The outlines library is designed to help structure outputs from language models, but as of my last knowledge update in October 2023, it may not fully support streaming contexts directly. If you're looking to enforce a specific output format from your LLM in a streaming manner, here are some steps and suggestions:
Check Library Updates: Since libraries are frequently updated, check the official documentation or GitHub repository for any recent changes regarding streaming support.
Custom Formatting: If outlines lacks streaming capabilities, consider implementing a custom solution. You can create a wrapper around the LLM output to enforce the desired format. This would involve parsing the streamed output and validating it against the specified format.
Pydantic Integration: Continue using Pydantic for response validation. Once you receive the output from the LLM, you can pass it to a Pydantic model to ensure it conforms to your specifications.
Asynchronous Handling: Ensure your FastAPI setup is properly handling asynchronous calls, especially when dealing with streaming data. Use async functions to manage the flow of data efficiently.
Community Feedback: Since you mentioned others might have similar issues, consider reaching out in forums or communities like GitHub discussions, Stack Overflow, or dedicated Discord servers for FastAPI, RAG systems, or the specific libraries you are using.