Local Gemma 4 with OpenCode & llama.cpp | Build a Local RAG with LangChain | 🔴 Live

2026-04-04 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

This content details a live stream experiment building a local Retrieval Augmented Generation (RAG) application using the Gemma 4 26B parameter model and Open Code. The author sets up a GitHub repository and addresses initial Gemma 4 tokenizer fixes within llama.cpp, running on an M4 GPU with 48GB unified memory. The Gemma 4 model, despite its "effective" smaller size claims (e.g., 2B effective is 4.5B real parameters), requires substantial hardware. Benchmarks on Arena AI show Gemma 4 outperforming larger models like Coin 3.5. The RAG application, built with Streamlit and Ollama, aims to upload PDFs, convert them to markdown, and enable chat with dynamic model selection. Initial challenges included outdated LangChain library versions, model import errors, and RAG retrieval accuracy issues, which were progressively debugged by updating embedding models (e.g., nomic-embed-text, Quantri embeddings) and adjusting chunking strategies.

Key takeaway

For AI Engineers building local RAG applications, be prepared for Gemma 4's substantial hardware requirements, even for quantized versions. Your choice of embedding model significantly impacts retrieval accuracy, so prioritize robust, up-to-date embeddings like Quantri. Expect to iteratively debug library versions and optimize chunking strategies to achieve reliable RAG performance, especially when integrating new models and frameworks.

Key insights

Gemma 4 models, despite effective size claims, require significant hardware for local RAG applications.

Principles

• Effective model sizes can be misleading.
• Embedding model choice critically impacts RAG accuracy.
• Local RAG development requires iterative debugging.

Method

The RAG application development involved using Open Code for scaffolding, Streamlit for UI, Ollama for local LLM inference, PyMuPDF for PDF-to-markdown conversion, and Chroma DB for vector storage, with iterative debugging of LangChain imports and embedding models.

In practice

• Use `brew install llama.cpp head` for latest fixes.
• Update Ollama to version 0.20+ for Gemma 4 support.
• Experiment with chunk size and count for RAG retrieval.

Topics

• Gemma 4 Model
• Local RAG Application
• Open Code
• LangChain
• Llama.cpp

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

Related on AIssential

• Gemma 4 12B MTP Local Test | Coding, OCR, Visual RAG with llama.cpp — The Gemma 4 12B MTP model offers multimodal capabilities and efficiency, nearing larger MoE models, but with performance trade-offs.
• LAI #122: Word Embeddings Started in 1948, Not With Word2Vec — Effective AI deployment requires careful tuning of RAG pipelines, robust AI writing guidelines, and understanding foundational architectural principles.
• Is RAG Dead in 2026? | Build Local RAG from First Principles — RAG remains essential for grounding LLMs with external, specific knowledge to enhance accuracy and reduce hallucinations.
• Gemma 4 + Ollama = FREE Claude Code Setup! — Gemma 4 models offer high intelligence per parameter, enabling efficient local AI inference across diverse hardware.
• Top 5 Embedding Models for Your RAG Pipeline — Effective RAG pipelines rely on embedding models that balance retrieval performance, real-world adoption, and deployment practicality.
• The Open Source RAG Stack: A Complete Guide to Building Retrieval-Augmented Generation Systems — The open-source RAG stack offers a modular, customizable approach to building context-rich AI systems.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.