Marlond Criollo@merlimar39

It's no secret that when working with a new library, SDK, or API, software developers often waste hours and hours hopelessly poring over a sea of scattered documentation pages to find the one syntax example or function parameter datatype they needed. With the arrival of AI tools such as ChatGPT, sometimes developers can get lucky and get the exact code they need simply by asking the LLM. However, traditional LLM’s knowledge pools are limited to their training data, so when they are asked about perhaps newer tech, they may be rendered useless, or even worse, hallucinate and spew nonsense, wasting even more of a developer’s time. Pylibrarian is a special chatbot that solves all of these headaches by granting LLM access to complete documentation for Python’s most popular libraries using RAG architecture. Pylibrarian was built by processing, embedding (using cohere.embed), and storing documentation pages into Weaviate’s vector database. Upon a user query, we can semantically search for the most relevant pages of documentation to that query. Using Cohere’s chat endpoint’s document mode, the chatbot synthesizes a response citing the documents, leading to far more consistent, grounded responses.