236 lines
10 KiB
Markdown
236 lines
10 KiB
Markdown
# DidiBrain
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A claim-verification knowledge cache for **Didi**, a disinformation analysis
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application. DidiBrain sits between Didi's backend and Didi's live
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web-gathering module and speaks the same HTTP contract, answering from
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pre-ingested atoms + multilingual semantic retrieval + LLM stance
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classification — typically in 1.5-6 seconds instead of 20+ seconds for a
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live web crawl.
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Built on top of [Atomic](https://github.com/kenforthewin/atomic), a Rust
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knowledge-graph backend. DidiBrain adds the scraping, claim-level extraction,
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reranker integration, NLI stance layer, HTTP contract, and the contradiction
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audit job on top.
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---
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## What it is, in one paragraph
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Didi's backend currently verifies claims by calling a live
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`POST /v1/gather` endpoint that does web search + fetch + score. That's
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slow (~23 s) and expensive on premium engines. DidiBrain exposes the same
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HTTP contract but serves responses from a locally-grown **knowledge graph**:
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- **Atoms** (documents and atomic claims) are stored in a Postgres +
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pgvector backend with multilingual embeddings (BGE-M3, 1024-dim).
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- **Claim extraction** runs Qwen 3.5 397B over every ingested document to
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pull out verifiable atomic claims with source quotes and stance.
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- **Retrieval** uses vector kNN + BGE-reranker-v2-m3 cross-encoder for
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precision.
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- **NLI stance vs query** classifies each piece of evidence as SUPPORTS,
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CONTRADICTS, or NEUTRAL relative to the input claim — giving Didi the
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signal it really needs for a disinfo verdict.
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- **`brain_meta.cache_status`** tells Didi HIT / PARTIAL / MISS so the
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backend can cleanly fall back to the expensive web module when the brain
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doesn't have relevant knowledge yet.
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The brain is also **self-populating**: whenever Didi falls back to the web
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module and gets fresh evidence, it can POST the result to `/v1/ingest`
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and DidiBrain will create new atoms + queue claim extraction in the
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background. Next time a similar claim arrives, it's a HIT.
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## Tech stack
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| Layer | Tech |
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|---|---|
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| **Brain storage** | [Atomic](https://github.com/kenforthewin/atomic) (Rust) on Postgres 16 + pgvector |
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| **Embeddings** | `BAAI/bge-m3` via vLLM OpenAI-compat (1024 dim, 8K ctx, multilingual) |
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| **Reranker** | `BAAI/bge-reranker-v2-m3` cross-encoder via vllm-rerank-api |
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| **LLM (reasoning)** | `Qwen3.5-397B-A17B` (MoE) via llama.cpp through an OpenAI-compat router |
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| **Service** | Python 3.12, FastAPI, Uvicorn, Pydantic v2, httpx, structlog, tenacity |
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| **Deployment** | Docker + docker-compose (3 services: api, atomic, postgres) |
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## Quick start — local dev
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Assumes Docker Desktop running and `.env` filled with reachable upstream
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endpoints.
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```bash
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cd didibrain
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# 1. Copy env template and fill upstream URLs
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cp .env.example .env
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# Edit LLM_ROUTER_URL, EMBEDDING_URL, RERANKER_URL to reachable addresses
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# 2. Bring up the stack (atomic + postgres + brain_api)
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docker compose -f infra/docker-compose.yml --env-file .env up -d --build
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# 3. Create a Python venv for operator scripts
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python3 -m venv .venv
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.venv/bin/pip install httpx pydantic pydantic-settings structlog \
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python-dotenv tenacity rich
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# 4. Bootstrap Atomic (claim instance, configure BGE-M3 provider)
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.venv/bin/python scripts/02_bootstrap_atomic.py
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# 5. Seed the canonical tag taxonomy
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.venv/bin/python scripts/04_seed_taxonomy.py
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# 6. (Optional) Import seed Wikipedia corpus — first topic: vaccines
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.venv/bin/python scripts/05_import_wikipedia_seed.py
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# 7. (Optional) Extract atomic claims from the imported documents
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.venv/bin/python scripts/07_run_extraction.py
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# 8. Verify it works
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curl -fsS http://localhost:8090/health
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.venv/bin/python scripts/09_brain_api_demo.py
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```
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Open the interactive Swagger UI at **http://localhost:8090/docs** to poke
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the 5 endpoints live.
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## Production deploy — fresh Linux server
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A single `bash` script does the whole thing:
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```bash
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scp -r didibrain/ user@server:~/
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ssh user@server
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cd ~/didibrain
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cp .env.example .env
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vim .env # set LLM/BGE/reranker URLs for this network
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./scripts/bootstrap_deploy.sh # preflight → build → up → seed → smoke test
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```
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The script is idempotent — re-run it any time to pick up from wherever it
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was interrupted. See `scripts/bootstrap_deploy.sh` for the exact flow and
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optional env flags (`BRAIN_IMPORT_CORPUS=0` to skip the seed import, etc.).
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Alternatively, run the numbered `scripts/XX_*.py` files in order by hand —
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they are all idempotent and explicit.
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## HTTP API — the contract
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Brain-API speaks **Didi's existing web-gathering module contract**, 1:1 at
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the response-shape level, with additive `brain_meta` fields that older
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backends safely ignore.
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| Endpoint | Purpose | Typical latency |
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|---|---|---|
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| `GET /health` | liveness | <10 ms |
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| `GET /docs` | interactive Swagger UI | — |
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| `GET /redoc` | read-only API docs | — |
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| `GET /openapi.json` | machine-readable schema | — |
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| `POST /v1/search` | flat list of doc-level search results | 200-400 ms |
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| `POST /v1/fetch` | look up atoms by URL → extracted text | 30-100 ms |
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| `POST /v1/gather` | claim → ranked evidence with NLI stance | 5-6 s (1.5 s without NLI) |
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| `POST /v1/image-search` | stub (always empty list) | <5 ms |
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| `POST /v1/ingest` | populate brain from web-module output | variable (async extraction) |
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The response from `/v1/gather` matches the existing web-module shape
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exactly plus an additive `brain_meta` object on the top level and inside
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each `evidence[].provenance`. The key signal to branch on:
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```python
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resp = requests.post(f"{BRAIN_URL}/v1/gather", json={"claim": text}).json()
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if resp["brain_meta"]["cache_status"] == "MISS":
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resp = requests.post(f"{WEB_MODULE_URL}/v1/gather", json={"claim": text}).json()
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# optional self-populating:
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requests.post(f"{BRAIN_URL}/v1/ingest",
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json={"claim": text, "evidence": resp["evidence"]})
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```
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See `STATUS.md` for the full field reference.
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## Architecture
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```
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Didi backend
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│
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│ POST /v1/gather {claim}
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▼
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┌────────────────────────────────────────────────────┐
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│ brain_api :8090 (FastAPI, Uvicorn) │
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│ │
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│ stage context → language detection │
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│ stage retrieval → Atomic semantic search (top 50) │
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│ stage rerank → BGE cross-encoder (top 15) │
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│ stage nli → Qwen 397B stance vs query │
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│ stage evidence → group by parent doc, shape │
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└──────┬──────────────────────────┬──────────────────┘
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│ │
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│ HTTP (docker DNS) │ HTTPS (VPN)
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▼ ▼
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┌────────────────┐ ┌───────────────────────┐
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│ atomic-server │ │ BGE-M3 embeddings │
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│ :8080 (8088) │◀──SQL──│ & BGE reranker │
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└──────┬─────────┘ │ (vLLM) │
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│ └───────────────────────┘
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▼
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┌────────────────┐ ┌───────────────────────┐
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│ postgres │ │ Qwen 3.5 397B-A17B │
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│ + pgvector │ │ via LLM router │
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│ :5432 (5434) │ │ (llama.cpp + vLLM) │
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└────────────────┘ └───────────────────────┘
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```
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## Project structure
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```
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didibrain/
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├── infra/docker-compose.yml # 3-service stack
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├── brain_api/ # FastAPI service (the main deliverable)
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├── shared/ # config, clients, taxonomy (reused everywhere)
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├── extractor/ # claim extraction (host jobs + /v1/ingest)
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├── lint/ # cross-corpus contradiction detection
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├── scripts/ # operator CLI tools (numbered 01-11)
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│ └── bootstrap_deploy.sh # fresh-server bootstrap script
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├── STATUS.md # end-of-session snapshot + troubleshooting
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└── README.md # this file
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```
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Detailed file-by-file rundown is in `STATUS.md`.
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## Status
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- [x] Upstream stack validation (LLM + BGE + reranker sanity gate)
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- [x] Atomic running on Postgres + pgvector in compose
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- [x] Canonical tag taxonomy (79 tags, 7 root namespaces)
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- [x] First Wikipedia import (19 documents, EN+RO, vaccines topic)
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- [x] Claim extraction (513 atoms, 1.2% hallucination filter)
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- [x] Document-level retrieval validated (cross-lingual cosine 0.88-0.94)
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- [x] Claim-level retrieval validated
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- [x] brain_api HTTP service with Didi contract (5 endpoints)
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- [x] brain_api dockerized (self-sufficient, taxonomy auto-refresh)
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- [x] NLI stance vs query in `/v1/gather`
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- [x] Lint pass contradiction detection (code + smoke test)
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- [x] Deployment bootstrap script
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- [ ] Corpus expansion — more topics, more sources (next session)
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- [ ] Full Lint run on diverse corpus
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- [ ] Bearer auth on brain_api (when exposing beyond loopback)
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- [ ] Optional `atomic-web` frontend service
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## Operational notes
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- **Resource footprint**: ~200 MB RAM total across the 3 containers; brain-api
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idles at ~6% CPU, spikes to ~20% during a `/v1/gather` with NLI.
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- **Image size**: brain-api Docker image is ~253 MB (Python 3.12-slim base).
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- **VPN dependency**: BGE and the LLM router live on a VPN-routed 10.11.10.x
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network. If the VPN drops, `/v1/gather` returns 500 because Atomic cannot
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embed the query. Confirm upstream reachability before debugging anything
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else when search starts failing.
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- **Self-sufficient startup**: brain_api pulls the current taxonomy from
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Atomic at startup, so there is no baked `_tag_ids.json` in the image and
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the container is portable across environments.
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- **Idempotency**: every operator script (taxonomy seeder, Wikipedia
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importer, claim extractor, Lint pass) is idempotent via state files or
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URL-based dedup. Re-running is always safe.
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## License & upstream
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DidiBrain itself is private (not open source). It builds on top of
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[Atomic](https://github.com/kenforthewin/atomic) (MIT). The Atomic
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upstream clone at `D:\didi_brain\atomic\` is untouched — `git pull`
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from upstream is always safe and doesn't conflict with anything in
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this repo.
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