dify/api/services/hit_testing_service.py

import logging
import time

from core.rag.datasource.retrieval_service import RetrievalService
from core.rag.models.document import Document
from core.rag.retrieval.retrival_methods import RetrievalMethod
from extensions.ext_database import db
from models.account import Account
from models.dataset import Dataset, DatasetQuery, DocumentSegment

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 HitTestingService:
    @classmethod
    def retrieve(cls, dataset: Dataset, query: str, account: Account, retrieval_model: dict, limit: int = 10) -> dict:
        if dataset.available_document_count == 0 or dataset.available_segment_count == 0:
            return {
                "query": {
                    "content": query,
                    "tsne_position": {"x": 0, "y": 0},
                },
                "records": [],
            }

        start = time.perf_counter()

        # get retrieval model , if the model is not setting , using default
        if not retrieval_model:
            retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model

        all_documents = RetrievalService.retrieve(
            retrival_method=retrieval_model.get("search_method", "semantic_search"),
            dataset_id=dataset.id,
            query=cls.escape_query_for_search(query),
            top_k=retrieval_model.get("top_k", 2),
            score_threshold=retrieval_model.get("score_threshold", 0.0)
            if retrieval_model["score_threshold_enabled"]
            else None,
            reranking_model=retrieval_model.get("reranking_model", None)
            if retrieval_model["reranking_enable"]
            else None,
            reranking_mode=retrieval_model.get("reranking_mode")
            if retrieval_model.get("reranking_mode")
            else "reranking_model",
            weights=retrieval_model.get("weights", None),
        )

        end = time.perf_counter()
        logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")

        dataset_query = DatasetQuery(
            dataset_id=dataset.id, content=query, source="hit_testing", created_by_role="account", created_by=account.id
        )

        db.session.add(dataset_query)
        db.session.commit()

        return cls.compact_retrieve_response(dataset, query, all_documents)

    @classmethod
    def compact_retrieve_response(cls, dataset: Dataset, query: str, documents: list[Document]):
        i = 0
        records = []
        for document in documents:
            index_node_id = document.metadata["doc_id"]

            segment = (
                db.session.query(DocumentSegment)
                .filter(
                    DocumentSegment.dataset_id == dataset.id,
                    DocumentSegment.enabled == True,
                    DocumentSegment.status == "completed",
                    DocumentSegment.index_node_id == index_node_id,
                )
                .first()
            )

            if not segment:
                i += 1
                continue

            record = {
                "segment": segment,
                "score": document.metadata.get("score", None),
            }

            records.append(record)

            i += 1

        return {
            "query": {
                "content": query,
            },
            "records": records,
        }

    @classmethod
    def hit_testing_args_check(cls, args):
        query = args["query"]

        if not query or len(query) > 250:
            raise ValueError("Query is required and cannot exceed 250 characters")

    @staticmethod
    def escape_query_for_search(query: str) -> str:
        return query.replace('"', '\\"')