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AI & Machine Learning Workflows

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Create an AI Data Analyst Chatbot for Automated Data Retrieval and Analysis
Automate your data analysis by building an AI-powered chatbot that retrieves and processes data from Google Sheets or databases using n8n.
n8nFree
Automate Weekly AI Research Summaries with arXiv and Weaviate
Automatically gather AI and machine learning articles from arXiv, enrich them with topic categories, and generate a concise weekly summary email using Weaviate.
n8nFree
Perform Hybrid Search on Legal Datasets Using Qdrant and BM25/mxbai
This workflow demonstrates how to conduct a hybrid search on a legal dataset using Qdrant, combining BM25 keyword retrieval and mxbai semantic retrieval for effective legal question-answering.
n8nFree

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Build a PDF Document RAG System with Mistral OCR, Qdrant, and Gemini AI

This workflow is designed to **process PDF documents** using **Mistral's OCR** capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (**RAG**) for answering questions. Here's how it functions: Once configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications. --- ### **Benefits** * **End-to-End Automation** No manual interaction is needed: documents are read, processed, and made queryable with minimal setup. * **Scalable and Modular** The workflow uses subflows and batching, making it easy to scale and customize. * **Multi-Model Support** Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelligent answering—taking advantage of the strengths of each. * **Real-Time Q&A** With RAG integration, users can query document content through natural language and receive accurate responses grounded in the PDF data. * **Light or Full Mode** Users can choose to index full page content or only summarized text, optimizing for either performance or richness. --- ### **How It Works** 1. **PDF Processing with Mistral OCR**: - The workflow starts by uploading a PDF file to Mistral's API, which performs OCR to extract text and metadata. - The extracted content is split into manageable chunks (e.g., pages or sections) for further processing. 2. **Vector Storage in Qdrant**: - The extracted text is converted into embeddings using OpenAI's embedding model. - These embeddings are stored in a Qdrant vector database, enabling efficient similarity searches for RAG. 3. **Question-Answering with RAG**: - When a user submits a question via a chat interface, the workflow retrieves relevant text chunks from Qdrant using vector similarity. - A language model (Google Gemini) generates answers based on the retrieved context, providing accurate and context-aware responses. 4. **Optional Summarization**: - The workflow includes an optional summarization step using Google Gemini to condense the extracted text for faster processing or lighter RAG usage. --- ### **Set Up Steps** To deploy this workflow in n8n, follow these steps: 1. **Configure Qdrant Database**: - Replace `QDRANT_URL` and `COLLECTION` in the Create collection and Refresh collection nodes with your Qdrant instance details. - Ensure the Qdrant collection is configured with the correct vector size (e.g., 1536 for OpenAI embeddings) and distance metric (e.g., Cosine). 2. **Set Up Credentials**: - Add credentials for: - **Mistral Cloud API** (for OCR processing). - **OpenAI API** (for embeddings). - **Google Gemini API** (for chat and summarization). - **Google Drive** (if sourcing PDFs from Drive). - **Qdrant API** (for vector storage). 3. **PDF Source Configuration**: - If using Google Drive, specify the folder ID in the Search PDFs node. - Alternatively, modify the workflow to accept PDFs from other sources (e.g., direct uploads or external APIs). 4. **Customize Text Processing**: - Adjust chunk size and overlap in the Token Splitter node to optimize for your document type. - Choose between raw text or summarized content for RAG by toggling between the Set page and Summarization Chain nodes. 5. **Test the RAG**: - Trigger the workflow manually or via a chat message to verify OCR, embedding, and Qdrant storage. - Use the Question and Answer Chain node to test query responses. 6. **Optional Sub-Workflows**: - The workflow supports execution as a sub-workflow for batch processing (e.g., handling multiple PDFs). --- ### **Need help customizing?** [Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/).

Index Legal Documents for Hybrid Search Using Qdrant, OpenAI, and BM25

Transform and index a legal dataset into Qdrant for hybrid retrieval, combining dense and sparse vectors for semantic and keyword-based searches.

Automate Legal Q&A with AI and Document Retrieval via Telegram

This workflow automates legal question and answer sessions by leveraging AI to retrieve information from a pre-indexed legal document library. It integrates OpenAI, Pinecone, and Telegram to provide instant, accurate legal assistance.

Automate Real-Time Google Sheets Analysis with AI Chatbot

Leverage AI to transform Google Sheets data into actionable insights through a conversational chatbot interface, ideal for non-technical users seeking real-time analysis.

Automate PDF Q&A System with LlamaIndex, OpenAI, and Pinecone

This workflow automates the process of parsing, normalizing, and storing PDF content for Retrieval-Augmented Generation (RAG) using LlamaIndex, OpenAI embeddings, and Pinecone. Ideal for developers and teams handling structured documents like insurance policies.

Automate Compliance Validation with AI and Vector Databases

This workflow automates the compliance validation process by using AI to compare uploaded documents against specified policies or procedures. It utilizes vector embeddings to ensure accurate and efficient analysis.

Legal Agreement Summariser

This Zap helps streamline the process of summarising legal agreements. It's triggered when a new file is added to a specified Google Drive. folder. The workflow then uses AI to summarise the agreement and extract key terms like dates, amounts, and counterparty names, and then creates a new record in a Notion database with the summarised information.

Automate Legal Document Analysis with AI and Google Integration

This workflow automates the analysis of PDF documents using AI, extracting and summarizing key information, and distributing results via Gmail and Google Sheets. Ideal for legal, compliance, or contract review scenarios.

Automated Backup of Transaction Logs for Enhanced Data Management

This workflow automates the backup process of transaction logs, ensuring data integrity and easy retrieval.

Automate Weekly AI-Generated Robotics Newsletters

This workflow automates the aggregation of real-time robotics news, filters and deduplicates the content, and uses OpenAI's GPT model to generate a polished weekly newsletter. Ideal for media publishers and content creators, it ensures timely, AI-curated journalism ready for publication or email delivery.

Automate Document Embedding and Q&A with Voyage-Context-3 and MongoDB Atlas

This workflow automates the process of embedding research papers using Voyage-Context-3 and storing them in MongoDB Atlas. It also sets up a Q&A system to evaluate the effectiveness of the embeddings.

Automate Document Indexing from Google Drive to Pinecone with OpenAI Embeddings

This workflow monitors a Google Drive folder for new documents, processes them with OpenAI embeddings, and indexes them in a Pinecone vector database for efficient retrieval-augmented generation (RAG) and semantic search.

Automate Blog Engagement with GPT-5 Generated Comments for WordPress

This workflow automatically generates realistic comments for your WordPress articles using AI. It makes your blog look more active, improves engagement, and can even support SEO by adding keyword-relevant comments. ## How It Works - Fetches all published blog posts from your WordPress site via the REST API. - Builds a tailored AI prompt using the article's title, excerpt, and content. - Uses OpenAI to generate a short, natural-sounding comment (some positive, some neutral, some longer, some shorter). - Assigns a random commenter name and email. - Posts the generated comment back to WordPress. ### Requirements 1. n8n version: 1.49.0 or later (recommended). 2. Active OpenAI API key. 3. WordPress site with REST API enabled. 4. WordPress API credentials (username + application password). ## Setup Instructions - Import this workflow into n8n. - Add your credentials in n8n > Credentials: - OpenAI API (API key). - WordPress API (username + application password). - Replace the sample URL https://example.com with your own WordPress site URL. - Execute manually or schedule it to run periodically. ### Categories - AI & Machine Learning - WordPress - Content Marketing - Engagement #### Tags - ai - openai - wordpress - comments - automation - engagement - n8n

Ask a Human for Help When the AI Doesn't Know the Answer

This is a workflow that tries to answer user queries using the standard GP-4 model. If it can't answer, it sends a message to Slack to ask for human help. It prompts the user to supply an email address. This workflow is used in [Advanced AI examples | Ask a human](https://docs.n8n.io/advanced-ai/examples/human-fallback/) in the documentation. To use this workflow: 1. Load it into your n8n instance. 2. Add your credentials as prompted by the notes. 3. Configure the Slack node to use your Slack details, or swap out Slack for a different service.

E-commerce Product Fine-tuning With Bright Data and OpenAI

![finetuning gpt4o.PNG](fileId:1577) This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automates the process of scraping product data from e-commerce websites and using it to fine-tune a custom OpenAI GPT model for generating high-quality marketing copy and product descriptions. ### Main Use Cases - [Fine-tune OpenAI models](https://platform.openai.com/docs/guides/fine-tuning/fine-tuning) with real product data from hundreds of supported e-commerce websites for marketing content generation. - Create custom AI models specialized in writing compelling product descriptions across different industries and platforms. - Automate the entire pipeline from data collection to model training using Bright Data's extensive scraper library. - Generate marketing copy using your custom-trained model via an interactive chat interface. ### How it works The workflow operates in two main phases: model training and model usage, organized into these stages: 1. **Data Collection & Processing** - Manually triggered to start the fine-tuning process. - Uses [Bright Data's web scraper](https://brightdata.com/products/web-scraper) to extract product information from any supported e-commerce platform (Amazon, eBay, Shopify stores, Walmart, Target, and hundreds of other websites). - Collects product titles, brands, features, descriptions, ratings, and availability status from your chosen platform. - Easily customizable to scrape from different websites by simply changing the dataset configuration and product URLs. 2. **Training Data Preparation** - A Code node processes the scraped product data to create training examples in OpenAI's required JSONL format. - For each product, generates a complete training example with: - System message defining the AI's role as a marketing assistant. - User prompt containing specific product details (title, brand, features, original description snippet). - Assistant response providing an ideal marketing description template. - Compiles all training examples into a single JSONL file ready for OpenAI fine-tuning. 3. **Model Fine-tuning** - Uploads the training file to OpenAI using the OpenAI File Upload node. - Initiates a fine-tuning job via HTTP Request to OpenAI's fine-tuning API using the GPT-4o-mini model as the base. - The fine-tuning process runs on OpenAI's servers to create your custom model. 4. **Interactive Chat Interface** - Provides a chat trigger that allows real-time interaction with your fine-tuned model. - An AI Agent node connects to your custom-trained OpenAI model. - Users can chat with the model to generate product descriptions, marketing copy, or other content based on the training. 5. **Custom Model Integration** - The OpenAI Chat Model node is configured to use your specific fine-tuned model ID. - Delivers responses trained on your product data for consistent, high-quality marketing content. ### Summary Flow: Manual Trigger → Scrape E-commerce Products (Bright Data) → Process & Format Training Data (Code) → Upload Training File (OpenAI) → Start Fine-tuning Job (HTTP Request) | Parallel: Chat Trigger → AI Agent → Custom Fine-tuned Model Response ### Benefits: - Fully automated pipeline from raw product data to trained AI model. - Works with hundreds of different e-commerce websites through Bright Data's extensive scraper library. - Creates specialized models trained on real e-commerce data for authentic marketing copy across various industries. - Scalable solution that can be adapted to different product categories, niches, or websites. - Interactive chat interface for immediate access to your custom-trained model. - Cost-effective fine-tuning using OpenAI's most efficient model (GPT-4o-mini). - Easily customizable with different websites, product URLs, training prompts, and model configurations. ### Setup Requirements: - Bright Data API credentials for web scraping (supports hundreds of e-commerce websites). - OpenAI API key with fine-tuning access. - Replace placeholder credential IDs and model IDs with your actual values. - Customize the product URLs list and Bright Data dataset for your specific website and use case. - The workflow can be adapted for any e-commerce platform supported by Bright Data's scraping infrastructure.

Binance Spot Market Quant AI Agent - Advanced Trading Reports with GPT-4o

Leverage AI for generating structured swing-trading reports on Binance Spot Market with advanced indicators.

Automated Stock Analysis and Recommendation Workflow

This workflow automates the process of stock analysis, providing insights and recommendations based on technical and sentiment analysis.

Evaluation Metrics: Answer Similarity

### This n8n template demonstrates how to calculate the evaluation metric Similarity which in this scenario, measures the consistency of the agent. The scoring approach is adapted from the open-source evaluations project [RAGAS](https://docs.ragas.io/) and you can see the source here [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py) ### How it works - This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation. - For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them. - A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination. ### Requirements - n8n version 1.94+ - Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)

Detect Hallucinations Using Specialised Ollama Model Bespoke-Minicheck

# Fact-Checking Workflow Documentation ## Overview This workflow is designed for automated fact-checking of texts. It uses AI models to compare a given text with a list of facts and identify potential discrepancies or hallucinations. ## Components ### 1. Input - The workflow can be initiated in two ways: a) Manually via the When Clicking Test Workflow Trigger b) By calling from another workflow via the When Executed by Another Workflow trigger - Required inputs: - `facts`: A list of verified facts - `text`: The text to be checked ### 2. Text Preparation - The Code node splits the input text into individual sentences - Takes into account date specifications and list elements ### 3. Fact Checking - Each sentence is individually compared with the given facts - Uses the bespoke-minicheck Ollama model for verification - The model responds with Yes or No for each sentence ### 4. Filtering and Aggregation - Sentences marked as No (not fact-based) are filtered - The filtered results are aggregated ### 5. Summary - A larger language model (Qwen2.5) creates a summary of the results - The summary contains: - Number of incorrect factual statements - List of incorrect statements - Final assessment of the article's accuracy ## Usage 1. Ensure the bespoke-minicheck model is installed in Ollama (`ollama pull bespoke-minicheck`) 2. Prepare a list of verified facts 3. Enter the text to be checked 4. Start the workflow 5. The results are output as a structured summary ## Notes - The workflow ignores small talk and focuses on verifiable factual statements - Accuracy depends on the quality of the provided facts and the performance of the AI models ## Customization Options - The summarization function can be adjusted or removed to return only the raw data of the issues found - The AI models used can be exchanged if needed This workflow provides an efficient method for automated fact-checking and can be easily integrated into larger systems or editorial workflows.

Multi-Jurisdiction Smart Contract Compliance Monitor with Automated Alerts

### **How it works** - **Regulatory monitoring**: Continuously tracks changes in laws, regulations, and compliance requirements across multiple jurisdictions. - **Contract analysis**: AI-powered review of existing contracts to identify compliance gaps and risks. - **Automated alerts**: Real-time notifications when regulatory changes affect your contracts or business operations. - **Compliance reporting**: Generates audit-ready reports and documentation for regulatory compliance. ### **Set up steps** - **Legal databases**: Connect to legal research platforms (Westlaw, LexisNexis, EUR-Lex). - **Contract repository**: Integrate with your contract management system or document storage. - **Regulatory feeds**: Configure government and regulatory body RSS feeds and APIs. - **AI legal analysis**: Set up OpenAI or specialized legal AI for contract analysis. - **Compliance calendar**: Integrate with calendar systems for deadline tracking. - **Audit trail**: Configure logging and documentation systems for compliance records. ### **Key Features** - **Multi-jurisdiction monitoring**: Tracks regulatory changes across different countries and regions. - **Risk assessment**: Automatically scores compliance risks and potential impact. - **Real-time alerts**: Instant notifications when regulations affecting your business change. - **Gap analysis**: Identifies discrepancies between current contracts and new requirements. - **AI-powered analysis**: Uses natural language processing to understand legal text. - **Compliance dashboard**: Visual overview of compliance status across all contracts. - **Automated remediation**: Suggests contract amendments and compliance actions. - **Mobile notifications**: Critical compliance alerts on mobile devices. ### **Compliance areas monitored** - **Data protection**: GDPR, CCPA, and other privacy regulations. - **Financial services**: Banking regulations, securities law, anti-money laundering. - **Healthcare**: HIPAA, medical device regulations, pharmaceutical compliance. - **Employment law**: Labor regulations, workplace safety, discrimination laws. - **Environmental**: ESG requirements, environmental protection regulations. - **Industry-specific**: Sector-specific regulations and standards. ### **Contract types supported** - **Vendor agreements**: Supplier contracts and service agreements. - **Employment contracts**: Employee agreements and contractor terms. - **Data processing agreements**: Privacy and data handling contracts. - **Customer agreements**: Terms of service and customer contracts. - **Partnership agreements**: Joint ventures and strategic partnerships. - **Licensing agreements**: Software licenses and intellectual property. ### **Automated responses** - **Low risk (0-30)**: Routine monitoring and documentation. - **Medium risk (31-60)**: Enhanced review and stakeholder notification. - **High risk (61-80)**: Immediate legal review and action planning. - **Critical risk (81-100)**: Emergency legal intervention and compliance measures. ### **Integration capabilities** - **Legal research**: Westlaw, LexisNexis, Bloomberg Law. - **Document management**: SharePoint, Google Drive, Dropbox. - **Contract systems**: DocuSign, PandaDoc, ContractWorks. - **Communication tools**: Slack, Teams, email for legal team alerts. - **Calendar systems**: Outlook, Google Calendar for compliance deadlines. This workflow ensures continuous legal compliance by monitoring regulatory changes and automatically assessing their impact on your contracts and business operations.

Automate Your Content Creation: Unsplash to Pinterest Workflow

Effortlessly transform images from Unsplash into engaging Pinterest posts with this automated workflow.

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