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88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
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 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 models.dataset import Dataset
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class DatasetTool(BaseTool):
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"""Tool for querying a Dataset."""
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dataset: Dataset
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k: int = 2
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def _run(self, tool_input: str) -> str:
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if self.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=self.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(tool_input, search_kwargs={'k': self.k})
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else:
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model_credentials = LLMBuilder.get_model_credentials(
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tenant_id=self.dataset.tenant_id,
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model_provider=LLMBuilder.get_default_provider(self.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=self.dataset,
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config=current_app.config,
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embeddings=embeddings
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)
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documents = vector_index.search(
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tool_input,
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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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hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
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hit_callback.on_tool_end(documents)
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return str("\n".join([document.page_content for document in documents]))
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async def _arun(self, tool_input: str) -> str:
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model_credentials = LLMBuilder.get_model_credentials(
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tenant_id=self.dataset.tenant_id,
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model_provider=LLMBuilder.get_default_provider(self.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=self.dataset,
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config=current_app.config,
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embeddings=embeddings
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)
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documents = await vector_index.asearch(
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tool_input,
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search_type='similarity',
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search_kwargs={
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'k': 10
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}
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)
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hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
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hit_callback.on_tool_end(documents)
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return str("\n".join([document.page_content for document in documents]))
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