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| from langchain_groq import ChatGroq | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain_core.documents import Document | |
| from dotenv import load_dotenv | |
| import os | |
| from src.config import GROQ_API_KEY, LLM_MODEL, LLM_TEMPERATURE, RETRIEVAL_K | |
| load_dotenv() | |
| def ensure_groq_key(): | |
| if not GROQ_API_KEY: | |
| raise RuntimeError("GROQ_API_KEY is not configured. Set it in .env or Hugging Face secrets.") | |
| def format_docs(docs: list[Document]) -> str: return "\n\n".join(doc.page_content for doc in docs) | |
| def build_qa_chain(vectorstore): | |
| ensure_groq_key() | |
| llm = ChatGroq( | |
| model=LLM_MODEL, | |
| api_key=GROQ_API_KEY, | |
| temperature=LLM_TEMPERATURE | |
| ) | |
| prompt = ChatPromptTemplate.from_template("""You are a research assistant. Use the context below to answer the question accurately and concisely. | |
| If the answer is not in the context, say "I don't have enough information in the provided document." | |
| Context: {context} | |
| Question: {question} | |
| Answer:""") | |
| retriever = vectorstore.as_retriever(search_kwargs={"k": RETRIEVAL_K}) | |
| chain = ( | |
| { | |
| "context": lambda x: format_docs(retriever.invoke(x["question"])), | |
| "question": lambda x: x["question"] | |
| } | |
| | prompt | |
| | llm | |
| | StrOutputParser() | |
| ) | |
| return chain | |
| def get_answer_with_sources(vectorstore, question: str) -> tuple[str, list]: | |
| ensure_groq_key() | |
| retriever = vectorstore.as_retriever(search_kwargs={"k": RETRIEVAL_K}) | |
| docs = retriever.invoke(question) | |
| context = format_docs(docs) | |
| llm = ChatGroq( | |
| model=LLM_MODEL, | |
| api_key=GROQ_API_KEY, | |
| temperature=LLM_TEMPERATURE | |
| ) | |
| prompt = ChatPromptTemplate.from_template("""You are a research assistant. Use the context below to answer the question accurately and concisely. | |
| If the answer is not in the context, say "I don't have enough information in the provided document." | |
| Context: {context} | |
| Question: {question} | |
| Answer:""") | |
| chain = prompt | llm | StrOutputParser() | |
| answer = chain.invoke({"context": context, "question": question}) | |
| sources = [doc.page_content[:200] for doc in docs] | |
| return answer, sources |