[AUR-338, AUR-406, AUR-407] Export pipeline to config for PromptUI. Construct PromptUI dynamically based on config. (#16)

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/`

tests/test_promptui.py

@@ -0,0 +1,86 @@
+import pytest
+
+from kotaemon.contribs.promptui.config import export_pipeline_to_config
+from kotaemon.contribs.promptui.ui import build_from_dict
+
+
+@pytest.fixture()
+def simple_pipeline_cls(tmp_path):
+    """Create a pipeline class that can be used"""
+    from typing import List
+
+    from theflow import Node
+
+    from kotaemon.base import BaseComponent
+    from kotaemon.embeddings import AzureOpenAIEmbeddings
+    from kotaemon.llms.completions.openai import AzureOpenAI
+    from kotaemon.pipelines.retrieving import (
+        RetrieveDocumentFromVectorStorePipeline,
+    )
+    from kotaemon.vectorstores import ChromaVectorStore
+
+    class Pipeline(BaseComponent):
+        vectorstore_path: str = str(tmp_path)
+        llm: Node[AzureOpenAI] = Node(
+            default=AzureOpenAI,
+            default_kwargs={
+                "openai_api_base": "https://test.openai.azure.com/",
+                "openai_api_key": "some-key",
+                "openai_api_version": "2023-03-15-preview",
+                "deployment_name": "gpt35turbo",
+                "temperature": 0,
+                "request_timeout": 60,
+            },
+        )
+
+        @Node.decorate(depends_on=["vectorstore_path"])
+        def retrieving_pipeline(self):
+            vector_store = ChromaVectorStore(self.vectorstore_path)
+            embedding = AzureOpenAIEmbeddings(
+                model="text-embedding-ada-002",
+                deployment="embedding-deployment",
+                openai_api_base="https://test.openai.azure.com/",
+                openai_api_key="some-key",
+            )
+
+            return RetrieveDocumentFromVectorStorePipeline(
+                vector_store=vector_store, embedding=embedding
+            )
+
+        def run_raw(self, text: str) -> str:
+            matched_texts: List[str] = self.retrieving_pipeline(text)
+            return self.llm("\n".join(matched_texts)).text[0]
+
+    return Pipeline
+
+
+Pipeline = simple_pipeline_cls
+
+
+class TestPromptConfig:
+    def test_export_prompt_config(self, simple_pipeline_cls):
+        """Test if the prompt config is exported correctly"""
+        pipeline = simple_pipeline_cls()
+        config_dict = export_pipeline_to_config(pipeline)
+        config = list(config_dict.values())[0]
+
+        assert "inputs" in config, "inputs should be in config"
+        assert "text" in config["inputs"], "inputs should have config"
+
+        assert "params" in config, "params should be in config"
+        assert "vectorstore_path" in config["params"]
+        assert "llm.deployment_name" in config["params"]
+        assert "llm.openai_api_base" in config["params"]
+        assert "llm.openai_api_key" in config["params"]
+        assert "llm.openai_api_version" in config["params"]
+        assert "llm.request_timeout" in config["params"]
+        assert "llm.temperature" in config["params"]
+
+
+class TestPromptUI:
+    def test_uigeneration(self, simple_pipeline_cls):
+        """Test if the gradio UI is exposed without any problem"""
+        pipeline = simple_pipeline_cls()
+        config = export_pipeline_to_config(pipeline)
+
+        build_from_dict(config)