Hey hacker news, we launched a few weeks ago as a GPT-powered chatbot for developer docs, and quickly realized that the value of what we’re doing isn’t the chatbot itself. Rather, it’s the time we save developers by automating the extraction of data from their SaaS tools (Github, Zendesk, Salesforce, etc) and helping transform it to contextually relevant chunks that fit into GPT’s context window. A lot of companies are building prototypes with GPT right now and they’re all using some combination of Langchain/Llama Index + Weaviate/Pinecone + GPT3.5/GPT4 as their stack for retrieval augmented generation (RAG). This works great for prototypes, but what we learned was that as you scale your RAG app to more users and ingest more sources of content, it becomes a real pain to manage your data pipelines. For example, if you want to ingest your developer docs, process it into chunks of <500 tokens, and add those chunks to a vector store, you can build a prototype with Langchain fairly qui...