Agents in LangChain (Advanced)

from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import SerpAPIWrapper

# Initialize LLM
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Create search tool
search = SerpAPIWrapper()
tools = [search]

# Create ReAct agent
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.REACT_DOCSTORE,
    verbose=True
)

response = agent.invoke({"input": "What is the latest news about electric vehicles in India?"})
print(response["output"])

from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import SerpAPIWrapper, PythonREPLTool

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

search = SerpAPIWrapper()
python_tool = PythonREPLTool()
tools = [
    Tool(name="Search", func=search.run,
         description="Search the internet for information"),
    Tool(name="PythonREPL", func=python_tool.run,
         description="Run Python code for calculation or analysis")
]
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
response = agent.invoke({"input": "Search the population of Delhi and calculate 5% of that number using Python."})
print(response["output"])

from langchain.tools import tool

@tool
def get_employee_details(emp_id: str) -> str:
    """Get employee details from internal system."""
    # Your business logic here
    return f"Employee {emp_id} - Name: Rahul, Dept: IT"

# Use this tool with an agent
# tools = [get_employee_details]
# agent = initialize_agent(tools, llm, agent=..., verbose=True)