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import os
from dotenv import load_dotenv
from typing import Literal, List, Dict, TypedDict
from langchain_groq import ChatGroq
from pydantic import BaseModel, Field
from langsmith import traceable
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display
load_dotenv()
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
llm = ChatGroq(model="qwen-2.5-32b")
# Graph state
class State(TypedDict):
blog: str
topic: str
feedback: str
good_or_revise: str
class Feedback(BaseModel):
grade: Literal["good", "needs revision"] = Field(
description="Decide if the blog is entertaining, concise with maxiumum of 400 characters, with subtitles and a conclusion or needs revision.",
)
feedback: str = Field(
description="If the blog is not good, provide feedback on how to improve it.",
)
evaluator = llm.with_structured_output(Feedback)
# Nodes
@traceable
def llm_call_generator(state: State):
"""LLM generates a blog"""
if state.get("feedback"):
msg = llm.invoke(
f"Write a blog about {state['topic']} but take into account the feedback: {state['feedback']}"
)
else:
msg = llm.invoke(f"Write a blog about {state['topic']}")
# Debugging print statement
print("Generated blog content:", msg.content)
return {"blog": msg.content} # Ensure this key is returned!
@traceable
def llm_call_evaluator(state: State):
"""LLM evaluates the blog"""
grade = evaluator.invoke(f"Grade the blog {state['blog']}")
return {"good_or_revise": grade.grade, "feedback": grade.feedback}
@traceable
def route_blog(state: State):
"""Route back to blog generator or end based upon feedback from evaluator"""
if state["good_or_revise"] == "good":
return "Accepted"
elif state["good_or_revise"] == "needs revision":
return "llm_call_generator"
# Build workflow
optimizer_builder = StateGraph(State)
# Add the nodes
optimizer_builder.add_node("llm_call_generator", llm_call_generator)
optimizer_builder.add_node("llm_call_evaluator", llm_call_evaluator)
# Add edges to connect nodes
optimizer_builder.add_edge(START, "llm_call_generator")
optimizer_builder.add_edge("llm_call_generator", "llm_call_evaluator")
optimizer_builder.add_conditional_edges(
"llm_call_evaluator",
route_blog,
{
"Accepted": END,
"llm_call_generator": "llm_call_generator",
},
)
# Compile the workflow
optimizer_workflow = optimizer_builder.compile()
# Show the workflow
display(Image(optimizer_workflow.get_graph().draw_mermaid_png()))
# Invoke
state = optimizer_workflow.invoke({"topic": "Vibe Coding"})
print(state["blog"])
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