File size: 2,547 Bytes
ea71a81 4df7450 ea71a81 4df7450 af51700 4df7450 ea71a81 af51700 ea71a81 | 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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from src.retrieve import get_hybrid_retriever
from src import config
SYSTEM_PROMPT = """You are a Clinical Genomic Assistant.
You MUST answer strictly using the provided context.
RULES:
1. Do NOT use external knowledge.
2. If answer is not clearly present, say: "Insufficient evidence."
3. Always prioritize exact values (numbers, percentages, trial IDs, dates).
4. If multiple pieces of context exist, combine them carefully.
5. Be precise and clinical — no vague statements.
When relevant, include:
- Drug name
- Mutation
- Clinical outcome
Context:
{context}
"""
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
def get_rag_chain():
retriever = get_hybrid_retriever()
llm = ChatOpenAI(model=config.LLM_MODEL, temperature=0)
prompt = ChatPromptTemplate.from_messages([
("system", SYSTEM_PROMPT),
("user", "{question}")
])
def chain_with_source(query: str):
docs = retriever.invoke(query)
# LLM based relevance scoring
scored_docs = []
for doc in docs:
score_prompt = f"""
Query: {query}
Document: {doc.page_content}
Score relevance from 1 to 10 (only number).
"""
score = llm.invoke(score_prompt).content.strip()
try:
score = int(score)
except:
score = 5
scored_docs.append((score, doc))
# Sort and keep top 5
docs = [doc for _, doc in sorted(scored_docs, key=lambda x: x[0], reverse=True)[:5]]
context = format_docs(docs)
answer = (prompt | llm | StrOutputParser()).invoke({
"context": context,
"question": query
})
context_data = []
for doc in docs:
page = doc.metadata.get("page", "N/A")
# Metadata key varies by loader- TextLoader uses 'source', PyMuPDFLoader uses 'file_path'
source = doc.metadata.get("source", doc.metadata.get("file_path", "Unknown"))
context_data.append({
"content": doc.page_content,
"metadata": f"Source: {source} | Page: {page}"
})
return {"answer": answer, "contexts": context_data}
return chain_with_source |