edge AIscrapingRaspberry Pi

Run Generative Models Locally on Pi HAT+ to Enrich Scraped Content Before Indexing

ccrawl

2026-01-30

10 min read

Use Raspberry Pi 5 + AI HAT+ to run LLMs at the edge and summarize or entity-tag scraped pages before indexing.

Hook: Stop shipping noise to your index — preprocess at the edge

Large crawl pools and content farms flood indexes with low-quality pages, increasing indexing costs and diluting signal. If you manage crawlers for large, dynamic sites, you need fast, reliable ways to reduce noise, extract entities, and summarize content before it ever hits your central index. The 2025–26 wave of edge AI hardware makes that practical: a Raspberry Pi 5 paired with the new AI HAT+ can run lightweight LLMs and compact NLP models locally to preprocess scraped pages for indexing, privacy, and efficiency.

What this walkthrough delivers (practical outcomes)

Step-by-step setup to run quantized LLMs and NER on Raspberry Pi 5 + AI HAT+
Example preprocessing pipeline: scrape → edge summarize & tag → push to OpenSearch/Meili
Code snippets: systemd service, Dockerfile, Python ingestion worker, llama.cpp invocation
Operational guidance: model selection, quantization, monitoring, CI/CD integration

Why preprocess at the edge in 2026?

Edge AI is now mainstream for production scraping pipelines. Late 2025 and early 2026 saw broad support for ARM-optimized runtimes (ONNX Runtime ARM, optimized ONNX runtime builds, and vendor NPU SDKs) that make running compact, quantized LLMs on devices like the Pi 5 plausible. Business benefits include:

Reduced bandwidth: send summaries and entity payloads, not entire pages.
Lower indexing cost: smaller documents and targeted fields index faster and consume less storage.
Privacy & compliance: minimize PII sent off-device by applying redaction rules locally.
Faster feedback loops: early detection of duplicate or low-quality content before it wastes crawl budget or index quotas.

Architecture overview

We'll build a simple, resilient pipeline:

Scraper (Scrapy, Playwright) scrapes pages and posts raw HTML to an edge ingestion endpoint.
Raspberry Pi 5 + AI HAT+ runs an ingestion worker (Python) that:

cleans HTML → extracts text
runs a locally hosted LLM for summarization (quantized model via llama.cpp or ONNX runtime)
runs a small NER model (spaCy or a compact LLM prompting step) to extract entities
optionally performs redaction and quality scoring

Worker packages a compact JSON (title, summary, entities, canonical URL, quality_score) and pushes to the central index (OpenSearch/Meili/Elasticsearch).

Hardware & software checklist

Raspberry Pi 5 (64-bit OS recommended; Ubuntu 24.04 or Raspberry Pi OS 64-bit)
AI HAT+ (model released late 2025; provides an NPU and accelerated runtimes)
16–32 GB USB swap or NVMe (optional but helpful for larger models)
Cooling (active heatsink) — inference workloads push CPU/NPU thermals
Central index: OpenSearch/Meili/Elasticsearch accessible over LAN/VPN

Model selection guidelines (2026 perspective)

Edge devices still can't run the largest LLMs. Use these principles:

Choose a compact open-weight model: prefer 1–7B parameter models that have community quantized gguf/onnx builds.
Quantize aggressively: int8 or int4 gguf quantizations (via llama.cpp or community tools) reduce RAM and speed inference.
Use small, efficient NER models for entity extraction (spaCy small or distilled transformer NER models) — or ask the LLM to extract entities if the model is tuned for instruction tasks.
Prefer models with permissive licenses and local-use support for offline inference.

Prepare the Pi: OS and base packages

# On your workstation or terminal connected to the Pi
sudo apt update && sudo apt upgrade -y
sudo apt install -y git build-essential python3 python3-venv python3-pip curl jq
# Optional: install Docker
curl -fsSL https://get.docker.com | sh
sudo usermod -aG docker $USER

Install AI HAT+ runtime

Follow the vendor SDK for AI HAT+ to install the NPU runtime and drivers. By 2026 most runtimes expose standard backends (ONNX Runtime with NPU provider, or an optimized llama.cpp build). Example (generic):

# pseudo-commands — check your HAT's vendor docs
git clone https://github.com/ai-hat/sdk.git
cd sdk
sudo ./install_runtime.sh

Build llama.cpp optimized for aarch64

llama.cpp remains one of the most portable ways to run quantized GGUF models locally. Build with ARM NEON support and, if available, HAT-specific NPU acceleration flags.

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
# Enable ARM optimizations (example — flags depend on version)
make clean && make -j4 CFLAGS="-O3 -march=armv8-a+crypto+fp16"

Acquire and convert a compact model

Use community-quantized models in gguf format. If you only have PyTorch/ONNX weights, convert them to gguf or a llama.cpp-supported format and quantize.

# Example: place model.gguf in /home/pi/models/
mkdir -p ~/models
# (download or scp) model.gguf -> ~/models/model.gguf

Edge worker: ingestion + summarization + entity extraction

Below is a compact Python worker that receives scraped HTML, extracts text, runs summarization via a llama.cpp subprocess call, and runs spaCy for entity extraction. Adapt the llama.cpp call to your build or ONNX runtime.

#!/usr/bin/env python3
# edge_worker.py
import os
import subprocess
import json
from flask import Flask, request, jsonify
from bs4 import BeautifulSoup
import spacy

MODEL_PATH = '/home/pi/models/model.gguf'
LLAMA_BIN = '/home/pi/llama.cpp/main'

nlp = spacy.load('en_core_web_sm')  # small, fast NER
app = Flask(__name__)

def extract_text(html):
    soup = BeautifulSoup(html, 'html.parser')
    for s in soup(['script','style']):
        s.extract()
    text = soup.get_text(separator=' ', strip=True)
    return ' '.join(text.split())

def summarize_with_llama(text, max_tokens=150):
    prompt = f"Summarize the following web page into 3 concise bullets:\n\n{text}\n\nSummary:".replace('"','\\"')
    proc = subprocess.run([LLAMA_BIN, '-m', MODEL_PATH, '-p', prompt, '--max-tokens', str(max_tokens)],
                          capture_output=True, text=True, timeout=30)
    return proc.stdout.strip()

def extract_entities(text):
    doc = nlp(text)
    entities = []
    for ent in doc.ents:
        entities.append({'text': ent.text, 'label': ent.label_})
    return entities

@app.route('/ingest', methods=['POST'])
def ingest():
    payload = request.json or {}
    html = payload.get('html') or ''
    url = payload.get('url')
    if not html or not url:
        return jsonify({'error':'missing html or url'}), 400

    text = extract_text(html)
    # heuristics: skip too-short or low-content pages
    if len(text) < 200:
        return jsonify({'status':'skipped','reason':'too_short','url':url}), 200

    summary = summarize_with_llama(text[:16000])  # cap input
    entities = extract_entities(text[:5000])

    doc = {
        'url': url,
        'summary': summary,
        'entities': entities,
        'raw_text_snippet': text[:2000]
    }

    # push to central index (example OpenSearch)
    # requests.post('http://opensearch.local:9200/docs/_doc', json=doc)

    return jsonify({'status':'ok','doc':doc}), 200

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

Notes on the example

Keep the LLM input truncated. Edge models have limited context; use the first N characters or run a light content prioritizer.
spaCy's small model is CPU-friendly. For other languages, use language-specific compact models.
Replace the llama.cpp CLI with an ONNX runtime call if your AI HAT+ provides an ONNX provider for the NPU.

Index mapping: what to store in your central index

Design your index schema to accept both raw and processed signals. Example fields:

url (keyword)
title (text)
summary (text, boosted for search)
entities (nested: text, label, confidence)
quality_score (float)
raw_text_snippet (text)
edge_processing_meta (device_id, model_version, timestamp)

Operational tips & 2026 trends to apply

Version models: store model_version with every document to allow reprocessing when you upgrade models.
Quantization & memory: prefer int8 or int4 gguf; 3B models are a practical sweet spot for Pi-class devices in 2026.
Monitoring: export latency, errors, swap usage to Prometheus/Grafana. Edge devices need health checks and auto-restarts.
Graceful degradation: if the local NPU is busy or a model fails, have a fallback path (lightweight spaCy-only processing or enqueue for central processing).
CI/CD: run lightweight inference tests in GitHub Actions and deploy model binaries via signed artifact storage to devices. See a practical CI/CD workflow review for ideas on packaging artifacts and rollout.
Security: sign model binaries and use mTLS for pushing to central index to prevent tampering; combine that with a secure agent policy for device-side enforcement.

Integrating into developer workflows & CI/CD

Edge preprocessing belongs in the same version control and test matrix as your crawler. Recommended steps:

Store model fingerprints and preprocessing scripts in the repo.
Unit test the text extraction and entity mapping locally with sample pages.
Use GitHub Actions to run small inference smoke tests (use a tiny model or mock CLI) to validate changes.
Push artifacts to an authenticated artifact store; devices pull updates via a controlled rollout.

Performance expectations and benchmarks (realistic)

Benchmarks vary by model, quantization, and whether the AI HAT+ accelerates the runtime. In production setups we've seen:

Summarization with a quantized 3B model: often sub-5s on Pi 5 + AI HAT+ for a truncated page (2–4k tokens).
NER with spaCy small: tens to hundreds of ms per page.
End-to-end preprocessing time: typically 1–7s depending on input size and model.

These reductions allow you to drop heavy HTML payloads and index only processed artifacts, reducing central storage and bandwidth by up to 40–70% depending on your content and retention policy.

Edge privacy & regulatory benefits

Processing scraped pages on-device enables surgical redaction of PII and controlled retention. In regulated environments (GDPR, CCPA, or industry-specific rules in 2026), that can be a major compliance win: you never send raw sensitive data to a cloud model or index unless explicit policy allows it.

Tip: implement a policy module that checks for PII patterns (emails, SSNs) and either masks them locally or routes the document for manual review.

Scaling: fleet management and reliability

When you run many Pi+HAT devices across data centers or remote sites, consider:

Over-the-air updates with rollback (Delta updates for model files)
Centralized logging and metrics aggregation (Fluentd/Vector to backend)
Device health checks with auto-reprovisioning and fallback routing
Edge orchestration: use lightweight container orchestrators or systemd templates for deterministic behavior
Consider how market orchestration and hyperlocal policies affect device placement and network topology.

Example systemd unit to keep the worker running

[Unit]
Description=Edge preprocessing worker
After=network.target

[Service]
User=pi
WorkingDirectory=/home/pi/edge-preproc
ExecStart=/usr/bin/python3 /home/pi/edge-preproc/edge_worker.py
Restart=on-failure
RestartSec=5

[Install]
WantedBy=multi-user.target

Common pitfalls and how to avoid them

Thermal throttling: use active cooling and monitor temps — inference tasks can trigger throttling which increases latency.
Memory pressure: quantize models and cap input size. Provide swap but avoid excessive swapping.
Model drift: track model_version and periodically reprocess high-value docs when you upgrade models.
Network partitions: buffer outputs locally (Redis or local queue) and retry pushes to the index to avoid data loss.

Advanced: hybrid edge+cloud orchestration

For top-tier pipelines in 2026, adopt a hybrid approach:

Run cheap, high-precision signals at the edge (summary, NER, quality_score).
Route ambiguous or high-value pages to cloud-hosted larger LLMs for deeper analysis.
Use a decision DAG to classify documents at the edge and choose the right processing tier.

Actionable checklist (do this next)

Provision a Pi 5 + AI HAT+ and install the NPU runtime.
Build llama.cpp or ONNX runtimes and test with a small gguf quantized model.
Deploy the Python ingestion worker as a systemd service and run sample pages through it.
Integrate the edge output into your index mapping and run a small-scale pilot.
Measure latency, bandwidth, and index size improvements; iterate on model size and quantization.

Closing: The future of crawler tooling in 2026

Edge AI is no longer experimental. With small, quantized LLMs and dedicated HAT runtimes, devices like the Raspberry Pi 5 + AI HAT+ let you push intelligence to the network edge. For teams managing large-scale crawling and indexing, that translates into lower costs, better data quality, and faster diagnosis of crawl and indexability problems.

Practical takeaway: start small—deploy one Pi+HAT to handle a subset of your crawl queue, measure improvements in bandwidth and index health, then scale the pattern. Treat models as first-class deployable artifacts, instrument aggressively, and design for graceful fallback.

Call to action

Ready to prototype? Clone the companion repo (edge-preproc-example), flash a Pi 5, and run the sample pipeline to see how much you can reduce indexing costs in a single weekend. If you want a checklist or a deployment template for fleets of Pi+HAT devices integrated into CI/CD, contact our team or explore the crawl.page resources for enterprise crawler tooling and edge AI blueprints.

crawl

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