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chore: extract retrival method literal values into enum (#5060)

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This commit is contained in:
Bowen Liang
2024-06-19 16:05:27 +08:00
committed by GitHub
parent 9d5a89eab6
commit c923684edd
10 changed files with 47 additions and 19 deletions
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13
api/core/rag/datasource/retrieval_service.py
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@@ -6,11 +6,12 @@ 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 core.rag.retrieval.retrival_methods import RetrievalMethod
from extensions.ext_database import db
from models.dataset import Dataset
default_retrieval_model = {
'search_method': 'semantic_search',
'search_method': RetrievalMethod.SEMANTIC_SEARCH,
'reranking_enable': False,
'reranking_model': {
'reranking_provider_name': '',
@@ -47,7 +48,7 @@ class RetrievalService:
threads.append(keyword_thread)
keyword_thread.start()
# retrieval_model source with semantic
if retrival_method == 'semantic_search' or retrival_method == 'hybrid_search':
if RetrievalMethod.is_support_semantic_search(retrival_method):
embedding_thread = threading.Thread(target=RetrievalService.embedding_search, kwargs={
'flask_app': current_app._get_current_object(),
'dataset_id': dataset_id,
@@ -63,7 +64,7 @@ class RetrievalService:
embedding_thread.start()
# retrieval source with full text
if retrival_method == 'full_text_search' or retrival_method == 'hybrid_search':
if RetrievalMethod.is_support_fulltext_search(retrival_method):
full_text_index_thread = threading.Thread(target=RetrievalService.full_text_index_search, kwargs={
'flask_app': current_app._get_current_object(),
'dataset_id': dataset_id,
@@ -85,7 +86,7 @@ class RetrievalService:
exception_message = ';\n'.join(exceptions)
raise Exception(exception_message)
if retrival_method == 'hybrid_search':
if retrival_method == RetrievalMethod.HYBRID_SEARCH:
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents = data_post_processor.invoke(
query=query,
@@ -141,7 +142,7 @@ class RetrievalService:
)
if documents:
if reranking_model and retrival_method == 'semantic_search':
if reranking_model and retrival_method == RetrievalMethod.SEMANTIC_SEARCH:
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents.extend(data_post_processor.invoke(
query=query,
@@ -173,7 +174,7 @@ class RetrievalService:
top_k=top_k
)
if documents:
if reranking_model and retrival_method == 'full_text_search':
if reranking_model and retrival_method == RetrievalMethod.FULL_TEXT_SEARCH:
data_post_processor = DataPostProcessor(str(dataset.tenant_id), reranking_model, False)
all_documents.extend(data_post_processor.invoke(
query=query,