Move contextual information handling from noise filtering to extraction
and centralize LLM call logic. Wrap blocking transcription and state
update calls in asyncio.to_thread to prevent event loop blocking.
Update transcriber model size to base.
- Add log_queue to PipelineOrchestrator and log LLM inputs to UI
- Use entity_name for lore update logs instead of topic
- Pass log_queue into ConfirmationApp to display logs in UI
- Introduce a log pane and left/right pane layout in the UI
- Poll and render log messages via a new poll_log_updates worker
- Run log polling with Textual workers to avoid GC issues
- Fix ListView insertion by wrapping ListItem in a list
- Relax RAG similarity threshold from 0.7 to 0.5
- Add ContextPipeline for async RAG lookups
- Implement RAG result summarization via LLMProcessor
- Add CLI flag for PDF ingestion
- Strip markdown code blocks from LLM responses
- Update TUI context display to use ListItems
- Add RAG capabilities using LlamaIndex and ChromaDB
- Implement RAGManager for PHB indexing and retrieval
- Integrate RAG pipeline into orchestrator to trigger queries based on extracted entities
- Update TUI to include a 3-column layout with a real-time Context Pane
- Define ContextUpdate data models in src/llm/models.py
- Update requirements.txt with new dependencies
Implement a sliding window audio buffer and update the transcriber to
use WhisperX for transcription, alignment, and speaker identification.
Update the pipeline to handle and store speaker-attributed transcripts.
Additionally, update the LLM processor's reasoning parameter to
"enable_thinking".
- Implement Silero VAD for dynamic audio chunking
- Add support for Ollama and vLLM backends
- Harden extraction prompts for strict JSON output
- Refactor TUI worker to handle proposals asynchronously
charles