bump version

.gitignore

@@ -10,6 +10,8 @@ core/__pycache__/
 # C extensions
 *.so
 
+.streamlit
+
 # Distribution / packaging
 .Python
 build/
@@ -139,4 +141,4 @@ dmypy.json
 # project file 
 project.yaml
 
-.idea/
+.idea/

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

@@ -10,7 +10,7 @@ from omegaconf import OmegaConf
 from vectara_agentic.agent import Agent
 from vectara_agentic.tools import ToolsFactory, VectaraToolFactory
 from vectara_agentic.agent_config import AgentConfig
-from vectara_agentic.sub_query_workflow import SubQuestionQueryWorkflow
+from vectara_agentic.sub_query_workflow import SequentialSubQuestionsWorkflow
 
 from dotenv import load_dotenv
 load_dotenv(override=True)
@@ -36,6 +36,7 @@ tickers = {
 years = range(2015, 2025)
 initial_prompt = "How can I help you today?"
 
+
 # Tool to get the income statement for a given company and year using the FMP API
 @lru_cache(maxsize=256)
 def fmp_income_statement(
@@ -94,8 +95,10 @@ class AgentTools:
         self.tools_factory = ToolsFactory()
         self.agent_config = agent_config
         self.cfg = _cfg
-        self.vec_factory = VectaraToolFactory(vectara_api_key=_cfg.api_key,
-                                              vectara_corpus_key=_cfg.corpus_key)
+        self.vec_factory = VectaraToolFactory(
+            vectara_api_key=_cfg.api_key,
+            vectara_corpus_key=_cfg.corpus_key
+        )
 
     def get_tools(self):
         class QueryTranscriptsArgs(BaseModel):
@@ -117,12 +120,12 @@ class AgentTools:
             You can ask this tool any question about the company including risks, opportunities, financial performance, competitors and more.
             """,
             tool_args_schema = QueryTranscriptsArgs,
-            reranker = "multilingual_reranker_v1", rerank_k = 100, rerank_cutoff = 0.1,
+            reranker = "multilingual_reranker_v1", rerank_k = 100, rerank_cutoff = 0.2,
             n_sentences_before = 2, n_sentences_after = 4, lambda_val = 0.005,
             summary_num_results = 15,
             vectara_summarizer = summarizer,
             include_citations = True,
-            verbose=False,
+            verbose = False,
         )
 
         class SearchTranscriptsArgs(BaseModel):
@@ -157,7 +160,7 @@ class AgentTools:
                 [ask_transcripts, search_transcripts]
         )
 
-def initialize_agent(_cfg, agent_progress_callback=None):
+def initialize_agent(_cfg, agent_progress_callback=None) -> Agent:
     financial_bot_instructions = """
     - You are a helpful financial assistant, with expertise in financial reporting, in conversation with a user. 
     - Use the 'fmp_income_statement' tool (with the company ticker and year) to obtain financial data.
@@ -183,7 +186,7 @@ def initialize_agent(_cfg, agent_progress_callback=None):
         agent_progress_callback=agent_progress_callback,
         query_logging_callback=query_logging,
         verbose=True,
-        #workflow_cls=SubQuestionQueryWorkflow,
+        workflow_cls=SequentialSubQuestionsWorkflow,
     )
 
     agent.report()

app.py

@@ -1,10 +1,15 @@
+
 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())
@@ -17,5 +22,6 @@ async def main():
 
 if __name__ == "__main__":
    st.set_page_config(page_title="Financial Assistant", layout="wide")
+
    nest_asyncio.apply()
    asyncio.run(main())

requirements.txt

@@ -1,9 +1,8 @@
 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==0.2.11

st_app.py

@@ -1,9 +1,10 @@
-from PIL import Image
+
 import sys
 import re
 
+from PIL import Image
+
 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
@@ -13,6 +14,12 @@ from agent import initialize_agent, get_agent_config
 
 initial_prompt = "How can I help you today?"
 
+# def pil_to_base64(img):
+#     buffered = BytesIO()
+#     img.save(buffered, format="PNG")
+#     return base64.b64encode(buffered.getvalue()).decode()
+
+
 def format_log_msg(log_msg: str):
     max_log_msg_size = 500
     return log_msg if len(log_msg) <= max_log_msg_size else log_msg[:max_log_msg_size]+'...'
@@ -50,7 +57,7 @@ def agent_progress_callback(status_type: AgentStatusType, msg: str):
 
 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
@@ -71,7 +78,10 @@ async def launch_bot():
         st.session_state.first_turn = True
         st.session_state.show_logs = False
         if 'agent' not in st.session_state:
-            st.session_state.agent = initialize_agent(cfg, agent_progress_callback=agent_progress_callback)
+            st.session_state.agent = initialize_agent(
+                cfg, 
+                agent_progress_callback=agent_progress_callback, 
+            )
         else:
             st.session_state.agent.clear_memory()
 
@@ -115,7 +125,8 @@ async def launch_bot():
     # Display chat messages
     for message in st.session_state.messages:
         with st.chat_message(message["role"], avatar=message["avatar"]):
-            st.write(message["content"])
+            st.markdown(message["content"], unsafe_allow_html=True)
+#            st.write(message["content"])
 
     example_container = st.empty()
     with example_container:
@@ -144,10 +155,11 @@ async def launch_bot():
         with st.chat_message("assistant", avatar='🤖'):
             st.session_state.status = st.status('Processing...', expanded=False)
             response = st.session_state.agent.chat(st.session_state.prompt)
-            
-            # from vectara_agentic.sub_query_workflow import SubQuestionQueryWorkflow
-            # response = await st.session_state.agent.run(inputs=SubQuestionQueryWorkflow.InputsModel(query=st.session_state.prompt))
             res = escape_dollars_outside_latex(response.response)
+
+            #from vectara_agentic.sub_query_workflow import SequentialSubQuestionsWorkflow
+            #response = await st.session_state.agent.run(inputs=SequentialSubQuestionsWorkflow.InputsModel(query=st.session_state.prompt))
+            #res = escape_dollars_outside_latex(response.response)
             
             #response = await st.session_state.agent.achat(st.session_state.prompt)
             #res = escape_dollars_outside_latex(response.response)
@@ -155,10 +167,9 @@ async def launch_bot():
             #res = await st.session_state.agent.astream_chat(st.session_state.prompt)
             #response = ''.join([token async for token in res.async_response_gen()])
             #res = escape_dollars_outside_latex(response)
-            
+
             message = {"role": "assistant", "content": res, "avatar": '🤖'}
             st.session_state.messages.append(message)
-            st.markdown(res)
 
         send_amplitude_data(
             user_query=st.session_state.messages[-2]["content"],