## Turn 1

Private home-lab Python/FastAPI service ("ai-flight-recorder").
FastAPI, llama.cpp (OpenAI compatible), SQLite, Docker Compose.
Proxying LLM requests, recording metadata/usage, running benchmarks, reporting metrics.

        1.  Long benchmark outputs $\rightarrow$ durable artifacts.
        2.  Reasoning output measurement (without disabling it).
        3.  Enhanced benchmark metadata for comparisons.
        4.  Privacy/Redaction (emails, Teams content).
        5.  Auditable model profile changes.
        6.  Handling high token counts (timeouts, storage, UI).
Deployment-grade engineering dossier (10k-14k tokens).
Executive Summary, Current Architecture, Failure Modes, Data Model Changes, Artifact Storage, Reasoning Budget, Benchmark Runner, Reporting Plane, Privacy/Redaction, Deployment Plan, Test Plan, Rollback Plan, Open Questions.
Concrete schemas, CLI examples, metrics, migration notes, 20+ acceptance tests.

    *   *Executive Summary:* High-level goal: Transition from a "recorder" to a "benchmark and observability platform." Focus on stability, privacy, and scalability for long-context reasoning.
    *   *Current Architecture:* Describe the flow: Client $\rightarrow$ FastAPI Proxy $\rightarrow$ llama.cpp $\rightarrow$ SQLite. Mention the specific model (Qwen3.6-35B).
    *   *Failure Modes:*
        *   SQLite locking on high-concurrency benchmarks.
        *   Memory exhaustion on huge response previews.
        *   Timeout crashes on 10k+ token outputs.
        *   PII leakage in logs/DB.
        *   Lack of versioning for model profiles.
    *   *Data Model Changes:*
        *   `runs` table: Add `model_version`, `config_hash`.
        *   `llm_requests` table: Split `response_text` into `preview` and `artifact_path`. Add `reasoning_tokens`.
        *   `model_profiles` table: New table for auditing changes.
    *   *Artifact Storage:* Move from DB blobs to filesystem (S3-compatible or local mount). JSONL format for raw outputs.
    *   *Reasoning Budget:* How to capture `<thought>` tags. Measuring "thinking time" vs "generation time."
    *   *Benchmark Runner:* Async execution, batching, artifact generation, metadata tagging.
    *   *Reporting Plane:* Metrics for tokens/sec (reasoning vs content), cost/latency per model, accuracy trends.
    *   *Privacy/Redaction:* Regex-based scrubbing for emails/Teams IDs before DB write.
    *   *Deployment Plan:* Step-by-step (DB migration $\rightarrow$ Volume mount $\rightarrow$ App update).
    *   *Test Plan:* 20+ tests covering edge cases (huge tokens, PII, timeouts).
    *   *Rollback Plan:* Snapshotting SQLite, Docker image tagging.

    *   *Detailing the Data Model:* I need to provide SQL schemas.
    *   *Detailing the Artifacts:* Define the directory structure: `/data/artifacts/{run_id}/{request_id}.json`.
    *   *Detailing the Reasoning:* Explain how to parse the `reasoning_content` field (if provided by llama.cpp) or regex for `<thought>` tags.
    *   *Detailing the Redaction:* Create a `Redactor` class with specific patterns.
    *   *Expanding the Benchmark Runner:* Describe the `Campaign` object and the `Evaluator` loop.

    *   *Self-Correction during drafting:* I initially thought about using PostgreSQL, but the prompt says SQLite. I must stick to SQLite but suggest WAL mode for concurrency.
    *   *Expanding the "High Token" section:* Discuss `StreamingResponse` and how to avoid loading the whole response into memory before writing to the DB.
    *   *Adding the "Model Profile" audit:* Create a `profile_history` table.