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knowledge entities fix
This commit is contained in:
jyong
parent
02337cbb09
commit
9e37021387
@ -1,194 +0,0 @@
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import threading
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from typing import Optional
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from flask import Flask, current_app
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from langchain.tools import BaseTool
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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.rerank.rerank import RerankRunner
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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': 'semantic_search',
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'reranking_enable': False,
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'reranking_model': {
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'reranking_provider_name': '',
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'reranking_model_name': ''
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},
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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(BaseTool):
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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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tenant_id: str
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dataset_ids: list[str]
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top_k: int = 2
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score_threshold: Optional[float] = None
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reranking_provider_name: str
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reranking_model_name: str
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return_resource: bool
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retriever_from: str
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hit_callbacks: list[DatasetIndexToolCallbackHandler] = []
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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}',
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tenant_id=tenant_id,
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dataset_ids=dataset_ids,
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**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(target=self._retriever, 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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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 = RerankRunner(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 'score' in item.metadata and item.metadata['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(segments,
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key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
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float('inf')))
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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.content} answer:{segment.answer}')
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else:
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document_context_list.append(segment.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(
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id=segment.dataset_id
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).first()
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document = Document.query.filter(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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async def _arun(self, tool_input: str) -> str:
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raise NotImplementedError()
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def _retriever(self, flask_app: Flask, dataset_id: str, query: str, all_documents: list,
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hit_callbacks: list[DatasetIndexToolCallbackHandler]):
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with flask_app.app_context():
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dataset = db.session.query(Dataset).filter(
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Dataset.tenant_id == self.tenant_id,
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Dataset.id == dataset_id
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).first()
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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 if dataset.retrieval_model else 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(retrival_method='keyword_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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)
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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(retrival_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=self.top_k,
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score_threshold=retrieval_model['score_threshold']
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if retrieval_model['score_threshold_enabled'] else None,
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reranking_model=retrieval_model['reranking_model']
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if retrieval_model['reranking_enable'] else None
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)
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all_documents.extend(documents)
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@ -1,159 +0,0 @@
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from typing import Optional
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from langchain.tools import BaseTool
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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.rag.datasource.retrieval_service import RetrievalService
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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': 'semantic_search',
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'reranking_enable': False,
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'reranking_model': {
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'reranking_provider_name': '',
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'reranking_model_name': ''
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},
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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(BaseTool):
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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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tenant_id: str
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dataset_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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@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}',
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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 = db.session.query(Dataset).filter(
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Dataset.tenant_id == self.tenant_id,
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Dataset.id == self.dataset_id
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).first()
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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 if dataset.retrieval_model else 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(retrival_method='keyword_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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)
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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(retrival_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=self.top_k,
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score_threshold=retrieval_model['score_threshold']
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if retrieval_model['score_threshold_enabled'] else None,
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reranking_model=retrieval_model['reranking_model']
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if retrieval_model['reranking_enable'] else 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 'score' in item.metadata and item.metadata['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(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(segments,
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key=lambda segment: index_node_id_to_position.get(segment.index_node_id,
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float('inf')))
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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.content} answer:{segment.answer}')
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else:
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document_context_list.append(segment.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(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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async def _arun(self, tool_input: str) -> str:
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raise NotImplementedError()
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@ -49,5 +49,5 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
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query_variable_selector: list[str]
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dataset_ids: list[str]
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retrieval_mode: Literal['single', 'multiple']
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multiple_retrieval_config: MultipleRetrievalConfig
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singleRetrievalConfig: SingleRetrievalConfig
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multiple_retrieval_config: Optional[MultipleRetrievalConfig]
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singleRetrievalConfig: Optional[SingleRetrievalConfig]
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