Today, we intend to equip you with a solid understanding of what AI embeddings are, their use cases, the function of Vector databases in enabling embeddings, and the fine distinctions between fine-tuning and embeddings. By the end of this blog, you should have a grasp on the purpose and workings of AI embeddings, and be ready to build your AI apps using this technology.

Unveiling AI Embeddings

AI Embeddings enable us to gauge the similarity between different pieces of text.

Their core lies in the concept of representing data items as vectors, comparable to stars spread in the outer space. In this 3D space, the 'distance' between these 'stars' signifies the similarity between embeddings. So, just like outer space, a vector database houses these vector embeddings, where a shorter distance between embeddings signifies a higher similarity.

Embeddings can be leveraged in various ways: for searching or recommending terms based on their relatedness, or building chatbots that possess knowledge of proprietary data like a PDF. This ensures that the chatbot only responds when the answer is based on the context of the data, eliminating the risk of hallucination, where chatbots fabricate information if they do not know the answer.

Use Cases of AI Embeddings

The versatility of embeddings finds applications in several domains:

1. Search Recommendations: They enable search for related terms or recommend similar items.
2. Classification: Grouping related items together.
3. Anomaly Detection: Identifying data points that don't match the norm.
4. Clustering: Assembling similar data points.

Creating Vector Embeddings

Vector embeddings are created by sending raw data (like a paragraph of text) through an embedding model. The OpenAI API, for instance, allows us to send data and receive back a vector embedding, which we can then send to a vector database. While OpenAI is a popular and efficient choice, there are numerous other methods available to create embeddings.

Building a Chatbot with AI Embeddings

Now, let's delve into a real-life application – creating a chatbot that responds based on information in a PDF.

1. The process begins with converting the PDF into text, which is then divided into smaller data chunks (e.g., 250 words each).
2. These chunks are then converted into vector embeddings and stored in a vector database.
3. When a user asks a question, it is converted into a vector embedding using OpenAI. A query is then run with a vector database like Pinecone, which returns the ten most similar vector embeddings.
4. These embeddings are used to create an OpenAI completion prompt, instructing the model to answer the question based on the information given from the embeddings. If it cannot answer the question based on this information, it will respond with "I do not know the answer."

Vector Databases and Their Role

Vector databases, like Pinecone, are specialized databases designed to store and search for embeddings. Traditional databases can't house embeddings due to the need for a 3D storage system, like the outer space analogy. A vector database not only stores these embeddings but also enables the search for relatedness between vectors.

Fine-Tuning Vs. Embeddings

In certain scenarios, fine-tuning may be preferred over embeddings. Fine-tuning helps improve an AI-based model to perform better on a specific task. For instance, if you want a model to get better at cracking jokes, you can fine-tune it on numerous examples of jokes that align with your humor.

Embeddings, on the other hand, are best suited when the AI needs to answer specific questions based on proprietary information accurately. Thus, it's crucial to make an informed decision on whether to use fine-tuning or embeddings based on the specific requirements of your AI application.

As we close this blog post, we hope this gives you an introductory understanding of AI embeddings. Feel free to reach out if you have any questions. Don't forget to check out our fine-tuning course to dive deeper into AI model training.

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