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| from langchain_community.embeddings.fastembed import FastEmbedEmbeddings | |
| from langchain_groq import ChatGroq | |
| import os | |
| import json | |
| from typing import List, Dict | |
| class LLMProcessor: | |
| def __init__(self): | |
| """Initialize embedding model and Groq LLM""" | |
| self.api_key = os.getenv("GROQ_API_KEY") | |
| # Use FastEmbed instead of SentenceTransformer | |
| self.embed_model = FastEmbedEmbeddings() | |
| self.llm = ChatGroq( | |
| model_name="mixtral-8x7b-32768", | |
| api_key=self.api_key | |
| ) | |
| def format_context(self, chunks: List[Dict]) -> str: | |
| """Format retrieved chunks into a structured context for the LLM""" | |
| context_parts = [] | |
| for chunk in chunks: | |
| try: | |
| headings = json.loads(chunk['headings']) | |
| if headings: | |
| context_parts.append(f"Section: {' > '.join(headings)}") | |
| except: | |
| pass | |
| if chunk['page']: | |
| context_parts.append(f"Page {chunk['page']}:") | |
| context_parts.append(chunk['text']) | |
| context_parts.append("-" * 40) | |
| return "\n".join(context_parts) | |
| def generate_answer(self, context: str, question: str) -> str: | |
| """Generate answer using structured context""" | |
| prompt = f"""Use the following pieces of information to answer the user's question. | |
| If you don't know the answer, just say that you don't know,if it is out of context say that it is out of context and also try to provide the answer and don't be rude. | |
| Format the answer in multiple short paragraphs, making it readable without horizontal scrolling in markdown. | |
| Context: {context} | |
| Question: {question} | |
| Only return the helpful answer below and nothing else. | |
| Helpful answer: | |
| """ | |
| return self.llm.invoke(prompt) | |