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import threading
from typing import Optional
from flask import Flask, current_app
from core.rag.data_post_processor.data_post_processor import DataPostProcessor
from core.rag.datasource.keyword.keyword_factory import Keyword
from core.rag.datasource.vdb.vector_factory import Vector
from extensions.ext_database import db
from models.dataset import Dataset
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 RetrievalService:
@classmethod
def retrieve(cls, retrival_method: str, dataset_id: str, query: str,
top_k: int, score_threshold: Optional[float] = .0, reranking_model: Optional[dict] = None):
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0:
return []
all_documents = []
threads = []
# retrieval_model source with keyword
if retrival_method == 'keyword_search':
keyword_thread = threading.Thread(target=RetrievalService.keyword_search, kwargs={
'flask_app': current_app._get_current_object(),
'dataset_id': dataset_id,
'query': query,
'top_k': top_k,
'all_documents': all_documents
})
threads.append(keyword_thread)
keyword_thread.start()
# retrieval_model source with semantic
if retrival_method == 'semantic_search' or retrival_method == 'hybrid_search':
embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
'flask_app': current_app._get_current_object(),
'dataset_id': dataset_id,
'query': query,
'top_k': top_k,
'score_threshold': score_threshold,
'reranking_model': reranking_model,
'all_documents': all_documents,
'retrival_method': retrival_method
})
threads.append(embedding_thread)
embedding_thread.start()
# retrieval source with full text
if retrival_method == 'full_text_search' or retrival_method == 'hybrid_search':
full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search, kwargs={
'flask_app': current_app._get_current_object(),
'dataset_id': dataset_id,
'query': query,
'retrival_method': retrival_method,
'score_threshold': score_threshold,
'top_k': top_k,
'reranking_model': reranking_model,
'all_documents': all_documents
})
threads.append(full_text_index_thread)
full_text_index_thread.start()
for thread in threads:
thread.join()
if retrival_method == 'hybrid_search':
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents = data_post_processor.invoke(
query=query,
documents=all_documents,
score_threshold=score_threshold,
top_n=top_k
)
return all_documents
@classmethod
def keyword_search(cls, flask_app: Flask, dataset_id: str, query: str,
top_k: int, all_documents: list):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
keyword = Keyword(
dataset=dataset
)
documents = keyword.search(
query,
top_k=top_k
)
all_documents.extend(documents)
@classmethod
def embedding_search(cls, flask_app: Flask, dataset_id: str, query: str,
top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
all_documents: list, retrival_method: str):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
vector = Vector(
dataset=dataset
)
documents = vector.search_by_vector(
query,
search_type='similarity_score_threshold',
top_k=top_k,
score_threshold=score_threshold,
filter={
'group_id': [dataset.id]
}
)
if documents:
if reranking_model and retrival_method == 'semantic_search':
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents.extend(data_post_processor.invoke(
query=query,
documents=documents,
score_threshold=score_threshold,
top_n=len(documents)
))
else:
all_documents.extend(documents)
@classmethod
def full_text_index_search(cls, flask_app: Flask, dataset_id: str, query: str,
top_k: int, score_threshold: Optional[float], reranking_model: Optional[dict],
all_documents: list, retrival_method: str):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
vector_processor = Vector(
dataset=dataset,
)
documents = vector_processor.search_by_full_text(
query,
top_k=top_k
)
if documents:
if reranking_model and retrival_method == 'full_text_search':
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents.extend(data_post_processor.invoke(
query=query,
documents=documents,
score_threshold=score_threshold,
top_n=len(documents)
))
else:
all_documents.extend(documents)
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