AI embeddings have been making waves in the world of artificial intelligence, revolutionizing the way we interact with and build AI applications. In this video, we'll dive into the fundamentals of AI embeddings, explore their use cases, and discuss how they differ from fine-tuning. If you're interested in applying what you learn here to build your own AI chatbot using embeddings and vector databases, be sure to check out the link in our description for our full "Building Apps with AI" course.

Understanding Vector Databases

Vector databases, such as Pinecone, are specialized databases that enable AI embeddings. Unlike traditional databases, vector databases store data in a high-dimensional space, allowing for efficient similarity search and retrieval. Popular vector databases like Pinecone make it easy to store and query embeddings, enabling powerful applications like semantic search and recommendation systems.

Use Cases for AI Embeddings

AI embeddings have a wide range of applications, including:

• Similarity Search: Finding related content based on the semantic similarity of embeddings.
• AI Search and Recommendation systems: Providing personalized recommendations based on user preferences and item similarities.
• Building AI Chatbots with proprietary data: Creating chatbots that can answer questions based on specific knowledge bases, such as PDFs or web pages.
• Anomaly detection and clustering: Identifying unusual data points and grouping similar items together.

Creating AI Embeddings

To create AI embeddings, you first need to convert your raw data, such as text paragraphs, into vector representations. This is typically done using an embedding model, like the ones provided by OpenAI's API. You can send your data to the OpenAI API, which will process it through their model and return a vector embedding. These embeddings can then be stored in a vector database for efficient retrieval and similarity search.

Building a Chatbot with AI Embeddings

One exciting application of AI embeddings is building chatbots that can answer questions based on proprietary data, such as a PDF document. Here's a high-level overview of the process:

1. Convert the PDF to text and chunk it into smaller pieces (e.g., 250-word chunks).
2. Convert each chunk into a vector embedding using OpenAI's API and store the embeddings in a vector database like Pinecone.
3. When a user asks a question, convert the question into a vector embedding using OpenAI's API.
4. Query the vector database to find the most similar embeddings to the question.
5. Use the retrieved embeddings to generate a response using OpenAI's completion API, instructing the model to only answer based on the provided context.

This approach ensures that the chatbot only provides information that is relevant to the uploaded PDF, reducing the risk of hallucination or making up information.

Fine-tuning vs Embeddings

While both fine-tuning and embeddings are techniques used to enhance AI models, they serve different purposes. Fine-tuning involves training a pre-trained model on a specific task or domain, such as joke generation or sentiment analysis, to improve its performance in that area. Embeddings, on the other hand, are used to represent data in a high-dimensional space, enabling similarity search and retrieval based on proprietary information.

At No Code MBA, we offer courses on both fine-tuning and embeddings. If you're interested in learning more about fine-tuning and how to apply it to your AI projects, be sure to check out our dedicated course on the topic.

Building AI Apps with No-Code Tools

No-code tools like Airtable and Bubble have made it easier than ever to build AI applications without extensive coding knowledge. These tools allow you to connect to AI APIs, such as OpenAI and Stable Diffusion, and integrate their capabilities into your applications seamlessly. With the power of AI embeddings and no-code tools, the possibilities for creating innovative AI apps are endless.

If you want to take your AI skills to the next level and learn how to build your own AI apps using no-code tools, we invite you to sign up for No Code MBA. Our comprehensive courses cover everything from the basics of no-code development to advanced topics like AI embeddings and fine-tuning. Sign up now and start your journey towards becoming an AI app builder!

FAQ (Frequently Asked Questions)

What are AI embeddings?

AI embeddings are vector representations of data items that allow us to measure the similarity between different pieces of data. They enable powerful applications like semantic search, recommendation systems, and chatbots that can answer questions based on proprietary data.

What is the difference between fine-tuning and embeddings?

Fine-tuning involves training a pre-trained AI model on a specific task or domain to improve its performance in that area. Embeddings, on the other hand, are used to represent data in a high-dimensional space, enabling similarity search and retrieval based on proprietary information.

Can I build AI apps without coding?

Yes! No-code tools like Airtable and Bubble make it possible to build AI applications without extensive coding knowledge. These tools allow you to connect to AI APIs and integrate their capabilities into your applications seamlessly.

How can I learn more about building AI apps?

No Code MBA offers comprehensive courses on building AI apps using no-code tools. Our courses cover topics like AI embeddings, fine-tuning, and integrating AI APIs into your applications. Sign up now to start learning!