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[[package]] name = "sentry-sdk" version = "1.39.2" @@ -7660,17 +7576,6 @@ files = [ [package.dependencies] tencentcloud-sdk-python-common = "3.0.1183" -[[package]] -name = "threadpoolctl" -version = "3.5.0" -description = "threadpoolctl" -optional = false -python-versions = ">=3.8" -files = [ - {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"}, - {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"}, -] - [[package]] name = "tidb-vector" version = "0.0.9" @@ -9095,4 +9000,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "a31e1524d35da47f63f5e8d24236cbe14585e6a5d9edf9b734d517d24f83e287" +content-hash = "fdba75f08df361b7b0d89d375062fa9208a68d2a59597071c6e382285f6fccff" diff --git a/api/pyproject.toml b/api/pyproject.toml index 3c633675e2..90e825ea6c 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -163,7 +163,6 @@ redis = { version = "~5.0.3", extras = ["hiredis"] } replicate = "~0.22.0" resend = "~0.7.0" safetensors = "~0.4.3" -scikit-learn = "1.2.2" sentry-sdk = { version = "~1.39.2", extras = ["flask"] } sqlalchemy = "~2.0.29" tencentcloud-sdk-python-hunyuan = "~3.0.1158" diff --git a/api/services/hit_testing_service.py b/api/services/hit_testing_service.py index 0378370d88..9bcf828712 100644 --- a/api/services/hit_testing_service.py +++ b/api/services/hit_testing_service.py @@ -1,9 +1,6 @@ import logging import time -import numpy as np -from sklearn.manifold import TSNE - from core.rag.datasource.retrieval_service import RetrievalService from core.rag.models.document import Document from core.rag.retrieval.retrival_methods import RetrievalMethod @@ -101,29 +98,6 @@ class HitTestingService: "records": records } - @classmethod - def get_tsne_positions_from_embeddings(cls, embeddings: list): - embedding_length = len(embeddings) - if embedding_length <= 1: - return [{'x': 0, 'y': 0}] - - noise = np.random.normal(0, 1e-4, np.array(embeddings).shape) - concatenate_data = np.array(embeddings) + noise - concatenate_data = concatenate_data.reshape(embedding_length, -1) - - perplexity = embedding_length / 2 + 1 - if perplexity >= embedding_length: - perplexity = max(embedding_length - 1, 1) - - tsne = TSNE(n_components=2, perplexity=perplexity, early_exaggeration=12.0) - data_tsne = tsne.fit_transform(concatenate_data) - - tsne_position_data = [] - for i in range(len(data_tsne)): - tsne_position_data.append({'x': float(data_tsne[i][0]), 'y': float(data_tsne[i][1])}) - - return tsne_position_data - @classmethod def hit_testing_args_check(cls, args): query = args['query']