mirror of
https://github.com/open-webui/open-webui
synced 2024-11-07 09:09:53 +00:00
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
import os
|
|
import torch
|
|
import numpy as np
|
|
from colbert.infra import ColBERTConfig
|
|
from colbert.modeling.checkpoint import Checkpoint
|
|
|
|
|
|
class ColBERT:
|
|
def __init__(self, name, **kwargs) -> None:
|
|
print("ColBERT: Loading model", name)
|
|
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
DOCKER = kwargs.get("env") == "docker"
|
|
if DOCKER:
|
|
# This is a workaround for the issue with the docker container
|
|
# where the torch extension is not loaded properly
|
|
# and the following error is thrown:
|
|
# /root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/segmented_maxsim_cpp.so: cannot open shared object file: No such file or directory
|
|
|
|
lock_file = (
|
|
"/root/.cache/torch_extensions/py311_cpu/segmented_maxsim_cpp/lock"
|
|
)
|
|
if os.path.exists(lock_file):
|
|
os.remove(lock_file)
|
|
|
|
self.ckpt = Checkpoint(
|
|
name,
|
|
colbert_config=ColBERTConfig(model_name=name),
|
|
).to(self.device)
|
|
pass
|
|
|
|
def calculate_similarity_scores(self, query_embeddings, document_embeddings):
|
|
|
|
query_embeddings = query_embeddings.to(self.device)
|
|
document_embeddings = document_embeddings.to(self.device)
|
|
|
|
# Validate dimensions to ensure compatibility
|
|
if query_embeddings.dim() != 3:
|
|
raise ValueError(
|
|
f"Expected query embeddings to have 3 dimensions, but got {query_embeddings.dim()}."
|
|
)
|
|
if document_embeddings.dim() != 3:
|
|
raise ValueError(
|
|
f"Expected document embeddings to have 3 dimensions, but got {document_embeddings.dim()}."
|
|
)
|
|
if query_embeddings.size(0) not in [1, document_embeddings.size(0)]:
|
|
raise ValueError(
|
|
"There should be either one query or queries equal to the number of documents."
|
|
)
|
|
|
|
# Transpose the query embeddings to align for matrix multiplication
|
|
transposed_query_embeddings = query_embeddings.permute(0, 2, 1)
|
|
# Compute similarity scores using batch matrix multiplication
|
|
computed_scores = torch.matmul(document_embeddings, transposed_query_embeddings)
|
|
# Apply max pooling to extract the highest semantic similarity across each document's sequence
|
|
maximum_scores = torch.max(computed_scores, dim=1).values
|
|
|
|
# Sum up the maximum scores across features to get the overall document relevance scores
|
|
final_scores = maximum_scores.sum(dim=1)
|
|
|
|
normalized_scores = torch.softmax(final_scores, dim=0)
|
|
|
|
return normalized_scores.detach().cpu().numpy().astype(np.float32)
|
|
|
|
def predict(self, sentences):
|
|
|
|
query = sentences[0][0]
|
|
docs = [i[1] for i in sentences]
|
|
|
|
# Embedding the documents
|
|
embedded_docs = self.ckpt.docFromText(docs, bsize=32)[0]
|
|
# Embedding the queries
|
|
embedded_queries = self.ckpt.queryFromText([query], bsize=32)
|
|
embedded_query = embedded_queries[0]
|
|
|
|
# Calculate retrieval scores for the query against all documents
|
|
scores = self.calculate_similarity_scores(
|
|
embedded_query.unsqueeze(0), embedded_docs
|
|
)
|
|
|
|
return scores
|