from omegaconf import OmegaConf import streamlit as st import os from PIL import Image import re import sys import datetime from pydantic import Field, BaseModel from vectara_agent.agent import Agent, AgentType, AgentStatusType from vectara_agent.tools import ToolsFactory tickers = { "AAPL": "Apple Computer", "GOOG": "Google", "AMZN": "Amazon", "SNOW": "Snowflake", "TEAM": "Atlassian", "TSLA": "Tesla", "NVDA": "Nvidia", "MSFT": "Microsoft", "AMD": "Advanced Micro Devices", "INTC": "Intel", "NFLX": "Netflix", } years = [2020, 2021, 2022, 2023, 2024] initial_prompt = "How can I help you today?" def create_tools(cfg): def get_company_info() -> list[str]: """ Returns a dictionary of companies you can query about their financial reports. 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 def get_valid_years() -> list[str]: """ Returns a list of the years for which financial reports are available. """ return years class QueryFinancialReportsArgs(BaseModel): query: str = Field(..., description="The user query.") year: int = Field(..., description=f"The year. an integer between {min(years)} and {max(years)}.") ticker: str = Field(..., description=f"The company ticker. Must be a valid ticket symbol from the list {tickers.keys()}.") tools_factory = ToolsFactory(vectara_api_key=cfg.api_key, vectara_customer_id=cfg.customer_id, vectara_corpus_id=cfg.corpus_id) query_financial_reports = tools_factory.create_rag_tool( tool_name = "query_financial_reports", tool_description = """ Given a company name and year, returns a response (str) to a user query about the company's financial reports for that year. make sure to provide the a valid company ticker and year. """, tool_args_schema = QueryFinancialReportsArgs, tool_filter_template = "doc.year = {year} and doc.ticker = '{ticker}'", reranker = "multilingual_reranker_v1", rerank_k = 100, n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.01, summary_num_results = 10, vectara_summarizer = 'vectara-summary-ext-24-05-med-omni', ) return (tools_factory.get_tools( [ get_company_info, get_valid_years, ] ) + tools_factory.standard_tools() + tools_factory.financial_tools() + tools_factory.guardrail_tools() + [query_financial_reports] ) def initialize_agent(agent_type: AgentType, _cfg): date = datetime.datetime.now().strftime("%Y-%m-%d") financial_bot_instructions = f""" - You are a helpful financial assistant, with expertise in finanal reporting, in conversation with a user. - Today's date is {date}. - Report in a concise and clear manner, and provide the most relevant information to the user. - Respond in a concise format by using appropriate units of measure (e.g., K for thousands, M for millions, B for billions). - Use tools when available instead of depending on your own knowledge. - When querying a tool for a numeric value or KPI, use a concise and non-ambiguous description of what you are looking for. - If you calculate a metric, make sure you have all the necessary information to complete the calculation. Don't guess. - Be very careful not to report results you are not confident about. - Always use any guardrails tools to ensure your responses are polite and do not discuss politices. """ def update_func(status_type: AgentStatusType, msg: str): output = f"{status_type.value} - {msg}" st.session_state.thinking_placeholder.text(output) agent = Agent( agent_type=agent_type, tools=create_tools(_cfg), topic="10-K annual financial reports", custom_instructions=financial_bot_instructions, update_func=update_func ) return agent def launch_bot(agent_type: AgentType): def reset(): cfg = st.session_state.cfg st.session_state.messages = [{"role": "assistant", "content": initial_prompt, "avatar": "🦖"}] st.session_state.thinking_message = "Agent at work..." st.session_state.agent = initialize_agent(agent_type, cfg) st.set_page_config(page_title="Financial Assistant", layout="wide") if 'cfg' not in st.session_state: cfg = OmegaConf.create({ 'customer_id': str(os.environ['VECTARA_CUSTOMER_ID']), 'corpus_id': str(os.environ['VECTARA_CORPUS_ID']), 'api_key': str(os.environ['VECTARA_API_KEY']), }) st.session_state.cfg = cfg reset() cfg = st.session_state.cfg # left side content with st.sidebar: image = Image.open('Vectara-logo.png') st.image(image, width=250) st.markdown("## Welcome to the financial assistant demo.\n\n\n") companies = ", ".join(tickers.values()) st.markdown( f"This assistant can help you with any questions about the financials of several companies:\n\n **{companies}**.\n" ) st.markdown("\n\n") if st.button('Start Over'): reset() st.markdown("---") st.markdown( "## How this works?\n" "This app was built with [Vectara](https://vectara.com).\n\n" "It demonstrates the use of Agentic Chat functionality with Vectara" ) st.markdown("---") if "messages" not in st.session_state.keys(): reset() # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"], avatar=message["avatar"]): st.write(message["content"]) # User-provided prompt if prompt := st.chat_input(): st.session_state.messages.append({"role": "user", "content": prompt, "avatar": '🧑‍💻'}) with st.chat_message("user", avatar='🧑‍💻'): print(f"Starting new question: {prompt}\n") st.write(prompt) # Generate a new response if last message is not from assistant if st.session_state.messages[-1]["role"] != "assistant": with st.chat_message("assistant", avatar='🤖'): with st.spinner(st.session_state.thinking_message): st.session_state.thinking_placeholder = st.empty() res = st.session_state.agent.chat(prompt) cleaned = re.sub(r'\[\d+\]', '', res).replace('$', '\\$') message = {"role": "assistant", "content": cleaned, "avatar": '🤖'} st.session_state.messages.append(message) st.session_state.thinking_placeholder.empty() st.rerun() sys.stdout.flush() if __name__ == "__main__": launch_bot(agent_type=AgentType.OPENAI)