How to set up a fully offline RAG implementation using Google Colab with Drive storage?

Other Questions RAG
1

I’m working on building a completely offline retrieval-augmented generation system using Google Colab that processes files from my Google Drive. My goal is to avoid any external API calls to language model services.

I’ve downloaded a language model to my Drive folder and set up a Chroma vector database with my documents. However, I keep running into path-related errors when trying to load the local model.

Here’s my current setup:

from google.colab import drive
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from llama_cpp import Llama

drive.mount('/content/drive')

# Model configuration
gpu_layers = 1
batch_size = 512

# Local model file path
model_file_path = "my_folder/llama-model-7b.Q4_K_M.gguf"

# Set up the Llama model
llm_instance = Llama(
    model_path=model_file_path,
    n_gpu_layers=gpu_layers,
    n_batch=batch_size,
    n_ctx=2048,
    f16_kv=True,
    verbose=True,
)

# Function for handling conversations
def handle_chat(user_input: str, history: list) -> tuple:
    try:
        buffer_memory = ConversationBufferMemory(
            memory_key='chat_history',
            return_messages=False
        )
        
        conversation_chain = ConversationalRetrievalChain.from_llm(
            llm=llm_instance,
            retriever=doc_retriever,
            memory=buffer_memory,
            get_chat_history=lambda h: h,
        )

        response = conversation_chain({'question': user_input, 'chat_history': history})
        history.append((user_input, response['answer']))
        return '', history

    except Exception as error:
        history.append((user_input, str(error)))
        return '', history

I’m getting these errors:

Repo id must be in the form 'repo_name' or 'namespace/repo_name'

and

OSError: Incorrect path_or_model_id. Please provide either the path to a local folder or the repo_id of a model on the Hub.

I also attempted using AutoModelForCausalLM with a repo_type parameter set to “local”, but encountered the same issues. When I switch to downloading models from Hugging Face Hub directly, everything works fine.

Is it actually feasible to run a completely local RAG system within Google Drive storage? What’s the correct way to reference local model files?

2

You’re mixing different model loading approaches. You loaded the GGUF file with llama_cpp.Llama but then tried to plug it into LangChain’s ConversationalRetrievalChain, which expects a different interface. I hit this exact same issue building an offline system last month.

Wrap your Llama instance properly for LangChain: python from langchain.llms import LlamaCpp # Use LangChain's wrapper instead of direct llama_cpp llm_instance = LlamaCpp( model_path="/content/drive/MyDrive/my_folder/llama-model-7b.Q4_K_M.gguf", n_gpu_layers=1, n_batch=512, n_ctx=2048, f16_kv=True, verbose=True ) This keeps your offline setup but integrates properly with ConversationalRetrievalChain. You still need the full mounted Drive path, but using LangChain’s LlamaCpp wrapper kills those repo_id errors you’re seeing.

3

Your path setup is wrong. When you mount Google Drive in Colab, you need the full mounted path, not just the folder structure.

Change this:

model_file_path = "my_folder/llama-model-7b.Q4_K_M.gguf"

To this:

model_file_path = "/content/drive/MyDrive/my_folder/llama-model-7b.Q4_K_M.gguf"

I hit this exact problem last year on a client RAG project. The error messages are confusing because the library assumes you’re referencing a Hugging Face repo when it can’t find your local file.

Add a quick path check before loading:

import os
if os.path.exists(model_file_path):
    print(f"Model file found at: {model_file_path}")
else:
    print(f"Model file not found. Check your path.")

Make sure your GGUF file is actually in that Drive folder and the filename matches exactly. Case sensitivity matters.

For offline setups like this, here’s a helpful walkthrough covering the full pipeline:

One more thing - if you’re still getting errors after fixing the path, try loading the model with an absolute path first, then tackle the LangChain integration. That’ll help you figure out if it’s a path issue or library compatibility problem.

4

yeah, i faced this too. first, make sure ur model file is fully uploaded to Drive - huge GGUF files sometimes mess up halfway. also, check the file size against the original download to ensure it didn’t get corrupted or anything.