
Developed a proof of concept for local LLM integration in the mito-ds/mito repository, enabling Ollama as an alternative backend for completion endpoints. The implementation introduced a use_ollama flag and model or host configuration through environment variables, allowing flexible selection between Ollama and OpenAI for both standard and streaming completions. Python was used for backend development, with a focus on AI and API integration as well as configuration management. Safe defaults were established by disabling Ollama in the initial release, requiring explicit re-enablement. All changes were tracked via commits, providing traceability and laying groundwork for future reliability improvements.
April 2025 monthly summary for mito-ds/mito: Implemented Ollama local LLM integration as a Proof of Concept (POC), enabling local inference with a use_ollama flag and model/host configuration via environment variables. Updated request routing to allow Ollama as an alternative to OpenAI for both request_completions and stream_completions. Implemented safe defaults by temporarily disabling Ollama in the POC until explicit re-enablement. All changes are tracked via commits for traceability. No major bugs reported this month; focus was on delivering flexible, low-latency local inference options and groundwork for future reliability improvements.
April 2025 monthly summary for mito-ds/mito: Implemented Ollama local LLM integration as a Proof of Concept (POC), enabling local inference with a use_ollama flag and model/host configuration via environment variables. Updated request routing to allow Ollama as an alternative to OpenAI for both request_completions and stream_completions. Implemented safe defaults by temporarily disabling Ollama in the POC until explicit re-enablement. All changes are tracked via commits for traceability. No major bugs reported this month; focus was on delivering flexible, low-latency local inference options and groundwork for future reliability improvements.

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