Workflow Templates

🧑‍⚖️ AI Legal Assistant Agent — AI-Powered Legal Q&A with Document Retrieval Category: LegalTech / AI Agent / RAG / Chatbot Description: This no-code AI agent acts as a legal assistant chatbot that can answer user queries by retrieving information from a pre-indexed legal document library. It’s powered by OpenAI + Pinecone + Telegram and designed for law firms, compliance teams, or anyone who needs instant answers from contracts, policies, or regulatory documents. For more of such builds and step-by-step video tutorial, check out: https://www.youtube.com/@Automatewithmarc 🔍 How it Works: Telegram Trigger – Starts when a user sends a message via Telegram. AI Agent (Open AI Model) – Uses a retrieval-augmented generation (RAG) setup to understand the question and pull relevant context. Pinecone Vector Store – Searches across a vectorized legal contract library for relevant clauses or documents. OpenAI Embeddings – Converts uploaded documents into vector embeddings for efficient search. Memory Buffer – Maintains conversation flow and context for follow-up questions. Telegram Response – Sends the final AI-generated answer directly to the user. 🎯 Use Cases: In-house legal teams automating internal policy Q&A Law firms building client-facing legal bots Startups offering legal tech services with document-based queries Compliance teams monitoring contract terms and obligations ✅ Key Features: Real-time legal Q&A via Telegram Pinecone + OpenAI-powered vector search Retrieval-Augmented Generation (RAG) setup Factual, memory-aware assistant with fallback if info is unavailable Fully customizable and extendable ⚙️ Setup Instructions: Connect OpenAI, Pinecone, and Telegram credentials Upload your contracts or policy docs into Pinecone Customize the system prompt or expand document sources as needed Activate and test via Telegram This workflow is a solid foundation for any AI-powered legal assistant or chatbot solution—highly relevant for modern LegalOps and knowledge management teams.

Transform Images with AI Image Editor using FLUX.1 Kontext

by Nasser

For Who? Content Creators Youtube Automation Marketing Team How it works? 1 - Retrieve Base Image, Image Description and Situation from Airtable 2 - Generate Image Prompt 3 - Generate Image via Fal AI 4 - Verify if Image is generated 5 - Upload Image on Airtable 📺 YouTube Video Tutorial: SETUP Setup Input : The first part of the workflow can be replaced with anything else. You need as input a Prompt and the Base Image URL (publicly available). Setup Output : In this Workflow, the output is storing the image on Airtable but you can replace that with anything else but basically you have two options : Store the Generated Image somewhere : Keep everything like this and replace the last Airtable node with the Third Party you want to use. Use the Image directly in n8n : In HTTP Request "Generate Image" switch sync_mode to "true", remove all the following nodes and add "Extract form File" node (convert to Base64 String) APIs : For the following third-party integrations, replace ==[YOUR_API_TOKEN]== with your API Token or connect your account via Client ID / Secret to your n8n instance: Fal AI (FLUX KONTEXT MAX) : https://fal.ai/models/fal-ai/flux-pro/kontext/max/api#schema-input Airtable : https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.airtable/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.airtable

Cold Email Icebreaker Generator with Apify, GPT-4 & Website Scraping

by Nick Saraev

Deep Multiline Icebreaker System (AI-Powered Cold Email Personalization) Categories: Lead Generation, AI Marketing, Sales Automation This workflow creates an advanced AI-powered cold email personalization system that achieves 5-10% reply rates by generating deeply personalized multi-line icebreakers. The system scrapes comprehensive website data, analyzes multiple pages per prospect, and uses advanced AI prompting to create custom email openers that make recipients believe you've personally researched their entire business. Benefits Superior Response Rates** - Achieves 5-10% reply rates vs. 1-2% for standard cold email campaigns Deep Website Intelligence** - Scrapes and analyzes multiple pages per prospect, not just homepages Advanced AI Personalization** - Uses sophisticated prompting techniques with examples and formatting rules Complete Lead Pipeline** - From Apollo search to personalized icebreakers in Google Sheets Scalable Processing** - Handle hundreds of prospects with intelligent batching and error handling Revenue-Focused Approach** - System designed around proven $72K/month agency methodologies How It Works Apollo Lead Acquisition: Integrates directly with Apollo.io search URLs through Apify scraper Processes 500+ leads per search with comprehensive contact data Filters for prospects with both email addresses and accessible websites Multi-Page Website Scraping: Scrapes homepage to extract all internal website links Processes relative URLs and filters out external/irrelevant links Performs intelligent batching to prevent IP blocking during scraping Comprehensive Content Analysis: Converts HTML to markdown for efficient AI processing Uses GPT-4 to generate detailed abstracts of each webpage Aggregates insights from multiple pages into comprehensive prospect profiles Advanced AI Icebreaker Generation: Employs sophisticated prompting with system messages, examples, and formatting rules Uses proven icebreaker templates that reference non-obvious website details Generates personalized openers that imply deep manual research Smart Data Processing: Removes duplicate URLs and handles scraping errors gracefully Implements token limits to control AI processing costs Organizes final output in structured Google Sheets format Required Google Sheets Setup Create a Google Sheet with these exact tab and column structures: Search URLs Tab: URL - Contains Apollo.io search URLs for your target audiences Leads Tab (Output): first_name - Contact's first name last_name - Contact's last name email - Contact's email address website_url - Company website URL headline - Job title/position location - Geographic location phone_number - Contact phone (if available) multiline_icebreaker - AI-generated personalized opener Setup Instructions: Create Google Sheet with "Search URLs" and "Leads" tabs Add your Apollo search URLs to the first tab (one per row) Connect Google Sheets OAuth credentials in n8n Update the Google Sheets document ID in all sheet nodes The workflow reads from Search URLs and outputs to Leads automatically Apollo Search URL Format: Your search URLs should look like: https://app.apollo.io/#/people?personLocations[]=United%20States&personTitles[]=ceo&qKeywords=marketing%20agency&page=1 Business Use Cases AI Automation Agencies** - Generate high-converting prospect outreach for service-based businesses B2B Sales Teams** - Create personalized cold email campaigns that actually get responses Marketing Agencies** - Offer premium personalization services to clients Consultants** - Build authority through deeply researched prospect outreach SaaS Companies** - Improve demo booking rates through personalized messaging Professional Services** - Stand out from generic sales emails with custom insights Revenue Potential This system transforms cold email economics: 5-10x Higher Response Rates** than standard cold email approaches $72K/month proven methodology** - exact system used to scale successful AI agency Premium Positioning** - prospects assume you've done extensive manual research Scalable Personalization** - process hundreds of prospects daily vs. manual research Difficulty Level: Advanced Estimated Build Time: 3-4 hours Monthly Operating Cost: ~$150 (Apollo + Apify + OpenAI + Email platform APIs) Watch My Complete Live Build Want to see me build this entire deep personalization system from scratch? I walk through every component live - including the AI prompting strategies, website scraping logic, error handling, and the exact techniques that generate 5-10% reply rates. 🎥 See My Live Build Process: "I Deep-Personalized 1000+ Cold Emails Using THIS AI System (FREE TEMPLATE)" This comprehensive tutorial shows the real development process - including advanced AI prompting, multi-page scraping architecture, and the proven icebreaker templates that have generated over $72K/month in agency revenue. Set Up Steps Apollo & Apify Integration: Configure Apify account with Apollo scraper access Set up API credentials and test lead extraction Define target audience parameters and lead qualification criteria Google Sheets Database Setup: Create multi-sheet structure (Search URLs, Leads) Configure proper column mappings for lead data Set up Google Sheets API credentials and permissions Website Scraping Infrastructure: Configure HTTP request nodes with proper redirect handling Set up error handling for websites that can't be scraped Implement intelligent batching with split-in-batches nodes AI Content Processing: Set up OpenAI API credentials with appropriate rate limits Configure dual-AI approach (page summarization + icebreaker generation) Implement token limiting to control processing costs Advanced Icebreaker Generation: Configure sophisticated AI prompting with system messages Set up example-based learning with input/output pairs Implement formatting rules for natural-sounding personalization Quality Control & Testing: Test complete workflow with small prospect batches Validate AI output quality and personalization accuracy Monitor response rates and optimize messaging templates Advanced Optimizations Scale the system with: Industry-Specific Templates:** Customize icebreaker formats for different verticals A/B Testing Framework:** Test different AI prompt variations and templates CRM Integration:** Automatically add qualified responders to sales pipelines Response Tracking:** Monitor which personalization elements drive highest engagement Multi-Touch Sequences:** Create follow-up campaigns based on initial response data Important Considerations AI Token Management:** System includes intelligent token limiting to control OpenAI costs Scraping Ethics:** Built-in delays and error handling prevent website overload Data Quality:** Filtering logic ensures only high-quality prospects with accessible websites Scalability:** Batch processing prevents IP blocking during high-volume scraping Why This System Works The key to 5-10% reply rates lies in making prospects believe you've done extensive manual research: Non-obvious details from deep website analysis Natural language patterns that avoid template detection Company name abbreviation (e.g., "Love AMS" vs "Love AMS Professional Services") Multiple page insights aggregated into compelling narratives Check Out My Channel For more advanced automation systems and proven business-building strategies that generate real revenue, explore my YouTube channel where I share the exact methodologies used to build successful automation agencies.

Two-Stage Document Retrieval Chatbot with OpenAI and Supabase Vector Search

by Mark Shcherbakov

Video Guide I prepared a comprehensive guide demonstrating how to build a multi-level retrieval AI agent in n8n that smartly narrows down search results first by file descriptions, then retrieves detailed vector data for improved relevance and answer quality. Youtube Link Who is this for? This workflow suits developers, AI enthusiasts, and data engineers working with vector stores and large document collections who want to enhance the precision of AI retrieval by leveraging metadata-based filtering before deep content search. It helps users managing many files or documents and aiming to reduce noise and input size limits in AI queries. What problem does this workflow solve? Performing vector searches directly on large numbers of document chunks can degrade AI input quality and introduce noise. This workflow implements a two-stage retrieval process that first searches file descriptions to filter relevant files, then runs vector searches only within those files to fetch precise results. This reduces irrelevant data, improves answer accuracy, and optimizes performance when dealing with dozens or hundreds of files split into multiple pieces. What this workflow does This n8n workflow connects to a Supabase vector store to perform: Multi-level Retrieval:** File Description Search: Calls a Supabase RPC function to find files whose descriptions (metadata) best match the user query. It filters and limits the number of relevant files based on similarity scores. Document Chunk Retrieval: Uses retrieved file IDs to perform a second RPC call fetching detailed vector pieces only within those files, again filtered by similarity thresholds. OpenAI Integration:** The filtered document chunks and associated metadata (like file names and URLs) are passed to an OpenAI message node that includes system instructions to guide the AI in leveraging the knowledge base and linked resources for comprehensive responses. Custom Code Functions:** Two code nodes interact with Supabase stored procedures match_files and match_documents to perform the semantic searches with multiline metadata filtering unavailable in default vector filters. Helper Flows and SQL Setup:** Templates and SQL scripts prepare database tables and functions, with additional flows to generate embeddings from file description summaries using OpenAI. N8N Workflow Preparation: Create or verify Supabase account with vector store capability. Set up necessary database tables and RPC functions (match_files and match_documents) using provided SQL scripts. Replace all credentials in n8n nodes to connect to your Supabase and OpenAI accounts. Optionally upload document files and generate their vector embeddings and description summaries in a separate helper workflow. Main Workflow Logic: Code Function Node #1: Receives user query and calls the match_files RPC to retrieve file IDs by searching file descriptions with vector similarity thresholds and file limits. Code Function Node #2: Takes filtered file IDs, invokes match_documents RPC to fetch vector document chunks only from those files with additional similarity filtering and count limits. OpenAI Message Node: Combines fetched document pieces, their metadata (file URLs, similarity scores), and system prompts to generate precise AI-powered answers referencing the documents. This multi-tiered retrieval process improves search relevance and AI contextual understanding by smartly limiting vector search scope first to relevant files, then to specific document chunks, refining user query results.

Create a Telegram Bot with Mistral Nemotron AI and Conversation Memory

by Ajith joseph

🤖 Create a Telegram Bot with Mistral AI and Conversation Memory A sophisticated Telegram bot that provides AI-powered responses with conversation memory. This template demonstrates how to integrate any AI API service with Telegram, making it easy to swap between different AI providers like OpenAI, Anthropic, Google AI, or any other API-based AI model. 🔧 How it works The workflow creates an intelligent Telegram bot that: 💬 Maintains conversation history for each user 🧠 Provides contextual AI responses using any AI API service 📱 Handles different message types and commands 🔄 Manages chat sessions with clear functionality 🔌 Easily adaptable to any AI provider (OpenAI, Anthropic, Google AI, etc.) ⚙️ Set up steps 📋 Prerequisites 🤖 Telegram Bot Token (from @BotFather) 🔑 AI API Key (from any AI service provider) 🚀 n8n instance with webhook capability 🛠️ Configuration Steps 🤖 Create Telegram Bot Message @BotFather on Telegram Create new bot with /newbot command Save the bot token for credentials setup 🧠 Choose Your AI Provider OpenAI: Get API key from OpenAI platform Anthropic: Sign up for Claude API access Google AI: Get Gemini API key NVIDIA: Access LLaMA models Hugging Face: Use inference API Any other AI API service 🔐 Set up Credentials in n8n Add Telegram API credentials with your bot token Add Bearer Auth/API Key credentials for your chosen AI service Test both connections 🚀 Deploy Workflow Import the workflow JSON Customize the AI API call (see customization section) Activate the workflow Set webhook URL in Telegram bot settings ✨ Features 🚀 Core Functionality 📨 Smart Message Routing**: Automatically categorizes incoming messages (commands, text, non-text) 🧠 Conversation Memory**: Maintains chat history for each user (last 10 messages) 🤖 AI-Powered Responses**: Integrates with any AI API service for intelligent replies ⚡ Command Support**: Built-in /start and /clear commands 📱 Message Types Handled 💬 Text Messages**: Processed through AI model with context 🔧 Commands**: Special handling for bot commands ❌ Non-text Messages**: Polite error message for unsupported content 💾 Memory Management 👤 User-specific chat history storage 🔄 Automatic history trimming (keeps last 10 messages) 🌐 Global state management across workflow executions 🤖 Bot Commands /start 🎯 - Welcome message with bot introduction /clear 🗑️ - Clears conversation history for fresh start Regular text 💬 - Processed by AI with conversation context 🔧 Technical Details 🏗️ Workflow Structure 📡 Telegram Trigger - Receives all incoming messages 🔀 Message Filtering - Routes messages based on type/content 💾 History Management - Maintains conversation context 🧠 AI Processing - Generates intelligent responses 📤 Response Delivery - Sends formatted replies back to user 🤖 AI API Integration (Customizable) Current Example (NVIDIA): Model: mistralai/mistral-nemotron Temperature: 0.6 (balanced creativity) Max tokens: 4096 Response limit: Under 200 words 🔄 Easy to Replace with Any AI Service: OpenAI Example: { "model": "gpt-4", "messages": [...], "temperature": 0.7, "max_tokens": 1000 } Anthropic Claude Example: { "model": "claude-3-sonnet-20240229", "messages": [...], "max_tokens": 1000 } Google Gemini Example: { "contents": [...], "generationConfig": { "temperature": 0.7, "maxOutputTokens": 1000 } } 🛡️ Error Handling ❌ Non-text message detection and appropriate responses 🔧 API failure handling ⚠️ Invalid command processing 🎨 Customization Options 🤖 AI Provider Switching To use a different AI service, modify the "NVIDIA LLaMA Chat Model" node: 📝 Change the URL in HTTP Request node 🔧 Update the request body format in "Prepare API Request" node 🔐 Update authentication method if needed 📊 Adjust response parsing in "Save AI Response to History" node 🧠 AI Behavior 📝 Modify system prompt in "Prepare API Request" node 🌡️ Adjust temperature and response parameters 📏 Change response length limits 🎯 Customize model-specific parameters 💾 Memory Settings 📊 Adjust history length (currently 10 messages) 👤 Modify user identification logic 🗄️ Customize data persistence approach 🎭 Bot Personality 🎉 Update welcome message content ⚠️ Customize error messages and responses ➕ Add new command handlers 💡 Use Cases 🎧 Customer Support**: Automated first-line support with context awareness 📚 Educational Assistant**: Homework help and learning support 👥 Personal AI Companion**: General conversation and assistance 💼 Business Assistant**: FAQ handling and information retrieval 🔬 AI API Testing**: Perfect template for testing different AI services 🚀 Prototype Development**: Quick AI chatbot prototyping 📝 Notes 🌐 Requires active n8n instance for webhook handling 💰 AI API usage may have rate limits and costs (varies by provider) 💾 Bot memory persists across workflow restarts 👥 Supports multiple concurrent users with separate histories 🔄 Template is provider-agnostic - easily switch between AI services 🛠️ Perfect starting point for any AI-powered Telegram bot project 🔧 Popular AI Services You Can Use | Provider | Model Examples | API Endpoint Style | |----------|---------------|-------------------| | 🟢 OpenAI | GPT-4, GPT-3.5 | https://api.openai.com/v1/chat/completions | | 🔵 Anthropic | Claude 3 Opus, Sonnet | https://api.anthropic.com/v1/messages | | 🔴 Google | Gemini Pro, Gemini Flash | https://generativelanguage.googleapis.com/v1beta/models/ | | 🟡 NVIDIA | LLaMA, Mistral | https://integrate.api.nvidia.com/v1/chat/completions | | 🟠 Hugging Face | Various OSS models | https://api-inference.huggingface.co/models/ | | 🟣 Cohere | Command, Generate | https://api.cohere.ai/v1/generate | Simply replace the HTTP Request node configuration to switch providers!

Automate LLM Testing with GPT-4 Judge & Google Sheets Tracking

by Adam Janes

How it works The workflow loads a list of test cases from a Google Sheet (previous results stored from an LLM) For each test case, we execute a call to an LLM judge in parallel (using HTTP Request + Webhook nodes) The judge uses the Input, Output, and Reference Answer fields from the spreadsheet to mark each LLM response as Pass/Fail The results are logged into a separate sheet in the same Sheets file. Set up steps: Add your credentials for Google Sheets and OpenRouter (or replace the OpenRouter node with your favourite chat model). Make a copy of the example Sheet to populate it with you own test data. Run the workflow with the Execute Workflow button next to the Manual Trigger node.

Multi-User Telegram Bot to Summarize & Repurpose YouTube Videos with GPT-4o

by Aashiq

👤 Who’s it for This workflow is for content creators, marketers, educators, or anyone who wants to instantly summarize YouTube videos and repurpose them into different formats (LinkedIn post, tweet, etc.) via a simple Telegram chatbot. ⚙️ How it works This n8n automation listens for messages in Telegram. If the message contains a YouTube link, it: Extracts the video ID Fetches the video transcript using RapidAPI Cleans the transcript of any special characters Sends it to OpenAI to generate a summary If the message is not a link, it simply acts as an AI chatbot using OpenAI with memory support. ✅ Supports follow-up prompts like: “Make it shorter” “Turn this into a LinkedIn post” “Create a tweet thread” 🧑‍🤝‍🧑 Multi-User Support This Telegram bot supports multiple users simultaneously. It tracks memory and context separately for each user using Telegram's unique chat_id. ✅ Each user gets personalized AI replies ✅ Follow-up commands work per user ✅ No interference between users 🛠️ Requirements A Telegram bot token (get via @BotFather) An OpenAI API Key (from https://platform.openai.com/account/api-keys) A RapidAPI Key and Host (typically youtube-transcript3.p.rapidapi.com) > 🚨 API keys must be added manually — they are not included in the template. 🧩 How to Set It Up Configure the Telegram Trigger node with your bot token. In the HTTP Request node, set: X-RapidAPI-Key: your RapidAPI key X-RapidAPI-Host: your RapidAPI host URL Add your OpenAI API credentials to the AI Agent node. Use the provided sticky notes for guidance inside the workflow itself. 🎛️ How to Customize Modify AI prompt behavior in the AI Agent node Change the text formatting in the Code node Use a different transcript API if preferred Add commands like make it into a blog post, summarize in bullet points, etc. 📌 Notes All nodes are renamed to reflect their function API credentials are removed for security Includes colored boxes and sticky notes to guide the user Compatible with n8n cloud and self-hosted setups

🤖AI & Machine Learning

6 nodes114 views

AI-Powered Automated Outreach with Scheduling, Gemini, Gmail & Google Sheets

by Sarfaraz Muhammad Sajib

💬Communication & Collaboration

AI-Powered Automated Outreach Scheduling with Gemini, Gmail & Google Sheets Automate your lead generation and outreach process seamlessly using AI, Gmail, and Google Sheets—all within n8n. No complicated setup—just import, activate, and start reaching prospects with personalized messages generated by Google Gemini’s AI model. Quick Setup Import the Workflow Download and import the provided workflow into your n8n instance. Connect Your Accounts Authenticate your Google Sheets account. Connect your Gmail account for sending emails. Prepare the Spreadsheet Use this template to set up your leads and tracking sheet. Configure the Gemini API Obtain your Gemini API key. Here Add it to the Gemini API credentials within n8n. Set Scheduling Preferences Customize the Schedule Trigger node to control when the workflow runs. Edit Email Prompts Update the initial and follow-up email prompts to match your outreach tone and goals. Set Rate Limits Configure the rate limiting settings to comply with Gmail sending limits and avoid spam filters. Activate the Workflow Enable the workflow to begin automated outreach to your leads. Track and Manage Leads Monitor responses and update lead statuses directly in your Google Sheet. How It Works Schedule Trigger:** Automatically starts outreach based on your defined schedule Google Sheets Integration:** Fetches leads and updates their status after outreach Email Validation:** Checks if lead emails are valid before sending Website Scraper:** Gathers info from lead websites to personalize messages Google Gemini AI:** Generates tailored cold outreach messages optimized for high response Gmail Node:** Sends personalized emails directly from your Gmail account Core Features Pull leads automatically from Google Sheets Validate emails to avoid bounces Scrape lead websites for custom messaging context Generate AI-crafted outreach emails with dynamic personalization Send emails on schedule without manual intervention Update lead status to track outreach progress AI Integration Uses Google Gemini AI to create professional, friendly, and engaging outreach emails Dynamic prompt templates tailored to each lead’s company and website content Structured JSON output to easily map subject, greeting, and body content 💡 Usage Examples B2B cold outreach campaigns with personalized emails Automated follow-ups based on lead engagement Lead nurturing with context-aware messaging Sales prospecting workflows integrated into your CRM ✨ Benefits Save hours by automating personalized outreach Increase response rates with AI-optimized messaging Keep lead data organized and updated in Google Sheets Fully scalable and customizable n8n workflow Minimal setup, ready to run out-of-the-box

6 nodes107 views

Evaluation metric example: Correctness (judged by AI)

by David Roberts

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: whether an output matches an expected output (i.e. has the same meaning). The workflow takes questions about the causes of historical events and compares them with the reference answers in the dataset. We use an evaluation trigger to read in our dataset It is wired up in parallel with the regular chat trigger so that the workflow can be started from either one. More info If we're evaluating (i.e. the execution started from the evaluation trigger), we calculate the correctness metric using AI We pass this information back to n8n as a metric If we're not evaluating we avoid calculating the metric, to reduce cost

🤖AI & Machine Learning

4 nodes654 views

Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o

by HoangSP

Medical Q&A Chatbot for Urology using RAG with Pinecone and GPT-4o This template provides an AI-powered Q&A assistant for the Urology domain using Retrieval-Augmented Generation (RAG). It uses Pinecone for vector search and GPT-4o for conversational responses. 🧠 Use Case This chatbot is designed for clinics or medical pages that want to automate question answering for Urology-related conditions. It uses a vector store of domain knowledge to return verified responses. 🔧 Requirements ✅ OpenAI API key (GPT-4o or GPT-4o-mini) ✅ Pinecone account with an active index ✅ Verified Urology documents embedded into Pinecone ⚙️ Setup Instructions Create a Pinecone vector index and connect it using the Pinecone credentials node. Upload Urology-related documents to embed using the Create Embeddings for Urology Docs node. Customize the chatbot system message to reflect your medical specialty. Deploy this chatbot on your website or link it with Telegram via the chat trigger node. 🛠️ Components chatTrigger: Listens for user messages and starts the workflow. Medical AI Agent: GPT-based agent guided by domain-specific instructions. RAG Tool Vector Store: Fetches relevant documents from Pinecone using vector search. Memory Buffer: Maintains conversation context. Create Embeddings for Urology Docs: Encodes documents into vector format. 📝 Customization You can replace the knowledge base with any other medical domain by: Updating the documents stored in Pinecone. Modifying the system prompt in the AI Agent node. 📣 CTA This chatbot is ideal for clinics, medical consultants, or educational websites wanting a reliable AI assistant in Urology.

🤖AI & Machine Learning

6 nodes2642 views

Form with Dynamic Dropdown Field

by Obsidi8n