Update app.py

app.py

@@ -36,5 +36,65 @@ def getEmbedding(text):
         return []
     encoded = embedding.encode(text)
     return encoded.tolist()
+
+
+# Connect to MongoDB
+def get_mongo_client(mongo_uri):
+    try:
+        client = pymongo.MongoClient(mongo_uri)
+        print("Connection to MongoDB successful")
+        return client
+    except pymongo.errors.ConnectionFailure as e:
+        print(f"Connection failed: {e}")
+        return None
+
+if not mongo_uri:
+    print("MONGO_URI not set in env")
+
+mongo_client = get_mongo_client(mongo_uri)
+
+mongo_db = mongo_client['recipes']
+mongo_collection = mongo_db['recipesCollection']
+
+def vector_search(user_query, collection):
+    query_embedding = getEmbedding(user_query)
+    if query_embedding is None:
+        return "Invalid query or embedding gen failed"
+    vector_search_stage = {
+        "$vectorSearch": {
+            "index": "vector_index",
+            "queryVector": query_embedding,
+            "path": "embedding",
+            "numCandidates": 150,  # Number of candidate matches to consider
+            "limit": 4  # Return top 4 matches
+        }
+    }
+
+    unset_stage = {
+        "$unset": "embedding"  # Exclude the 'embedding' field from the results
+    }
+
+    project_stage = {
+        "$project": {
+            "_id": 0,  # Exclude the _id field
+            "name": 1,
+            "minutes": 1,
+            "tags": 1,
+            "n_steps": 1,
+            "description": 1,
+            "ingredients": 1,
+            "n_ingredients": 1,
+            "formatted_nutrition": 1,
+            "formatted_steps": 1,
+            "score": {
+                "$meta": "vectorSearchScore"  # Include the search score
+            }
+        }
+    }
+
+    pipeline = [vector_search_stage, unset_stage, project_stage]
+    results = mongo_collection.aggregate(pipeline)
+    return list(results)
+    
 print("HELLO WORLD")
-st.title("Results")
+st.title("qwerty :D")