Update app.py

app.py

@@ -16,9 +16,29 @@ collection = client_chroma.get_or_create_collection(name=collection_name)
 embedding_function = embedding_functions.DefaultEmbeddingFunction()
 
 client = Client("Qwen/Qwen2.5-72B-Instruct")
-def ask_llm(llm_prompt_input): 
+def ask_llm(llm_prompt_input):
+    # Erstelle Embedding für den Prompt
+    query_embedding = embedding_function([llm_prompt_input])[0]
+    
+    # Führe die Ähnlichkeitssuche durch
+    results = collection.query(
+        query_embeddings=[query_embedding],
+        n_results=3
+    )
+    
+    # Formatiere die Ergebnisse
+    formatted_results = []
+    for i, doc in enumerate(results["documents"][0]):
+        metadata = results["metadatas"][0][i]
+        filename = metadata["filename"]
+        formatted_results.append(f"{doc}\n")
+
+    queri = "\n".join(formatted_results)
+    #return "\n".join(formatted_results)
+   
+    
     result = client.predict(
-		query=f"{llm_prompt_input}",
+		query=f"{llm_prompt_input} kontext:{queri}",
 		history=[],
 		system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
 		api_name="/model_chat"