Vector Databases and Embeddings With ChromaDB
The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots like OpenAI’s ChatGPT, and perhaps you’ve even experienced their remarkable ability to reason about natural language processing (NLP) problems.
Modern LLMs, while imperfect, can accurately solve a wide range of problems and provide correct answers to many questions. However, due to the limits of their training and the number of text tokens they can process, LLMs aren’t a silver bullet for all tasks.
You wouldn’t expect an LLM to deliver relevant responses about topics that don’t appear in its training data. For example, if you asked ChatGPT