1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
|
from edges.grade_documents import grade_documents
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from nodes.generate_answer import generate_answer
# Try relative imports first (works when imported as module)
from nodes.generate_query_or_respond import generate_query_or_respond
from nodes.rewrite_question import rewrite_question
from tools.retriever_tool import retriever_tool
from azure.ai.agentserver.langgraph import from_langgraph
workflow = StateGraph(MessagesState)
# Define the nodes we will cycle between
workflow.add_node(generate_query_or_respond)
workflow.add_node("retrieve", ToolNode([retriever_tool]))
workflow.add_node(rewrite_question)
workflow.add_node(generate_answer)
workflow.add_edge(START, "generate_query_or_respond")
# Decide whether to retrieve
workflow.add_conditional_edges(
"generate_query_or_respond",
# Assess LLM decision (call `retriever_tool` tool or respond to the user)
tools_condition,
{
# Translate the condition outputs to nodes in our graph
"tools": "retrieve",
END: END,
},
)
# Edges taken after the `action` node is called.
workflow.add_conditional_edges(
"retrieve",
# Assess agent decision
grade_documents,
)
workflow.add_edge("generate_answer", END)
workflow.add_edge("rewrite_question", "generate_query_or_respond")
# Compile
graph = workflow.compile()
if __name__ == "__main__":
from_langgraph(graph).run()
|
