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195e631 | 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 | # # app/graph/rag_answer_node.py
# from langchain_core.prompts import PromptTemplate
# from app.core.llm_engine import run_llm
# from app.core.prompts.rag_prompt import rag_prompt
# # rag_prompt = PromptTemplate(
# # input_variables=["context", "query"],
# # template=(
# # "You are a document intelligence system.\n"
# # "Answer ONLY using the provided context.\n"
# # "If answer is not present, say: 'Not in document'.\n\n"
# # "Keep response concise:\n"
# # "- Short explanation\n"
# # "- Bullet points if useful\n"
# # "- Max 120 words\n\n"
# # "Avoid repeating the question.\n\n"
# # "Context:\n{context}\n\n"
# # "Question:\n{query}\n\n"
# # "Answer:"
# # )
# # )
# def rag_answer_node(state):
# response = run_llm(rag_prompt, {
# "context": state.get("context", ""),
# "query": state.get("query")
# })
# return {
# **state,
# "final_answer": response
# }
from app.core.llm_engine import llm
from app.core.prompts.rag_prompt import rag_prompt
from langchain_core.output_parsers import StrOutputParser
chain = rag_prompt | llm | StrOutputParser()
def rag_answer_node(state):
response = chain.invoke({
"context": state.get("context", ""),
"query": state.get("query")
})
return {
**state,
"final_answer": response
} |