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import logging
import time
import numpy as np
from sklearn.manifold import TSNE
from core.embedding.cached_embedding import CacheEmbedding
from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from core.rag.datasource.entity.embedding import Embeddings
from core.rag.datasource.retrieval_service import RetrievalService
from core.rag.models.document import Document
from extensions.ext_database import db
from models.account import Account
from models.dataset import Dataset, DatasetQuery, DocumentSegment
default_retrieval_model = {
'search_method': 'semantic_search',
'reranking_enable': False,
'reranking_model': {
'reranking_provider_name': '',
'reranking_model_name': ''
},
'top_k': 2,
'score_threshold_enabled': False
}
class HitTestingService:
@classmethod
def retrieve(cls, dataset: Dataset, query: str, account: Account, retrieval_model: dict, limit: int = 10) -> dict:
if dataset.available_document_count == 0 or dataset.available_segment_count == 0:
return {
"query": {
"content": query,
"tsne_position": {'x': 0, 'y': 0},
},
"records": []
}
start = time.perf_counter()
# get retrieval model , if the model is not setting , using default
if not retrieval_model:
retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
# get embedding model
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
provider=dataset.embedding_model_provider,
model=dataset.embedding_model
)
embeddings = CacheEmbedding(embedding_model)
all_documents = RetrievalService.retrieve(retrival_method=retrieval_model['search_method'],
dataset_id=dataset.id,
query=query,
top_k=retrieval_model['top_k'],
score_threshold=retrieval_model['score_threshold']
if retrieval_model['score_threshold_enabled'] else None,
reranking_model=retrieval_model['reranking_model']
if retrieval_model['reranking_enable'] else None
)
end = time.perf_counter()
logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")
dataset_query = DatasetQuery(
dataset_id=dataset.id,
content=query,
source='hit_testing',
created_by_role='account',
created_by=account.id
)
db.session.add(dataset_query)
db.session.commit()
return cls.compact_retrieve_response(dataset, embeddings, query, all_documents)
@classmethod
def compact_retrieve_response(cls, dataset: Dataset, embeddings: Embeddings, query: str, documents: list[Document]):
text_embeddings = [
embeddings.embed_query(query)
]
text_embeddings.extend(embeddings.embed_documents([document.page_content for document in documents]))
tsne_position_data = cls.get_tsne_positions_from_embeddings(text_embeddings)
query_position = tsne_position_data.pop(0)
i = 0
records = []
for document in documents:
index_node_id = document.metadata['doc_id']
segment = db.session.query(DocumentSegment).filter(
DocumentSegment.dataset_id == dataset.id,
DocumentSegment.enabled == True,
DocumentSegment.status == 'completed',
DocumentSegment.index_node_id == index_node_id
).first()
if not segment:
i += 1
continue
record = {
"segment": segment,
"score": document.metadata.get('score', None),
"tsne_position": tsne_position_data[i]
}
records.append(record)
i += 1
return {
"query": {
"content": query,
"tsne_position": query_position,
},
"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']
if not query or len(query) > 250:
raise ValueError('Query is required and cannot exceed 250 characters')
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