fixes

app/backend/main.py

@@ -4,6 +4,8 @@
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from typing import Optional, Dict, Any
+from src.lm.query_executor import execute_weaviate_query
+from src.models import WeaviateQuery
 
 app = FastAPI()
 
@@ -18,9 +20,25 @@
 
 import json
 import os
+import weaviate
 
 QUERIES_FILE = "synthetic-weaviate-queries-with-results.json"
 
+# Get API keys from environment variables
+WEAVIATE_URL = os.getenv("WEAVIATE_URL")
+WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY") 
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+print("Connecting to Weaviate...")
+weaviate_client = weaviate.connect_to_weaviate_cloud(
+    cluster_url=WEAVIATE_URL,
+    auth_credentials=weaviate.auth.AuthApiKey(WEAVIATE_API_KEY),
+    headers = {
+        "X-OpenAI-Api-Key": OPENAI_API_KEY
+    }
+)
+print("Successfully connected to Weaviate...")
+
 with open(QUERIES_FILE, 'r') as f:
     synthetic_query_data = json.load(f)
 
@@ -60,20 +78,31 @@ async def get_data():
 class QueryUpdate(BaseModel):
     index: int
     updated_query: Dict[Any, Any]
-    updated_result: str
 
 @app.put("/update-query")
 async def update_query(query_update: QueryUpdate):
+    print(query_update)
     try:
-        if 0 <= query_update.index < len(synthetic_query_data):
-            synthetic_query_data[query_update.index]["query"] = query_update.updated_query
-            synthetic_query_data[query_update.index]["ground_truth_query_result"] = query_update.updated_result
-
-            with open(QUERIES_FILE, 'w') as f:
-                json.dump(synthetic_query_data, f, indent=2)
-
-            return {"message": "Query and result updated successfully"}
-        else:
+        # 1) Validate the requested index
+        if not (0 <= query_update.index < len(synthetic_query_data)):
             raise HTTPException(status_code=404, detail="Query index not found")
+
+        # 2) Overwrite the existing "query" field with the updated query data
+        synthetic_query_data[query_update.index]["query"] = query_update.updated_query
+
+        # 3) Build a WeaviateQuery object from the updated query dictionary
+        updated_query_obj = WeaviateQuery(**query_update.updated_query)
+
+        # 4) Execute the query to get the new ground-truth result
+        final_response = execute_weaviate_query(weaviate_client, updated_query_obj)
+
+        # 5) Update ground_truth_query_result and save changes to disk
+        synthetic_query_data[query_update.index]["ground_truth_query_result"] = final_response
+
+        with open(QUERIES_FILE, 'w') as f:
+            json.dump(synthetic_query_data, f, indent=2)
+
+        return {"message": "Query and ground truth result updated successfully"}
+
     except Exception as e:
         raise HTTPException(status_code=400, detail=str(e))

data/print-query-execution.py

@@ -1,6 +1,7 @@
 import weaviate
 import weaviate.classes as wvc
 import json
+import time
 import pandas as pd
 import os
 from src.models import WeaviateQuery
@@ -68,12 +69,14 @@
         )
 
         # Load and insert data from corresponding CSV
-        csv_path = f'./{collection_name}.csv'
+        start_time = time.time()
+        csv_path = f'./data-for-use-cases/{collection_name}.csv'
         if os.path.exists(csv_path):
             df = pd.read_csv(csv_path)
             collection_obj = weaviate_client.collections.get(collection_name)
             for _, row in df.iterrows():
                 collection_obj.data.insert(properties=row.to_dict())
+            print(f"Loading data for {collection_name} took {time.time() - start_time:.2f} seconds")
 
         created_collections.add(collection_name)
 
@@ -87,20 +90,12 @@
     try:
         result = execute_weaviate_query(weaviate_client, query)
         query_data['ground_truth_query_result'] = result
+        print(f"\033[92mQuery executed successfully\033[0m")  # Green text
+        print(f"Query result: {result}")  # Print the query result
     except Exception as e:
         failed_queries += 1
-        print("\nQuery:", query_data['query']['corresponding_natural_language_query'])
-        print("\nQuery details:")
-        print(f"Target collection: {query.target_collection}")
-        print(f"Search query: {query.search_query}")
-        print(f"Integer filters: {query.integer_property_filter}")
-        print(f"Text filters: {query.text_property_filter}")
-        print(f"Boolean filters: {query.boolean_property_filter}")
-        print(f"Integer aggregations: {query.integer_property_aggregation}")
-        print(f"Text aggregations: {query.text_property_aggregation}")
-        print(f"Boolean aggregations: {query.boolean_property_aggregation}")
-        print(f"Group by: {query.groupby_property}")
-        print(f"\033[91mQuery execution failed: {str(e)}\033[0m")  # Red text
+        print(f"\033[91mQuery execution failed\033[0m")  # Red text
+        print(f"Error: {str(e)}")  # Print the error message
         query_data['ground_truth_query_result'] = "QUERY EXECUTION FAILED"
         print("Connecting to Weaviate...")
         weaviate_client = weaviate.connect_to_weaviate_cloud(
@@ -122,4 +117,4 @@
 print(f"\nResults saved to {output_path}")
 
 # Close the Weaviate client connection
-weaviate_client.close()
+weaviate_client.close()