dify/api/core/tool/dataset_retriever_tool.py

import json
from typing import Type

from flask import current_app
from langchain.tools import BaseTool
from pydantic import Field, BaseModel

from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.conversation_message_task import ConversationMessageTask
from core.embedding.cached_embedding import CacheEmbedding
from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
from core.index.vector_index.vector_index import VectorIndex
from core.model_providers.error import LLMBadRequestError, ProviderTokenNotInitError
from core.model_providers.model_factory import ModelFactory
from extensions.ext_database import db
from models.dataset import Dataset, DocumentSegment, Document


class DatasetRetrieverToolInput(BaseModel):
    query: str = Field(..., description="Query for the dataset to be used to retrieve the dataset.")


class DatasetRetrieverTool(BaseTool):
    """Tool for querying a Dataset."""
    name: str = "dataset"
    args_schema: Type[BaseModel] = DatasetRetrieverToolInput
    description: str = "use this to retrieve a dataset. "

    tenant_id: str
    dataset_id: str
    k: int = 3
    conversation_message_task: ConversationMessageTask
    return_resource: str
    retriever_from: str

    @classmethod
    def from_dataset(cls, dataset: Dataset, **kwargs):
        description = dataset.description
        if not description:
            description = 'useful for when you want to answer queries about the ' + dataset.name

        description = description.replace('\n', '').replace('\r', '')
        return cls(
            name=f'dataset-{dataset.id}',
            tenant_id=dataset.tenant_id,
            dataset_id=dataset.id,
            description=description,
            **kwargs
        )

    def _run(self, query: str) -> str:
        dataset = db.session.query(Dataset).filter(
            Dataset.tenant_id == self.tenant_id,
            Dataset.id == self.dataset_id
        ).first()

        if not dataset:
            return f'[{self.name} failed to find dataset with id {self.dataset_id}.]'

        if dataset.indexing_technique == "economy":
            # use keyword table query
            kw_table_index = KeywordTableIndex(
                dataset=dataset,
                config=KeywordTableConfig(
                    max_keywords_per_chunk=5
                )
            )

            documents = kw_table_index.search(query, search_kwargs={'k': self.k})
            return str("\n".join([document.page_content for document in documents]))
        else:

            try:
                embedding_model = ModelFactory.get_embedding_model(
                    tenant_id=dataset.tenant_id,
                    model_provider_name=dataset.embedding_model_provider,
                    model_name=dataset.embedding_model
                )
            except LLMBadRequestError:
                return ''
            except ProviderTokenNotInitError:
                return ''
            embeddings = CacheEmbedding(embedding_model)

            vector_index = VectorIndex(
                dataset=dataset,
                config=current_app.config,
                embeddings=embeddings
            )

            if self.k > 0:
                documents = vector_index.search(
                    query,
                    search_type='similarity_score_threshold',
                    search_kwargs={
                        'k': self.k,
                        'filter': {
                            'group_id': [dataset.id]
                        }
                    }
                )
            else:
                documents = []

            hit_callback = DatasetIndexToolCallbackHandler(dataset.id, self.conversation_message_task)
            hit_callback.on_tool_end(documents)
            document_score_list = {}
            if dataset.indexing_technique != "economy":
                for item in documents:
                    document_score_list[item.metadata['doc_id']] = item.metadata['score']
            document_context_list = []
            index_node_ids = [document.metadata['doc_id'] for document in documents]
            segments = DocumentSegment.query.filter(DocumentSegment.dataset_id == self.dataset_id,
                                                    DocumentSegment.completed_at.isnot(None),
                                                    DocumentSegment.status == 'completed',
                                                    DocumentSegment.enabled == True,
                                                    DocumentSegment.index_node_id.in_(index_node_ids)
                                                    ).all()

            if segments:
                index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
                sorted_segments = sorted(segments,
                                         key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
                                                                                           float('inf')))
                for segment in sorted_segments:
                    if segment.answer:
                        document_context_list.append(f'question:{segment.content} answer:{segment.answer}')
                    else:
                        document_context_list.append(segment.content)
                if self.return_resource:
                    context_list = []
                    resource_number = 1
                    for segment in sorted_segments:
                        context = {}
                        document = Document.query.filter(Document.id == segment.document_id,
                                                         Document.enabled == True,
                                                         Document.archived == False,
                                                         ).first()
                        if dataset and document:
                            source = {
                                'position': resource_number,
                                'dataset_id': dataset.id,
                                'dataset_name': dataset.name,
                                'document_id': document.id,
                                'document_name': document.name,
                                'data_source_type': document.data_source_type,
                                'segment_id': segment.id,
                                'retriever_from': self.retriever_from
                            }
                            if dataset.indexing_technique != "economy":
                                source['score'] = document_score_list.get(segment.index_node_id)
                            if self.retriever_from == 'dev':
                                source['hit_count'] = segment.hit_count
                                source['word_count'] = segment.word_count
                                source['segment_position'] = segment.position
                                source['index_node_hash'] = segment.index_node_hash
                            if segment.answer:
                                source['content'] = f'question:{segment.content} \nanswer:{segment.answer}'
                            else:
                                source['content'] = segment.content
                            context_list.append(source)
                        resource_number += 1
                    hit_callback.return_retriever_resource_info(context_list)

            return str("\n".join(document_context_list))

    async def _arun(self, tool_input: str) -> str:
        raise NotImplementedError()