graph/chains/retrieval_grader.py

from langchain_deepseek import ChatDeepSeek
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

llm = ChatDeepSeek(model="deepseek-chat", temperature=0)

class GradeDocuments(BaseModel):
    """Binary score for relevance check on retrieved documents."""

    binary_score: str = Field(
        description="Documents are relevant to the question, 'yes' or 'no'"
    )

structured_llm_grader = llm.with_structured_output(GradeDocuments)

system_prompt = """
You are a grader assessing whether an LLM generation is grounded in /supported by a set of retrieved facts.\n
If the document contains keyword or semantic meaning related to question, grade it as relevant.\n
Give a binary score 'yes' or 'no'. 'Yes means that the answer is grounded in / supported by the set of facts.
"""

grade_prompt = ChatPromptTemplate.from_messages(
    [
        ('system', system_prompt),
        ("human", "Retrieved document: {document} User question: {question}")
    ]
)

retrieval_grader = grade_prompt | structured_llm_grader