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Curiosity Didn’t Kill the Cat, It Built a Bot: Customizing LLMs With Your Own Content
- To turn a fun, engaging ChatGPT interaction into a fully functional, customized AI feature requires a specific set of steps
- Python must be installed along with Homebrew, a tool for installing and maintaining different Python versions, and then use Python’s virtual environment feature to create an isolated test space exclusively for working with LLMs.
- Request an OpenAI key and save it somewhere securely to work with LLMs.
- Create an assistant and provide it with a set of files to reference by storing them in a vectorDB that is queried for relevant information.
- Python is used to handle the lookup and stream responses by using async functions, which is valuable when creating a web UI. The async functions enable seamless interaction through the web UI, ensuring that each request to the assistant builds upon the previous conversation.
- The real challenge is converting the example to use a streaming response from an OpenAI Assistant with a Thread instead of storing the prompt history and submitting it with each new prompt.
- Sandgarden is an industry-leading AI development platform that offers the flexibility and speed to grow your AI solution as you refine and develop it further.
- Sandgarden provides everything from data management to custom AI integrations, and can help you get started faster with building your own AI-powered tool.
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