Word Embeddings: Giving Your ChatBot Context For Better Answers

OpenAI's ChatGPT, built on large language models such as GPT-3, demonstrates remarkable intelligence but is limited by a knowledge cutoff in 2021 and a propensity to fabricate information when uncertain. To overcome these limitations and enhance its ability to answer questions, a strategy involving the use of Python, OpenAI API, and word embeddings is proposed. This approach entails creating a bot capable of generating continuous integration pipelines formatted in YAML for Semaphore CI/CD by providing relevant context to the model. The process involves calculating word embeddings of prompts, querying a vector database for semantically similar documents, and supplying this context to the model to improve accuracy. The implementation uses Pinecone for managing vector data and OpenAI's text-embedding-ada-002 for computing embeddings. Despite the model's inability to learn beyond its training data, this method allows it to respond more accurately by leveraging its text comprehension abilities. Additionally, the bot can be adapted to function as a conversation partner by managing the token limits through strategies such as message summarization or using models with larger token capacities. The success of this approach hinges on high-quality, curated context data, emphasizing the art and experimentation involved in tuning the bot for specific needs.