Airtable + GPT: Prototyping a Light-weight RAG System with No-Code Instruments

Picture by Editor | ChatGPT

# Introduction

 
Prepared for a sensible walkthrough with little to no code concerned, relying on the strategy you select? This tutorial exhibits learn how to tie collectively two formidable instruments — OpenAI‘s GPT fashions and the Airtable cloud-based database — to prototype a easy, toy-sized retrieval-augmented era (RAG) system. The system accepts question-based prompts and makes use of textual content knowledge saved in Airtable because the information base to provide grounded solutions. In case you’re unfamiliar with RAG methods, or need a refresher, don’t miss this article series on understanding RAG.

# The Components

 
To apply this tutorial your self, you may want:

• An Airtable account with a base created in your workspace.
• An OpenAI API key (ideally a paid plan for flexibility in mannequin alternative).
• A Pipedream account — an orchestration and automation app that allows experimentation beneath a free tier (with limits on every day runs).

# The Retrieval-Augmented Technology Recipe

 
The method to construct our RAG system isn’t purely linear, and a few steps may be taken in numerous methods. Relying in your stage of programming information, you could go for a code-free or practically code-free strategy, or create the workflow programmatically.

In essence, we’ll create an orchestration workflow consisting of three components, using Pipedream:

1. Set off: much like an online service request, this ingredient initiates an motion circulation that passes by way of the subsequent components within the workflow. As soon as deployed, that is the place you specify the request, i.e., the consumer immediate for our prototype RAG system.
2. Airtable block: establishes a connection to our Airtable base and particular desk to make use of its knowledge because the RAG system’s information base. We’ll add some textual content knowledge to it shortly inside Airtable.
3. OpenAI block: connects to OpenAI’s GPT-based language fashions utilizing an API key and passes the consumer immediate alongside the context (retrieved Airtable knowledge) to the mannequin to acquire a response.

However first, we have to create a brand new desk in our Airtable base containing textual content knowledge. For this instance, I created an empty desk with three fields (ID: one-line textual content, Supply: one-line textual content, Content material: lengthy textual content), after which imported knowledge from this publicly available small dataset containing textual content with fundamental information about Asian nations. Use the CSV and hyperlink choices to import the information into the desk. Extra details about creating tables and importing knowledge is in this article.

After getting your information base prepared in Airtable, let’s change to Pipedream for the remainder of the method! Register if you happen to haven’t accomplished so but, and log in. There are a number of methods to provoke a workflow. Certainly one of them is by describing what you wish to create to Pipedream’s AI agent, which is intuitive however could lead to errors needing fixes, as we’ll illustrate shortly. Pipedream has two parallel UIs: one for manually constructing and enhancing workflows, and one for conversations with its AI agent, referred to as String.

Pipedream’s AI conversational interface (String)

In case you go for letting String construct an approximate model of your required workflow, describe clearly what to construct, with a immediate which will look one thing like:

I would wish to orchestrate a easy toy RAG software that makes use of an Airtable desk containing textual content, and OpenAI’s GPT fashions (I’ve a paid API key).

Pipedream’s language mannequin could return a solution so that you can approve (at your individual danger), like this:

I am going to create a easy RAG (Retrieval-Augmented Technology) agent that connects your Airtable desk with OpenAI’s GPT fashions. This is what I am going to construct:

Arrange a set off to simply accept queries (by way of webhook or guide execution)
Search and retrieve related textual content out of your Airtable desk based mostly on the question
Use the retrieved textual content as context for OpenAI GPT to generate an knowledgeable response
Return the AI-generated reply that is grounded in your Airtable knowledge
This will provide you with a fundamental RAG system the place you’ll be able to ask questions and get solutions based mostly on the content material saved in your Airtable, enhanced by GPT’s reasoning capabilities.

For the sake of experimentation and “seeing what occurs,” approve the prompt plan of action to acquire a three-block workflow much like the one within the picture proven above.

Since this isn’t the core Pipedream editor UI, change to it by clicking “Open Pipedream” within the higher proper nook. A brand new tab will open with the precise Pipedream workflow editor.

For the set off block, a URL is robotically generated with a syntax much like this one I obtained for mine: https://eoupscprutt37xx.m.pipedream.web. Click on it and, within the settings pane that opens on the right-hand aspect, guarantee the primary couple of choices are set to “Full HTTP request” and “Return a static response.”

For the second block (Airtable motion) there could also be a little bit work to do. First, connect with your Airtable base. In case you’re working in the identical browser, this could be simple: check in to Airtable from the pop-up window that seems after clicking “Join new account,” then observe the on-screen steps to specify the bottom and desk to entry:

Pipedream workflow editor: connecting to Airtable

Right here comes the tough half (and a motive I deliberately left an imperfect immediate earlier when asking the AI agent to construct the skeleton workflow): there are a number of varieties of Airtable actions to select from, and the precise one we want for a RAG-style retrieval mechanism is “Record data.” Chances are high, this isn’t the motion you see within the second block of your workflow. If that’s the case, take away it, add a brand new block within the center, choose “Airtable,” and select “Record data.” Then reconnect to your desk and check the connection to make sure it really works.

That is what a efficiently examined connection appears like:

Pipedream workflow editor: testing connection to Airtable

Final, arrange and configure OpenAI entry to GPT. Maintain your API key useful. In case your third block’s secondary label isn’t “Generate RAG response,” take away the block and exchange it with a brand new OpenAI block with this subtype.

Begin by establishing an OpenAI connection utilizing your API key:

Establishing OpenAI connection

The consumer query subject ought to be set as {{ steps.set off.occasion.physique.check }}, and the information base data (your textual content “paperwork” for RAG from Airtable) have to be set as {{ steps.list_records.$return_value }}.

You possibly can hold the remaining as default and check, however you could encounter parsing errors widespread to those sorts of workflows, prompting you to leap again to String for assist and automated fixes utilizing the AI agent. Alternatively, you’ll be able to immediately copy and paste the next into the OpenAI element’s code subject on the backside for a sturdy answer:

import openai from "@pipedream/openai"

export default defineComponent({
  identify: "Generate RAG Response",
  description: "Generate a response utilizing OpenAI based mostly on consumer query and Airtable information base content material",
  kind: "motion",
  props: {
    openai,
    mannequin: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    query: {
      kind: "string",
      label: "Consumer Query",
      description: "The query from the webhook set off",
      default: "{{ steps.set off.occasion.physique.check }}",
    },
    knowledgeBaseRecords: {
      kind: "any",
      label: "Information Base Data",
      description: "The Airtable data containing the information base content material",
      default: "{{ steps.list_records.$return_value }}",
    },
  },
  async run({ $ }) {
    // Extract consumer query
    const userQuestion = this.query;
    
    if (!userQuestion) {
      throw new Error("No query offered from the set off");
    }

    // Course of Airtable data to extract content material
    const data = this.knowledgeBaseRecords;
    let knowledgeBaseContent = "";
    
    if (data && Array.isArray(data)) {
      knowledgeBaseContent = data
        .map(document => {
          // Extract content material from fields.Content material
          const content material = document.fields?.Content material;
          return content material ? content material.trim() : "";
        })
        .filter(content material => content material.size > 0) // Take away empty content material
        .be a part of("nn---nn"); // Separate completely different information base entries
    }

    if (!knowledgeBaseContent) {
      throw new Error("No content material present in information base data");
    }

    // Create system immediate with information base context
    const systemPrompt = `You're a useful assistant that solutions questions based mostly on the offered information base. Use solely the knowledge from the information base beneath to reply questions. If the knowledge isn't accessible within the information base, please say so.

Information Base:
${knowledgeBaseContent}

Directions:
- Reply based mostly solely on the offered information base content material
- Be correct and concise
- If the reply isn't within the information base, clearly state that the knowledge isn't accessible
- Cite related components of the information base when attainable`;

    // Put together messages for OpenAI
    const messages = [
      {
        role: "system",
        content: systemPrompt,
      },
      {
        role: "user",
        content: userQuestion,
      },
    ];

    // Name OpenAI chat completion
    const response = await this.openai.createChatCompletion({
      $,
      knowledge: {
        mannequin: this.mannequin,
        messages: messages,
        temperature: 0.7,
        max_tokens: 1000,
      },
    });

    const generatedResponse = response.generated_message?.content material;

    if (!generatedResponse) {
      throw new Error("Did not generate response from OpenAI");
    }

    // Export abstract for consumer suggestions
    $.export("$abstract", `Generated RAG response for query: "${userQuestion.substring(0, 50)}${userQuestion.size > 50 ? '...' : ''}"`);

    // Return the generated response
    return {
      query: userQuestion,
      response: generatedResponse,
      model_used: this.mannequin,
      knowledge_base_entries: data ? data.size : 0,
      full_openai_response: response,
    };
  },
})

If no errors or warnings seem, you have to be prepared to check and deploy. Deploy first, after which check by passing a consumer question like this within the newly opened deployment tab:

Testing deployed workflow with a immediate asking what’s the capital of Japan

If the request is dealt with and all the things runs accurately, scroll all the way down to see the response returned by the GPT mannequin accessed within the final stage of the workflow:

GPT mannequin response

Effectively accomplished! This response is grounded within the information base we inbuilt Airtable, so we now have a easy prototype RAG system that mixes Airtable and GPT fashions by way of Pipedream.

# Wrapping Up

 
This text confirmed learn how to construct, with little or no coding, an orchestration workflow to prototype a RAG system that makes use of Airtable textual content databases because the information base for retrieval and OpenAI’s GPT fashions for response era. Pipedream permits defining orchestration workflows programmatically, manually, or aided by its conversational AI agent. By means of the creator’s experiences, we succinctly showcased the professionals and cons of every strategy.
 
 

Iván Palomares Carrascosa is a pacesetter, author, speaker, and adviser in AI, machine studying, deep studying & LLMs. He trains and guides others in harnessing AI in the true world.