Añado cosas, ni idea

app.py

@@ -7,6 +7,36 @@ with gr.Blocks() as demo:
     msg = gr.Textbox()
     clear = gr.ClearButton([msg, chatbot])
 
+    model = AutoModelForSequenceClassification.from_pretrained("/path/to/serialized/model/")
+
+    tokenizer = AutoTokenizer.from_pretrained("/path/to/serialized/tokenizer/")
+    
+    query_pipeline = transformers.pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        torch_dtype=torch.float16,
+        device_map="auto", max_new_tokens=200)
+
+    vectordb = Chroma.from_documents(documents=all_splits, embedding=embeddings, persist_directory="chroma_db")
+
+    def test_rag(pipeline, query):
+      docs = vectordb.similarity_search_with_score(query)
+      context = []
+      for doc,score in docs:
+        if(score<7):
+          doc_details = doc.to_json()['kwargs']
+          context.append( doc_details['page_content'])
+      if(len(context)!=0):
+        messages = [{"role": "user", "content": "Basándote en la siguiente información: " + "\n".join(context) + "\n Responde en castellano a la pregunta: " + query}]
+        prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+        answer = outputs[0]["generated_text"]
+        return answer[answer.rfind("[/INST]")+8:],docs
+      else:
+        return "No tengo información para responder a esta pregunta",docs
+
+    
     def respond(message, chat_history):
         bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
         chat_history.append((message, bot_message))