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Add eval-driven development skill (#1013)
* Add eval-driven development skill * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
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Yiou Li
GitHub
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skills/eval-driven-dev/references/pixie-api.md
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skills/eval-driven-dev/references/pixie-api.md
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# pixie API Reference
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## Configuration
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All settings read from environment variables at call time. By default,
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every artefact lives inside a single `pixie_qa` project directory:
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| Variable | Default | Description |
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| ------------------- | -------------------------- | ---------------------------------- |
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| `PIXIE_ROOT` | `pixie_qa` | Root directory for all artefacts |
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| `PIXIE_DB_PATH` | `pixie_qa/observations.db` | SQLite database file path |
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| `PIXIE_DB_ENGINE` | `sqlite` | Database engine (currently sqlite) |
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| `PIXIE_DATASET_DIR` | `pixie_qa/datasets` | Directory for dataset JSON files |
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---
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## Instrumentation API (`pixie`)
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```python
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from pixie import enable_storage, observe, start_observation, flush, init, add_handler
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```
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| Function / Decorator | Signature | Notes |
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| -------------------- | ------------------------------------------------------------ | --------------------------------------------------------------------------------------------------- |
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| `enable_storage()` | `() → StorageHandler` | Idempotent. Creates DB, registers handler. Call at app startup. |
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| `init()` | `(*, capture_content=True, queue_size=1000) → None` | Called internally by `enable_storage`. Idempotent. |
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| `observe` | `(name=None) → decorator` | Wraps a sync or async function. Captures all kwargs as `eval_input`, return value as `eval_output`. |
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| `start_observation` | `(*, input, name=None) → ContextManager[ObservationContext]` | Manual span. Call `obs.set_output(v)` and `obs.set_metadata(key, value)` inside. |
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| `flush` | `(timeout_seconds=5.0) → bool` | Drains the queue. Call after a run before using CLI commands. |
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| `add_handler` | `(handler) → None` | Register a custom handler (must call `init()` first). |
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---
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## CLI Commands
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```bash
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# Dataset management
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pixie dataset create <name>
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pixie dataset list
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pixie dataset save <name> # root span (default)
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pixie dataset save <name> --select last_llm_call # last LLM call
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pixie dataset save <name> --select by_name --span-name <name>
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pixie dataset save <name> --notes "some note"
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echo '"expected value"' | pixie dataset save <name> --expected-output
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# Run eval tests
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pixie test [path] [-k filter_substring] [-v]
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```
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**`pixie dataset save` selection modes:**
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- `root` (default) — the outermost `@observe` or `start_observation` span
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- `last_llm_call` — the most recent LLM API call span in the trace
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- `by_name` — a span matching the `--span-name` argument (takes the last matching span)
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---
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## Eval Harness (`pixie`)
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```python
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from pixie import (
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assert_dataset_pass, assert_pass, run_and_evaluate, evaluate,
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EvalAssertionError, Evaluation, ScoreThreshold,
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capture_traces, MemoryTraceHandler,
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last_llm_call, root,
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)
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```
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### Key functions
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**`assert_dataset_pass(runnable, dataset_name, evaluators, *, dataset_dir=None, passes=1, pass_criteria=None, from_trace=None)`**
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- Loads dataset by name, runs `assert_pass` with all items.
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- `runnable`: callable `(eval_input) → None` (sync or async). Must instrument itself.
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- `evaluators`: list of evaluator callables.
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- `pass_criteria`: defaults to `ScoreThreshold()` (all scores >= 0.5).
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- `from_trace`: `last_llm_call` or `root` — selects which span to evaluate.
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**`assert_pass(runnable, eval_inputs, evaluators, *, evaluables=None, passes=1, pass_criteria=None, from_trace=None)`**
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- Same, but takes explicit inputs (and optionally `Evaluable` items for expected outputs).
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**`run_and_evaluate(evaluator, runnable, eval_input, *, expected_output=..., from_trace=None)`**
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- Runs `runnable(eval_input)`, captures traces, evaluates. Returns one `Evaluation`.
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**`ScoreThreshold(threshold=0.5, pct=1.0)`**
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- `threshold`: min score per item (default 0.5).
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- `pct`: fraction of items that must meet threshold (default 1.0 = all).
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- Example: `ScoreThreshold(0.7, pct=0.8)` = 80% of cases must score ≥ 0.7.
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**`Evaluation(score, reasoning, details={})`** — frozen result. `score` is 0.0–1.0.
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**`capture_traces()`** — context manager; use for in-memory trace capture without DB.
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**`last_llm_call(trace)`** / **`root(trace)`** — `from_trace` helpers.
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---
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## Evaluators
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### Heuristic (no LLM needed)
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| Evaluator | Use when |
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| -------------------------------- | --------------------------------------------------- |
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| `ExactMatchEval(expected=...)` | Output must exactly equal the expected string |
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| `LevenshteinMatch(expected=...)` | Partial string similarity (edit distance) |
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| `NumericDiffEval(expected=...)` | Normalised numeric difference |
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| `JSONDiffEval(expected=...)` | Structural JSON comparison |
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| `ValidJSONEval(schema=None)` | Output is valid JSON (optionally matching a schema) |
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| `ListContainsEval(expected=...)` | Output list contains expected items |
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### LLM-as-judge (require OpenAI key or compatible client)
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| Evaluator | Use when |
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| ----------------------------------------------------- | ----------------------------------------- |
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| `FactualityEval(expected=..., model=..., client=...)` | Output is factually accurate vs reference |
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| `ClosedQAEval(expected=..., model=..., client=...)` | Closed-book QA comparison |
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| `SummaryEval(expected=..., model=..., client=...)` | Summarisation quality |
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| `TranslationEval(expected=..., language=..., ...)` | Translation quality |
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| `PossibleEval(model=..., client=...)` | Output is feasible / plausible |
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| `SecurityEval(model=..., client=...)` | No security vulnerabilities in output |
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| `ModerationEval(threshold=..., client=...)` | Content moderation |
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| `BattleEval(expected=..., model=..., client=...)` | Head-to-head comparison |
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### RAG / retrieval
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| Evaluator | Use when |
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| ------------------------------------------------- | ------------------------------------------ |
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| `ContextRelevancyEval(expected=..., client=...)` | Retrieved context is relevant to query |
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| `FaithfulnessEval(client=...)` | Answer is faithful to the provided context |
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| `AnswerRelevancyEval(client=...)` | Answer addresses the question |
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| `AnswerCorrectnessEval(expected=..., client=...)` | Answer is correct vs reference |
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### Custom evaluator template
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```python
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from pixie import Evaluation, Evaluable
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async def my_evaluator(evaluable: Evaluable, *, trace=None) -> Evaluation:
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# evaluable.eval_input — what was passed to the observed function
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# evaluable.eval_output — what the function returned
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# evaluable.expected_output — reference answer (UNSET if not provided)
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score = 1.0 if "expected pattern" in str(evaluable.eval_output) else 0.0
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return Evaluation(score=score, reasoning="...")
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```
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---
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## Dataset Python API
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```python
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from pixie import DatasetStore, Evaluable
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store = DatasetStore() # reads PIXIE_DATASET_DIR
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store.create("my-dataset") # create empty
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store.create("my-dataset", items=[...]) # create with items
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store.append("my-dataset", Evaluable(...)) # add one item
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store.get("my-dataset") # returns Dataset
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store.list() # list names
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store.remove("my-dataset", index=2) # remove by index
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store.delete("my-dataset") # delete entirely
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```
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**`Evaluable` fields:**
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- `eval_input`: the input (what `@observe` captured as function kwargs)
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- `eval_output`: the output (return value of the observed function)
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- `eval_metadata`: dict of extra info (trace_id, span_id, provider, token counts, etc.) — always includes `trace_id` and `span_id`
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- `expected_output`: reference answer for comparison (`UNSET` if not provided)
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---
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## ObservationStore Python API
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```python
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from pixie import ObservationStore
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store = ObservationStore() # reads PIXIE_DB_PATH
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await store.create_tables()
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# Read traces
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await store.list_traces(limit=10, offset=0) # → list of trace summaries
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await store.get_trace(trace_id) # → list[ObservationNode] (tree)
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await store.get_root(trace_id) # → root ObserveSpan
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await store.get_last_llm(trace_id) # → most recent LLMSpan
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await store.get_by_name(name, trace_id=None) # → list of spans
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# ObservationNode
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node.to_text() # pretty-print span tree
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node.find(name) # find a child span by name
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node.children # list of child ObservationNode
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node.span # the underlying span (ObserveSpan or LLMSpan)
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```
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