Langchain Autonomous Agent Searches Google And Writes Blog Post

blog-bot.py

import argparse
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.tools import Tool
import os
from dotenv import load_dotenv
from langchain.llms.openai import OpenAI
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain import hub
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory

# Set up 2 agents, 1 to research with google, 1 to write blog post

# Set up argument parser
parser = argparse.ArgumentParser(
    description='Run a query using Self Ask With Search agent and generate a blog post.')
parser.add_argument('-q', '--query', required=True, help='The topic to research.')
args = parser.parse_args()

# Load the .env file
load_dotenv()

# Define a function to check if the output is complete


def is_output_complete(output):
    return 'Finished Chain' in output or len(output) >= 1500


# Retrieve the GOOGLE_CSE_ID and GOOGLE_API_KEY environment variables
GOOGLE_CSE_ID = os.getenv('GOOGLE_CSE_ID')
GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')

# Initialize the LLM with OpenAI
research_llm = OpenAI(temperature=0, verbose=True)

# Initialize the Google Search API Wrapper with the necessary keys
search = GoogleSearchAPIWrapper()

# Create a list of Tool objects for the Google Search
tools = [
    Tool(
        name="Google Search",
        description="Useful for research and when you need to ask with search. Search Google for recent results.",
        func=search.run,
    )
]

# Initialize the agent with the tools and LLM
memory = ConversationBufferMemory(
    memory_key="chat_history", return_messages=True)

# Define the first agent to search for information
search_agent_executor = initialize_agent(
    tools,
    research_llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
    memory=memory,
    handle_parsing_errors=True  # Add this line to handle parsing errors

)

# Define the prompt for the second agent to write the blog post
#blog_post_prompt = hub.pull("hwchase17/react-chat-json")
blog_post_chat_model = ChatOpenAI(temperature=0, model="gpt-4", verbose=True)

# Define the second agent to write the blog post
write_blog_agent_executor = initialize_agent(
    tools,
    blog_post_chat_model,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    verbose=True,
    memory=memory,
    handle_parsing_errors=True
)

# Run the first agent to get the search results
search_results = search_agent_executor.invoke({"input": args.query})["output"]

# Prepare the input for the second agent, including the search results as context
input_for_blog_post = f"Write a 1000-word blog post reacting to the following information: {search_results}. When you are done writing the post put Finished Chain. at the end."

# Run the second agent to generate the blog post
blog_post = ""
current_input = input_for_blog_post
while not is_output_complete(blog_post):
    output = write_blog_agent_executor.invoke(
        {"input": current_input})["output"]
    # Append the new output to the blog post
    blog_post += output
    # Check if the output is already complete
    # Check the entire blog post, not just the new output
    if is_output_complete(blog_post):
        break
    #print(blog_post)
    # Update the current_input to include the entire blog post to signal continuation
    current_input = f"Continue writing the 1000-word blog post from the last sentence: {blog_post}. When you are done writing the post put Finished Chain. at the end."

# Print the blog post
print(blog_post)