updated

Dockerfile

@@ -4,7 +4,9 @@ WORKDIR /app
 
 COPY ./requirements.txt /app/requirements.txt
     
-RUN pip3 install --no-cache-dir -r /app/requirements.txt
+RUN pip3 install --no-cache-dir --upgrade pip
+RUN pip3 install --no-cache-dir wheel setuptools build
+RUN pip3 install --no-cache-dir --use-pep517 -r /app/requirements.txt
 
 # User
 RUN useradd -m -u 1000 user

agent.py

@@ -4,15 +4,30 @@ from pydantic import Field, BaseModel
 from omegaconf import OmegaConf
 
 from vectara_agentic.agent import Agent
-from vectara_agentic.tools import VectaraToolFactory
+from vectara_agentic.tools import VectaraToolFactory, ToolsFactory
 
 from dotenv import load_dotenv
 load_dotenv(override=True)
 
 initial_prompt = "How can I help you today?"
 
+tickers = {
+    "GOOG": "Google", 
+    "NVDA": "Nvidia",
+    "META": "Meta",
+    "BKNG": "Bookings holding",
+}
+
 def create_assistant_tools(cfg):
 
+    def get_company_info() -> list[str]:
+        """
+        Returns a dictionary of companies you can query about. Always check this before using any other tool.
+        The output is a dictionary of valid ticker symbols mapped to company names.
+        You can use this to identify the companies you can query about, and their ticker information.
+        """
+        return tickers
+
     class QueryHMC(BaseModel):
         query: str = Field(description="The user query.")
         ticker: Optional[str] = Field(
@@ -41,7 +56,7 @@ def create_assistant_tools(cfg):
         vectara_corpus_key=cfg.corpus_key
     )
     
-    summarizer = 'vectara-experimental-summary-ext-2023-12-11-med-omni'
+    summarizer = 'vectara-summary-table-md-query-ext-jan-2025-gpt-4o'
     #summarizer = 'vectara-summary-ext-24-05-med-omni'
     ask_hmc = vec_factory.create_rag_tool(
         tool_name = "ask_hmc",
@@ -59,16 +74,17 @@ def create_assistant_tools(cfg):
             {
                 "type": "mmr",
                 "diversity_bias": 0.05,
-                "limit": 20
+                "limit": 50
             }
         ],
         n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
         vectara_summarizer = summarizer,
         summary_num_results = 10,
         include_citations = True,
-        verbose=False,
+        verbose = True,
     )
-    return [ask_hmc]
+    tools_factory = ToolsFactory()
+    return [ask_hmc] + [tools_factory.create_tool(get_company_info)]
 
 def initialize_agent(_cfg, agent_progress_callback=None):
     bot_instructions = """
@@ -77,6 +93,7 @@ def initialize_agent(_cfg, agent_progress_callback=None):
       If the tool responds with "I don't have enough information to answer", try rephrasing the question.
     - Use the year, quarter, filing_type and ticker arguments to the 'ask_hmc' tool to get more specific answers.
     - Note that 10Q reports exist for quarters 1, 2, 3 and for the 4th quarter there is a 10K report.
+    - If the 'ask_hmc' tool does not return any results, check the year and ticker and try calling it again with the right values.
     """
 
     agent = Agent(
@@ -84,6 +101,7 @@ def initialize_agent(_cfg, agent_progress_callback=None):
         topic="Endowment fund management",
         custom_instructions=bot_instructions,
         agent_progress_callback=agent_progress_callback,
+        verbose=True,
     )
     agent.report()
     return agent

app.py

@@ -1,10 +1,13 @@
 import streamlit as st
+import torch
 from st_app import launch_bot
 import uuid
 
 import nest_asyncio
 import asyncio
 
+torch.classes.__path__ = []
+
 # Setup for HTTP API Calls to Amplitude Analytics
 if 'device_id' not in st.session_state:
     st.session_state.device_id = str(uuid.uuid4())

requirements.txt

@@ -1,9 +1,9 @@
 omegaconf==2.3.0
 python-dotenv==1.0.1
-streamlit==1.41.1
-streamlit_pills==0.3.0
+streamlit==1.43.2
 streamlit_feedback==0.1.3
 uuid==1.30
 langdetect==1.0.9
 langcodes==3.4.0
-vectara-agentic==0.2.5
+vectara-agentic==0.2.9
+torch==2.6.0

st_app.py

@@ -3,7 +3,6 @@ import sys
 import re
 
 import streamlit as st
-from streamlit_pills import pills
 from streamlit_feedback import streamlit_feedback
 
 from utils import thumbs_feedback, escape_dollars_outside_latex, send_amplitude_data
@@ -15,7 +14,7 @@ initial_prompt = "How can I help you today?"
 
 def show_example_questions():        
     if len(st.session_state.example_messages) > 0 and st.session_state.first_turn:            
-        selected_example = pills("Queries to Try:", st.session_state.example_messages, index=None)
+        selected_example = st.pills("Queries to Try:", st.session_state.example_messages, default=None)
         if selected_example:
             st.session_state.ex_prompt = selected_example
             st.session_state.first_turn = False