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"""
File call_llm.py - LLM node với function calling
"""
import json
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage
from langchain_core.tools import tool
from src.state.graph_state import TransportationState
from src.config.setting import settings
@tool
def predict_transportation_mode(country: str, pack_price: float, project_code: str, vendor: str) -> dict:
"""Dự đoán phương thức vận chuyển tối ưu"""
from src.app.schema.transportation import TransportationRequest
from src.app.api.predict import predict_transportation
request = TransportationRequest(
country=country,
pack_price=pack_price,
project_code=project_code,
vendor=vendor
)
response = predict_transportation(request)
return response.dict()
@tool
def get_transportation_options() -> dict:
"""Lấy danh sách options vận chuyển"""
from src.app.api.predict import get_transportation_options as get_options
return get_options()
class CallLLMNode:
def __init__(self):
self.llm = ChatGoogleGenerativeAI(
model="gemini-1.5-pro",
google_api_key=settings.GEMINI_API_KEY
)
self.llm_with_tools = self.llm.bind_tools([
predict_transportation_mode,
get_transportation_options
])
def _create_system_message(self) -> str:
"""System prompt cho transportation AI"""
return """Bạn là chuyên gia logistics và vận chuyển. Bạn có 2 functions:
1. predict_transportation_mode(country, pack_price, project_code, vendor)
- Dự đoán phương thức vận chuyển tối ưu
- Cần đủ 4 tham số
2. get_transportation_options()
- Lấy danh sách options có sẵn
- Không cần tham số
QUY TẮC:
- "dự đoán", "tối ưu", "nên chọn" → gọi predict_transportation_mode
- "options", "danh sách", "có gì" → gọi get_transportation_options
- Thiếu thông tin → hỏi ngược ngay
- Trả lời luật/quy định từ kiến thức"""
def __call__(self, state: TransportationState) -> TransportationState:
try:
messages = [
SystemMessage(content=self._create_system_message()),
HumanMessage(content=state["user_message"])
]
response = self.llm_with_tools.invoke(messages)
if response.tool_calls:
print(f"✅ Function calls detected: {len(response.tool_calls)}")
# Execute tools và lưu results
tool_messages = []
for tool_call in response.tool_calls:
func_name = tool_call["name"]
args = tool_call["args"]
print(f"🔧 Calling {func_name} with args: {args}")
if func_name == "predict_transportation_mode":
result = predict_transportation_mode.invoke(args)
elif func_name == "get_transportation_options":
result = get_transportation_options.invoke({})
else:
result = {"error": f"Unknown function: {func_name}"}
print(f"📊 Result: {result}")
# Save function call info
state["function_calls_made"].append({
"function_name": func_name,
"arguments": args,
"result": result
})
tool_messages.append(ToolMessage(
content=json.dumps(result),
tool_call_id=tool_call["id"]
))
# Get final response
final_messages = messages + [response] + tool_messages
final_response = self.llm.invoke(final_messages)
state["ai_response"] = final_response.content
print("✅ Function calling completed successfully")
else:
state["ai_response"] = response.content
print("ℹ️ No function calls needed")
state["current_step"] = "completed"
return state
except Exception as e:
print(f"❌ Error in LLM call: {e}")
state["error_message"] = f"LLM Error: {str(e)}"
state["current_step"] = "error"
return state
def create_call_llm_node() -> CallLLMNode:
"""Factory function to create CallLLMNode"""
return CallLLMNode()
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