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Scrub stale NN- prefixes missed by rename sweep
The rename PR's perl sweep keyed on ``examples/NN-name`` paths, which caught directory references but missed three other forms of stale numeric prefix: - Link DISPLAY text in docs/concepts/llms.md (three bullets in the "Where to next" section + one inline reference): the link targets were updated correctly but the text still read ``00 - Hello, world`` / ``07 - Multimodal prompt`` / ``09 - Tool use``. Rewriting to drop the prefix and match the by-name style used elsewhere. - Standalone example-name references in source comments (no ``examples/`` prefix), e.g. ``00-hello-world`` and ``01-routing-and-subgraphs`` in three example main.py files. Stripping the leading digit-digit-dash. Surfaced by CoPilot review on PR #120 (line 338 + 598 + 600 threads), plus a broader git-grep sweep to catch the source- comment cases the threads didn't flag.
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docs/concepts/llms.md

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@@ -334,8 +334,8 @@ Wiring the loop as a graph cycle: a `call_llm` node, a
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to `call_llm` when `tool_calls` are present and forward to a
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termination node when they aren't. A turn cap on the routing function
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prevents runaway loops on a model that stays in tool-calling forever.
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See [`09 - Tool use`](../examples/tool-use.md) for the runnable
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shape.
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See [the tool-use example](../examples/tool-use.md) for the
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runnable shape.
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### Controlling tool-call behavior with `tool_choice`
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@@ -591,10 +591,10 @@ classifier won't do this for them.
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- [API reference: `openarmature.llm`](../reference/llm.md) for the
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full surface: message types, `Response`, `RuntimeConfig`, every
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error class, validation helpers.
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- [Examples: 00 - Hello, world](../examples/hello-world.md) for a
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- [Examples: Hello, world](../examples/hello-world.md) for a
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runnable graph exercising both `response_schema` forms in one
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pipeline.
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- [Examples: 09 - Tool use](../examples/tool-use.md) for the
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- [Examples: Tool use](../examples/tool-use.md) for the
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agent-loop pattern with two local tools.
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- [Examples: 07 - Multimodal prompt](../examples/multimodal-prompt.md)
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- [Examples: Multimodal prompt](../examples/multimodal-prompt.md)
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for content blocks alongside versioned prompts.

examples/explicit-subgraph-mapping/main.py

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@@ -207,7 +207,7 @@ async def synthesize(s: ComparisonState) -> Mapping[str, Any]:
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# with no way to express "this site reads A, that site reads B." Both
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# sites would write `parent.summary` and clobber each other.
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#
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# - A custom `ProjectionStrategy` (the 01-routing-and-subgraphs approach)
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# - A custom `ProjectionStrategy` (the routing-and-subgraphs approach)
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# would have to differ per call site; you'd write two distinct projection
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# classes that do the same thing in mirror image. That's exactly the
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# boilerplate `ExplicitMapping` removes.

examples/nested-subgraphs/main.py

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@@ -223,7 +223,7 @@ async def pick_doc(s: DocQAState) -> Mapping[str, Any]:
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# return only part of the title. Accept either direction of containment
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# over the lowercased strings; strict equality is too brittle for
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# free-form output. A production app would constrain the model with
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# response_schema (see 00-hello-world) so the reply is guaranteed to be
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# response_schema (see hello-world) so the reply is guaranteed to be
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# a valid title.
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match = next(
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(d for d in CORPUS if d["title"].lower() in reply or reply in d["title"].lower()),

examples/routing-and-subgraphs/main.py

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custom `ProjectionStrategy` for the parent ↔ subgraph boundary, and the
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`merge` reducer for dict accumulation.
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Three graph features that `00-hello-world` only touched lightly:
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Three graph features that `hello-world` only touched lightly:
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1. **Conditional edges.** The entry node classifies the question and the
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graph routes to one of two branches based on that classification.
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shape what comes back out), we write a `ProjectionStrategy` by hand.
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LLM calls go through ``openarmature.llm.OpenAIProvider`` (same pattern as
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``00-hello-world``) so the example reads as the recommended path rather
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``hello-world``) so the example reads as the recommended path rather
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than as "openai with some openarmature on top."
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**Configuration** (env vars; OpenAI defaults shown):

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