Spaces:
Running
Running
File size: 13,847 Bytes
39b7b6a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 |
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)
|