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add error msg for hit test (#4704)

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Jyong 2024-05-28 14:54:53 +08:00 committed by GitHub
parent e6f6a59f3b
commit 1b2d862973
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1 changed files with 81 additions and 64 deletions
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145
api/core/rag/datasource/retrieval_service.py
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@ -33,6 +33,7 @@ class RetrievalService:
return []
all_documents = []
threads = []
exceptions = []
# retrieval_model source with keyword
if retrival_method == 'keyword_search':
keyword_thread = threading.Thread(target=RetrievalService.keyword_search, kwargs={
@ -40,7 +41,8 @@ class RetrievalService:
'dataset_id': dataset_id,
'query': query,
'top_k': top_k,
'all_documents': all_documents
'all_documents': all_documents,
'exceptions': exceptions,
})
threads.append(keyword_thread)
keyword_thread.start()
@ -54,7 +56,8 @@ class RetrievalService:
'score_threshold': score_threshold,
'reranking_model': reranking_model,
'all_documents': all_documents,
'retrival_method': retrival_method
'retrival_method': retrival_method,
'exceptions': exceptions,
})
threads.append(embedding_thread)
embedding_thread.start()
@ -69,7 +72,8 @@ class RetrievalService:
'score_threshold': score_threshold,
'top_k': top_k,
'reranking_model': reranking_model,
'all_documents': all_documents
'all_documents': all_documents,
'exceptions': exceptions,
})
threads.append(full_text_index_thread)
full_text_index_thread.start()
@ -77,6 +81,10 @@ class RetrievalService:
for thread in threads:
thread.join()
if exceptions:
exception_message = ';\n'.join(exceptions)
raise Exception(exception_message)
if retrival_method == 'hybrid_search':
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents = data_post_processor.invoke(
@ -89,82 +97,91 @@ class RetrievalService:
@classmethod
def keyword_search(cls, flask_app: Flask, dataset_id: str, query: str,
top_k: int, all_documents: list):
top_k: int, all_documents: list, exceptions: list):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
try:
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
keyword = Keyword(
dataset=dataset
)
keyword = Keyword(
dataset=dataset
)
documents = keyword.search(
query,
top_k=top_k
)
all_documents.extend(documents)
documents = keyword.search(
query,
top_k=top_k
)
all_documents.extend(documents)
except Exception as e:
exceptions.append(str(e))
@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):
all_documents: list, retrival_method: str, exceptions: list):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
try:
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
vector = Vector(
dataset=dataset
)
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]
}
)
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)
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)
except Exception as e:
exceptions.append(str(e))
@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):
all_documents: list, retrival_method: str, exceptions: list):
with flask_app.app_context():
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
try:
dataset = db.session.query(Dataset).filter(
Dataset.id == dataset_id
).first()
vector_processor = Vector(
dataset=dataset,
)
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)
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)
except Exception as e:
exceptions.append(str(e))