c6dd01e820
From pipeline > config > UI. Provide example project for promptui - Pipeline to config: `kotaemon.contribs.promptui.config.export_pipeline_to_config`. The config follows schema specified in this document: https://cinnamon-ai.atlassian.net/wiki/spaces/ATM/pages/2748711193/Technical+Detail. Note: this implementation exclude the logs, which will be handled in AUR-408. - Config to UI: `kotaemon.contribs.promptui.build_from_yaml` - Example project is located at `examples/promptui/`
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
import pytest
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from kotaemon.contribs.promptui.config import export_pipeline_to_config
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from kotaemon.contribs.promptui.ui import build_from_dict
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@pytest.fixture()
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def simple_pipeline_cls(tmp_path):
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"""Create a pipeline class that can be used"""
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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 (
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RetrieveDocumentFromVectorStorePipeline,
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)
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from kotaemon.vectorstores import ChromaVectorStore
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class Pipeline(BaseComponent):
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vectorstore_path: str = str(tmp_path)
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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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return Pipeline
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Pipeline = simple_pipeline_cls
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class TestPromptConfig:
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def test_export_prompt_config(self, simple_pipeline_cls):
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"""Test if the prompt config is exported correctly"""
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pipeline = simple_pipeline_cls()
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config_dict = export_pipeline_to_config(pipeline)
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config = list(config_dict.values())[0]
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assert "inputs" in config, "inputs should be in config"
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assert "text" in config["inputs"], "inputs should have config"
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assert "params" in config, "params should be in config"
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assert "vectorstore_path" in config["params"]
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assert "llm.deployment_name" in config["params"]
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assert "llm.openai_api_base" in config["params"]
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assert "llm.openai_api_key" in config["params"]
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assert "llm.openai_api_version" in config["params"]
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assert "llm.request_timeout" in config["params"]
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assert "llm.temperature" in config["params"]
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class TestPromptUI:
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def test_uigeneration(self, simple_pipeline_cls):
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"""Test if the gradio UI is exposed without any problem"""
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pipeline = simple_pipeline_cls()
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config = export_pipeline_to_config(pipeline)
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build_from_dict(config)
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