updates

app.py

@@ -8,7 +8,6 @@ from translate import Translator
 
 
 from llama_index.indices.managed.vectara import VectaraIndex
-from llama_index.indices.managed.vectara.query import VectaraQueryEngine
 from llama_index.core.agent import ReActAgent
 from llama_index.llms.openai import OpenAI
 from llama_index.core.tools import QueryEngineTool, ToolMetadata
@@ -24,17 +23,13 @@ def launch_bot():
         cfg = st.session_state.cfg
         llm = OpenAI(model="gpt-4o", temperature=0)        
         tr_prompt = Translator(to_lang=languages[cfg.language]).translate(initial_prompt)
-        print(tr_prompt)
         st.session_state.messages = [{"role": "assistant", "content": tr_prompt, "avatar": "🦖"}]
         vectara = VectaraIndex(vectara_api_key=cfg.api_key, 
                                vectara_customer_id=cfg.customer_id, 
                                vectara_corpus_id=cfg.corpus_id)
-        vectara_retriever = vectara.as_retriever(n_sentences_before=5, n_sentences_after=10, 
-                                                 lambda_val=0.005, similarity_top_k=10)
         vectara_tool = QueryEngineTool(
-            query_engine = VectaraQueryEngine.from_args(retriever=vectara_retriever, summary_enabled=True, 
-                                                        summary_num_results=5, summary_response_lang = languages[cfg.language],
-                                                        summary_prompt_name="vectara-summary-ext-24-05-large"),
+            query_engine = vectara.as_query_engine(summary_enabled=True, summary_num_results=5, summary_response_lang = languages[cfg.language],
+                                                   summary_prompt_name="vectara-summary-ext-24-05-large"),
             metadata = ToolMetadata(name="Vectara", 
                                     description="Vectara Query Engine that is able to answer any questions about the Justice Harvard class."),
         )
@@ -42,9 +37,8 @@ def launch_bot():
         st.session_state.agent = ReActAgent.from_tools(
             tools=[vectara_tool], llm=llm, 
             context = f'''
-                You are a teacher assistant at Justice Harvard course. You are helping a student with his questions.
-                The student is {cfg.student_age} years old, so make sure to adapt your response as appropriate to that age.
-                You respond in the {cfg.style} style.
+                You are a teacher assistant for the Justice Harvard course, helping a {cfg.student_age} years old student with his or her questions.
+                Evaluate the response from tools and respond in the {cfg.style} learning style.
                 ''',
             verbose=True
         )