app.py

from omegaconf import OmegaConf
import streamlit as st
import os
from PIL import Image
import sys
import datetime
import requests
from dotenv import load_dotenv
from typing import Tuple
from bs4 import BeautifulSoup

from pydantic import Field, BaseModel
from vectara_agent.agent import Agent, AgentStatusType
from vectara_agent.tools import ToolsFactory

initial_prompt = "How can I help you today?"

load_dotenv(override=True)

get_headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:98.0) Gecko/20100101 Firefox/98.0",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.5",
    "Accept-Encoding": "gzip, deflate",
    "Connection": "keep-alive",
}

def create_tools(cfg):    
    
    class QueryHackerNews(BaseModel):
        query: str = Field(..., description="The user query.")

    tools_factory = ToolsFactory(vectara_api_key=cfg.api_key, 
                                 vectara_customer_id=cfg.customer_id, 
                                 vectara_corpus_id=cfg.corpus_id)
    ask_hackernews_semantic = tools_factory.create_rag_tool(
        tool_name = "ask_hackernews_semantic",
        tool_description = """
        Responds to query based on information in hacker news from the last 6 months.
        Performs a semantic search to find relevant information.
        Use this tool to perform pure semantic search.
        """,
        tool_args_schema = QueryHackerNews,
        reranker = "multilingual_reranker_v1", rerank_k = 100, 
        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.0,
        summary_num_results = 10,
        vectara_summarizer = 'vectara-summary-ext-24-05-med-omni',
        include_citations = True,
    )

    ask_hackernews_hybrid = tools_factory.create_rag_tool(
        tool_name = "ask_hackernews_keyword",
        tool_description = """
        Responds to query based on information in hacker news from the last 6 months
        performs a hybrid search (both semantic and keyword) to find relevant information.
        Use this tool when some amount of keyword search is expected to work better than semantic search,
        For example, when you are looking for specific keywords or use rare words in the query.
        """,
        tool_args_schema = QueryHackerNews,
        reranker = "multilingual_reranker_v1", rerank_k = 100, 
        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.1,
        summary_num_results = 10,
        vectara_summarizer = 'vectara-summary-ext-24-05-med-omni',
        include_citations = True,
    )

    def get_top_stories(
            n_stories: int = Field(default=10, description="The number of top stories to return.")
    ) -> list[str]:
        """
        Get the top stories from hacker news.
        Returns a list of story IDS for the top stories right now
        """
        db_url = 'https://hacker-news.firebaseio.com/v0/'
        top_stories = requests.get(f"{db_url}topstories.json").json()
        return top_stories[:n_stories]

    def get_show_stories(
            n_stories: int = Field(default=10, description="The number of top SHOW HN stories to return.")
    ) -> list[str]:
        """
        Get the top SHOW HN stories from hacker news.
        Returns a list of story IDS for the top SHOW HN stories right now
        """
        db_url = 'https://hacker-news.firebaseio.com/v0/'
        top_stories = requests.get(f"{db_url}showstories.json").json()
        return top_stories[:n_stories]

    def get_ask_stories(
            n_stories: int = Field(default=10, description="The number of top ASK HN stories to return.")
    ) -> list[str]:
        """
        Get the top ASK HN stories from hacker news.
        Returns a list of story IDS for the top ASK HN stories right now
        """
        db_url = 'https://hacker-news.firebaseio.com/v0/'
        top_stories = requests.get(f"{db_url}askstories.json").json()
        return top_stories[:n_stories]

    def get_story_details(
            story_id: str = Field(..., description="The story ID.")
    ) -> Tuple[str, str]:
        """
        Get the title of a story from hacker news.
        Returns:
         - The title of the story (str)
         - The main URL of the story (str)
         - The external link pointed to in the story (str)
        """
        db_url = 'https://hacker-news.firebaseio.com/v0/'
        story = requests.get(f"{db_url}item/{story_id}.json").json()
        story_url = f'https://news.ycombinator.com/item?id={story_id}'
        return story['title'], story_url, story['url'], 

    def get_story_text(
            story_id: str = Field(..., description="The story ID.")
    ) -> str:
        """
        Get the text of the story from hacker news (original text + all comments)
        Returns the extracted text of the story as a string.
        """
        url = f'https://news.ycombinator.com/item?id={story_id}'
        html = requests.get(url, headers=get_headers).text
        soup = BeautifulSoup(html, 'html5lib')
        for element in soup.find_all(['script', 'style']):
            element.decompose()
        text = soup.get_text(" ", strip=True).replace('\n', ' ')
        return text


    return (
        tools_factory.get_tools(
                [
                    get_top_stories, 
                    get_show_stories,
                    get_ask_stories,
                    get_story_details,
                    get_story_text,
                ]
            ) +
        tools_factory.standard_tools() + 
        tools_factory.guardrail_tools() +
        [ask_hackernews_semantic, ask_hackernews_hybrid]
    )

def initialize_agent(_cfg):
    date = datetime.datetime.now().strftime("%Y-%m-%d")
    bot_instructions = f"""
    - You are a helpful assistant, answering user questions about content from hacker news. 
    - Today's date is {date}.
    - Never discuss politics, and always respond politely.
    - Use tools when available instead of depending on your own knowledge. 
    - If a tool provides citations, you can include them in your response to provide more context.
    - If a tool cannot respond properly, retry with a rephrased question or ask the user for more information.
    - Be very careful not to report results you are not confident about.
    """

    def update_func(status_type: AgentStatusType, msg: str):
        output = f"{status_type.value} - {msg}"
        st.session_state.log_messages.append(output)

    agent = Agent(
        tools=create_tools(_cfg),
        topic="hacker news",
        custom_instructions=bot_instructions,
        update_func=update_func
    )
    return agent

def launch_bot():
    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(cfg)
        st.session_state.log_messages = []
        st.session_state.show_logs = False

    st.set_page_config(page_title="Hacker News Bot", 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 hacker news assistant demo.\n\n\n")

        st.markdown("\n\n")
        bc1, bc2 = st.columns([1, 1])
        with bc1:
            if st.button('Start Over'):
                reset()
        with bc2:
            if st.button('Show Logs'):
                st.session_state.show_logs = not st.session_state.show_logs

        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 RAG 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):
                res = st.session_state.agent.chat(prompt)
                res = res.replace('$', '\\$')  # escape dollar sign for markdown
            message = {"role": "assistant", "content": res, "avatar": '🤖'}
            st.session_state.messages.append(message)
            st.rerun()

    # Display log messages in an expander
    if st.session_state.show_logs:
        with st.expander("Agent Log Messages", expanded=True):
            for msg in st.session_state.log_messages:
                st.write(msg)
            if st.button('Close Logs'):
                st.session_state.show_logs = False
                st.rerun()

    sys.stdout.flush()

if __name__ == "__main__":
    launch_bot()