Default Embedding Function in Chroma

Chroma, a powerful vector database, provides a flexible framework for embedding data points. While you can customize the embedding function to suit your specific needs, Chroma offers a default embedding function that is often suitable for many use cases. In this guide, we will explore the default embedding function in Chroma and its characteristics.

Understanding the Default Embedding Function

Chroma’s default embedding function is designed to provide a reasonable baseline for most applications. It is based on a pre-trained language model, typically a transformer-based model, that has been trained on a large corpus of text. This pre-trained model is capable of capturing semantic relationships between words and phrases, making it suitable for various tasks such as text classification, question answering, and document retrieval.

Key Characteristics of the Default Embedding Function

• Pre-trained Model: The default embedding function is based on a pre-trained language model, which has been trained on a large amount of text data.
• Semantic Understanding: The model is capable of understanding the semantic meaning of words and phrases, allowing it to generate embeddings that capture the underlying relationships between different pieces of text.
• Flexibility: The default embedding function can be used for a wide range of applications, including text classification, question answering, and document retrieval.
• Performance: While the default embedding function may not be optimal for every use case, it often provides reasonable performance and can be a good starting point.

Customizing the Embedding Function

If the default embedding function does not meet your specific requirements, you can customize it by using a different pre-trained model or fine-tuning the model on your own dataset. This can help you achieve better results for certain tasks.