enable vector search on package_name and package_details columns

Author: chatrtham

src/fastapi_app/get_token.py

@@ -0,0 +1,13 @@
+import asyncio
+import os
+
+from azure.identity import DefaultAzureCredential
+
+async def get_token():
+    credential = DefaultAzureCredential()
+    token = credential.get_token("https://ossrdbms-aad.database.windows.net/.default")
+    return token.token
+
+
+token = asyncio.run(get_token())
+print(token)

src/fastapi_app/postgres_models.py

@@ -84,7 +84,7 @@ def to_str_for_rag(self):
 """
 
     def to_str_for_embedding(self):
-        return f"Name: {self.package_name} Description: {self.package_details} Type: {self.meta_keywords}"
+        return f"Name: {self.package_name} Description: {self.package_details}"
 
 
 # Define HNSW index to support vector similarity search through the vector_cosine_ops access method (cosine distance).

src/fastapi_app/rag_advanced.py

@@ -71,7 +71,7 @@ async def run(
         results = await self.searcher.search_and_embed(
             query_text,
             top=top,
-            # enable_vector_search=vector_search,
+            enable_vector_search=vector_search,
             enable_text_search=text_search,
             filters=filters,
         )

src/fastapi_app/update_embeddings.py

@@ -1,18 +1,21 @@
 import asyncio
 
+from azure.identity.aio import DefaultAzureCredential
 from dotenv import load_dotenv
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import async_sessionmaker
 
 from fastapi_app.embeddings import compute_text_embedding
 from fastapi_app.openai_clients import create_openai_embed_client
-from fastapi_app.postgres_engine import create_postgres_engine
+from fastapi_app.postgres_engine import create_postgres_engine_from_env
 from fastapi_app.postgres_models import Item
 
+load_dotenv()
 
 async def update_embeddings():
-    engine = await create_postgres_engine()
-    openai_embed_client, openai_embed_model, openai_embed_dimensions = await create_openai_embed_client()
+    engine = await create_postgres_engine_from_env()
+    azure_credential = DefaultAzureCredential()
+    openai_embed_client, openai_embed_model, openai_embed_dimensions = await create_openai_embed_client(azure_credential)
 
     async with async_sessionmaker(engine, expire_on_commit=False)() as session:
         async with session.begin():