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LangChain-Free RAG Chatbot: Budget 2024 (Part II — Google Colab)

Running LLMs locally is powerful, but what if you want to leverage cloud GPU without paying cloud bills? Google Colab offers free GPU access, and it's a perfect environment for building and testing AI applications. This tutorial takes the framework-free RAG approach from Part I and adapts it to run on Colab's free GPU.

The Problem

Part I showed you how to build a custom RAG chatbot on your local CPU. But not everyone has powerful hardware at home. GPUs accelerate inference significantly, and most developers don't have high-end GPUs locally. Cloud GPU solutions are expensive—AWS GPU instances can produce surprise bills quickly.

Enter Google Colab: free GPU access, pre-installed Python and libraries, and a notebook interface perfect for development and experimentation.

The Difference from Part I

Everything from Part I (document loading, chunking, embeddings, FAISS) remains the same. The key difference: we leverage Colab's free GPU to run the Llamafile LLM faster, making inference snappier and enabling larger models.

  • Same RAG Pipeline: Identical approach to chunking, embeddings, and retrieval
  • Cloud GPU: Colab's T4 GPU (free tier) provides ~5-10x faster inference than CPU
  • Hassle-Free Setup: No driver installation, no CUDA configuration—Colab handles it
  • Notebook-Friendly: Execute code cells interactively, experiment easily

Key Differences from Part I

Model Selection: Part I uses smaller models optimized for CPU (TinyLlama). Part II can use larger, more capable models (Mistral-7B) thanks to GPU availability.

Llamafile Execution: Part I runs Llamafile as a server on CPU. Part II uses subprocess to run Llamafile during inference only, leveraging GPU when available.

Notebook Environment: Google Colab notebooks allow interactive development, easy experimentation, and built-in visualization.

Prerequisites

  • Google Account (free)
  • Access to Google Colab (free, at colab.google.com)
  • Basic Python knowledge
  • Familiarity with Part I (optional but recommended)

Setup Steps

  1. Download Llamafile and Models — Google Colab cells handle this
  2. Install Dependencies — Standard pip in Colab environment
  3. Load and Process Your Document — Same code as Part I
  4. Create Embeddings & Vector Store — Identical to Part I
  5. Run Llamafile with GPU — Subprocess-based execution leveraging GPU
  6. Chat with Your Bot — Interactive inference in notebook

Example Workflow

# In Colab notebook cells:

# 1. Download models (runs once)
!wget -O llamafile https://github.com/Mozilla-Ocho/llamafile/releases/download/0.8.12/llamafile-0.8.12
!wget -O mistral-7b-instruct-q5.gguf https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q5_0.gguf

# 2. Load and index your document
chunks = ChunkManager(docs=budget_doc).chunk_by_words_with_overlap(250, 100)
faiss_index = FAISS()
faiss_index.fit(chunks)

# 3. Query and infer
query = "What are the key budget allocations for education?"
similar_chunks = faiss_index.search(query, k=10)
answer = run_llamafile_subprocess("llamafile", "mistral-7b-instruct-q5.gguf", 4096, 9999, prompt)
print(answer)

Why Colab for Experimentation

  • Zero Setup Cost: No local GPU, no cloud subscription needed
  • Instant GPU Access: T4 GPU available in free tier (with limitations)
  • Reproducible Notebooks: Share your work easily with others
  • Data Integration: Easy integration with Google Drive and datasets
  • Free CUDA: All GPU drivers and CUDA pre-installed

Performance Gains

On Colab T4 GPU vs. Local CPU: - Inference Speed: ~5-10x faster depending on model size - Larger Models: Can run Mistral-7B or Mixtral vs. TinyLlama - Batch Processing: Faster embedding generation for larger documents

Limitations & Considerations

  • Session Timeouts: Colab sessions end after ~12 hours; save your work
  • Limited GPU Hours: Free tier has usage limits (typically 100 GPU hours/week)
  • Disk Space: Colab provides ~110GB, but models take space; clean up after
  • No Persistent Storage: Save results to Google Drive for persistence

Moving Beyond Colab

Once you've prototyped on Colab, you can: - Deploy to cloud servers (AWS EC2 with GPU, Google Cloud, Azure) - Run locally on your own GPU (RTX 4090, etc.) - Package as a Docker container for portability - Create a web API using FastAPI or Flask

Resources

Full Tutorial: Chat with India's Budget 2024 (Part II): Without LangChain on Free Google Colab GPU

Code & Notebooks: AIMP Labs GitHub Repository

Foundation: New to this approach? Start with Part I for the core RAG concepts.

Next Steps: Ready to deploy? Consider containerizing this chatbot or converting it to a web service.