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