48c839d927
This is the snapshot the production landing site (nibiru-framework.com) is deployed from. Brings together the recent splash + docs migration to the v4 "Cosmos" design system, the new in-framework AI module, and the framework groundwork that backs the framework-reference extraction. What lands: - docs/: Astro + Starlight site with the v4 dark cosmic palette, GalaxyHero canvas constellation, Mission Control chat (wired to /api/oracle → api.neuronetz.ai via providers.mjs Ollama), 5-panel MMVC stage (Model · AI · Module · Controller · View), translated EN/DE/JA/ES/FR content, PWA + sitemap + llms.txt + Umami analytics. - docs/design-system/: canonical mockup bundle (source/index-v2.html for splash, source/docs-system.html + preview/ for docs, SPEC.md, tokens). - docs/scripts/extraction/framework-reference-v2.md: deep framework reference (~1.6k lines, file:line citations, every public factory and idiom — basis for the LoRA training corpus. - application/module/ai/: AI module with chat / embed / RAG / agent plugins, plus pdoQuery / httpGet / fileRead tools and Modelfile + smoke-test in training/. - application/module/users/: user / ACL / form-factory traits used as the reference plugin pattern for the framework docs. - application/settings/config/database/: schema + seed migrations including the AI module tables (200–203). - Form factory + autogenerator changes the framework-reference-v2 covers. Production secrets stay out: docs/.env, settings.production.ini and ai.production.ini are all gitignored (.example files are in tree). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
29 lines
1.6 KiB
SQL
29 lines
1.6 KiB
SQL
-- =============================================================================
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-- ai_rag_chunk
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--
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-- One row per text chunk in a RAG collection. The embedding column stores
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-- the vector as a base64-packed Float32Array (4 bytes/dim). Cosine search
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-- is done in PHP after fetching all chunks for the collection — fine up
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-- to ~10k chunks per collection. For larger sets, drop in a vector index
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-- extension (pgvector, MySQL HeatWave LakeHouse vector) and update
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-- Rag::search() accordingly.
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-- =============================================================================
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CREATE TABLE IF NOT EXISTS ai_rag_chunk (
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ai_rag_chunk_id INT(11) NOT NULL AUTO_INCREMENT,
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ai_rag_chunk_collection_id INT(11) NOT NULL,
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ai_rag_chunk_text MEDIUMTEXT NOT NULL,
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ai_rag_chunk_metadata JSON NULL,
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ai_rag_chunk_embedding LONGTEXT NOT NULL, -- base64-packed Float32Array
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ai_rag_chunk_token_count INT(11) NOT NULL DEFAULT 0,
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ai_rag_chunk_source VARCHAR(512) NULL, -- denormalised from metadata for indexing
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ai_rag_chunk_created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
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PRIMARY KEY (ai_rag_chunk_id),
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KEY ai_rag_chunk_collection_idx (ai_rag_chunk_collection_id),
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KEY ai_rag_chunk_source_idx (ai_rag_chunk_source),
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CONSTRAINT ai_rag_chunk_collection_fk
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FOREIGN KEY (ai_rag_chunk_collection_id)
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REFERENCES ai_rag_collection (ai_rag_collection_id)
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ON DELETE CASCADE
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) ENGINE = InnoDB DEFAULT CHARSET = utf8mb4 COLLATE = utf8mb4_unicode_ci;
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