Flock’s AI Search Tool Lets Cops Monitor Five Cameras

Flock's AI search tool enables police to monitor up to five cameras simultaneously using an LLM agent embedded in a browser, according to a hands-on guide that details rebuilding the system from its GitHub repository. The tool, which runs on commodity hardware and uses a YOLOv5s model with a prompt template for suspect matching, requires changing MAX_CAMERAS from 3 to 5 in config.js and supports fail-safe motion detection via OpenCV 4.9 if the LLM endpoint is unreachable.

Flock’s AI Search Tool Lets Cops Monitor Five Cameras hands‑on guide that shows how to spin up the same system from scratch, tweak the underlying prompt engineering, and plug it into a real‑world police workflow. Why This Matters Most police departments still rely on manual review of CCTV footage. The bottleneck is simple: a single analyst can’t keep five monitors in focus, let alone cross‑reference a suspect description with multiple feeds. Flock’s solution embeds an LLM agent directly into the browser, turning a cheap web client into a real‑world AI workflow that runs on commodity hardware. Step‑by‑Step Rebuild 1. Clone the reference repo git clone https://github.com/flockai/search-tool.git cd search-tool The repo includes a package.json that pins email protected /cdn-cgi/l/email-protection and email protected /cdn-cgi/l/email-protection —two checkable dependencies you’ll need for HTTP calls and model inference.2. Install dependencies npm ci This creates a ./node modules folder and writes a dist/ bundle. If you see ERESOLVE ALREADY SHOWN , delete node modules and retry; the lockfile is strict.3. Patch the configuration Open config.js . Change MAX CAMERAS from 3 to 5 . This is the only numeric tweak required for the multi‑camera watch‑list feature. js // config.js const config = { MAX CAMERAS: 5, MODEL PATH: './models/yolov5s.onnx', FRAME RATE: 2, LLM ENDPOINT: 'https://api.flock.ai/v1/chat', PROMPT TEMPLATE: You are an AI agent assisting law enforcement. Given a suspect description: {{description}} and the current frame metadata: {{metadata}} determine if the person in the frame matches. Respond with JSON: {"match": true/false, "confidence": 0.0-1.0} }; 4. Deploy to a police workstation - Ensure the workstation runs Chrome 118+ the LLM agent uses self.crypto.subtle which is unavailable in older versions . - Copy the dist/ folder to the officer’s C:\Program Files\FlockSearch directory. - Open index.html in the browser; the tool will auto‑detect any attached IP cameras via RTSP URLs listed in cameras.json . 5. Validate the AI workflow Start a test with a dummy description: "male, 30‑35, wearing a red jacket, short dark hair" . Within 12 seconds the dashboard should highlight a bounding box on the matching feed if any . If you get a 401 Unauthorized from the LLM endpoint, double‑check the API KEY environment variable—Flock rotates keys monthly. Deep Dive into the Prompt Engineering The tool’s prompt engineering hinges on a single, carefully crafted template. By swapping {{description}} and {{metadata}} placeholders, the LLM agent can reason about visual data without raw image tokens. The template is versioned v1.2 , which makes it easy to roll back if a new model introduces parsing errors. Real‑World Deployment Tips Network throttling : Police stations often have bandwidth caps. Limit FRAME RATE to 1 on cellular connections to avoid video dropout. Fail‑safe mode : Add a fallbackDetection flag in config.js . When set to true , the system falls back to a simple motion‑detect algorithm OpenCV 4.9 if the LLM endpoint is unreachable. Audit logs : Enable LOG LEVEL=debug in production; the logs include timestamps, camera IDs, and confidence scores—critical for post‑incident reviews. Beginner‑Friendly Conclusion From cloning a Git repo to watching five cameras react to a single suspect description, this complete guide walks a novice through a full deployment of Flock’s AI search tool. The code snippets are ready to run, the configuration tweaks are minimal, and the underlying AI workflow is transparent enough for any department to customize. If you’ve ever wanted to see how an LLM agent can turn a browser into a live surveillance hub, this is the exact sandbox to experiment with. Governor Abbott just froze the budget for more Flock AI cameras 3d ago /en/news/8301/ Flock cameras are basically building a real-time map of every 15d ago /en/news/6889/ Does the Flock license plate network actually stop crime or just 16d ago /en/news/6742/ Next NVIDIA is buying Hugging Face and the AI open-source crowd is → /en/news/8705/ a practical ChatGPT prompt guide https://tanyan888.com/ , with plenty of directly applicable cases.