Update app.py

app.py

@@ -181,6 +181,23 @@ def retrieval(query: str):
     try:
         query_embedding = get_text_embedding(query)
         results = collection.query(query_embeddings=[query_embedding], n_results=5)
-        return {"results": results.get("documents", [])}
+        #return {"results": results.get("documents", [])}
+         # Set a similarity threshold (adjust as needed)
+        SIMILARITY_THRESHOLD = 0.7
+        
+        # Extract documents and similarity scores
+        documents = results.get("documents", [[]])[0]  # Ensure we get the first list
+        distances = results.get("distances", [[]])[0]  # Ensure we get the first list
+
+        # Filter results based on similarity threshold
+        filtered_results = [
+            doc for doc, score in zip(documents, distances) if score >= SIMILARITY_THRESHOLD
+        ]
+
+        # Return filtered results or indicate no match found
+        if filtered_results:
+            return {"results": filtered_results}
+        else:
+            return {"results": "No relevant match found in ChromaDB."}
     except Exception as e:
         return {"error": str(e)}