- Introduces embeviz - a simple side project for exploring text embeddings
- Uses backend API with GoFiber framework and frontend UI with React and React Router
- Provides interactive charts for visualizing computed projections
- Can label texts and select options for both projections and chunking
- Offers swagger docs for the API, in-memory data store, and persistent data store with QDrant
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
The api_base key can be used to point the OpenAI client library at a different API endpoint.
Function calling allows you to more reliably get structured data back from the model. For example, you can:
Create chatbots that answer questions by calling external APIs (e.g. like ChatGPT Plugins)
e.g. define functions like send_email(to: string, body: string), or get_current_weather(location: string, unit: 'celsius' | 'fahrenheit')
Convert natural language into API calls
e.g. convert "Who are my top customers?" to get_customers(min_revenue: int, created_before: string, limit: int) and call your internal API
Extract structured data from text
e.g. define a function called extract_data(name: string, birthday: string), or sql_query(query: string)
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