updated

agent.py

@@ -20,10 +20,10 @@ def create_assistant_tools(cfg):
             description="The company ticker.",
             examples=['GOOG', 'META']
         )
-        year: Optional[str] = Field(
+        year: int | str = Field(
             default=None,
-            description="The year of the report.",
-            examples=[2020, 2023]
+            description="The year of the report, or a string specifying a condition on the year",
+            examples=[2020, '>2021', '<2023', '>=2021', '<=2023', '[2021, 2023]', '[2021, 2023)']
         )
         quarter: Optional[int] = Field(
             default=None,
@@ -38,8 +38,7 @@ def create_assistant_tools(cfg):
 
     vec_factory = VectaraToolFactory(
         vectara_api_key=cfg.api_key,
-        vectara_customer_id=cfg.customer_id,
-        vectara_corpus_id=cfg.corpus_id
+        vectara_corpus_key=cfg.corpus_key
     )
     
     summarizer = 'vectara-experimental-summary-ext-2023-12-11-med-omni'
@@ -67,6 +66,7 @@ def create_assistant_tools(cfg):
         vectara_summarizer = summarizer,
         summary_num_results = 10,
         include_citations = True,
+        verbose=False,
     )
     return [ask_hmc]
 
@@ -91,8 +91,7 @@ def initialize_agent(_cfg, agent_progress_callback=None):
 
 def get_agent_config() -> OmegaConf:
     cfg = OmegaConf.create({
-        'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']),
-        'corpus_id': str(os.environ['VECTARA_CORPUS_ID']),
+        'corpus_key': str(os.environ['VECTARA_CORPUS_KEY']),
         'api_key': str(os.environ['VECTARA_API_KEY']),
         'examples': os.environ.get('QUERY_EXAMPLES', None),
         'demo_name': "Harvard Management Company",

requirements.txt

@@ -6,4 +6,4 @@ streamlit_feedback==0.1.3
 uuid==1.30
 langdetect==1.0.9
 langcodes==3.4.0
-vectara-agentic==0.1.22
+vectara-agentic==0.2.1

st_app.py

@@ -140,8 +140,8 @@ async def launch_bot():
     if st.session_state.prompt:
         with st.chat_message("assistant", avatar='🤖'):
             st.session_state.status = st.status('Processing...', expanded=False)
-            res = st.session_state.agent.chat(st.session_state.prompt)
-            res = escape_dollars_outside_latex(res)
+            response = st.session_state.agent.chat(st.session_state.prompt)
+            res = escape_dollars_outside_latex(response.response)
             message = {"role": "assistant", "content": res, "avatar": '🤖'}
             st.session_state.messages.append(message)
             st.markdown(res)