DiDi — LOT 1 · Arhitectură și fluxuri (Sistem de Inteligență Artificială)

flowchart LR subgraph LOT1["LOT 1 — Platforma AI"] subgraph M4["Modul 4 — Cache"] redis["Redis / cache distribuit
rezultate · locks · pub/sub · rate-limit"] end subgraph M1["Modul 1 — Inferență LLM"] gw["LLM Gateway (FastAPI)"] vllm["vLLM — Qwen3.5 (GPU)"] llama["llama.cpp (CPU, fallback)"] end subgraph M6["Modul 6 — Dashboard Admin AI"] dash["Dashboard Admin
monitorizare · metrici GPU · stare servicii"] end subgraph M5["Modul 5 — RAG / Brain"] rag["RAG Service"] emb["Embeddings API"] vs["Vector Store (pgvector)"] rr["Rerank API"] end subgraph M3["Modul 3 — Web Crawl"] web["Web Crawl API
căutare · content extraction"] end subgraph M2["Modul 2 — Extractoare ML"] whisper["Whisper API
transcriere audio"] video["Video Analysis API
ffmpeg + frame sampling"] buster["BusterX (vLLM)
deepfake detection"] sandbox["Sandbox Cod
EXIF · ELA · spectrograme"] end end subgraph LOT2["LOT 2 — Backend (consumator API REST)"] orch["Orchestrator"] workers["Workers Analiză"] end gw --> vllm gw -. fallback .-> llama rag --> emb rag --> vs rag --> rr video --> buster workers -->|POST /v1/chat/completions| gw workers -->|POST /v1/audio/transcriptions| whisper workers -->|POST /analyze/video| video workers -->|POST /v1/gather| web workers -->|POST /v1/query| rag workers -->|POST /analyze| sandbox orch --- workers redis -. cache .-> gw redis -. cache .-> rag

sequenceDiagram participant B as Backend (LOT 2) participant G as LLM Gateway (FastAPI) participant V as vLLM Qwen3.5 participant L as llama.cpp (fallback) B->>G: POST /v1/chat/completions {model, messages[], temperature} G->>G: Verificare cache alt Cache hit G-->>B: 200 {cached response} else Cache miss G->>V: Forward request alt vLLM disponibil V->>V: Generare răspuns (GPU) V-->>G: 200 {response, usage} else vLLM indisponibil G->>L: Fallback request L->>L: Generare răspuns (CPU) L-->>G: 200 {response, usage} end G->>G: Stocare în cache G-->>B: 200 {response, usage, model_used} end

sequenceDiagram participant B as Backend (LOT 2) participant G as LLM Gateway (FastAPI) participant V as vLLM Qwen3.5 B->>G: POST /v1/chat/completions {messages:[{user, [text, image_url]}]} G->>V: Forward multimodal request V->>V: Procesare imagine + text (GPU) V->>V: OCR, descriere, detecție artefacte AI V-->>G: 200 {response: structured JSON} Note over G: Răspuns structurat: description, ocr_text, ai_artifacts, anomalies G-->>B: 200 {response, model_used}

sequenceDiagram participant B as Backend (LOT 2) participant VA as Video Analysis API participant F as ffmpeg participant BX as BusterX (vLLM) B->>VA: POST /analyze/video {video_url} VA->>VA: Download video VA->>F: Extragere cadre (frame sampling configurabil) F-->>VA: Cadre extrase (N imagini) loop Pentru fiecare cadru VA->>BX: POST analiză cadru BX->>BX: Detecție artefacte deepfake BX-->>VA: {score, artifacts, confidence} end VA->>VA: Agregare scoruri per cadru VA->>VA: Calculare verdict final VA-->>B: 200 {verdict, score, evidence_frames, analysis_details}

sequenceDiagram participant B as Backend (LOT 2) participant R as RAG Service participant E as Embeddings API participant VS as Vector Store participant RR as Rerank API B->>R: POST /v1/query {query, top_k, filters} R->>E: Embedding query text E-->>R: Vector embedding R->>VS: Nearest neighbor search {vector, top_k: 20} VS-->>R: Documente candidat (cu scoruri similaritate) R->>RR: Reranking documente {query, documents} RR-->>R: Documente reordonate (scoruri relevanță) R->>R: Filtrare top_k final R-->>B: 200 {documents:[{text, score, source, metadata}]}

sequenceDiagram participant B as Backend (LOT 2) participant W as Web Crawl API participant S as Motor căutare (SearXNG / provideri) participant P as Pagini Web B->>W: POST /v1/gather {claim, max_sources} W->>S: Căutare web {query: claim} S-->>W: Rezultate căutare (URLs + snippets) loop Pentru fiecare URL relevant W->>P: Fetch pagină P-->>W: Conținut HTML W->>W: Extragere conținut structurat (text, metadate, autor, dată) end W->>W: Structurare evidence package W-->>B: 200 {sources:[{url, title, content, snippet, date, credibility_indicators}]}

sequenceDiagram participant B as Backend (LOT 2) participant SB as Sandbox API participant PY as Python Runtime (izolat) B->>SB: POST /analyze {media_url, analysis_type} SB->>SB: Download fișier media (stocare temporară) alt Imagine SB->>PY: Execuție EXIF extraction PY-->>SB: Metadata EXIF SB->>PY: Execuție Error Level Analysis PY-->>SB: Rezultat ELA else Audio SB->>PY: Execuție spectrogramă PY-->>SB: Analiză spectrogramă else Video SB->>PY: Execuție frame analysis PY-->>SB: Semnături digitale + anomalii end SB->>SB: Cleanup fișiere temporare SB-->>B: 200 {metadata, anomalies, signatures, analysis_type} Note over SB: Sandbox: izolare completă, timeout, restricții resurse, audit