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synced 2026-05-04 17:38:04 +08:00
refactor: tool
This commit is contained in:
Yeuoly
@ -0,0 +1,193 @@
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import threading
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from flask import Flask, current_app
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from pydantic import BaseModel, Field
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from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
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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.datasource.retrieval_service import RetrievalService
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from core.rag.rerank.rerank_model import RerankModelRunner
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
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from extensions.ext_database import db
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from models.dataset import Dataset, Document, DocumentSegment
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default_retrieval_model = {
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"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
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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": 2,
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"score_threshold_enabled": False,
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}
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class DatasetMultiRetrieverToolInput(BaseModel):
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query: str = Field(..., description="dataset multi retriever and rerank")
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class DatasetMultiRetrieverTool(DatasetRetrieverBaseTool):
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"""Tool for querying multi dataset."""
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name: str = "dataset_"
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args_schema: type[BaseModel] = DatasetMultiRetrieverToolInput
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description: str = "dataset multi retriever and rerank. "
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dataset_ids: list[str]
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reranking_provider_name: str
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reranking_model_name: str
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@classmethod
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def from_dataset(cls, dataset_ids: list[str], tenant_id: str, **kwargs):
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return cls(
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name=f"dataset_{tenant_id.replace('-', '_')}", tenant_id=tenant_id, dataset_ids=dataset_ids, **kwargs
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)
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def _run(self, query: str) -> str:
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threads = []
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all_documents = []
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for dataset_id in self.dataset_ids:
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retrieval_thread = threading.Thread(
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target=self._retriever,
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kwargs={
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"flask_app": current_app._get_current_object(),
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"dataset_id": dataset_id,
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"query": query,
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"all_documents": all_documents,
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"hit_callbacks": self.hit_callbacks,
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},
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)
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threads.append(retrieval_thread)
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retrieval_thread.start()
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for thread in threads:
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thread.join()
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# do rerank for searched documents
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model_manager = ModelManager()
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rerank_model_instance = model_manager.get_model_instance(
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tenant_id=self.tenant_id,
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provider=self.reranking_provider_name,
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model_type=ModelType.RERANK,
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model=self.reranking_model_name,
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)
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rerank_runner = RerankModelRunner(rerank_model_instance)
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all_documents = rerank_runner.run(query, all_documents, self.score_threshold, self.top_k)
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for hit_callback in self.hit_callbacks:
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hit_callback.on_tool_end(all_documents)
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document_score_list = {}
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for item in all_documents:
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if item.metadata.get("score"):
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document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
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document_context_list = []
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index_node_ids = [document.metadata["doc_id"] for document in all_documents]
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segments = DocumentSegment.query.filter(
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DocumentSegment.dataset_id.in_(self.dataset_ids),
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DocumentSegment.completed_at.isnot(None),
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DocumentSegment.status == "completed",
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DocumentSegment.enabled == True,
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DocumentSegment.index_node_id.in_(index_node_ids),
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).all()
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if segments:
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index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
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sorted_segments = sorted(
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segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
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)
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for segment in sorted_segments:
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if segment.answer:
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document_context_list.append(f"question:{segment.get_sign_content()} answer:{segment.answer}")
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else:
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document_context_list.append(segment.get_sign_content())
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if self.return_resource:
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context_list = []
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resource_number = 1
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for segment in sorted_segments:
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dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
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document = Document.query.filter(
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Document.id == segment.document_id,
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Document.enabled == True,
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Document.archived == False,
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).first()
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if dataset and document:
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source = {
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"position": resource_number,
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"dataset_id": dataset.id,
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"dataset_name": dataset.name,
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"document_id": document.id,
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"document_name": document.name,
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"data_source_type": document.data_source_type,
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"segment_id": segment.id,
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"retriever_from": self.retriever_from,
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"score": document_score_list.get(segment.index_node_id, None),
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}
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if self.retriever_from == "dev":
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source["hit_count"] = segment.hit_count
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source["word_count"] = segment.word_count
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source["segment_position"] = segment.position
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source["index_node_hash"] = segment.index_node_hash
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if segment.answer:
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source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
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else:
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source["content"] = segment.content
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context_list.append(source)
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resource_number += 1
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for hit_callback in self.hit_callbacks:
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hit_callback.return_retriever_resource_info(context_list)
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return str("\n".join(document_context_list))
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def _retriever(
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self,
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flask_app: Flask,
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dataset_id: str,
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query: str,
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all_documents: list,
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hit_callbacks: list[DatasetIndexToolCallbackHandler],
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):
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with flask_app.app_context():
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dataset = (
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db.session.query(Dataset).filter(Dataset.tenant_id == self.tenant_id, Dataset.id == dataset_id).first()
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)
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if not dataset:
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return []
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for hit_callback in hit_callbacks:
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hit_callback.on_query(query, dataset.id)
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# get retrieval model , if the model is not setting , using default
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retrieval_model = dataset.retrieval_model or default_retrieval_model
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if dataset.indexing_technique == "economy":
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# use keyword table query
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documents = RetrievalService.retrieve(
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retrieval_method="keyword_search",
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dataset_id=dataset.id,
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query=query,
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top_k=retrieval_model.get("top_k") or 2,
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)
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if documents:
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all_documents.extend(documents)
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else:
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if self.top_k > 0:
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# retrieval source
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documents = RetrievalService.retrieve(
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retrieval_method=retrieval_model["search_method"],
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dataset_id=dataset.id,
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query=query,
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top_k=retrieval_model.get("top_k") or 2,
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score_threshold=retrieval_model.get("score_threshold", 0.0)
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if retrieval_model["score_threshold_enabled"]
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else 0.0,
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reranking_model=retrieval_model.get("reranking_model", None)
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if retrieval_model["reranking_enable"]
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else None,
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reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
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weights=retrieval_model.get("weights", None),
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)
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all_documents.extend(documents)
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@ -0,0 +1,33 @@
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from abc import abstractmethod
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from typing import Any, Optional
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from msal_extensions.persistence import ABC
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from pydantic import BaseModel, ConfigDict
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from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
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class DatasetRetrieverBaseTool(BaseModel, ABC):
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"""Tool for querying a Dataset."""
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name: str = "dataset"
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description: str = "use this to retrieve a dataset. "
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tenant_id: str
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top_k: int = 2
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score_threshold: Optional[float] = None
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hit_callbacks: list[DatasetIndexToolCallbackHandler] = []
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return_resource: bool
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retriever_from: str
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model_config = ConfigDict(arbitrary_types_allowed=True)
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@abstractmethod
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def _run(
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self,
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*args: Any,
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**kwargs: Any,
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) -> Any:
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"""Use the tool.
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Add run_manager: Optional[CallbackManagerForToolRun] = None
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to child implementations to enable tracing,
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"""
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149
api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py
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149
api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py
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@ -0,0 +1,149 @@
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from pydantic import BaseModel, Field
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from core.rag.datasource.retrieval_service import RetrievalService
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from core.rag.retrieval.retrieval_methods import RetrievalMethod
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from core.tools.tool.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
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from extensions.ext_database import db
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from models.dataset import Dataset, Document, DocumentSegment
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default_retrieval_model = {
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"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
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"reranking_enable": False,
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"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
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"reranking_mode": "reranking_model",
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"top_k": 2,
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"score_threshold_enabled": False,
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}
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class DatasetRetrieverToolInput(BaseModel):
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query: str = Field(..., description="Query for the dataset to be used to retrieve the dataset.")
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class DatasetRetrieverTool(DatasetRetrieverBaseTool):
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"""Tool for querying a Dataset."""
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name: str = "dataset"
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args_schema: type[BaseModel] = DatasetRetrieverToolInput
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description: str = "use this to retrieve a dataset. "
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dataset_id: str
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@classmethod
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def from_dataset(cls, dataset: Dataset, **kwargs):
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description = dataset.description
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if not description:
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description = "useful for when you want to answer queries about the " + dataset.name
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description = description.replace("\n", "").replace("\r", "")
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return cls(
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name=f"dataset_{dataset.id.replace('-', '_')}",
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tenant_id=dataset.tenant_id,
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dataset_id=dataset.id,
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description=description,
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**kwargs,
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)
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def _run(self, query: str) -> str:
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dataset = (
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db.session.query(Dataset).filter(Dataset.tenant_id == self.tenant_id, Dataset.id == self.dataset_id).first()
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)
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if not dataset:
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return ""
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for hit_callback in self.hit_callbacks:
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hit_callback.on_query(query, dataset.id)
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# get retrieval model , if the model is not setting , using default
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retrieval_model = dataset.retrieval_model or default_retrieval_model
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if dataset.indexing_technique == "economy":
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# use keyword table query
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documents = RetrievalService.retrieve(
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retrieval_method="keyword_search", dataset_id=dataset.id, query=query, top_k=self.top_k
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)
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return str("\n".join([document.page_content for document in documents]))
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else:
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if self.top_k > 0:
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# retrieval source
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documents = RetrievalService.retrieve(
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retrieval_method=retrieval_model.get("search_method", "semantic_search"),
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dataset_id=dataset.id,
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query=query,
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top_k=self.top_k,
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score_threshold=retrieval_model.get("score_threshold", 0.0)
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if retrieval_model["score_threshold_enabled"]
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else 0.0,
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reranking_model=retrieval_model.get("reranking_model", None)
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if retrieval_model["reranking_enable"]
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else None,
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reranking_mode=retrieval_model.get("reranking_mode") or "reranking_model",
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weights=retrieval_model.get("weights", None),
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)
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else:
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documents = []
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for hit_callback in self.hit_callbacks:
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hit_callback.on_tool_end(documents)
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document_score_list = {}
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if dataset.indexing_technique != "economy":
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for item in documents:
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if item.metadata.get("score"):
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document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
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document_context_list = []
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index_node_ids = [document.metadata["doc_id"] for document in documents]
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segments = DocumentSegment.query.filter(
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DocumentSegment.dataset_id == self.dataset_id,
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DocumentSegment.completed_at.isnot(None),
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DocumentSegment.status == "completed",
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DocumentSegment.enabled == True,
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DocumentSegment.index_node_id.in_(index_node_ids),
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).all()
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if segments:
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index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
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sorted_segments = sorted(
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segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
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)
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for segment in sorted_segments:
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if segment.answer:
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document_context_list.append(f"question:{segment.get_sign_content()} answer:{segment.answer}")
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else:
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document_context_list.append(segment.get_sign_content())
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if self.return_resource:
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context_list = []
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resource_number = 1
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for segment in sorted_segments:
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context = {}
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document = Document.query.filter(
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Document.id == segment.document_id,
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Document.enabled == True,
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Document.archived == False,
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).first()
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if dataset and document:
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source = {
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"position": resource_number,
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"dataset_id": dataset.id,
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"dataset_name": dataset.name,
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"document_id": document.id,
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"document_name": document.name,
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"data_source_type": document.data_source_type,
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"segment_id": segment.id,
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"retriever_from": self.retriever_from,
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"score": document_score_list.get(segment.index_node_id, None),
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}
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if self.retriever_from == "dev":
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source["hit_count"] = segment.hit_count
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source["word_count"] = segment.word_count
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source["segment_position"] = segment.position
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source["index_node_hash"] = segment.index_node_hash
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if segment.answer:
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source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
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else:
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source["content"] = segment.content
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context_list.append(source)
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resource_number += 1
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for hit_callback in self.hit_callbacks:
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hit_callback.return_retriever_resource_info(context_list)
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return str("\n".join(document_context_list))
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