Embedding Model

When we use big language models (like the ones that chat or answer questions), we often want to help them understand not just the language, but also specific information from texts or documents we have. However, these models don't understand words and sentences the way we do; they need everything turned into numbers. This is where embeddings come into play.

Why We Need Embeddings

Embeddings are a way to convert words, phrases, or whole documents into a form (specifically, number lists or vectors) that these models can understand. This conversion is crucial because:

• Understanding Context: It helps the model grasp not just the words, but also the meaning and context. For example, the word "bank" can mean the side of a river or a place where money is kept, and embeddings help the model figure out which meaning is being used.

• Handling Variety: Different words or phrases can express similar ideas. Embeddings help the model see these similarities by placing similar meanings close together in a number space.

The Use of Embeddings

Embeddings have several important uses:

• Improving Model Performance: By converting text into a form that the model understands better, embeddings help improve how well the model can answer questions, make recommendations, or even create new text that makes sense.

• Customizing Responses: When we use embeddings to include specific information (like from a particular book or website), it helps the model tailor its responses more closely to that information, making it more useful and accurate for specific topics.

• Facilitating Learning: Embeddings can help models learn new information or update their understanding based on new texts or documents, making them more adaptable and knowledgeable over time.

GenAI Stack and Embeddings

The GenAI Stack supports various embedding models like OpenAI Embeddings, Cohere Embeddings, and HuggingFace Embeddings. This variety allows you to choose the best embedding approach for your needs, ensuring that your language model can understand and work with the specific texts or documents you're interested in. By using embeddings, you're essentially translating the unique language of your data into a language that the model can understand, greatly enhancing the model's ability to provide relevant and accurate responses.

Mostly embedding components are connected to vector database so that the vector database can use the embedding function to embed the incoming documents.

Example of embedding component paired in a data pipeline:

Conclusion

There are different types fo embeddings we support. To know more about embedding refer the component documentation here.