Files
breakpilot-compliance/admin-compliance/lib/sdk/agents/advisor-rag.ts
T
Benjamin Admin 49171e841f feat(advisor): Evidence Workspace — structured panes, markdown, sources as knowledge units
Rebuilds the Compliance Advisor floating widget from a plain chat into an Evidence
Workspace: pinned last question, markdown-rendered answer (clean prose), and separate
panes for Sources (hierarchical Knowledge Units), Figures (C8, conditional) and
Footnotes (C-FN), plus a stats bar (Quellen/Regelwerke/Diagramme/Fußnoten). Scrollable
turn history; stays a floating icon on every SDK page.

Architecture (user direction): the frontend renders ONLY structured evidence and NEVER
parses the answer text. The proxy now returns a JSON AdvisorEvidenceMeta line followed
by the streamed markdown answer; advisor-rag exposes structured results; an adapter maps
RAG/compiler output to the frontend envelope. Figures/footnotes wire in once the
RAG-ingestion contract lands (requested on the board) — figures pane is conditional.

- lib/sdk/advisor/{evidence,evidence-adapter}.ts (+ adapter test, 7 cases)
- components/sdk/advisor/* panes + in-house safe Markdown (no new dep, no dangerouslySetInnerHTML) + test
- useAdvisorStream (meta-line parse + streamed answer) + useAdvisorEmail (escaped)
- proxy: evidence-meta-v1 envelope + clean-prose prompt (no inline citations)
- tsc clean, 11 vitest pass, check-loc 0. ESLint not installed in this node_modules -> CI lints on push.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-07-01 07:46:37 +02:00

125 lines
4.3 KiB
TypeScript

/**
* Compliance-Advisor RAG-Suche.
*
* Fragt den Authority Router der ai-compliance-sdk (`/sdk/v1/rag/retrieve`) mit NUR der
* Query ab — der Router waehlt selbst die Collections (Broad-Authority-Base + KB-2026.1-Slice
* bei in-scope), embeddet mit bge-m3 (prod: ollama-embed), merged + authority-ranked. Der
* Advisor bleibt damit collection-agnostisch (Vertrag: Compiler -> Collections -> Retriever
* -> Advisor); die fruehere Multi-Collection-Logik liegt jetzt im Retriever.
*
* `retrieveAdvisorEvidence` liefert die STRUKTURIERTEN Treffer (fuer das Evidence-Workspace-
* Frontend, das nur strukturierte Daten rendert und nie den Antworttext parst) UND den
* vorformatierten Kontext-Block fuer den LLM-Prompt. Fehler werden geschluckt (graceful).
*/
const SDK_URL =
process.env.SDK_API_URL || process.env.SDK_URL || 'http://ai-compliance-sdk:8090'
const DEFAULT_USER = '00000000-0000-0000-0000-000000000001'
const DEFAULT_TENANT =
process.env.DEFAULT_TENANT_ID || '9282a473-5c95-4b3a-bf78-0ecc0ec71d3e'
export interface SdkRagResult {
text?: string
regulation_code?: string
regulation_name?: string
regulation_short?: string
article_label?: string
article?: string
paragraph?: string
sub?: string
citation_style?: string
is_recital?: boolean
category?: string
source_url?: string
score?: number
}
/** Raw RAG response. `figures`/`footnotes` (C8 / C-FN) are passed through untyped until the
* RAG-ingestion contract is finalized (board), then mapped in the evidence-adapter. */
interface SdkRagResponse {
results?: SdkRagResult[]
figures?: unknown[]
footnotes?: unknown[]
}
interface ScoredPassage {
content: string
source: string
score: number
}
/** Normalisiert eine ai-sdk-RAG-Antwort auf {content, source, score} (fuer den Prompt-Kontext). */
export function mapSdkResults(results: SdkRagResult[] | undefined): ScoredPassage[] {
return (results || [])
.map((r) => ({
content: r.text || '',
// Fundstelle: article_label ist die fertig formatierte, druckbare Quelle aus der
// Ingestion ("BDSG § 38 Abs. 1"); Fallback baut sie aus den strukturierten Feldern.
source:
(r.article_label && r.article_label.trim()) ||
[r.regulation_short || r.regulation_name || r.regulation_code, r.article, r.paragraph, r.sub]
.filter(Boolean)
.join(' ') ||
'Unbekannt',
score: typeof r.score === 'number' ? r.score : 0,
}))
.filter((p) => p.content)
}
/** Formatiert die Top-Passagen als Kontext-Block fuer den System-Prompt. */
function formatContext(passages: ScoredPassage[]): string {
if (passages.length === 0) return ''
return passages
.map((r, i) => `[Quelle ${i + 1}: ${r.source}]\n${r.content}`)
.join('\n\n---\n\n')
}
/** EIN collection-agnostischer Aufruf an die ai-sdk. Fehler -> leeres Ergebnis (graceful). */
async function fetchRag(query: string): Promise<SdkRagResponse> {
try {
const res = await fetch(`${SDK_URL}/sdk/v1/rag/retrieve`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-User-ID': DEFAULT_USER,
'X-Tenant-ID': DEFAULT_TENANT,
},
body: JSON.stringify({ query, top_k: 8 }),
signal: AbortSignal.timeout(15000),
})
if (res.ok) return ((await res.json()) as SdkRagResponse) || {}
} catch {
// graceful: keine Verbindung -> Antwort ohne RAG-Kontext
}
return {}
}
export interface AdvisorEvidenceRaw {
contextText: string
results: SdkRagResult[]
figures?: unknown[]
footnotes?: unknown[]
}
/**
* Strukturierte Evidence + Prompt-Kontext aus EINEM Retrieval. Das Frontend bekommt die
* `results` (und kuenftig `figures`/`footnotes`) als Daten; der `contextText` geht in den
* LLM-Prompt. Reihenfolge der authority-geordneten Top-K bleibt erhalten.
*/
export async function retrieveAdvisorEvidence(query: string): Promise<AdvisorEvidenceRaw> {
const data = await fetchRag(query)
const results = data.results || []
return {
contextText: formatContext(mapSdkResults(results)),
results,
figures: Array.isArray(data.figures) ? data.figures : undefined,
footnotes: Array.isArray(data.footnotes) ? data.footnotes : undefined,
}
}
/** Abwaertskompatibel: nur der Prompt-Kontext als String. */
export async function queryAdvisorRAG(query: string): Promise<string> {
return (await retrieveAdvisorEvidence(query)).contextText
}