Refactor the index component and update the MVP insurance accordingly (#90)

Refactor the `kotaemon/pipelines` module to `kotaemon/indices`. Create the VectorIndex.

Note: currently I place `qa` to be inside `kotaemon/indices` since at the moment we only have `qa` in RAG. At the same time, I think `qa` can be an independent module in `kotaemon/qa`. Since this can be changed later, I still go at the 1st option for now to observe if we can change it later.

tests/simple_pipeline.py

@@ -5,8 +5,8 @@ from theflow.utils.modules import ObjectInitDeclaration as _
 
 from kotaemon.base import BaseComponent
 from kotaemon.embeddings import AzureOpenAIEmbeddings
+from kotaemon.indices import VectorRetrieval
 from kotaemon.llms.completions.openai import AzureOpenAI
-from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
 from kotaemon.storages import ChromaVectorStore
 
 
@@ -20,16 +20,14 @@ class Pipeline(BaseComponent):
         request_timeout=60,
     )
 
-    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",
-            ),
-        )
+    retrieving_pipeline: VectorRetrieval = VectorRetrieval.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: