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Add legal context enrichment from Qdrant vector corpus to the two highest-priority modules (Requirements AI assistant and DSFA drafting engine). Go SDK: - Add SearchCollection() with collection override + whitelist validation - Refactor Search() to delegate to shared searchInternal() Python backend: - New ComplianceRAGClient proxying POST /sdk/v1/rag/search (error-tolerant) - AI assistant: enrich interpret_requirement() and suggest_controls() with RAG - Requirements API: add ?include_legal_context=true query parameter Admin (Next.js): - Extract shared queryRAG() utility from chat route - Inject RAG legal context into v1 and v2 draft pipelines Tests for all three layers (Go, Python, TypeScript shared utility). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
41 lines
1.3 KiB
TypeScript
41 lines
1.3 KiB
TypeScript
/**
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* Shared RAG query utility for the Drafting Engine.
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*
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* Queries the DSFA RAG corpus via klausur-service for relevant legal context.
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* Used by both chat and draft routes.
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*/
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const KLAUSUR_SERVICE_URL = process.env.KLAUSUR_SERVICE_URL || 'http://klausur-service:8086'
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/**
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* Query the RAG corpus for relevant legal documents.
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*
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* @param query - The search query (e.g. "DSFA Art. 35 DSGVO")
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* @param topK - Number of results to return (default: 3)
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* @returns Formatted string of legal context, or empty string on error
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*/
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export async function queryRAG(query: string, topK = 3): Promise<string> {
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try {
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const url = `${KLAUSUR_SERVICE_URL}/api/v1/dsfa-rag/search?query=${encodeURIComponent(query)}&top_k=${topK}`
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const res = await fetch(url, {
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headers: { 'Content-Type': 'application/json' },
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signal: AbortSignal.timeout(10000),
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})
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if (!res.ok) return ''
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const data = await res.json()
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if (data.results?.length > 0) {
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return data.results
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.map(
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(r: { source_name?: string; source_code?: string; content?: string }, i: number) =>
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`[Quelle ${i + 1}: ${r.source_name || r.source_code || 'Unbekannt'}]\n${r.content || ''}`
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)
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.join('\n\n---\n\n')
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}
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return ''
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} catch {
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return ''
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}
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}
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