W8: graph-as-context injection + exact-match merge
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@@ -12,6 +12,10 @@ import { createTeacherTools } from "./tools.ts";
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* The model is injectable so tests can pass a MockLanguageModelV4 and drive
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* deterministic scripted lessons (see tests/chat-flow.test.ts).
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*
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* The optional graphContext (buildGraphContext() in src/core/graph-context.ts,
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* wired by the chat-handler per request) is appended after the composed
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* fragments; absent/empty means a new learner — nothing is injected.
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*
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* The system prompt is COMPOSED: system-prompt.md is the base, then every
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* src/agent/prompts/*.md fragment in sorted filename order (workers add
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* fragments — nobody edits shared sections of the base prompt). Prefix
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@@ -36,7 +40,19 @@ function composeInstructions(): string {
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const instructions = composeInstructions();
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export function createTeacherAgent(model?: LanguageModel, tools?: ToolSet) {
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/**
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* Compose the full instruction string: base prompt + fragments, then — when
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* the session carries graph context (W8, vision §1 "the graph as session
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* context") — the auto-generated knowledge-graph block appended after them as
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* a clearly-delimited section (the 50-graph-context.md fragment teaches the
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* teacher how to read it). The fragment mechanism itself stays untouched.
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*/
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function withGraphContext(graphContext: string | null | undefined): string {
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if (!graphContext) return instructions;
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return `${instructions}\n\n---\n\n${graphContext}`;
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}
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export function createTeacherAgent(model?: LanguageModel, tools?: ToolSet, graphContext?: string | null) {
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return new ToolLoopAgent({
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model:
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model ??
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@@ -45,7 +61,7 @@ export function createTeacherAgent(model?: LanguageModel, tools?: ToolSet) {
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// they stream as reasoning parts into the UI's collapsible block.
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reasoning: { enabled: true, effort: "medium" },
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}),
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instructions,
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instructions: withGraphContext(graphContext),
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// Tools are per-request (plan tools close over chatId); chat-handler
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// passes createTeacherTools({ chatId }). Tests pass a mock model and get
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// the default question-only set.
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