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import os
from typing import Literal
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from langgraph.graph import MessagesState
from pydantic import BaseModel, Field
GRADE_PROMPT = (
"You are a grader assessing relevance of a retrieved document to a user question. \n "
"Here is the retrieved document: \n\n {context} \n\n"
"Here is the user question: {question} \n"
"If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n"
"Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."
)
# highlight-next-line
class GradeDocuments(BaseModel):
"""Grade documents using a binary score for relevance check."""
binary_score: str = Field(
description="Relevance score: 'yes' if relevant, or 'no' if not relevant"
)
load_dotenv()
deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", "gpt-4o")
grader_model = init_chat_model(f"azure_openai:{deployment_name}")
def grade_documents(
state: MessagesState,
) -> Literal["generate_answer", "rewrite_question"]:
"""Determine whether the retrieved documents are relevant to the question."""
question = state["messages"][0].content
context = state["messages"][-1].content
prompt = GRADE_PROMPT.format(question=question, context=context)
response = (
grader_model
# highlight-next-line
.with_structured_output(GradeDocuments).invoke(
[{"role": "user", "content": prompt}]
)
)
score = response.binary_score
if score == "yes":
return "generate_answer"
else:
return "rewrite_question"
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