import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
from typing import Optional
from flask import Flask, current_app
from sqlalchemy.orm import load_only
from configs import dify_config
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.embedding.retrieval import RetrievalSegments
from core.rag.index_processor.constant.index_type import IndexType
from core.rag.models.document import Document
from core.rag.rerank.rerank_type import RerankMode
from core.rag.retrieval.retrieval_methods import RetrievalMethod
from extensions.ext_database import db
from models.dataset import ChildChunk, Dataset, DocumentSegment
from models.dataset import Document as DatasetDocument
from services.external_knowledge_service import ExternalDatasetService
default_retrieval_model = {
"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
"reranking_enable": False,
"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
"top_k": 2,
"score_threshold_enabled": False,
}
class RetrievalService:
# Cache precompiled regular expressions to avoid repeated compilation
@classmethod
def retrieve(
cls,
retrieval_method: str,
dataset_id: str,
query: str,
top_k: int,
score_threshold: Optional[float] = 0.0,
reranking_model: Optional[dict] = None,
reranking_mode: str = "reranking_model",
weights: Optional[dict] = None,
):
if not query:
return []
dataset = cls._get_dataset(dataset_id)
if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0:
return []
all_documents: list[Document] = []
exceptions: list[str] = []
# Optimize multithreading with thread pools
with ThreadPoolExecutor(max_workers=dify_config.RETRIEVAL_SERVICE_EXECUTORS) as executor: # type: ignore
futures = []
if retrieval_method == "keyword_search":
futures.append(
executor.submit(
cls.keyword_search,
flask_app=current_app._get_current_object(), # type: ignore
dataset_id=dataset_id,
query=query,
top_k=top_k,
all_documents=all_documents,
exceptions=exceptions,
)
)
if RetrievalMethod.is_support_semantic_search(retrieval_method):
futures.append(
executor.submit(
cls.embedding_search,
flask_app=current_app._get_current_object(), # type: ignore
dataset_id=dataset_id,
query=query,
top_k=top_k,
score_threshold=score_threshold,
reranking_model=reranking_model,
all_documents=all_documents,
retrieval_method=retrieval_method,
exceptions=exceptions,
)
)
if RetrievalMethod.is_support_fulltext_search(retrieval_method):
futures.append(
executor.submit(
cls.full_text_index_search,
flask_app=current_app._get_current_object(), # type: ignore
dataset_id=dataset_id,
query=query,
top_k=top_k,
score_threshold=score_threshold,
reranking_model=reranking_model,
all_documents=all_documents,
retrieval_method=retrieval_method,
exceptions=exceptions,
)
)
concurrent.futures.wait(futures, timeout=30, return_when=concurrent.futures.ALL_COMPLETED)
if exceptions:
raise ValueError(";\n".join(exceptions))
if retrieval_method == RetrievalMethod.HYBRID_SEARCH.value:
data_post_processor = DataPostProcessor(
str(dataset.tenant_id), reranking_mode, reranking_model, weights, 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 external_retrieve(cls, dataset_id: str, query: str, external_retrieval_model: Optional[dict] = None):
dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
if not dataset:
return []
all_documents = ExternalDatasetService.fetch_external_knowledge_retrieval(
dataset.tenant_id, dataset_id, query, external_retrieval_model or {}
)
return all_documents
@classmethod
def _get_dataset(cls, dataset_id: str) -> Optional[Dataset]:
return db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
@classmethod
def keyword_search(
cls, flask_app: Flask, dataset_id: str, query: str, top_k: int, all_documents: list, exceptions: list
):
with flask_app.app_context():
try:
dataset = cls._get_dataset(dataset_id)
if not dataset:
raise ValueError("dataset not found")
keyword = Keyword(dataset=dataset)
documents = keyword.search(cls.escape_query_for_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,
retrieval_method: str,
exceptions: list,
):
with flask_app.app_context():
try:
dataset = cls._get_dataset(dataset_id)
if not dataset:
raise ValueError("dataset not found")
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 reranking_model.get("reranking_model_name")
and reranking_model.get("reranking_provider_name")
and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH.value
):
data_post_processor = DataPostProcessor(
str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, 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,
retrieval_method: str,
exceptions: list,
):
with flask_app.app_context():
try:
dataset = cls._get_dataset(dataset_id)
if not dataset:
raise ValueError("dataset not found")
vector_processor = Vector(dataset=dataset)
documents = vector_processor.search_by_full_text(cls.escape_query_for_search(query), top_k=top_k)
if documents:
if (
reranking_model
and reranking_model.get("reranking_model_name")
and reranking_model.get("reranking_provider_name")
and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH.value
):
data_post_processor = DataPostProcessor(
str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, 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))
@staticmethod
def escape_query_for_search(query: str) -> str:
return query.replace('"', '\\"')
@classmethod
def format_retrieval_documents(cls, documents: list[Document]) -> list[RetrievalSegments]:
"""Format retrieval documents with optimized batch processing"""
if not documents:
return []
try:
# Collect document IDs
document_ids = {doc.metadata.get("document_id") for doc in documents if "document_id" in doc.metadata}
if not document_ids:
return []
# Batch query dataset documents
dataset_documents = {
doc.id: doc
for doc in db.session.query(DatasetDocument)
.filter(DatasetDocument.id.in_(document_ids))
.options(load_only(DatasetDocument.id, DatasetDocument.doc_form, DatasetDocument.dataset_id))
.all()
}
records = []
include_segment_ids = set()
segment_child_map = {}
# Process documents
for document in documents:
document_id = document.metadata.get("document_id")
if document_id not in dataset_documents:
continue
dataset_document = dataset_documents[document_id]
if not dataset_document:
continue
if dataset_document.doc_form == IndexType.PARENT_CHILD_INDEX:
# Handle parent-child documents
child_index_node_id = document.metadata.get("doc_id")
child_chunk = (
db.session.query(ChildChunk).filter(ChildChunk.index_node_id == child_index_node_id).first()
)
if not child_chunk:
continue
segment = (
db.session.query(DocumentSegment)
.filter(
DocumentSegment.dataset_id == dataset_document.dataset_id,
DocumentSegment.enabled == True,
DocumentSegment.status == "completed",
DocumentSegment.id == child_chunk.segment_id,
)
.options(
load_only(
DocumentSegment.id,
DocumentSegment.content,
DocumentSegment.answer,
)
)
.first()
)
if not segment:
continue
if segment.id not in include_segment_ids:
include_segment_ids.add(segment.id)
child_chunk_detail = {
"id": child_chunk.id,
"content": child_chunk.content,
"position": child_chunk.position,
"score": document.metadata.get("score", 0.0),
}
map_detail = {
"max_score": document.metadata.get("score", 0.0),
"child_chunks": [child_chunk_detail],
}
segment_child_map[segment.id] = map_detail
record = {
"segment": segment,
}
records.append(record)
else:
child_chunk_detail = {
"id": child_chunk.id,
"content": child_chunk.content,
"position": child_chunk.position,
"score": document.metadata.get("score", 0.0),
}
segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
segment_child_map[segment.id]["max_score"] = max(
segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
)
else:
# Handle normal documents
index_node_id = document.metadata.get("doc_id")
if not index_node_id:
continue
segment = (
db.session.query(DocumentSegment)
.filter(
DocumentSegment.dataset_id == dataset_document.dataset_id,
DocumentSegment.enabled == True,
DocumentSegment.status == "completed",
DocumentSegment.index_node_id == index_node_id,
)
.first()
)
if not segment:
continue
include_segment_ids.add(segment.id)
record = {
"segment": segment,
"score": document.metadata.get("score"), # type: ignore
}
records.append(record)
# Add child chunks information to records
for record in records:
if record["segment"].id in segment_child_map:
record["child_chunks"] = segment_child_map[record["segment"].id].get("child_chunks") # type: ignore
record["score"] = segment_child_map[record["segment"].id]["max_score"]
return [RetrievalSegments(**record) for record in records]
except Exception as e:
db.session.rollback()
raise e