Edisson Garcia
Templates by Edisson Garcia
Message buffer system with Redis for efficient processing
🚀 Message-Batching Buffer Workflow (n8n) This workflow implements a lightweight message-batching buffer using Redis for temporary storage and a JavaScript consolidation function to merge messages. It collects incoming user messages per session, waits for a configurable inactivity window or batch size threshold, consolidates buffered messages via custom code, then clears the buffer and returns the combined response—all without external LLM calls. --- 🔑 Key Features Redis-backed buffer queues incoming messages per context_id. Centralized Config Parameters node to adjust thresholds and timeouts in one place. Dynamic wait time based on message length (configurable minWords, waitLong, waitShort). Batch trigger fires on inactivity timeout or when buffer_count ≥ batchThreshold. Zero-cost consolidation via built-in JavaScript Function (consolidate buffer)—no GPT-4 or external API required. --- ⚙️ Setup Instructions Extract Session & Message Trigger: When chat message received (webhook) or When clicking ‘Test workflow’ (manual). Map inputs: set variables context_id and message into a Set node named Mock input data (for testing) or a proper mapping node in production. Config Parameters Add a Set node Config Parameters with: minWords: 3 Word threshold waitLong: 10 Timeout (s) for long messages waitShort: 20 Timeout (s) for short messages batchThreshold: 3 Messages to trigger batch early All downstream nodes reference these JSON values dynamically. Determine Wait Time Node: get wait seconds (Code) JS code: js const msg = $json.message || ''; const wordCount = msg.split(/\s+/).filter(w => w).length; const { minWords, waitLong, waitShort } = items[0].json; const waitSeconds = wordCount < minWords ? waitShort : waitLong; return [{ json: { contextid: $json.contextid, message: msg, waitSeconds } }]; Buffer Message in Redis Buffer messages: LPUSH bufferin:{{$json.contextid}} with payload {text, timestamp}. Set buffer\count increment: INCR buffercount:{{$json.context_id}} with TTL {{$json.waitSeconds + 60}}. Set last\seen: record lastseen:{{$json.context_id}} timestamp with same TTL. Check & Set Waiting Flag Get waiting\reply: if null, Set waiting\reply to true with TTL {{$json.waitSeconds}}; else exit. Wait for Inactivity WaitSeconds (webhook): pauses for {{$json.waitSeconds}} seconds before batch evaluation. Check Batch Trigger Get last\seen and Get buffer\count. IF (now - last_seen) ≥ waitSeconds 1000 OR buffer_count ≥ batchThreshold, proceed; else use Wait node to retry. Consolidate Buffer consolidate buffer (Code): js const j = items[0].json; const raw = Array.isArray(j.buffer) ? j.buffer : []; const buffer = raw.map(x => { try { return typeof x === 'string' ? JSON.parse(x) : x; } catch { return null; } }).filter(Boolean); buffer.sort((a, b) => new Date(a.timestamp) - new Date(b.timestamp)); const texts = buffer.map(e => e.text?.trim()).filter(Boolean); const unique = [...new Set(texts)]; const message = unique.join(' '); return [{ json: { contextid: j.contextid, message } }]; Cleanup & Respond Delete Redis keys: bufferin, buffercount, waitingreply, lastseen (for the context_id). Return consolidated message to the user via your chat integration. --- 🛠 Customization Guidance Adjust thresholds by editing the Config Parameters node. Change concatenation (e.g., line breaks) by modifying the join separator in the consolidation code. Add filters (e.g., ignore empty or system messages) inside the consolidation Function. Monitor performance: for very high volume, consider sharding Redis keys by date or user segments. --- © 2025 Innovatex • Automation & AI Solutions • innovatexiot.carrd.co • LinkedIn
By Edisson GarciaEnhance Google Drive images with Gemini 2.5 Flash AI
🚀 Google Drive Image Enhancement with Gemini nano banana This workflow automates image enhancement by integrating Google Drive with Google Gemini. It fetches unprocessed images from a source folder, applies AI-driven transformations based on a customizable prompt (e.g., clean and realistic product backgrounds), and uploads the enhanced results into a destination folder—streamlining e-commerce catalog preparation or creative pipelines. --- 🔑 Key Features Customizable Prompt Node → Easily adjust the style/instructions for Gemini (e.g., backgrounds, lighting, focus). Google Drive Integration → Automatically fetches images from a source folder and uploads results to a target folder. AI Processing via Gemini → Converts original images to Base64, sends them with the prompt to Gemini, and returns enhanced versions. Image Filtering → Processes only files whose mimeType contains "image". Loop Handling → Iterates over all images in the source folder until all are processed. --- ⚙️ Setup Instructions Configure Prompt Open the promt node. Replace the text with your desired Gemini instructions (e.g., "Add a clean, realistic background for baby products"). Set Google Drive Folders In origin_folder → set Search Query to the name of the source folder (with unprocessed images). In destination_folder → set Search Query to the name of the target folder (to save results). Credentials Provide valid Google Drive OAuth2 credentials for both Drive nodes. Provide a Google Gemini API credential for the banana-request node. Run the Workflow Trigger from the init node. Workflow will download → convert → send to Gemini → reconvert → upload results automatically. --- 🛠 Customization Guidance Modify the prompt text to change how Gemini processes the images (background, style, product focus). Swap Search Query for folder IDs in Drive nodes if you need more precise targeting. Extend the workflow by chaining post-processing (e.g., watermarking, resizing, or tagging metadata). --- © 2025 Innovatex • Automation & AI Solutions • innovatexiot.carrd.co • LinkedIn ---
By Edisson Garcia