Skip to main content

AI Model & RAG Architecture

AI Model & RAG Architecture

Pregled svih AI modela i RAG (Retrieval-Augmented Generation) komponenti u ALAI sistemu. Datum: 2026-02-23. Izvor: verifikovan inventar iz filesystem-a i running servisa. Zadnji update: RAG System Upgrade (MC #1804) — unified embedding, HiveMind vector search, retrieval orchestrator, session archiver.

Pregled na jednoj stranici

+-----------------------------------------------------------------+
|                      CLAUDE CODE (Opus/Sonnet/Haiku)            |
|                     Primarni orkestrator - John                  |
|                  (Anthropic API, cloud, kontekst do 200K)        |
+-----------------------------------------------------------------+
                             |
          +------------------+------------------+
          v                  v                  v
   +-------------+   +-------------+   +-----------------+
   |  RAG Router  |   |  Tier Router |   |  MCP Servers    |
   |  (rag-mcp)   |   |  (6 tierova) |   |  email, figma,  |
   |              |   |              |   |  playwright, yt  |
   +------+---+--+   +------+------+   +-----------------+
          |   |              |
    +-----+   +----+         v
    v              v   +---------------+
+--------+  +--------+|    OLLAMA      |
| Cache  |  |  KB    || localhost:11434|
|flywheel|  |knowledge|+------+--------+
|  .db   |  |  .db   |       |
+--------+  +--------+       v
                       +---------------+
                       |  7 lokalnih   |
                       |   modela      |
                       +---------------+

+------------------------------------------------------------------+
|                  RETRIEVAL ORCHESTRATOR                            |
|              retrieval-orchestrator.js                             |
|  Parallel query -> HiveMind + KB + RAG + Sessions -> RRF merge   |
+------------------------------------------------------------------+
    |             |              |              |
    v             v              v              v
+--------+  +--------+   +--------+   +----------+
|HiveMind|  |Knowledge|   |  RAG   |   | Sessions |
|semantic|  |  DB     |   | Cache  |   |  (grep)  |
|13,473  |  |24,636   |   | 2,201  |   |   761    |
+--------+  +--------+   +--------+   +----------+

+-----------------------------------------------------------------+
|                     BOOKSTACK (Wiki)                             |
|           http://localhost:6875 - dokumentacija                  |
|       NE ucestvuje u RAG pipeline-u (covjek cita)               |
+-----------------------------------------------------------------+

1. Lokalni AI modeli (Ollama)

Server: http://localhost:11434 Hardware: Mac Studio M3 Ultra, 96 GB RAM LaunchAgent: homebrew.mxcl.ollama Config: ~/system/config/ollama.json

Instalirani modeli (ollama list, 2026-02-21)

Model Velicina Namjena Status
llama3.1:8b 4.9 GB Brzi classify/extract/filter (Tier 1) AKTIVAN
qwen2.5-coder:32b 19 GB Code review, debug, refaktor (Tier 2c) AKTIVAN
nomic-embed-text 274 MB Embeddings - 768-dim vektori za RAG AKTIVAN
alaiml-task-v1 986 MB Fine-tuned za MC task handling (Tier 2t) AKTIVAN
alaiml-tender-v1 986 MB Fine-tuned za tender analizu AKTIVAN
alaiml-email-v1 986 MB Fine-tuned za email klasifikaciju AKTIVAN
llama-guard3:8b 4.9 GB Content safety / guardrails AKTIVAN

Konfigurirani ali NE instalirani

Model Razlog Napomena
llama3.1:70b 42 GB - ne stane uvijek u RAM U config-u kao Tier 3 (complex reasoning)
qwen2.5:72b 47 GB - ne stane uvijek u RAM U config-u kao Tier 2 (general)

Wrapper toolsi:

  • ~/system/tools/ollama-engine.js - HTTP wrapper za generate/classify
  • ~/system/tools/ollama-tool-agent.js - Multi-turn agent sa READ-ONLY toolsima
  • ~/system/tools/agent-runner.js - Agent lifecycle (identity -> state -> HiveMind -> Ollama -> save)

2. Tier Routing (Task -> Model dispatch)

File: ~/system/tools/tier-router.js Config: ~/system/config/tier-routing.json

Svaki AI request ide kroz routing koji odlucuje koji model procesira:

Tier Engine Model Namjena
1 Ollama llama3.1:8b Trivijalno: classify, filter, extract
2 Ollama qwen2.5:72b* Medium: summarize, draft, analyze
2c Ollama qwen2.5-coder:32b Code: review, debug, simple fix
2t Ollama alaiml-task-v1 Task-specific: MC task handling
3 Ollama llama3.1:70b* Complex reasoning (NO code execution)
4 Human Queue - Critical: multi-file, architecture, decisions

Tier 2 i 3 modeli nisu trenutno instalirani. Fallback na Tier 2c.

Routing logika

  1. Caller-based - svaki daemon/agent ima fiksni tier:
    • email-agent, pipeline-watcher -> Tier 1
    • morning-routine, explore -> Tier 2
    • autowork-standard, validator -> Tier 2c
    • builder, interactive -> Tier 4 (human/Claude)
  2. Keyword fallback - skenira task tekst za keyword match
  3. Default - Tier 2

3. RAG System (Retrieval-Augmented Generation)

3.1 Arhitektura (v2, 2026-02-23)

                         Query dolazi
                              |
                              v
                  +------------------------+
                  |  Retrieval Orchestrator |  (retrieval-orchestrator.js)
                  |  Multi-store parallel   |
                  +-----+-----+-----+------+
                        |     |     |      |
           +------------+     |     |      +------------+
           v                  v     v                   v
    +-----------+     +-------+  +--------+     +-----------+
    |  HiveMind |     |  KB   |  |  RAG   |     |  Sessions |
    |  semantic |     | docs  |  | cache  |     |   grep    |
    |  13,473   |     |24,636 |  | 2,201  |     |    761    |
    +-----------+     +-------+  +--------+     +-----------+
           |               |          |
           +-------+-------+----------+
                   v
           +---------------+
           |  RRF Merge    |  Reciprocal Rank Fusion (k=60)
           |  Deduplicate  |
           +-------+-------+
                   |
                   v
            Top N results

Retrieval flow:

  1. Embed query jednom (nomic-embed-text, 768-dim)
  2. Parallel query svih 4 storea (HiveMind semantic, Knowledge DB, RAG Cache, Sessions grep)
  3. RRF Merge — Reciprocal Rank Fusion kombinira rankings iz svih izvora
  4. Return top N rezultata sa RRF score + source attribution

Inspirirano: Spring AI Modular RAG (RetrievalAugmentationAdvisor + MultiQueryExpander + ConcatenationDocumentJoiner)

3.2 Retrieval Orchestrator (NOVO, 2026-02-23)

File: ~/system/tools/retrieval-orchestrator.js MC Task: #1804

Centralni entry-point za sav retrieval u sistemu. Umjesto rucnog "BookStack PRVO -> HiveMind -> etc", orchestrator automatski paralelno pretrazuje sve storee i vraca rankirane rezultate.

CLI:

node retrieval-orchestrator.js query "tema" [--limit N] [--verbose] [--stores s1,s2]
node retrieval-orchestrator.js stats
node retrieval-orchestrator.js stores

Module:

const { RetrievalOrchestrator } = require('./retrieval-orchestrator');
const ro = new RetrievalOrchestrator();
const { results, meta } = await ro.query('tema', { limit: 5 });

Stores:

Store Tip pretrage Entries Izvor
hivemind Cosine similarity + LIKE fallback 13,473 hivemind.db
knowledge Cosine similarity (vector-db.js) 24,636 knowledge.db
rag Cosine similarity na RAG cache 2,201 flywheel.db
sessions Grep text search 761 fajlova ~/system/memory/sessions/

3.3 Vector Database

File: ~/system/tools/vector-db.js Tip: SQLite + Float32Array BLOB kolone (custom implementacija) Embedding model: nomic-embed-text (768-dim, lokalni, via Ollama) Nema: ChromaDB, FAISS, Pinecone, Weaviate, pgvector — sve je custom SQLite

UNIFIED EMBEDDING (2026-02-23): Svi toolsi koriste ISTI model (nomic-embed-text via Ollama):

  • vector-db.js — JS modul (originalni)
  • memory-indexer.py — Python indexer (prepisani sa sentence-transformers)
  • hivemind.js — HiveMind embeddings (novo)
  • session-archiver.js — Session embeddings (novo)
  • rag-router.js — RAG cache embeddings (originalni)

Prethodno: memory-indexer.py je koristio all-MiniLM-L6-v2 (384-dim) — razliciti vektorski prostori, cosine similarity izmedju njih je besmislen. Fiksirano u MC #1804.

Mogucnosti:

  • Semanticki search (cosine similarity)
  • Hybrid search (SQL WHERE + vektor ranking)
  • Kolekcije sa metadata kolonama
  • Bulk insert sa batching-om (32 docs/batch)

3.4 Knowledge Base (Document Store)

File: ~/system/tools/knowledge-base.js DB: ~/system/databases/knowledge.db

Velicina (2026-02-23): 24,636 entries (13,558 dokumenata + 11,075 memory-file chunks + 3 session chunks)

Schema:

  • kb_docs — metadata (source, title, tag, hash, chunk count)
  • documents — vektor-indeksirani chunkovi (content, embedding BLOB, tag)

Tagovi:

Tag kategorija Primjer tagova Entries
memory-file Svi ~/system/ MD fajlovi 11,075
Projekti lumiscare, drop, drop-architecture ~8,000
Patterns pattern-security, pattern-architecture ~500
System agents, system, rules, organization ~900
Sessions session 3+ (raste)

Dva indexera:

  • knowledge-base.js — URL/file ingestion sa auto-chunking, tagging, dedup
  • memory-indexer.py — ~/system/ MD file scanner, batch embedding, tag='memory-file'

3.5 RAG Flywheel (Cache + Ucenje)

File: ~/system/tools/rag-router.js DB: ~/system/databases/flywheel.db MCP Server: ~/system/tools/rag-mcp.js -> registrovan u ~/.claude/mcp.json

Flywheel metrike (live, 2026-02-23):

Metrika Vrijednost
Total queries 886
Cache hit rate 61.1%
Local model rate 4.4%
External rate 34.5%
Cache size 2,201 entries
Cost saved queries 580

MCP Tools (dostupni iz Claude Code sesije):

  • mcp__rag__rag_query(query, task_type) — Rutiraj upit kroz cache -> local -> external
  • mcp__rag__rag_learn(question, answer) — Dodaj Q&A u cache
  • mcp__rag__rag_stats() — Flywheel metrike

RAG Router flow (Progressive Enrichment):

  1. Cache search — cosine similarity na rag_cache (threshold 0.75)
  2. Local RAW — Ollama bez KB konteksta, confidence gate (0.75+)
  3. Local ENRICHED — Ollama SA knowledge.db kontekstom
  4. External — Flag za Claude Code

DB Schema (flywheel.db):

  • interactions — svaki query logiran (model, routing, cost, latency)
  • rag_cache — Q&A parovi sa embedding-om (query_embedding BLOB, response, hit_count, project_tag)
  • shadow_log — routing odluke + top 3 similarity scores

3.6 Session Archiver (NOVO, 2026-02-23)

File: ~/system/tools/session-archiver.js LaunchAgent: com.john.session-archiver (daily 03:00)

Upravlja lifecycleom session fajlova — cijenimo summary, cistimo raw transkripte.

Komande:

node session-archiver.js stats                    # Statistika
node session-archiver.js archive [--dry-run]      # Strip raw transkripata >14 dana
node session-archiver.js index [--limit N]        # Embeduj summarije u knowledge.db
node session-archiver.js cleanup [--dry-run]      # Archive + index (cron)

Stats (2026-02-23):

  • 761 session fajlova, 688 sa raw transkriptom
  • 21.5 MB total, 20 MB (93%) je raw transcript bulk
  • ~20 MB estimated savings od archivinga

4. HiveMind (Shared Memory Bus + Semantic Search)

File: ~/system/agents/hivemind/hivemind.js DB: ~/system/agents/hivemind/hivemind.db Tip: SQLite — keyword search + semantic vector search (od 2026-02-23)

Live stats (2026-02-23):

Metrika Vrijednost
Total intel entries 13,473
With embeddings ~13,473 (backfill u toku)
Memos 70+
Retencija 90 dana

Upgrade (MC #1804): HiveMind je dobio vektor search:

  • embedding BLOB kolona dodana u intel tabelu
  • Svaki novi post automatski embeduje poruku (best-effort, skip ako Ollama down)
  • Tri nova search moda:
Komanda Tip Opis
query "X" LIKE Keyword match (originalni, backward compat)
semantic-query "X" Cosine Embedding similarity search (top 5000 recent)
hybrid-query "X" LIKE + Cosine RRF Reciprocal Rank Fusion merge
backfill-embeddings Batch Embeduje entries bez vektora (32/batch)

Schema:

  • intel — agent poruke (agent, type, message, data, priority, embedding BLOB)
  • agents — registrovani agenti (name, role, status)
  • subscriptions — agent topic pretplate
  • memos — key-value memorija (key, value, access_count)

Intel tipovi: discovery, alert, opportunity, update, request, response, learning, error

Retencija: 90 dana za intel, 7 dana za event fajlove

5. Claude API (Anthropic)

Primarni AI: Claude Code (Opus za sesije, Sonnet/Haiku za agente)

Direktna API integracija:

  • ~/system/tools/comms-agent/claude-handler.ts - Anthropic SDK wrapper za automatske odgovore
  • ~/system/tools/comms-responder.js - Komunikacijski agent

Nema OpenAI API integracija u sistemu.

6. MCP Serveri

Server File Namjena
rag ~/system/tools/rag-mcp.js RAG query/learn/stats
email ~/system/tools/email-mcp-bridge.js Email operacije (2 accounta)
youtube-transcript @fabriqa.ai/youtube-transcript-mcp YouTube transkripti
playwright @playwright/mcp Browser automatizacija
figma @anthropic-ai/figma-mcp Figma dizajn pristup

7. Fine-tuned modeli (ALAI ML)

Tri custom modela trenirani na internim podacima:

Model Baza Namjena Velicina
alaiml-task-v1 llama3.1:8b (Modelfile) MC task klasifikacija i handling 986 MB
alaiml-tender-v1 llama3.1:8b (Modelfile) Tender analiza i filtriranje 986 MB
alaiml-email-v1 llama3.1:8b (Modelfile) Email klasifikacija i triage 986 MB

Retrain daemon: com.john.alaiml-retrain (LaunchAgent)

8. AutoCoder (Python Agent Framework)

Path: ~/system/services/autocoder/ Komponente:

  • agent.py - Glavni agent logic
  • agent_classifier.py - Task klasifikacija
  • parallel_orchestrator.py - Multi-agent orkestracija (53 KB)
  • mcp_server/ - MCP server

UI: LaunchAgent com.john.autocoder-ui (port 8888) Status: Instaliran, koristi se opcionalno kroz build mode.

9. Baze podataka (sve SQLite)

Baza Velicina Namjena Ima vektore? Entries
knowledge.db ~50 MB Document store (KB + memory-file + sessions) DA (BLOB 768-dim) 24,636
flywheel.db ~10 MB RAG cache + interaction log + routing DA (BLOB 768-dim) 2,201 cache + 886 interactions
hivemind.db ~30 MB Agent memory bus + memos + semantic search DA (BLOB 768-dim) 13,473
mission-control.db ~3 MB Task management NE 1,804+ tasks
events.db ~3 MB Event bus NE —
contacts.db ~50 KB Kontakti NE —
invoices.db ~40 KB Fakture NE —

Unified embedding model (od 2026-02-23): Sve 3 vektor-baze koriste ISTI model (nomic-embed-text 768-dim via Ollama). Nema mismatch-a.

Nema eksternih vektor baza (ChromaDB, FAISS, Pinecone, Weaviate, Qdrant, pgvector).

10. Sto POSTOJI vs Sto NE POSTOJI

Postoji (verifikovano 2026-02-23)

  • 7 lokalnih Ollama modela (ukljucujuci 3 fine-tuned)
  • Unified embedding model (nomic-embed-text, 768-dim, lokalni) — ISTI za sve storee
  • Custom vektor DB (SQLite + BLOB, cosine similarity)
  • Retrieval Orchestrator — 4-store parallel search sa RRF merge (NOVO)
  • RAG 3-tier routing sa flywheel cache-om (61.1% hit rate, 886 queries)
  • Knowledge base: 24,636 entries (documents + memory files + sessions)
  • HiveMind semantic search — cosine + hybrid + backfill (NOVO)
  • Session archiver — cleanup + embedding + daily cron (NOVO)
  • Tier router za task->model dispatch (6 tierova)
  • 5 MCP servera (RAG, email, YouTube, Playwright, Figma)
  • 3 ALAI fine-tuned modela
  • Usage tracking za sve AI pozive
  • Claude API integracija (comms-agent)

NE postoji

  • Nema cloud vektor baza (ChromaDB, Pinecone, Weaviate...)
  • Nema OpenAI API
  • Nema LangChain / LlamaIndex / LanceDB (custom implementacija, zero external deps)
  • Nema cloud embeddings (sve lokalno)
  • Nema GraphRAG (prevelik effort za nas obim)
  • Nema cross-encoder reranking (Ollama default dovoljan)
  • llama3.1:70b i qwen2.5:72b konfigurirani ali NE instalirani
  • BookStack NIJE dio RAG pipeline-a (samo human-readable wiki)

11. Arhitekturni princip

Cost-optimized hybrid: Cache prvo -> Lokalni modeli drugo -> Cloud API zadnji.

  • Svi embeddings su lokalni (Ollama nomic-embed-text, 768-dim)
  • Sav vektor storage je u SQLite BLOB kolonama (Float32Array)
  • Jedan embedding model za cijeli sistem — nema mismatch-a
  • Nema cloud zavisnosti za RAG
  • Claude API se koristi samo za ono sto lokalni modeli ne mogu
  • Fine-tuned modeli pokrivaju repetitivne domenske taskove (email, tender, MC tasks)
  • Retrieval orchestrator objedinjuje sve storee u jedan poziv sa RRF merge

Tool-First Protocol (retrieval redoslijed)

BookStack (human wiki) -> RAG MCP (mcp__rag__rag_query) -> Manifest
-> HiveMind (semantic-query) -> Internet -> Azuriraj bazu

Za programski retrieval: node retrieval-orchestrator.js query "tema" — automatski paralelno pretrazuje sve.

12. Changelog

Datum Promjena MC Task
2026-02-23 RAG System Upgrade: unified embedding, HiveMind vector search, retrieval orchestrator, session archiver #1804
2026-02-21 Initial document created — full system inventory —

No comments to display

Back to top