import shutil from typing import Optional, Union import huggingface_hub import safetensors import safetensors.torch import torch import torch.nn.functional as F import weave from datasets import Dataset, load_dataset from rich.progress import track from transformers import ( AutoModel, AutoTokenizer, BertPreTrainedModel, PreTrainedTokenizerFast, ) from medrag_multi_modal.retrieval.common import SimilarityMetric, argsort_scores from medrag_multi_modal.utils import ( fetch_from_huggingface, get_torch_backend, save_to_huggingface, ) class MedCPTRetriever(weave.Model): """ A class to retrieve relevant text chunks using MedCPT models. This class provides methods to index a dataset of text chunks and retrieve the most relevant chunks for a given query using MedCPT models. It uses separate models for encoding queries and articles, and supports both cosine similarity and Euclidean distance as similarity metrics. Args: query_encoder_model_name (str): The name of the model used for encoding queries. article_encoder_model_name (str): The name of the model used for encoding articles. chunk_size (Optional[int]): The maximum length of text chunks. vector_index (Optional[torch.Tensor]): The vector index of encoded text chunks. chunk_dataset (Optional[list[dict]]): The dataset of text chunks. """ query_encoder_model_name: str article_encoder_model_name: str chunk_size: Optional[int] _chunk_dataset: Optional[list[dict]] _query_tokenizer: PreTrainedTokenizerFast _article_tokenizer: PreTrainedTokenizerFast _query_encoder_model: BertPreTrainedModel _article_encoder_model: BertPreTrainedModel _vector_index: Optional[torch.Tensor] def __init__( self, query_encoder_model_name: str = "ncbi/MedCPT-Query-Encoder", article_encoder_model_name: str = "ncbi/MedCPT-Article-Encoder", chunk_size: Optional[int] = None, vector_index: Optional[torch.Tensor] = None, chunk_dataset: Optional[list[dict]] = None, ): super().__init__( query_encoder_model_name=query_encoder_model_name, article_encoder_model_name=article_encoder_model_name, chunk_size=chunk_size, ) self._query_tokenizer = AutoTokenizer.from_pretrained( self.query_encoder_model_name ) self._article_tokenizer = AutoTokenizer.from_pretrained( self.article_encoder_model_name ) self._query_encoder_model = AutoModel.from_pretrained( self.query_encoder_model_name ).to(get_torch_backend()) self._article_encoder_model = AutoModel.from_pretrained( self.article_encoder_model_name ).to(get_torch_backend()) self._chunk_dataset = chunk_dataset self._vector_index = vector_index def index( self, chunk_dataset: Union[str, Dataset], index_repo_id: Optional[str] = None, cleanup: bool = True, batch_size: int = 32, ): """ Indexes a dataset of text chunks using the MedCPT model and optionally saves the vector index. This method retrieves a dataset of text chunks from a specified source, encodes the text chunks into vector representations using the article encoder model, and stores the resulting vector index. If an `index_repo_id` is provided, the vector index is saved to disk in the safetensors format and optionally logged as a Huggingface artifact. !!! example "Example Usage" ```python import weave from dotenv import load_dotenv from medrag_multi_modal.retrieval.text_retrieval import MedCPTRetriever load_dotenv() retriever = MedCPTRetriever() retriever.index( chunk_dataset="geekyrakshit/grays-anatomy-chunks-test", index_repo_id="geekyrakshit/grays-anatomy-index-medcpt", ) ``` Args: chunk_dataset (str): The Huggingface dataset containing the text chunks to be indexed. Either a dataset repository name or a dataset object can be provided. index_repo_id (Optional[str]): The Huggingface repository of the index artifact to be saved. cleanup (bool, optional): Whether to delete the local index directory after saving the vector index. batch_size (int, optional): The batch size to use for encoding the corpus. """ self._chunk_dataset = ( load_dataset(chunk_dataset, split="chunks") if isinstance(chunk_dataset, str) else chunk_dataset ) corpus = [row["text"] for row in self._chunk_dataset] vector_indices = [] with torch.no_grad(): for idx in track( range(0, len(corpus), batch_size), description="Encoding corpus using MedCPT", ): batch = corpus[idx : idx + batch_size] encoded = self._article_tokenizer( batch, truncation=True, padding=True, return_tensors="pt", max_length=self.chunk_size, ).to(get_torch_backend()) batch_vectors = ( self._article_encoder_model(**encoded) .last_hidden_state[:, 0, :] .contiguous() ) vector_indices.append(batch_vectors) vector_index = torch.cat(vector_indices, dim=0) self._vector_index = vector_index if index_repo_id: index_save_dir = os.path.join( ".huggingface", index_repo_id.split("/")[-1] ) os.makedirs(index_save_dir, exist_ok=True) safetensors.torch.save_file( {"vector_index": self._vector_index.cpu()}, os.path.join(index_save_dir, "vector_index.safetensors"), ) commit_type = ( "update" if huggingface_hub.repo_exists(index_repo_id, repo_type="model") else "add" ) with open( os.path.join(index_save_dir, "config.json"), "w" ) as config_file: json.dump( { "query_encoder_model_name": self.query_encoder_model_name, "article_encoder_model_name": self.article_encoder_model_name, "chunk_size": self.chunk_size, }, config_file, indent=4, ) save_to_huggingface( index_repo_id, index_save_dir, commit_message=f"{commit_type}: Contriever index", ) if cleanup: shutil.rmtree(index_save_dir) @classmethod def from_index(cls, chunk_dataset: Union[str, Dataset], index_repo_id: str): """ Creates an instance of the class from a Huggingface repository. This method retrieves a vector index and metadata from a Huggingface repository. It also retrieves a dataset of text chunks from the specified source. The vector index is loaded from a safetensors file and moved to the appropriate device (CPU or GPU). The method then returns an instance of the class initialized with the retrieved model names, vector index, and chunk dataset. !!! example "Example Usage" ```python from medrag_multi_modal.retrieval.text_retrieval import MedCPTRetriever retriever = MedCPTRetriever.from_index( index_repo_id="ashwiniai/medrag-text-corpus-chunks-medcpt", chunk_dataset="ashwiniai/medrag-text-corpus-chunks", ) ``` Args: chunk_dataset (str): The Huggingface dataset containing the text chunks to be indexed. Either a dataset repository name or a dataset object can be provided. index_repo_id (Optional[str]): The Huggingface repository of the index artifact to be saved. Returns: An instance of the class initialized with the retrieved model name, vector index, and chunk dataset. """ index_dir = fetch_from_huggingface(index_repo_id, ".huggingface") with safetensors.torch.safe_open( os.path.join(index_dir, "vector_index.safetensors"), framework="pt" ) as f: vector_index = f.get_tensor("vector_index") device = torch.device(get_torch_backend()) vector_index = vector_index.to(device) with open(os.path.join(index_dir, "config.json"), "r") as config_file: metadata = json.load(config_file) chunk_dataset = ( load_dataset(chunk_dataset, split="chunks") if isinstance(chunk_dataset, str) else chunk_dataset ) return cls( query_encoder_model_name=metadata["query_encoder_model_name"], article_encoder_model_name=metadata["article_encoder_model_name"], chunk_size=metadata["chunk_size"], vector_index=vector_index, chunk_dataset=chunk_dataset, ) @weave.op() def retrieve( self, query: str, top_k: int = 2, metric: SimilarityMetric = SimilarityMetric.COSINE, ): """ Retrieves the top-k most relevant chunks for a given query using the specified similarity metric. This method encodes the input query into an embedding and computes similarity scores between the query embedding and the precomputed vector index. The similarity metric can be either cosine similarity or Euclidean distance. The top-k chunks with the highest similarity scores are returned as a list of dictionaries, each containing a chunk and its corresponding score. !!! example "Example Usage" ```python import weave from medrag_multi_modal.retrieval.text_retrieval import MedCPTRetriever weave.init(project_name="ml-colabs/medrag-multi-modal") retriever = MedCPTRetriever.from_index( index_repo_id="ashwiniai/medrag-text-corpus-chunks-medcpt", chunk_dataset="ashwiniai/medrag-text-corpus-chunks", ) retriever.retrieve(query="What is ribosome?") ``` Args: query (str): The input query string to search for relevant chunks. top_k (int, optional): The number of top relevant chunks to retrieve. Defaults to 2. metric (SimilarityMetric, optional): The similarity metric to use for scoring. Defaults to cosine similarity. Returns: list: A list of dictionaries, each containing a retrieved chunk and its relevance score. """ query = [query] device = torch.device(get_torch_backend()) with torch.no_grad(): encoded = self._query_tokenizer( query, truncation=True, padding=True, return_tensors="pt", ).to(device) query_embedding = self._query_encoder_model(**encoded).last_hidden_state[ :, 0, : ] query_embedding = query_embedding.to(device) if metric == SimilarityMetric.EUCLIDEAN: scores = torch.squeeze(query_embedding @ self._vector_index.T) else: scores = F.cosine_similarity(query_embedding, self._vector_index) scores = scores.cpu().numpy().tolist() scores = argsort_scores(scores, descending=True)[:top_k] retrieved_chunks = [] for score in scores: retrieved_chunks.append( { **self._chunk_dataset[score["original_index"]], **{"score": score["item"]}, } ) return retrieved_chunks @weave.op() def predict( self, query: str, top_k: int = 2, metric: SimilarityMetric = SimilarityMetric.COSINE, ): """ Predicts the most relevant chunks for a given query. This function uses the `retrieve` method to find the top-k relevant chunks from the dataset based on the input query. It allows specifying the number of top relevant chunks to retrieve and the similarity metric to use for scoring. !!! example "Example Usage" ```python import weave from medrag_multi_modal.retrieval.text_retrieval import MedCPTRetriever weave.init(project_name="ml-colabs/medrag-multi-modal") retriever = MedCPTRetriever.from_index( index_repo_id="ashwiniai/medrag-text-corpus-chunks-medcpt", chunk_dataset="ashwiniai/medrag-text-corpus-chunks", ) retriever.predict(query="What is ribosome?") ``` Args: query (str): The input query string to search for relevant chunks. top_k (int, optional): The number of top relevant chunks to retrieve. Defaults to 2. metric (SimilarityMetric, optional): The similarity metric to use for scoring. Defaults to cosine similarity. Returns: list: A list of dictionaries, each containing a retrieved chunk and its relevance score. """ return self.retrieve(query, top_k, metric)