How to Cost-Effectively Integrate ChatGPT💰?
Large language models like GPT-3 have undoubtedly changed the way we approach natural language processing (NLP), providing us with unprecedented levels of accuracy and fluency in generating text. However, integrating these models into real-world applications can be a challenge, both in terms of cost and complexity.
That’s where LangChain comes in — it’s a framework that simplifies the process of developing applications powered by language models. LangChain provides a streamlined approach to building NLP-powered applications, making it easier to use large language models while reducing the complexity of integration.
One exciting application of LangChain is its ability to enable the development of question-answering (QA) systems. These systems are particularly useful across industries including finance, healthcare, legal and so on, where the ability to quickly and accurately answer questions can make a significant difference.
We recently tested LangChain for a QA system that works with PDF documents. We opted to use Sentence-transformers for embedding and Qdrant for vector storage and retrieval instead of relying solely on OpenAI models, which can be costly at scale. By doing so, we were able to reduce costs by up to 20x while still achieving high performance.
