Create This RAG n8n AI Agent in 30 Minutes (You'll Use This Daily)

Key Concepts

AI Agent, RAG (Retrieval Augmented Generation), Vector Database, Pinecone, Embeddings, Text Splitting, Document Loader, Chatbot, Google Drive Integration, AirTable Integration, System Prompt, Tools.

Google Drive Integration and Data Ingestion

• Trigger: The workflow is initiated when a new document is added to a specific Google Drive folder. The trigger is set to "on changes involving a specific folder."
• Google Cloud Console Setup: Connecting Google Drive requires setting up a project in the Google Cloud Console, enabling the Google Drive API, creating credentials (client ID and client secret), and configuring the OAuth consent screen.
• Polling: Naden uses polling to check the Google Drive folder for changes. The polling interval is initially set to 1 minute, but is adjusted to 30 minutes using a cron expression to avoid exceeding the execution limits of the Naden plan.
• Downloading the File: Once a new file is detected, the workflow downloads it using the Google Drive "Download a file" module, using the file ID obtained from the trigger data.
• Example: The video uses a contract document as an example.

Pinecone Vector Database Setup

• Pinecone Account: A Pinecone account is created to store the company's data in a vector database.
• Vector Database: A vector database stores text data in a way that allows for efficient retrieval of information based on semantic similarity.
• Index Creation: An index is created in Pinecone, named "nn3" in the video. The embedding model is configured to "text embedding three small."
• API Key: An API key is generated in Pinecone and used to connect the Pinecone account to Naden.
• Namespace: A namespace, "sales," is used to separate the data within the Pinecone index.
• Embeddings: Embeddings are numerical representations of text that capture semantic meaning. The video explains how embeddings help machines understand the similarity between words.
  • Example: The words "car" and "automobile" have different spellings but similar meanings.
• Document Loader: A document loader converts files (e.g., PDFs) into a format accessible for embedding and retrieval. The default data loader in Naden is used, with the data type set to binary data.
• Text Splitter: A text splitter breaks up larger chunks of text into smaller pieces. The recursive character text splitter is used to keep the meaning intact by breaking up text by paragraphs or sentences.
• Data Transformation: The document is broken up into smaller pieces (32 in the example) and converted into vector embeddings, which are stored in the Pinecone database.

AI Agent Workflow

• Trigger: The workflow is triggered by a chat message received in the Naden chat interface.
• AI Agent Component: The AI Agent component is used to build an AI agent that can answer questions and generate tasks. The agent type is set to "tools."
• Chat Model: The chat model is the "brain" of the AI agent. The OpenAI chat model is used, with GPT-4o-mini selected as the model.
• Memory: Memory is added to the AI agent to remember past conversations. The context window length is set to 5, meaning the agent will remember the last 5 messages.
• Tools: Tools are used to extend the capabilities of the AI agent.
  • Vector Store Question Answer Tool: This tool allows the AI agent to answer questions based on the data stored in the Pinecone vector database.
    • The tool is named "Pinecone Vector store."
    • The description is "Call this tool when a customer has a question and you need to provide an answer to that person."
    • The model is set to GPT-4o-mini.
    • The Pinecone account and index ("nn3") are selected.
    • The namespace is set to "sales."
    • The embedding model is set to "text embedding three small."
  • AirTable Integration: This tool allows the AI agent to create tasks in AirTable.
    • An AirTable account is connected to Naden using a personal access token.
    • The "Create Record" action is used to create new tasks in AirTable.
    • Expressions are used to dynamically fill in the task name and description using AI.
    • The status is set to "To Do."
• System Prompt: The system prompt is used to instruct the AI agent on how to behave. The prompt is updated to instruct the agent to answer questions and create tasks in AirTable if necessary.
  • Example: "Your helpful assistant, please make sure to answer any questions from the user to the best of your ability, and if the user has any tasks for you to complete, please send the task over to AirTable and notify the user that we're currently working on the task and will let them know when it's done."
• Testing: The AI agent is tested by asking questions about the contract document. The agent successfully answers questions and creates tasks in AirTable.
• Alternative Integrations: The video mentions that the AI agent can be integrated with other platforms, such as Twilio (text messaging) and Telegram.

Conclusion

The video demonstrates how to build an AI agent that can be trained on a company's data and used to answer questions and generate tasks. The AI agent uses a RAG system, which involves storing data in a vector database (Pinecone), retrieving relevant information based on user queries, and generating responses using a chat model (OpenAI). The AI agent can be integrated with various platforms, such as Google Drive, AirTable, Twilio, and Telegram. The video emphasizes the potential of AI agents to improve customer service and automate tasks for small to medium-sized businesses.