Production AI agent systems from your architecture

from langgraph.graph import StateGraph, END
from typing import TypedDict

class WorkflowState(TypedDict):
    input: str
    context: list[str]
    result: str | None

def retrieve(state: WorkflowState) -> WorkflowState:
    docs = vector_store.similarity_search(state["input"], k=4)
    return {**state, "context": [d.page_content for d in docs]}

def generate(state: WorkflowState) -> WorkflowState:
    response = llm.invoke([
        SystemMessage(content="Use the context to answer."),
        HumanMessage(content=f"Context: {state['context']}\n\nQ: {state['input']}"),
    ])
    return {**state, "result": response.content}

graph = StateGraph(WorkflowState)
graph.add_node("retrieve", retrieve)
graph.add_node("generate", generate)
graph.add_edge("retrieve", "generate")
graph.add_edge("generate", END)
graph.set_entry_point("retrieve")
app = graph.compile()