Real-world application examples

Release validation should use application-shaped inputs, not only spans assembled from isolated attribute fixtures. The repository includes a vendor-key-free replay of a real TraceSense banking voice-RAG turn:

examples/real_world/voice_rag_replay.py

The replay reads an actual WAV recording and the TraceSense knowledge-base corpus. It emits one trace containing separate speech-to-text, local embedding, retrieval, and grounded-answer spans. Audio is attached only to the speech span and must be represented as type=audio with media_mime_type=audio/wav.

Run it against the remote collector:

python examples/real_world/voice_rag_replay.py `
  --audio REDACTED-PATH/apps/tracesense/data/samples/audio/audio_001_account_balance.wav `
  --corpus REDACTED-PATH/apps/tracesense/data/kb_corpus.py `
  --transcript "Please check my account balance and recent transactions." `
  --endpoint http://192.168.18.128:4318

The example does not claim to call an ASR, embedding, vector database, or LLM vendor. It validates the telemetry path with the application payload shape while remaining runnable without paid credentials. The checked-in Chaos Lab WAV and corpus are deterministic lab fixtures, not production customer data.

TraceSense document OCR

The companion replay covers the other multimodal workflow already implemented in the Chaos Lab: PDF/image ingestion and structured document extraction.

python examples/real_world/document_ocr_replay.py `
  --document REDACTED-PATH/apps/tracesense/data/samples/documents/invoice_001.pdf `
  --endpoint http://192.168.18.128:4318

The emitted OCR span must report type=document and media_mime_type=application/pdf for a PDF. An image input must report type=image and its image MIME type. Use --no-export for a local shape-only check. This replay validates file handling and telemetry; it does not claim that a provider-backed OCR model ran. For the provider-backed path, run the Chaos Lab's apps/tracesense/tools/extract_document.py with Ollama available.

The broader application workflow is documented in the Chaos Lab's apps/tracesense/agents/voice_retrieval_agent.py and apps/tracesense/tools/extract_document.py: Voice RAG runs ASR → embedding → Qdrant retrieval → reranking → grounded answer → TTS, while OCR runs document bytes through multimodal extraction. The lab's apps/traceinsure document processor is a separate insurance workflow and currently uses simulated claim-field extraction, so it should not be presented as a live OCR provider trace.