rag_backend.py

import os
from llama_cpp import Llama
from llama_index.core import VectorStoreIndex, Settings, SimpleDirectoryReader, load_index_from_storage, StorageContext
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.huggingface import HuggingFaceEmbedding


Settings.llm = None

class Backend:
    def __init__(self):
        self.llm = None
        self.llm_model = None
        self.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
        self.PERSIST_DIR = "./db"
        os.makedirs(self.PERSIST_DIR, exist_ok=True)
        
    def load_model(self, model_path):
        self.llm = Llama(
            model_path=f"models/{model_path}",
            flash_attn=True,
            n_gpu_layers=81,
            n_batch=1024,
            n_ctx=8192,
        )
        self.llm_model = model_path
        
        
    def create_index_for_query_engine(self, matched_path):
        print(f"Attempting to read files from: {matched_path}")
        
        documents = []
        for root, dirs, files in os.walk(matched_path):
            for file in files:
                file_path = os.path.join(root, file)
                try:
                    with open(file_path, 'r', encoding='utf-8') as f:
                        content = f.read()
                        doc = Document(text=content, metadata={"source": file_path})
                        documents.append(doc)
                    print(f"Successfully read file: {file_path}")
                except Exception as e:
                    print(f"Error reading file {file_path}: {str(e)}")
        
        print(f"Number of documents loaded: {len(documents)}")

        storage_context = StorageContext.from_defaults()
        nodes = SentenceSplitter(chunk_size=256, chunk_overlap=64, paragraph_separator="\n\n").get_nodes_from_documents(documents)
        index = VectorStoreIndex(nodes, embed_model=self.embed_model)
        query_engine = index.as_query_engine(
                        similarity_top_k=4, response_mode="tree_summarize"
                        )
        index.storage_context.persist(persist_dir=self.PERSIST_DIR)
        
        return query_engine


    # here we're leveraging an already constructed and stored FAISS index
    def load_index_for_query_engine(self):
        storage_context = StorageContext.from_defaults(persist_dir=self.PERSIST_DIR)
        index = load_index_from_storage(storage_context, embed_model=self.embed_model)
        
        query_engine = index.as_query_engine(
                        similarity_top_k=4, response_mode="tree_summarize"
                        )
        return query_engine


    def generate_prompt(self, query_engine, message):
        relevant_chunks = query_engine.retrieve(message)
        print(f"Found: {len(relevant_chunks)} relevant chunks")
        
        prompt = "Considera questo come tua base di conoscenza personale:\n==========Conoscenza===========\n"
        for idx, chunk in enumerate(relevant_chunks):
            print(f"{idx + 1}) {chunk.text[:64]}...")
            prompt += chunk.text + "\n\n"   
        prompt += "\n======================\nDomanda: " + message
        return prompt