Upgrade the declarative pipeline for cleaner interface (#51)

tests/simple_pipeline.py

@@ -1,7 +1,7 @@
 import tempfile
 from typing import List
 
-from theflow import Node
+from theflow.utils.modules import ObjectInitDeclaration as _
 
 from kotaemon.base import BaseComponent
 from kotaemon.embeddings import AzureOpenAIEmbeddings
@@ -11,33 +11,27 @@ from kotaemon.vectorstores import ChromaVectorStore
 
 
 class Pipeline(BaseComponent):
-    vectorstore_path: str = str(tempfile.mkdtemp())
-    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,
-        },
+    llm: AzureOpenAI = AzureOpenAI.withx(
+        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
+    retrieving_pipeline: RetrieveDocumentFromVectorStorePipeline = (
+        RetrieveDocumentFromVectorStorePipeline.withx(
+            vector_store=_(ChromaVectorStore).withx(path=str(tempfile.mkdtemp())),
+            embedding=AzureOpenAIEmbeddings.withx(
+                model="text-embedding-ada-002",
+                deployment="embedding-deployment",
+                openai_api_base="https://test.openai.azure.com/",
+                openai_api_key="some-key",
+            ),
         )
+    )
 
     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 self.llm("\n".join(matched_texts)).text