ai-agent.py

# consider for agents lab part 1
# ref: https://dev.to/timesurgelabs/how-to-make-an-ai-agent-in-10-minutes-with-langchain-3i2n
# pip install -U duckduckgo-search
# pip install -U langchain-community
import requests
from bs4 import BeautifulSoup
from dotenv import load_dotenv
from langchain.tools import Tool, DuckDuckGoSearchResults
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOllama
from langchain.chains import LLMChain
from langchain.agents import initialize_agent, AgentType

ddg_search = DuckDuckGoSearchResults()

HEADERS = {
    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:90.0) Gecko/20100101 Firefox/90.0'
}

def parse_html(content) -> str:
    soup = BeautifulSoup(content, 'html.parser')
    text_content_with_links = soup.get_text()
    return text_content_with_links

def fetch_web_page(url: str) -> str:
    response = requests.get(url, headers=HEADERS)
    return parse_html(response.content)

web_fetch_tool = Tool.from_function(
    func=fetch_web_page,
    name="WebFetcher",
    description="Fetches the content of a web page"
)

prompt_template = "Summarize the following content: {content}"
llm = ChatOllama(model="llama2")
llm_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(prompt_template)
)

summarize_tool = Tool.from_function(
    func=llm_chain.run,
    name="Summarizer",
    description="Summarizes a web page"
)

tools = [ddg_search, web_fetch_tool, summarize_tool]

agent = initialize_agent(
    tools=tools,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    llm=llm,
    verbose=True
)

prompt = "Research how to use the requests library in Python. Use your tools to search and summarize content into a guide on how to use the requests library."

print(agent.run(prompt))