This task involves adding basic LLM-based autocompletion capabilities to the LSP server. The goal is to leverage a language model (LLM) to provide intelligent, context-aware code suggestions that enhance the development experience in MeTTa. This initial implementation will focus on integrating the LLM with the LSP server and displaying autocompletion suggestions in the editor.
Task Description
Integrate LLM API:
Choose an LLM API (e.g., OpenAI's GPT-3.5 or GPT-4) to provide autocompletion suggestions.
Implement a connection between the LSP server and the chosen LLM API.
Ensure that the integration allows for sending code context to the LLM and receiving suggestions.
Implement Basic Autocompletion:
Modify the LSP server to process autocompletion requests by sending relevant code context to the LLM.
Parse the LLM's response to extract useful autocomplete suggestions.
Display these suggestions in the editor's autocompletion dropdown.
Optimize and Handle Edge Cases:
Ensure that the autocompletion system handles various code contexts, including incomplete or partially typed expressions.
Implement basic error handling for cases where the LLM API fails or returns irrelevant results.
Test the Integration:
Test the autocompletion feature with various coding scenarios in MeTTa to ensure it provides relevant and accurate suggestions.
Verify that the integration works smoothly in the supported editors (e.g., VSCode, Neovim).
Documentation:
Update the LSP server documentation to include instructions on enabling and using the LLM-based autocompletion.
Provide basic troubleshooting steps for common issues that might arise.
Expected Outcome
Working Autocompletion: The LSP server should provide intelligent autocompletion suggestions powered by the LLM, enhancing the coding experience in MeTTa.
Seamless Integration: The feature should integrate smoothly with existing editor environments and not introduce significant latency or errors.
Basic Documentation: Users should have clear instructions on how to enable and use the new autocompletion feature.
This task involves adding basic LLM-based autocompletion capabilities to the LSP server. The goal is to leverage a language model (LLM) to provide intelligent, context-aware code suggestions that enhance the development experience in MeTTa. This initial implementation will focus on integrating the LLM with the LSP server and displaying autocompletion suggestions in the editor.
Task Description
Integrate LLM API:
Implement Basic Autocompletion:
Optimize and Handle Edge Cases:
Test the Integration:
Documentation:
Expected Outcome