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* test(plugins): capture opt-in artifact RED (18 failed, 13 passed) * feat(plugins): add typed opt-in artifacts (31 ToolNode tests pass) * test(extensions): require API 0.2.6 (version RED verified) * build(extensions): pin API 0.2.6 (installed wheel guards GREEN) * test(examples): verify plugin artifacts through checkpoint restart * test(extensions): reproduce artifact key coercion (8 RED cases) * fix(extensions): reject non-string artifact keys (185 tests GREEN) * fix(frontend): list bundled retrieval example in extension catalog * test(mcp): reconcile recovered notification at stored due time --------- Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
Synthetic retrieval with plugin tool artifacts
This is a small, read-only example for ModelTool(response_format="content_and_artifact").
It searches two fixed synthetic documents in memory. It makes no network or
model requests, reads no user files, and has no UI, database, ingestion, or write
operations. All users see the same demonstration corpus; it is not a private
knowledge base or a factual company source.
The package imports only Python's standard library and deerflow-extension-api
0.2.6 or newer. It does not import private DeerFlow harness modules.
Enable explicitly
Install this trusted local package using the normal extension workflow:
deerflow extensions install /absolute/path/to/examples/deerflow-extension-retrieval
The contribution is disabled by default. To enable it, set the installed entry's
configuration to enabled: true and restart the Gateway. For a source package
already on the Python path, the equivalent deployment entry is:
plugins:
- use: deerflow_extension_retrieval:install
config:
enabled: true
The registered model tool performs case-insensitive literal matching. For
example, the query release returns the synthetic release note. Empty queries
and additional arguments are rejected by the tool schema.
What the example proves
The handler returns PluginToolResult: readable text is model-visible, while a
JSON artifact contains schema, synthetic, and structured matches. The host
assigns the tool name and call ID. The artifact contains no secret information;
artifacts are retained in state and API output, not a secret-storage boundary.
The integration test loads this package through load_extensions, dispatches
the real ToolNode with ToolOutputBudgetMiddleware, saves to SQLite, closes
and reopens the saver, reads through CheckpointStateAccessor, and serializes
the restored state. A later turn also checks the standard model-message
conversion receives text rather than the artifact. Both full and delta
checkpoint modes are covered, including content truncation.
The artifact is generic example metadata. It does not claim to be a native RAGFlow citation, enable citation cards, or participate in source-aware budget pruning or subagent source forwarding. Generic artifacts can outlive truncated content; adding an artifact does not establish evidence authority.
From an existing DeerFlow backend development environment with API 0.2.6:
uv run pytest tests/test_retrieval_plugin_artifacts.py -q