Building a Local AI

Want to run your own private AI assistant that can answer questions about your local documents—without sending anything to the cloud? In this guide, we’ll walk through how to build a fully local AI system using:

Ollama or llama.cpp for running large language models (LLMs)
Open-source frontends like Open WebUI or LibreChat
A retrieval-augmented generation (RAG) pipeline to query your own documents

Let’s dive in.

🏗️ Architecture Overview

Here’s what we’re building:

[Frontend UI] ⇄ [Local RAG Server] ⇄ [LLM Backend (Ollama or llama.cpp)]
                            ⇓
                   [Local Document Store]

The frontend provides a chat interface.
The RAG server handles document ingestion, chunking, embedding, and retrieval.
The LLM backend generates answers using retrieved context.
All components run locally, ensuring privacy and full control.

🧰 Tools You’ll Need

Component	Options	Description
LLM Backend	Ollama, llama.cpp	Run open-source LLMs like LLaMA, Mistral, or Gemma locally
Frontend UI	Open WebUI, LibreChat	Chat interface for interacting with your AI
RAG Framework	llama-index, LangChain	Frameworks to build document-aware AI
Embedding Model	sentence-transformers, InstructorXL	Convert text into vector embeddings
Vector Store	Chroma, FAISS	Store and search document chunks

🧠 Step 1: Set Up the LLM Backend

Option A: Using Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run a model (e.g., Mistral)
ollama run mistral

Ollama handles model downloading, quantization, and serving via an HTTP API. It’s the easiest way to get started.

Option B: Using llama.cpp

# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# Run a model
./main -m ./models/llama-2-7b.ggmlv3.q4_0.bin -p "Hello, AI!"

You’ll need to download a quantized .gguf or .bin model file separately.

📄 Step 2: Ingest Local Documents

Use llama-index or LangChain to:

Load documents (PDFs, TXT, DOCX, etc.)
Chunk them into manageable pieces
Embed them into vectors
Store them in a vector database

Example with llama-index:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("docs").load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist("storage/")

🔍 Step 3: Enable Retrieval-Augmented Generation (RAG)

Now, connect your vector store to the LLM. When a user asks a question:

Embed the query
Search for relevant document chunks
Feed them as context to the LLM

Example with llama-index and Ollama:

from llama_index.llms import Ollama
from llama_index import ServiceContext

llm = Ollama(model="mistral")
service_context = ServiceContext.from_defaults(llm=llm)

query_engine = index.as_query_engine(service_context=service_context)
response = query_engine.query("What is our refund policy?")
print(response)

💬 Step 4: Add a Chat Frontend

Option A: Open WebUI

git clone https://github.com/open-webui/open-webui
cd open-webui
docker compose up

Connects directly to Ollama
Clean, modern UI
Supports file uploads and chat history

Option B: LibreChat

git clone https://github.com/danny-avila/LibreChat
cd LibreChat
docker compose up

More customizable
Supports multiple providers (Ollama, OpenAI, etc.)

🛡️ Why Go Local?

Privacy: Your documents never leave your machine.
Speed: No latency from cloud APIs.
Cost: No API fees or subscriptions.
Control: Customize everything from models to UI.

🚀 Bonus: Automate with a Local RAG Server

Wrap your RAG pipeline in a FastAPI or Flask server:

POST /chat
{
  "query": "Summarize the Q3 report",
  "history": [...]
}

This lets your frontend send queries and receive context-aware answers from your local AI.

🧩 Final Thoughts

With tools like Ollama, llama.cpp, and open-source frontends, building your own local AI assistant is more accessible than ever. Whether you’re a developer, researcher, or privacy-conscious user, this stack gives you full control over your data and your AI.

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