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02d44c3 | 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 | from langchain_core.prompts import ChatPromptTemplate
from llm_factory import get_llm
from langchain_core.output_parsers import JsonOutputParser
from models import Patient
import os
# Initialize LLM
llm = get_llm(model_type="text", temperature=0.1)
# Parser
parser = JsonOutputParser(pydantic_object=Patient)
# Prompt
system_prompt = """You are a medical receptionist agent. Your goal is to extract patient information from a natural language introduction.
Extract the following fields: name, age, gender, and any mentioned medical history.
If a field is missing, leave it as null or infer it if obvious.
Return the result as a JSON object matching the Patient schema.
"""
prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
("user", "{input}")
])
chain = prompt | llm | parser
async def run_intake_agent(user_input: str) -> Patient:
try:
result = await chain.ainvoke({"input": user_input})
return Patient(**result)
except Exception as e:
# Fallback or error handling
print(f"Error in intake agent: {e}")
return Patient(name="Unknown", age=0, gender="Unknown")
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