LOT 1 - Optimizare script build -Instalare mono comanda

ai_platform/modules/didi_brain/README.md

@@ -23,7 +23,7 @@ HTTP contract but serves responses from a locally-grown **knowledge graph**:
 
 - **Atoms** (documents and atomic claims) are stored in a Postgres +
   pgvector backend with multilingual embeddings (BGE-M3, 1024-dim).
-- **Claim extraction** runs Qwen 3.5 397B over every ingested document to
+- **Claim extraction** runs Qwen3.5 over every ingested document to
   pull out verifiable atomic claims with source quotes and stance.
 - **Retrieval** uses vector kNN + BGE-reranker-v2-m3 cross-encoder for
   precision.
@@ -46,9 +46,9 @@ background. Next time a similar claim arrives, it's a HIT.
 | **Brain storage** | [Atomic](https://github.com/kenforthewin/atomic) (Rust) on Postgres 16 + pgvector |
 | **Embeddings** | `BAAI/bge-m3` via vLLM OpenAI-compat (1024 dim, 8K ctx, multilingual) |
 | **Reranker** | `BAAI/bge-reranker-v2-m3` cross-encoder via vllm-rerank-api |
-| **LLM (reasoning)** | `Qwen3.5-397B-A17B` (MoE) via llama.cpp through an OpenAI-compat router |
+| **LLM (reasoning)** | `qwen3.5` via vLLM through an OpenAI-compat router (`MODEL_FAST` enabled) |
 | **Service** | Python 3.12, FastAPI, Uvicorn, Pydantic v2, httpx, structlog, tenacity |
-| **Deployment** | Docker + docker-compose (3 services: api, atomic, postgres) |
+| **Deployment** | Docker + docker-compose (4 services: api, atomic, postgres, scheduler) |
 
 ## Quick start — local dev
 
@@ -88,7 +88,7 @@ curl -fsS http://localhost:8090/health
 ```
 
 Open the interactive Swagger UI at **http://localhost:8090/docs** to poke
-the 5 endpoints live.
+the full route set live (~30 routes).
 
 ## Production deploy — fresh Linux server
 
@@ -127,6 +127,21 @@ backends safely ignore.
 | `POST /v1/gather` | claim → ranked evidence with NLI stance | 5-6 s (1.5 s without NLI) |
 | `POST /v1/image-search` | stub (always empty list) | <5 ms |
 | `POST /v1/ingest` | populate brain from web-module output | variable (async extraction) |
+| `POST /v1/verification_cache` | write claim verification result (claims cache) | <50 ms |
+| `POST /v1/analysis_atom/lookup` | read cached analysis result (techniques / ai_tampered / claims) | <50 ms |
+| `POST /v1/analysis_atom` | write analysis atom (silver/bronze by confidence) | <50 ms |
+| `PATCH /v1/analysis_atom/{id}` | promote atom to gold (moderator review) | <50 ms |
+| `GET /v1/analysis_atom/stats` | per-tier/component counts + 24h hit rate | <20 ms |
+| `POST /v1/canonicalize` | temporal claim disambiguation (Pilon 7) | LLM-bound |
+| `POST /v1/cache/invalidate` | mass invalidation with `dry_run` (Pilon 8) | variable |
+| `GET /v1/cache/audit_log` | paginated audit browser | <50 ms |
+| `GET/PATCH /v1/fact_status/*` | versioned fact-status layer (list/detail/versions/override) | <50 ms |
+
+This is the full set served by `brain_api` (~30 routes including the FastAPI
+auto docs); the `/v1/gather`, `/v1/search`, `/v1/fetch`, `/v1/ingest`,
+`/v1/image-search` group is the web-gathering contract, the rest are the
+brain-owned cache + freshness-defense layers. See `INDEX.md` for the
+canonical route list.
 
 The response from `/v1/gather` matches the existing web-module shape
 exactly plus an additive `brain_meta` object on the top level and inside
@@ -156,11 +171,11 @@ Didi backend
 │  stage context    → language detection               │
 │  stage retrieval  → Atomic semantic search (top 50)  │
 │  stage rerank     → BGE cross-encoder (top 15)       │
-│  stage nli        → Qwen 397B stance vs query        │
+│  stage nli        → Qwen3.5 stance vs query          │
 │  stage evidence   → group by parent doc, shape       │
 └──────┬──────────────────────────┬──────────────────┘
        │                          │
-       │ HTTP (docker DNS)        │ HTTPS (VPN)
+       │ HTTP (docker DNS)        │ HTTP (docker DNS)
        ▼                          ▼
 ┌────────────────┐        ┌───────────────────────┐
 │ atomic-server  │        │  BGE-M3 embeddings    │
@@ -169,9 +184,9 @@ Didi backend
        │                  └───────────────────────┘
        ▼
 ┌────────────────┐        ┌───────────────────────┐
-│ postgres       │        │  Qwen 3.5 397B-A17B   │
+│ postgres       │        │  qwen3.5              │
 │ + pgvector     │        │  via LLM router       │
-│  :5432 (5434)  │        │  (llama.cpp + vLLM)   │
+│  :5432 (5434)  │        │  (vLLM)               │
 └────────────────┘        └───────────────────────┘
 ```
 
@@ -179,7 +194,7 @@ Didi backend
 
 ```
 didibrain/
-├── infra/docker-compose.yml       # 3-service stack
+├── infra/docker-compose.yml       # 4-service stack (api, atomic, postgres, scheduler)
 ├── brain_api/                     # FastAPI service (the main deliverable)
 ├── shared/                        # config, clients, taxonomy (reused everywhere)
 ├── extractor/                     # claim extraction (host jobs + /v1/ingest)
@@ -201,7 +216,7 @@ Detailed file-by-file rundown is in `STATUS.md`.
 - [x] Claim extraction (513 atoms, 1.2% hallucination filter)
 - [x] Document-level retrieval validated (cross-lingual cosine 0.88-0.94)
 - [x] Claim-level retrieval validated
-- [x] brain_api HTTP service with Didi contract (5 endpoints)
+- [x] brain_api HTTP service with Didi contract (full route set, ~30 routes)
 - [x] brain_api dockerized (self-sufficient, taxonomy auto-refresh)
 - [x] NLI stance vs query in `/v1/gather`
 - [x] Lint pass contradiction detection (code + smoke test)
@@ -213,16 +228,20 @@ Detailed file-by-file rundown is in `STATUS.md`.
 
 ## Operational notes
 
-- **Resource footprint**: ~200 MB RAM total across the 3 containers; brain-api
+- **Resource footprint**: ~200 MB RAM total across the 4 containers; brain-api
   idles at ~6% CPU, spikes to ~20% during a `/v1/gather` with NLI.
 - **Image size**: brain-api Docker image is ~253 MB (Python 3.12-slim base).
-- **VPN dependency**: BGE and the LLM router live on a VPN-routed 10.11.10.x
-  network. If the VPN drops, `/v1/gather` returns 500 because Atomic cannot
-  embed the query. Confirm upstream reachability before debugging anything
-  else when search starts failing.
-- **Self-sufficient startup**: brain_api pulls the current taxonomy from
-  Atomic at startup, so there is no baked `_tag_ids.json` in the image and
-  the container is portable across environments.
+- **Upstream connectivity**: the LLM router, BGE-M3 embeddings, and the BGE
+  reranker are reached over Docker DNS as `didiAI-llm-api:14011`,
+  `didiAI-embeddings-api:14100`, and `didiAI-rerank-api:14200` on the shared
+  `didi-network`. If those upstreams are unreachable, `/v1/gather` returns 500
+  because Atomic cannot embed the query. Confirm upstream reachability before
+  debugging anything else when search starts failing.
+- **Startup**: brain_api refreshes the `TagResolver` from Atomic at startup;
+  in addition `shared/_tag_ids.json` is mounted into the container (compose
+  volume) so background extraction — which instantiates `TagResolver`
+  directly, bypassing the lifespan refresh — can still resolve canonical tag
+  UUIDs.
 - **Idempotency**: every operator script (taxonomy seeder, Wikipedia
   importer, claim extractor, Lint pass) is idempotent via state files or
   URL-based dedup. Re-running is always safe.