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126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
import re
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from typing import Type
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from flask import current_app
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.tools import BaseTool
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from pydantic import Field, BaseModel
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from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
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from core.embedding.cached_embedding import CacheEmbedding
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from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
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from core.index.vector_index.vector_index import VectorIndex
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from core.llm.llm_builder import LLMBuilder
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from extensions.ext_database import db
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from models.dataset import Dataset, DocumentSegment
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class DatasetRetrieverToolInput(BaseModel):
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dataset_id: str = Field(..., description="ID of dataset to be queried. MUST be UUID format.")
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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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k: int = 3
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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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description += '\nID of dataset MUST be ' + dataset.id
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return cls(
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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, dataset_id: str, query: str) -> str:
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pattern = r'\b[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}\b'
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match = re.search(pattern, dataset_id, re.IGNORECASE)
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if match:
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dataset_id = match.group()
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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 f'[{self.name} failed to find dataset with id {dataset_id}.]'
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if dataset.indexing_technique == "economy":
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# use keyword table query
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kw_table_index = KeywordTableIndex(
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dataset=dataset,
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config=KeywordTableConfig(
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max_keywords_per_chunk=5
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)
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)
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documents = kw_table_index.search(query, search_kwargs={'k': self.k})
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return str("\n".join([document.page_content for document in documents]))
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else:
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model_credentials = LLMBuilder.get_model_credentials(
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tenant_id=dataset.tenant_id,
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model_provider=LLMBuilder.get_default_provider(dataset.tenant_id, 'text-embedding-ada-002'),
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model_name='text-embedding-ada-002'
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)
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embeddings = CacheEmbedding(OpenAIEmbeddings(
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**model_credentials
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))
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vector_index = VectorIndex(
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dataset=dataset,
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config=current_app.config,
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embeddings=embeddings
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)
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if self.k > 0:
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documents = vector_index.search(
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query,
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search_type='similarity',
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search_kwargs={
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'k': self.k
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}
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)
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else:
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documents = []
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hit_callback = DatasetIndexToolCallbackHandler(dataset.id)
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hit_callback.on_tool_end(documents)
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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.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} \nanswer:{segment.answer}')
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else:
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document_context_list.append(segment.content)
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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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