Build Your Own AI Code Assistant: LocalLLM + Python Automation
Cloud-based AI code assistants are convenient, but they come with trade-offs. Your code snippets get sent to external servers, your usage patterns are tracked, and you're subject to rate limits and subscription fees. What if you could run a capable AI assistant locally on your machine, integrated directly into your development workflow?
In this tutorial, we'll build a privacy-first AI code assistant that runs entirely on your machine. You'll learn how to set up a local language model, wrap it with Python automation, and integrate it into your development environment. By the end, you'll have a tool that understands your codebase context and generates suggestions without ever leaving your machine.
Why Local LLMs Matter for Developers
Before diving into code, let's discuss why this matters. Local LLMs give you:
- Privacy: Your code never leaves your machine. No cloud logging, no data retention policies to worry about.
- Cost: Zero per-request fees. Run inference as much as you want.
- Customization: Fine-tune models on your specific codebase or domain.
- Offline capability: Work without internet connectivity.
- Latency control: No network round-trips; responses are instantaneous.
- Integration freedom: Direct programmatic access without API rate limits.
The trade-off? You need adequate hardware (GPU recommended but not required) and accept slightly lower performance compared to cutting-edge cloud models.
What You'll Need
- Python 3.9+
- 8GB+ RAM (16GB recommended)
- GPU with 6GB+ VRAM (optional but significantly faster)
- Ollama or LM Studio for running local models
- About 30 minutes to set everything up
Step 1: Install and Run a Local LLM
We'll use Ollama because it's the most developer-friendly option for running local models. It handles model downloads, optimization, and provides a simple API.
Installing Ollama
Head to ollama.ai and download the installer for your OS. Installation is straightforward—it creates a background service that manages models for you.
Once installed, open a terminal and pull a model. For code tasks, I recommend starting with mistral or neural-chat, which are fast and capable:
ollama pull mistral
This downloads the model (about 4GB for Mistral). You can also try smaller models like orca-mini (1.3GB) if space is constrained:
ollama pull orca-mini
Test that it's working:
ollama run mistral "Write a Python function that reverses a string"
You should see a response generated locally. Ollama runs on localhost:11434 by default.
Step 2: Create Your Python Wrapper
Now let's build a Python module that communicates with Ollama. This abstraction layer makes it easy to swap models or add features later.
First, install the required dependency:
pip install requests
Create a file called local_assistant.py:
python
import requests
import json
from typing import Optional
import os
class LocalCodeAssistant:
"""
A privacy-first code assistant powered by local LLMs.
Runs entirely on your machine without external API calls.
"""
def __init__(
self,
model: str = "mistral",
base_url: str = "http://localhost:11434",
temperature: float = 0.7,
context_window: int = 4096
):
"""
Initialize the local code assistant.
Args:
model: Name of the Ollama model to use
base_url: URL where Ollama is running
temperature: Creativity level (0.0-1.0)
context_window: Maximum tokens to consider
"""
self.model = model
self.base_url = base_url
self.temperature = temperature
self.context_window = context_window
self.conversation_history = []
# Verify connection
if not self._check_connection():
raise ConnectionError(
f"Cannot connect to Ollama at {base_url}. "
"Make sure Ollama is running: `ollama serve`"
)
def _check_connection(self) -> bool:
"""Verify Ollama is accessible."""
try:
response = requests.get(f"{self.base_url}/api/tags", timeout=2)
return response.status_code == 200
except requests.exceptions.RequestException:
return False
def _build_prompt(
self,
user_query: str,
context: Optional[str] = None,
system_prompt: Optional[str] = None
) -> str:
"""
Build a structured prompt with optional context.
Args:
user_query: The user's actual question
context: Additional code or context (e.g., current file)
system_prompt: Custom system instructions
Returns:
Formatted prompt string
"""
if system_prompt is None:
system_prompt = (
"You are an expert code assistant. Provide concise, "
"practical solutions. Include code examples when relevant. "
"Explain your reasoning briefly."
)
prompt_parts = [system_prompt]
if context:
prompt_parts.append(f"\n## Context:\n{context}")
prompt_parts.append(f"\n## Question:\n{user_query}")
return "\n".join(prompt_parts)
def generate(
self,
prompt: str,
context: Optional[str] = None,
system_prompt: Optional[str] = None,
stream: bool = False
) -> str:
"""
Generate a response from the local LLM.
Args:
prompt: The main prompt/question
context: Optional code context
system_prompt: Optional custom system instructions
stream: If True, yield tokens as they arrive
Returns:
Generated response or generator if stream=True
"""
full_prompt = self._build_prompt(prompt, context, system_prompt)
payload = {
"model": self.model,
"prompt": full_prompt,
"stream": stream,
"temperature": self.temperature,
"num_ctx": self.context_window
}
try:
response = requests.post(
f"{self.base_url}/api/generate",
json=payload,
timeout=300
)
response.raise_for_status()
if stream:
return self._stream_response(response)
else:
return self._parse_response(response)
except requests.exceptions.RequestException as e:
return f"Error communicating with Ollama: {str(e)}"
def _parse_response(self, response: requests.Response) -> str:
"""Extract text from non-streaming response."""
full_text = ""
for line in response.iter_lines():
if line:
data = json.loads(line)
full_text += data.get("response", "")
return full_text
def _stream_response(self, response: requests.Response):
"""Yield tokens from streaming response."""
for line in response.iter_lines():
if line:
data = json.loads(line)
yield data.get("response", "")
def explain_code(self, code: str) -> str:
"""Explain what a code snippet does."""
prompt = "Explain this code concisely, focusing on what it does and why:"
return self.generate(prompt, context=code)
def suggest_improvements(self, code: str) -> str:
"""Suggest improvements to a code snippet."""
prompt = (
"Review this code and suggest improvements for readability, "
"performance, or best practices. Be specific."
)
return self.generate(prompt, context=code)
def generate_tests(self, code: str, language: str = "python") -> str:
"""Generate unit tests for a code snippet."""
prompt =
---
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