mirror of
https://github.com/langgenius/dify.git
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697 lines
31 KiB
Python
697 lines
31 KiB
Python
import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any
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from flask import Flask, current_app
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from sqlalchemy import select
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from sqlalchemy.orm import Session, load_only
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from configs import dify_config
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from core.model_manager import ModelManager
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from core.model_runtime.entities.model_entities import ModelType
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from core.rag.data_post_processor.data_post_processor import DataPostProcessor
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from core.rag.datasource.keyword.keyword_factory import Keyword
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from core.rag.datasource.vdb.vector_factory import Vector
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from core.rag.embedding.retrieval import RetrievalSegments
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from core.rag.entities.metadata_entities import MetadataCondition
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from core.rag.index_processor.constant.doc_type import DocType
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from core.rag.index_processor.constant.index_type import IndexStructureType
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from core.rag.index_processor.constant.query_type import QueryType
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from core.rag.models.document import Document
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from core.rag.rerank.rerank_type import RerankMode
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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from core.tools.signature import sign_upload_file
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from extensions.ext_database import db
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from models.dataset import ChildChunk, Dataset, DocumentSegment, SegmentAttachmentBinding
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from models.dataset import Document as DatasetDocument
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from models.model import UploadFile
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from services.external_knowledge_service import ExternalDatasetService
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default_retrieval_model = {
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"search_method": RetrievalMethod.SEMANTIC_SEARCH,
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"reranking_enable": False,
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"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
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"top_k": 4,
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"score_threshold_enabled": False,
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}
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class RetrievalService:
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# Cache precompiled regular expressions to avoid repeated compilation
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@classmethod
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def retrieve(
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cls,
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retrieval_method: RetrievalMethod,
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dataset_id: str,
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query: str,
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top_k: int = 4,
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score_threshold: float | None = 0.0,
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reranking_model: dict | None = None,
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reranking_mode: str = "reranking_model",
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weights: dict | None = None,
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document_ids_filter: list[str] | None = None,
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attachment_ids: list | None = None,
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):
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if not query and not attachment_ids:
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return []
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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return []
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all_documents: list[Document] = []
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exceptions: list[str] = []
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# Optimize multithreading with thread pools
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with ThreadPoolExecutor(max_workers=dify_config.RETRIEVAL_SERVICE_EXECUTORS) as executor: # type: ignore
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futures = []
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retrieval_service = RetrievalService()
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if query:
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futures.append(
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executor.submit(
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retrieval_service._retrieve,
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flask_app=current_app._get_current_object(), # type: ignore
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retrieval_method=retrieval_method,
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dataset=dataset,
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query=query,
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top_k=top_k,
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score_threshold=score_threshold,
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reranking_model=reranking_model,
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reranking_mode=reranking_mode,
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weights=weights,
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document_ids_filter=document_ids_filter,
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attachment_id=None,
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all_documents=all_documents,
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exceptions=exceptions,
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)
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)
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if attachment_ids:
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for attachment_id in attachment_ids:
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futures.append(
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executor.submit(
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retrieval_service._retrieve,
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flask_app=current_app._get_current_object(), # type: ignore
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retrieval_method=retrieval_method,
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dataset=dataset,
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query=None,
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top_k=top_k,
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score_threshold=score_threshold,
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reranking_model=reranking_model,
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reranking_mode=reranking_mode,
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weights=weights,
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document_ids_filter=document_ids_filter,
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attachment_id=attachment_id,
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all_documents=all_documents,
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exceptions=exceptions,
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)
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)
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concurrent.futures.wait(futures, timeout=3600, return_when=concurrent.futures.ALL_COMPLETED)
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if exceptions:
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raise ValueError(";\n".join(exceptions))
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return all_documents
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@classmethod
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def external_retrieve(
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cls,
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dataset_id: str,
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query: str,
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external_retrieval_model: dict | None = None,
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metadata_filtering_conditions: dict | None = None,
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):
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stmt = select(Dataset).where(Dataset.id == dataset_id)
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dataset = db.session.scalar(stmt)
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if not dataset:
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return []
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metadata_condition = (
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MetadataCondition.model_validate(metadata_filtering_conditions) if metadata_filtering_conditions else None
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)
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all_documents = ExternalDatasetService.fetch_external_knowledge_retrieval(
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dataset.tenant_id,
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dataset_id,
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query,
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external_retrieval_model or {},
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metadata_condition=metadata_condition,
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)
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return all_documents
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@classmethod
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def _deduplicate_documents(cls, documents: list[Document]) -> list[Document]:
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"""Deduplicate documents based on doc_id to avoid duplicate chunks in hybrid search."""
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if not documents:
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return documents
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unique_documents = []
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seen_doc_ids = set()
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for document in documents:
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# For dify provider documents, use doc_id for deduplication
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if document.provider == "dify" and document.metadata is not None and "doc_id" in document.metadata:
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doc_id = document.metadata["doc_id"]
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if doc_id not in seen_doc_ids:
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seen_doc_ids.add(doc_id)
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unique_documents.append(document)
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# If duplicate, keep the one with higher score
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elif "score" in document.metadata:
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# Find existing document with same doc_id and compare scores
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for i, existing_doc in enumerate(unique_documents):
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if (
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existing_doc.metadata
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and existing_doc.metadata.get("doc_id") == doc_id
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and existing_doc.metadata.get("score", 0) < document.metadata.get("score", 0)
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):
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unique_documents[i] = document
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break
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else:
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# For non-dify documents, use content-based deduplication
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if document not in unique_documents:
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unique_documents.append(document)
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return unique_documents
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@classmethod
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def _get_dataset(cls, dataset_id: str) -> Dataset | None:
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with Session(db.engine) as session:
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return session.query(Dataset).where(Dataset.id == dataset_id).first()
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@classmethod
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def keyword_search(
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cls,
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flask_app: Flask,
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dataset_id: str,
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query: str,
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top_k: int,
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all_documents: list,
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exceptions: list,
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document_ids_filter: list[str] | None = None,
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):
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with flask_app.app_context():
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try:
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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keyword = Keyword(dataset=dataset)
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documents = keyword.search(
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cls.escape_query_for_search(query), top_k=top_k, document_ids_filter=document_ids_filter
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)
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all_documents.extend(documents)
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except Exception as e:
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exceptions.append(str(e))
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@classmethod
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def embedding_search(
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cls,
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flask_app: Flask,
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dataset_id: str,
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query: str,
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top_k: int,
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score_threshold: float | None,
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reranking_model: dict | None,
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all_documents: list,
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retrieval_method: RetrievalMethod,
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exceptions: list,
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document_ids_filter: list[str] | None = None,
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query_type: QueryType = QueryType.TEXT_QUERY,
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):
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with flask_app.app_context():
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try:
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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vector = Vector(dataset=dataset)
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documents = []
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if query_type == QueryType.TEXT_QUERY:
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documents.extend(
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vector.search_by_vector(
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query,
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search_type="similarity_score_threshold",
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top_k=top_k,
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score_threshold=score_threshold,
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filter={"group_id": [dataset.id]},
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document_ids_filter=document_ids_filter,
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)
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)
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if query_type == QueryType.IMAGE_QUERY:
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if not dataset.is_multimodal:
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return
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documents.extend(
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vector.search_by_file(
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file_id=query,
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top_k=top_k,
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score_threshold=score_threshold,
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filter={"group_id": [dataset.id]},
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document_ids_filter=document_ids_filter,
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)
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)
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if documents:
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if (
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reranking_model
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and reranking_model.get("reranking_model_name")
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and reranking_model.get("reranking_provider_name")
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and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH
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):
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data_post_processor = DataPostProcessor(
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str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL), reranking_model, None, False
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)
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if dataset.is_multimodal:
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model_manager = ModelManager()
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is_support_vision = model_manager.check_model_support_vision(
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tenant_id=dataset.tenant_id,
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provider=reranking_model.get("reranking_provider_name") or "",
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model=reranking_model.get("reranking_model_name") or "",
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model_type=ModelType.RERANK,
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)
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if is_support_vision:
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all_documents.extend(
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data_post_processor.invoke(
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query=query,
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documents=documents,
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score_threshold=score_threshold,
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top_n=len(documents),
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query_type=query_type,
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)
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)
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else:
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# not effective, return original documents
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all_documents.extend(documents)
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else:
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all_documents.extend(
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data_post_processor.invoke(
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query=query,
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documents=documents,
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score_threshold=score_threshold,
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top_n=len(documents),
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query_type=query_type,
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)
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)
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else:
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all_documents.extend(documents)
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except Exception as e:
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exceptions.append(str(e))
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@classmethod
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def full_text_index_search(
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cls,
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flask_app: Flask,
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dataset_id: str,
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query: str,
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top_k: int,
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score_threshold: float | None,
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reranking_model: dict | None,
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all_documents: list,
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retrieval_method: str,
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exceptions: list,
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document_ids_filter: list[str] | None = None,
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):
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with flask_app.app_context():
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try:
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dataset = cls._get_dataset(dataset_id)
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if not dataset:
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raise ValueError("dataset not found")
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vector_processor = Vector(dataset=dataset)
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documents = vector_processor.search_by_full_text(
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cls.escape_query_for_search(query), top_k=top_k, document_ids_filter=document_ids_filter
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)
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if documents:
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if (
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reranking_model
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and reranking_model.get("reranking_model_name")
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and reranking_model.get("reranking_provider_name")
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and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH
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):
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data_post_processor = DataPostProcessor(
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str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL), reranking_model, None, False
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)
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all_documents.extend(
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data_post_processor.invoke(
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query=query,
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documents=documents,
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score_threshold=score_threshold,
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top_n=len(documents),
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)
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)
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else:
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all_documents.extend(documents)
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except Exception as e:
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exceptions.append(str(e))
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@staticmethod
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def escape_query_for_search(query: str) -> str:
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return query.replace('"', '\\"')
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@classmethod
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def format_retrieval_documents(cls, documents: list[Document]) -> list[RetrievalSegments]:
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"""Format retrieval documents with optimized batch processing"""
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if not documents:
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return []
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try:
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# Collect document IDs
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document_ids = {doc.metadata.get("document_id") for doc in documents if "document_id" in doc.metadata}
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if not document_ids:
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return []
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# Batch query dataset documents
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dataset_documents = {
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doc.id: doc
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for doc in db.session.query(DatasetDocument)
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.where(DatasetDocument.id.in_(document_ids))
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.options(load_only(DatasetDocument.id, DatasetDocument.doc_form, DatasetDocument.dataset_id))
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.all()
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}
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records = []
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include_segment_ids = set()
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segment_child_map = {}
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segment_file_map = {}
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with Session(db.engine) as session:
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# Process documents
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for document in documents:
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segment_id = None
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attachment_info = None
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child_chunk = None
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document_id = document.metadata.get("document_id")
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if document_id not in dataset_documents:
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continue
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dataset_document = dataset_documents[document_id]
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if not dataset_document:
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continue
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if dataset_document.doc_form == IndexStructureType.PARENT_CHILD_INDEX:
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# Handle parent-child documents
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if document.metadata.get("doc_type") == DocType.IMAGE:
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attachment_info_dict = cls.get_segment_attachment_info(
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dataset_document.dataset_id,
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dataset_document.tenant_id,
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document.metadata.get("doc_id") or "",
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session,
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)
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if attachment_info_dict:
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attachment_info = attachment_info_dict["attchment_info"]
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segment_id = attachment_info_dict["segment_id"]
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else:
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child_index_node_id = document.metadata.get("doc_id")
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child_chunk_stmt = select(ChildChunk).where(ChildChunk.index_node_id == child_index_node_id)
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child_chunk = session.scalar(child_chunk_stmt)
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if not child_chunk:
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continue
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segment_id = child_chunk.segment_id
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if not segment_id:
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continue
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segment = (
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session.query(DocumentSegment)
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.where(
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DocumentSegment.dataset_id == dataset_document.dataset_id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == "completed",
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DocumentSegment.id == segment_id,
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)
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.options(
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load_only(
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DocumentSegment.id,
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DocumentSegment.content,
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DocumentSegment.answer,
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)
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)
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.first()
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)
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if not segment:
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continue
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if segment.id not in include_segment_ids:
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include_segment_ids.add(segment.id)
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if child_chunk:
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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map_detail = {
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"max_score": document.metadata.get("score", 0.0),
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"child_chunks": [child_chunk_detail],
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}
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segment_child_map[segment.id] = map_detail
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record = {
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"segment": segment,
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}
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if attachment_info:
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segment_file_map[segment.id] = [attachment_info]
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records.append(record)
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else:
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if child_chunk:
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child_chunk_detail = {
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"id": child_chunk.id,
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"content": child_chunk.content,
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"position": child_chunk.position,
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"score": document.metadata.get("score", 0.0),
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}
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segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
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segment_child_map[segment.id]["max_score"] = max(
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segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
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)
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if attachment_info:
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segment_file_map[segment.id].append(attachment_info)
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else:
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# Handle normal documents
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segment = None
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if document.metadata.get("doc_type") == DocType.IMAGE:
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attachment_info_dict = cls.get_segment_attachment_info(
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dataset_document.dataset_id,
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dataset_document.tenant_id,
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document.metadata.get("doc_id") or "",
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session,
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)
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if attachment_info_dict:
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attachment_info = attachment_info_dict["attchment_info"]
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segment_id = attachment_info_dict["segment_id"]
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document_segment_stmt = select(DocumentSegment).where(
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DocumentSegment.dataset_id == dataset_document.dataset_id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == "completed",
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DocumentSegment.id == segment_id,
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)
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segment = db.session.scalar(document_segment_stmt)
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if segment:
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segment_file_map[segment.id] = [attachment_info]
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else:
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index_node_id = document.metadata.get("doc_id")
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if not index_node_id:
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continue
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document_segment_stmt = select(DocumentSegment).where(
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DocumentSegment.dataset_id == dataset_document.dataset_id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == "completed",
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DocumentSegment.index_node_id == index_node_id,
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)
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segment = db.session.scalar(document_segment_stmt)
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if not segment:
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continue
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if segment.id not in include_segment_ids:
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include_segment_ids.add(segment.id)
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record = {
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"segment": segment,
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"score": document.metadata.get("score"), # type: ignore
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}
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if attachment_info:
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segment_file_map[segment.id] = [attachment_info]
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records.append(record)
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else:
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if attachment_info:
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attachment_infos = segment_file_map.get(segment.id, [])
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if attachment_info not in attachment_infos:
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attachment_infos.append(attachment_info)
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segment_file_map[segment.id] = attachment_infos
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# Add child chunks information to records
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for record in records:
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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"]
|
|
if record["segment"].id in segment_file_map:
|
|
record["files"] = segment_file_map[record["segment"].id] # type: ignore[assignment]
|
|
|
|
result = []
|
|
for record in records:
|
|
# Extract segment
|
|
segment = record["segment"]
|
|
|
|
# Extract child_chunks, ensuring it's a list or None
|
|
child_chunks = record.get("child_chunks")
|
|
if not isinstance(child_chunks, list):
|
|
child_chunks = None
|
|
|
|
# Extract files, ensuring it's a list or None
|
|
files = record.get("files")
|
|
if not isinstance(files, list):
|
|
files = None
|
|
|
|
# Extract score, ensuring it's a float or None
|
|
score_value = record.get("score")
|
|
score = (
|
|
float(score_value)
|
|
if score_value is not None and isinstance(score_value, int | float | str)
|
|
else None
|
|
)
|
|
|
|
# Create RetrievalSegments object
|
|
retrieval_segment = RetrievalSegments(
|
|
segment=segment, child_chunks=child_chunks, score=score, files=files
|
|
)
|
|
result.append(retrieval_segment)
|
|
|
|
return result
|
|
except Exception as e:
|
|
db.session.rollback()
|
|
raise e
|
|
|
|
def _retrieve(
|
|
self,
|
|
flask_app: Flask,
|
|
retrieval_method: RetrievalMethod,
|
|
dataset: Dataset,
|
|
query: str | None = None,
|
|
top_k: int = 4,
|
|
score_threshold: float | None = 0.0,
|
|
reranking_model: dict | None = None,
|
|
reranking_mode: str = "reranking_model",
|
|
weights: dict | None = None,
|
|
document_ids_filter: list[str] | None = None,
|
|
attachment_id: str | None = None,
|
|
all_documents: list[Document] = [],
|
|
exceptions: list[str] = [],
|
|
):
|
|
if not query and not attachment_id:
|
|
return
|
|
with flask_app.app_context():
|
|
all_documents_item: list[Document] = []
|
|
# Optimize multithreading with thread pools
|
|
with ThreadPoolExecutor(max_workers=dify_config.RETRIEVAL_SERVICE_EXECUTORS) as executor: # type: ignore
|
|
futures = []
|
|
if retrieval_method == RetrievalMethod.KEYWORD_SEARCH and query:
|
|
futures.append(
|
|
executor.submit(
|
|
self.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_item,
|
|
exceptions=exceptions,
|
|
document_ids_filter=document_ids_filter,
|
|
)
|
|
)
|
|
if RetrievalMethod.is_support_semantic_search(retrieval_method):
|
|
if query:
|
|
futures.append(
|
|
executor.submit(
|
|
self.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_item,
|
|
retrieval_method=retrieval_method,
|
|
exceptions=exceptions,
|
|
document_ids_filter=document_ids_filter,
|
|
query_type=QueryType.TEXT_QUERY,
|
|
)
|
|
)
|
|
if attachment_id:
|
|
futures.append(
|
|
executor.submit(
|
|
self.embedding_search,
|
|
flask_app=current_app._get_current_object(), # type: ignore
|
|
dataset_id=dataset.id,
|
|
query=attachment_id,
|
|
top_k=top_k,
|
|
score_threshold=score_threshold,
|
|
reranking_model=reranking_model,
|
|
all_documents=all_documents_item,
|
|
retrieval_method=retrieval_method,
|
|
exceptions=exceptions,
|
|
document_ids_filter=document_ids_filter,
|
|
query_type=QueryType.IMAGE_QUERY,
|
|
)
|
|
)
|
|
if RetrievalMethod.is_support_fulltext_search(retrieval_method) and query:
|
|
futures.append(
|
|
executor.submit(
|
|
self.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_item,
|
|
retrieval_method=retrieval_method,
|
|
exceptions=exceptions,
|
|
document_ids_filter=document_ids_filter,
|
|
)
|
|
)
|
|
concurrent.futures.wait(futures, timeout=300, return_when=concurrent.futures.ALL_COMPLETED)
|
|
|
|
if exceptions:
|
|
raise ValueError(";\n".join(exceptions))
|
|
|
|
# Deduplicate documents for hybrid search to avoid duplicate chunks
|
|
if retrieval_method == RetrievalMethod.HYBRID_SEARCH:
|
|
if attachment_id and reranking_mode == RerankMode.WEIGHTED_SCORE:
|
|
all_documents.extend(all_documents_item)
|
|
all_documents_item = self._deduplicate_documents(all_documents_item)
|
|
data_post_processor = DataPostProcessor(
|
|
str(dataset.tenant_id), reranking_mode, reranking_model, weights, False
|
|
)
|
|
|
|
query = query or attachment_id
|
|
if not query:
|
|
return
|
|
all_documents_item = data_post_processor.invoke(
|
|
query=query,
|
|
documents=all_documents_item,
|
|
score_threshold=score_threshold,
|
|
top_n=top_k,
|
|
query_type=QueryType.TEXT_QUERY if query else QueryType.IMAGE_QUERY,
|
|
)
|
|
|
|
all_documents.extend(all_documents_item)
|
|
|
|
@classmethod
|
|
def get_segment_attachment_info(
|
|
cls, dataset_id: str, tenant_id: str, attachment_id: str, session: Session
|
|
) -> dict[str, Any] | None:
|
|
upload_file = session.query(UploadFile).where(UploadFile.id == attachment_id).first()
|
|
if upload_file:
|
|
attachment_binding = (
|
|
session.query(SegmentAttachmentBinding)
|
|
.where(SegmentAttachmentBinding.attachment_id == upload_file.id)
|
|
.first()
|
|
)
|
|
if attachment_binding:
|
|
attchment_info = {
|
|
"id": upload_file.id,
|
|
"name": upload_file.name,
|
|
"extension": "." + upload_file.extension,
|
|
"mime_type": upload_file.mime_type,
|
|
"source_url": sign_upload_file(upload_file.id, upload_file.extension),
|
|
"size": upload_file.size,
|
|
}
|
|
return {"attchment_info": attchment_info, "segment_id": attachment_binding.segment_id}
|
|
return None
|