Build a Local AI-Powered WordPress Assistant to Analyze Plugins Offline

In today’s WordPress development landscape, efficiency, security, and deep code understanding are essential. What if you could harness the power of a GPT-grade AI assistant to review your WordPress plugins — all without uploading your files to the cloud? In this post, we’ll walk through how to set up a completely offline AI assistant that analyzes your WordPress plugins using state-of-the-art language models.

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🤖 What This AI Assistant Can Do

Once set up, the AI assistant can:

• Parse and analyze PHP, JavaScript, CSS, SCSS, JSON, and HTML files
• Help you understand hook usage, REST APIs, CPTs, user meta, and more
• Skip machine-compiled or non-human-readable files like .pot, .mo, .png, etc.
• Work 100% offline using Ollama and deepseek-coder
• Provide GPT-4-level insight for plugin auditing, refactoring, and documentation
• Show real-time progress while scanning files

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🚀 Requirements

• Python 3.10 or 3.11 (3.12+ may have some incompatibilities)
• Ollama installed (to run the local model)
• deepseek-coder pulled via Ollama
• tqdm for CLI progress bar
• Git (required if installing from GitHub sources)
• Basic familiarity with command line

We recommend using LocalWP for running your WordPress site during development.

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⚙️ Installation Steps

1. Install Required Python Packages

Open your terminal and run:

pip install --upgrade langchain langchain-community langchain-ollama faiss-cpu tqdm

2. Start the DeepSeek AI Model

In a new terminal window:

ollama pull deepseek-coder
ollama run deepseek-coder

Let this window run in the background.

3. Setup Plugin Assistant Folder

Extract the assistant package to your desired location, such as:

C:\Users\YourName\Local Sites\woocommerce\app\public\wp-content\ai-wp-dev-offline-assistant-final

Place your plugin inside the wp-plugin/ folder. For example:

wp-plugin\buddypress\

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📁 File Types It Covers

Only files with these extensions are scanned:

• .php – Plugin core logic
• .js / .ts – Front-end behavior
• .css / .scss – Styling, admin UI
• .json – Block definitions, config
• .html – Template or static content

All others like .pot, .po, .mo, .zip, .jpg, .png are automatically skipped.

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📊 Visual File Scan Progress

The assistant now includes a terminal-based progress bar, thanks to tqdm. You will see something like this as it processes files:

📂 Indexing 76 source files...
🔍 Processing:  54%|███████████████████▌              | 41/76 [00:04<00:02, 15.21it/s]

Skipped or failed files will be listed in real-time.

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🔧 Sample main.py File

Below is the core Python file to power your assistant. Save it as main.py inside your assistant folder:

import os
from tqdm import tqdm
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_ollama import OllamaEmbeddings, OllamaLLM
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA

plugin_path = r"C:\\Users\\YourName\\Local Sites\\woocommerce\\app\\public\\wp-content\\ai-wp-dev-offline-assistant\\wp-plugin"
valid_extensions = ('.php', '.js', '.css', '.json', '.html', '.scss', '.ts')

files = [
    os.path.join(dp, f)
    for dp, dn, filenames in os.walk(plugin_path)
    for f in filenames if f.endswith(valid_extensions)
]

print(f"📂 Indexing {len(files)} source files...")
docs = []
for file in tqdm(files, desc="🔍 Processing"):
    try:
        loader = TextLoader(file, autodetect_encoding=True)
        docs.extend(loader.load_and_split())
    except Exception as e:
        tqdm.write(f"❌ Skipped {file}: {e}")

splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=200)
chunks = splitter.split_documents(docs)

embeddings = OllamaEmbeddings(model="deepseek-coder")
db = FAISS.from_documents(chunks, embeddings)

llm = OllamaLLM(model="deepseek-coder")
qa = RetrievalQA.from_chain_type(llm=llm, retriever=db.as_retriever())

query = "Give a full technical breakdown of how this plugin handles PHP logic, JS interactivity, and CSS structure."
result = qa.invoke({ "query": query })
print("[Local AI Answer]")
print(result["result"])

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🚪 Optional (but Useful): Install Git and Check Versions

python --version
pip --version
git --version

Ensure Git is installed from: https://git-scm.com/download/win

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📄 Full CLI Setup Summary

Here is a quick recap of the terminal commands used:

# Install Python dependencies
pip install --upgrade langchain langchain-community langchain-ollama faiss-cpu tqdm

# Pull and run the Ollama model
ollama pull deepseek-coder
ollama run deepseek-coder

# (optional) Install Git
git --version

# Run the assistant
python main.py

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🧠 What Can You Ask It?

Here are some powerful prompts you can run:

• “List all add_action and add_filter calls in this plugin.”
• “What does this plugin do to manage custom user roles?”
• “Which AJAX handlers are defined in the JavaScript files?”
• “Explain how styles are applied to the BuddyPress profile page.”

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🎉 You’re All Set!

You now have a production-ready, completely offline AI WordPress assistant that can:

• Work with local plugin files directly
• Understand and parse your full dev stack
• Offer valuable insight without needing cloud API access
• Show real-time file scan progress in terminal

Whether you’re a plugin developer, agency, or freelancer, this tool can drastically improve your workflow.

Need a web-based chat UI or Markdown export of answers? Let us know in the comments and we’ll share an upgrade tutorial!

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Happy developing — powered by AI, fueled by WordPress.