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from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
import os
from dotenv import load_dotenv
load_dotenv()
# load model from HuggingFace
# def load_model(model_name="context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16"):
# ## load model and tokenizer
# # Configure quantization for memory efficiency
# quantization_config = BitsAndBytesConfig(
# load_in_4bit=True,
# bnb_4bit_quant_type="nf4",
# bnb_4bit_use_double_quant=True,
# bnb_4bit_compute_dtype=torch.float16,
# )
#
# tokenizer = AutoTokenizer.from_pretrained(model_name)
# model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config, device_map="auto")
#
# print("✅ Model and tokenizer loaded")
# return model, tokenizer
# -----------------------------
# Prompt Template
# -----------------------------
prompt = PromptTemplate(
input_variables=["context", "question"],
template="""
You are an experienced assistant specializing in question-answering tasks.
Utilize the provided context to respond to the question.
Rules:
- If the question refers to a **specific table**, note that tables may be identified by either:
• Roman numerals (I, II, III, IV, …)
• Arabic numerals (1, 2, 3, 4, …)
- Normalize references (e.g., "Table II" = "Table 2"). Always check both forms when matching.
- Only answer using information contained in that table.
- If the table is not found or the requested information is not in the table, respond with: "I don't know."
- If the question is about a **formula**:
• Extract the formula from the context (in LaTeX).
• Present it in a clean readable way:
- Use a block math display for clarity: $$ ... $$
- Then rewrite it inline in plain text (e.g., f_final^t = β·f_adapter^t + (1 - β)·f_original^t).
• Briefly explain what each symbol means if the context provides that information.
• If the formula is not found, respond with: "I don't know."
- If the question is not about a table or a formula, answer using the context as normal.
- Never provide an answer you are unsure about.
- Keep answers concise, factual, and easy for non-experts to read.
CONTEXT:
{context}
QUESTION: {question}
DETAILED RESPONSE:
""",
input_variable=["context", "question"]
)
#call api groq
llm = ChatGroq(
api_key=os.environ.get("GROQ_API_KEY"),
model="meta-llama/llama-4-scout-17b-16e-instruct",
temperature=0.3,
max_tokens=1024
)
print("✅ Using Groq LLM")
# Function to ask a question
def ask_question(question, context):
final_prompt = prompt.invoke({"context": context, "question": question})
answer = llm.invoke(final_prompt)
return answer.content
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