Add new data from Rohit's branch

.devcontainer/devcontainer.json

@@ -31,8 +31,7 @@
 				"ms-python.vscode-pylance",
 				"ms-python.vscode-python-envs",
 				"charliermarsh.ruff",
-				"mtxr.sqltools",
-				"mtxr.sqltools-driver-pg",
+				// TODO: Add PostgreSQL extension once its in marketplace
 				"esbenp.prettier-vscode",
 				"mechatroner.rainbow-csv",
 				"ms-vscode.vscode-node-azure-pack",

.vscode/settings.json

@@ -1,26 +1,20 @@
 {
-    "sqltools.connections": [
+    "pgsql.connections": [
         {
-            "previewLimit": 50,
+            "id": "92CC1089-BAD0-44A4-B071-A50A6EC12B67",
+            "groupId": "F3347CD6-9995-4EE9-8D98-D88DB010FA5B",
+            "authenticationType": "SqlLogin",
+            "connectTimeout": 15,
+            "applicationName": "vscode-pgsql",
+            "clientEncoding": "utf8",
+            "sslmode": "prefer",
             "server": "localhost",
-            "port": 5432,
-            "driver": "PostgreSQL",
-            "name": "local",
+            "user": "admin",
+            "password": "",
+            "savePassword": true,
             "database": "postgres",
-            "username": "admin",
-            "password": "postgres"
-        },
-        {
-            "name": "Azure database",
-            "driver": "PostgreSQL",
-            "server": "<HOSTNAME>.postgres.database.azure.com",
-            "port": 5432,
-            "database": "postgres",
-            "username": "<USERNAME>",
-            "askForPassword": true,
-            "pgOptions": {
-                "ssl": true
-            }
+            "profileName": "local-pg",
+            "expiresOn": 0
         }
     ],
     "python.testing.pytestArgs": [

convert_csv_json.py

@@ -3,7 +3,7 @@
 import json
 
 # Read CSV file - Using the correct dialect to handle quotes properly
-with open("data.csv", encoding="utf-8") as csv_file:
+with open("pittsburgh_restaurants.csv", encoding="utf-8") as csv_file:
     # Use the csv.reader with proper quoting parameters
     csv_reader = csv.reader(csv_file, quoting=csv.QUOTE_ALL, doublequote=True, escapechar="\\")
     header = next(csv_reader)  # Get the header row
@@ -42,10 +42,17 @@
             item[header[i]] = value
     # remove is_open column
     del item["is_open"]
+    del item["hours"]
+    del item["neighborhood"]
+    del item["tags"]
+    del item["vibe"]
+    del item["top_reviews"]
+    if item["price_level"] == "":
+        item["price_level"] = 2.0  # Assume 2.0 if empty
     json_data.append(item)
 
 # Write to JSON file
-with open("data.json", "w", encoding="utf-8") as f:
+with open("src/backend/fastapi_app/seed_data.json", "w", encoding="utf-8") as f:
     json.dump(json_data, f, indent=4, ensure_ascii=False)
 
 print(f"Successfully converted CSV data to JSON format with {len(json_data)} records")

src/backend/fastapi_app/api_models.py

@@ -42,22 +42,17 @@ class ChatRequest(BaseModel):
 
 
 class ItemPublic(BaseModel):
-    id: int
+    id: str
     name: str
-    location: str
     cuisine: str
     rating: int
     price_level: int
     review_count: int
-    hours: str
-    tags: list[str]
     description: str
     menu_summary: str
-    top_reviews: str
-    vibe: str
 
     def to_str_for_rag(self):
-        return f"Name:{self.name} Description:{self.description} Location:{self.location} Cuisine:{self.cuisine} Rating:{self.rating} Price Level:{self.price_level} Review Count:{self.review_count} Hours:{self.hours} Tags:{self.tags} Menu Summary:{self.menu_summary} Top Reviews:{self.top_reviews} Vibe:{self.vibe}"  # noqa: E501
+        return f"Name:{self.name} Description:{self.description} Cuisine:{self.cuisine} Rating:{self.rating} Price Level:{self.price_level} Review Count:{self.review_count} Menu Summary:{self.menu_summary}"  # noqa: E501
 
 
 class ItemWithDistance(ItemPublic):
@@ -75,7 +70,7 @@ class ThoughtStep(BaseModel):
 
 
 class RAGContext(BaseModel):
-    data_points: dict[int, ItemPublic]
+    data_points: dict[str, ItemPublic]
     thoughts: list[ThoughtStep]
     followup_questions: Optional[list[str]] = None
 

src/backend/fastapi_app/postgres_models.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
 from pgvector.sqlalchemy import Vector
-from sqlalchemy import VARCHAR, Index
-from sqlalchemy.dialects import postgresql
+from sqlalchemy import Index
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
 
 
@@ -13,19 +12,14 @@ class Base(DeclarativeBase):
 
 class Item(Base):
     __tablename__ = "items"
-    id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True)
+    id: Mapped[str] = mapped_column(primary_key=True)
     name: Mapped[str] = mapped_column()
-    location: Mapped[str] = mapped_column()
     cuisine: Mapped[str] = mapped_column()
     rating: Mapped[int] = mapped_column()
     price_level: Mapped[int] = mapped_column()
     review_count: Mapped[int] = mapped_column()
-    hours: Mapped[str] = mapped_column()
-    tags: Mapped[list[str]] = mapped_column(postgresql.ARRAY(VARCHAR))  # Array of strings
     description: Mapped[str] = mapped_column()
     menu_summary: Mapped[str] = mapped_column()
-    top_reviews: Mapped[str] = mapped_column()
-    vibe: Mapped[str] = mapped_column()
 
     # Embeddings for different models:
     embedding_3l: Mapped[Vector] = mapped_column(Vector(1024), nullable=True)  # text-embedding-3-large
@@ -42,10 +36,10 @@ def to_dict(self, include_embedding: bool = False):
         return model_dict
 
     def to_str_for_rag(self):
-        return f"Name:{self.name} Description:{self.description} Location:{self.location} Cuisine:{self.cuisine} Rating:{self.rating} Price Level:{self.price_level} Review Count:{self.review_count} Hours:{self.hours} Tags:{self.tags} Menu Summary:{self.menu_summary} Top Reviews:{self.top_reviews} Vibe:{self.vibe}"  # noqa: E501
+        return f"Name:{self.name} Description:{self.description} Cuisine:{self.cuisine} Rating:{self.rating} Price Level:{self.price_level} Review Count:{self.review_count} Menu Summary:{self.menu_summary}"  # noqa: E501
 
     def to_str_for_embedding(self):
-        return f"Name: {self.name} Description: {self.description} Cuisine: {self.cuisine} Tags: {self.tags} Menu Summary: {self.menu_summary} Top Reviews: {self.top_reviews} Vibe: {self.vibe}"  # noqa: E501
+        return f"Name: {self.name} Description: {self.description} Cuisine: {self.cuisine} Menu Summary: {self.menu_summary}"  # noqa: E501
 
 
 """

src/backend/fastapi_app/rag_advanced.py

@@ -119,6 +119,8 @@ async def prepare_context(self) -> tuple[list[ItemPublic], list[ThoughtStep]]:
         most_recent_response = run_results.new_items[-1]
         if isinstance(most_recent_response, ToolCallOutputItem):
             search_results = most_recent_response.output
+            if not isinstance(search_results, SearchResults):
+                raise ValueError(f"Error retrieving search results: {search_results}")
         else:
             raise ValueError("Error retrieving search results, model did not call tool properly")