4f189dc931
This CL implements: - The logic to export log to Excel. - Route the export logic in the UI. - Demonstrate this functionality in `./examples/promptui` project.
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import tempfile
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from typing import List
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from theflow import Node
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from kotaemon.base import BaseComponent
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from kotaemon.embeddings import AzureOpenAIEmbeddings
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from kotaemon.llms.completions.openai import AzureOpenAI
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from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
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from kotaemon.vectorstores import ChromaVectorStore
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class Pipeline(BaseComponent):
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vectorstore_path: str = str(tempfile.mkdtemp())
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llm: Node[AzureOpenAI] = Node(
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default=AzureOpenAI,
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default_kwargs={
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"openai_api_base": "https://test.openai.azure.com/",
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"openai_api_key": "some-key",
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"openai_api_version": "2023-03-15-preview",
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"deployment_name": "gpt35turbo",
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"temperature": 0,
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"request_timeout": 60,
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},
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)
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@Node.decorate(depends_on=["vectorstore_path"])
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def retrieving_pipeline(self):
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vector_store = ChromaVectorStore(self.vectorstore_path)
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embedding = AzureOpenAIEmbeddings(
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model="text-embedding-ada-002",
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deployment="embedding-deployment",
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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)
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return RetrieveDocumentFromVectorStorePipeline(
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vector_store=vector_store, embedding=embedding
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)
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def run_raw(self, text: str) -> str:
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matched_texts: List[str] = self.retrieving_pipeline(text)
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return self.llm("\n".join(matched_texts)).text[0]
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