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1d32142 | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from typing import Literal
from pydantic import BaseModel, Field
from langgraph.store.base import BaseStore
from langchain_core.runnables import RunnableConfig
class MessageClassifier(BaseModel):
"""Classification result for routing messages."""
message_type: Literal["emotional", "logical", "charity_search", "donor_search", "volunteer_search"] = Field(
...,
description="Classify message for routing to appropriate agent."
)
async def classify_message(state: dict, config: RunnableConfig, *, store: BaseStore, llm) -> dict:
"""Classify user message to route to appropriate agent.
Args:
state: Graph state containing messages
config: Runtime config with user_id, thread_id
store: Memory store (required by graph but not used here)
llm: Language model instance
Returns:
Dict with message_type for routing
"""
last_message = state["messages"][-1]
classifier_llm = llm.with_structured_output(MessageClassifier)
result = classifier_llm.invoke([
{
"role": "system",
"content": """Classify the user message into one of these categories:
Respond ONLY with valid JSON in this exact format:
{"message_type": "TYPE"}
Where TYPE is one of:
- 'emotional': Message requires emotional support, therapy, deals with feelings, or personal problems
- 'donor_search': Looking for donors in the database, finding people who donate, matching donors by criteria
- 'volunteer_search': Looking for volunteers in the database, finding people who volunteer, matching volunteers
- 'charity_search': Asking about charity organizations, nonprofits, wanting to research specific charities
- 'logical': Facts, information, logical analysis, practical solutions (default for general queries)
Examples:
- "Find donors interested in education in Singapore" → donor_search
- "Show me volunteers with tech skills" → volunteer_search
- "Tell me about Red Cross charity" → charity_search
- "I'm feeling sad today" → emotional
- "What is the capital of France?" → logical"""
},
{
"role": "user",
"content": last_message.content
}
])
return {"message_type": result.message_type}
def create_classifier(llm):
"""Factory to create classifier function with LLM bound.
Usage:
llm = ChatOllama(model="gpt-oss:120b-cloud")
classify = create_classifier(llm)
graph_builder.add_node("classifier", classify)
"""
async def classifier_node(state: dict, config: RunnableConfig, *, store: BaseStore):
return await classify_message(state, config, store=store, llm=llm)
return classifier_node
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