Livrare LOT 1 - Didi
This commit is contained in:
commit
5380c3fc63
990 changed files with 133308 additions and 0 deletions
236
ai_platform/modules/didi_brain/README.md
Normal file
236
ai_platform/modules/didi_brain/README.md
Normal file
|
|
@ -0,0 +1,236 @@
|
|||
# DidiBrain
|
||||
|
||||
A claim-verification knowledge cache for **Didi**, a disinformation analysis
|
||||
application. DidiBrain sits between Didi's backend and Didi's live
|
||||
web-gathering module and speaks the same HTTP contract, answering from
|
||||
pre-ingested atoms + multilingual semantic retrieval + LLM stance
|
||||
classification — typically in 1.5-6 seconds instead of 20+ seconds for a
|
||||
live web crawl.
|
||||
|
||||
Built on top of [Atomic](https://github.com/kenforthewin/atomic), a Rust
|
||||
knowledge-graph backend. DidiBrain adds the scraping, claim-level extraction,
|
||||
reranker integration, NLI stance layer, HTTP contract, and the contradiction
|
||||
audit job on top.
|
||||
|
||||
---
|
||||
|
||||
## What it is, in one paragraph
|
||||
|
||||
Didi's backend currently verifies claims by calling a live
|
||||
`POST /v1/gather` endpoint that does web search + fetch + score. That's
|
||||
slow (~23 s) and expensive on premium engines. DidiBrain exposes the same
|
||||
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
|
||||
pull out verifiable atomic claims with source quotes and stance.
|
||||
- **Retrieval** uses vector kNN + BGE-reranker-v2-m3 cross-encoder for
|
||||
precision.
|
||||
- **NLI stance vs query** classifies each piece of evidence as SUPPORTS,
|
||||
CONTRADICTS, or NEUTRAL relative to the input claim — giving Didi the
|
||||
signal it really needs for a disinfo verdict.
|
||||
- **`brain_meta.cache_status`** tells Didi HIT / PARTIAL / MISS so the
|
||||
backend can cleanly fall back to the expensive web module when the brain
|
||||
doesn't have relevant knowledge yet.
|
||||
|
||||
The brain is also **self-populating**: whenever Didi falls back to the web
|
||||
module and gets fresh evidence, it can POST the result to `/v1/ingest`
|
||||
and DidiBrain will create new atoms + queue claim extraction in the
|
||||
background. Next time a similar claim arrives, it's a HIT.
|
||||
|
||||
## Tech stack
|
||||
|
||||
| Layer | Tech |
|
||||
|---|---|
|
||||
| **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 |
|
||||
| **Service** | Python 3.12, FastAPI, Uvicorn, Pydantic v2, httpx, structlog, tenacity |
|
||||
| **Deployment** | Docker + docker-compose (3 services: api, atomic, postgres) |
|
||||
|
||||
## Quick start — local dev
|
||||
|
||||
Assumes Docker Desktop running and `.env` filled with reachable upstream
|
||||
endpoints.
|
||||
|
||||
```bash
|
||||
cd didibrain
|
||||
|
||||
# 1. Copy env template and fill upstream URLs
|
||||
cp .env.example .env
|
||||
# Edit LLM_ROUTER_URL, EMBEDDING_URL, RERANKER_URL to reachable addresses
|
||||
|
||||
# 2. Bring up the stack (atomic + postgres + brain_api)
|
||||
docker compose -f infra/docker-compose.yml --env-file .env up -d --build
|
||||
|
||||
# 3. Create a Python venv for operator scripts
|
||||
python3 -m venv .venv
|
||||
.venv/bin/pip install httpx pydantic pydantic-settings structlog \
|
||||
python-dotenv tenacity rich
|
||||
|
||||
# 4. Bootstrap Atomic (claim instance, configure BGE-M3 provider)
|
||||
.venv/bin/python scripts/02_bootstrap_atomic.py
|
||||
|
||||
# 5. Seed the canonical tag taxonomy
|
||||
.venv/bin/python scripts/04_seed_taxonomy.py
|
||||
|
||||
# 6. (Optional) Import seed Wikipedia corpus — first topic: vaccines
|
||||
.venv/bin/python scripts/05_import_wikipedia_seed.py
|
||||
|
||||
# 7. (Optional) Extract atomic claims from the imported documents
|
||||
.venv/bin/python scripts/07_run_extraction.py
|
||||
|
||||
# 8. Verify it works
|
||||
curl -fsS http://localhost:8090/health
|
||||
.venv/bin/python scripts/09_brain_api_demo.py
|
||||
```
|
||||
|
||||
Open the interactive Swagger UI at **http://localhost:8090/docs** to poke
|
||||
the 5 endpoints live.
|
||||
|
||||
## Production deploy — fresh Linux server
|
||||
|
||||
A single `bash` script does the whole thing:
|
||||
|
||||
```bash
|
||||
scp -r didibrain/ user@server:~/
|
||||
ssh user@server
|
||||
cd ~/didibrain
|
||||
cp .env.example .env
|
||||
vim .env # set LLM/BGE/reranker URLs for this network
|
||||
./scripts/bootstrap_deploy.sh # preflight → build → up → seed → smoke test
|
||||
```
|
||||
|
||||
The script is idempotent — re-run it any time to pick up from wherever it
|
||||
was interrupted. See `scripts/bootstrap_deploy.sh` for the exact flow and
|
||||
optional env flags (`BRAIN_IMPORT_CORPUS=0` to skip the seed import, etc.).
|
||||
|
||||
Alternatively, run the numbered `scripts/XX_*.py` files in order by hand —
|
||||
they are all idempotent and explicit.
|
||||
|
||||
## HTTP API — the contract
|
||||
|
||||
Brain-API speaks **Didi's existing web-gathering module contract**, 1:1 at
|
||||
the response-shape level, with additive `brain_meta` fields that older
|
||||
backends safely ignore.
|
||||
|
||||
| Endpoint | Purpose | Typical latency |
|
||||
|---|---|---|
|
||||
| `GET /health` | liveness | <10 ms |
|
||||
| `GET /docs` | interactive Swagger UI | — |
|
||||
| `GET /redoc` | read-only API docs | — |
|
||||
| `GET /openapi.json` | machine-readable schema | — |
|
||||
| `POST /v1/search` | flat list of doc-level search results | 200-400 ms |
|
||||
| `POST /v1/fetch` | look up atoms by URL → extracted text | 30-100 ms |
|
||||
| `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) |
|
||||
|
||||
The response from `/v1/gather` matches the existing web-module shape
|
||||
exactly plus an additive `brain_meta` object on the top level and inside
|
||||
each `evidence[].provenance`. The key signal to branch on:
|
||||
|
||||
```python
|
||||
resp = requests.post(f"{BRAIN_URL}/v1/gather", json={"claim": text}).json()
|
||||
if resp["brain_meta"]["cache_status"] == "MISS":
|
||||
resp = requests.post(f"{WEB_MODULE_URL}/v1/gather", json={"claim": text}).json()
|
||||
# optional self-populating:
|
||||
requests.post(f"{BRAIN_URL}/v1/ingest",
|
||||
json={"claim": text, "evidence": resp["evidence"]})
|
||||
```
|
||||
|
||||
See `STATUS.md` for the full field reference.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
Didi backend
|
||||
│
|
||||
│ POST /v1/gather {claim}
|
||||
▼
|
||||
┌────────────────────────────────────────────────────┐
|
||||
│ brain_api :8090 (FastAPI, Uvicorn) │
|
||||
│ │
|
||||
│ 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 evidence → group by parent doc, shape │
|
||||
└──────┬──────────────────────────┬──────────────────┘
|
||||
│ │
|
||||
│ HTTP (docker DNS) │ HTTPS (VPN)
|
||||
▼ ▼
|
||||
┌────────────────┐ ┌───────────────────────┐
|
||||
│ atomic-server │ │ BGE-M3 embeddings │
|
||||
│ :8080 (8088) │◀──SQL──│ & BGE reranker │
|
||||
└──────┬─────────┘ │ (vLLM) │
|
||||
│ └───────────────────────┘
|
||||
▼
|
||||
┌────────────────┐ ┌───────────────────────┐
|
||||
│ postgres │ │ Qwen 3.5 397B-A17B │
|
||||
│ + pgvector │ │ via LLM router │
|
||||
│ :5432 (5434) │ │ (llama.cpp + vLLM) │
|
||||
└────────────────┘ └───────────────────────┘
|
||||
```
|
||||
|
||||
## Project structure
|
||||
|
||||
```
|
||||
didibrain/
|
||||
├── infra/docker-compose.yml # 3-service stack
|
||||
├── brain_api/ # FastAPI service (the main deliverable)
|
||||
├── shared/ # config, clients, taxonomy (reused everywhere)
|
||||
├── extractor/ # claim extraction (host jobs + /v1/ingest)
|
||||
├── lint/ # cross-corpus contradiction detection
|
||||
├── scripts/ # operator CLI tools (numbered 01-11)
|
||||
│ └── bootstrap_deploy.sh # fresh-server bootstrap script
|
||||
├── STATUS.md # end-of-session snapshot + troubleshooting
|
||||
└── README.md # this file
|
||||
```
|
||||
|
||||
Detailed file-by-file rundown is in `STATUS.md`.
|
||||
|
||||
## Status
|
||||
|
||||
- [x] Upstream stack validation (LLM + BGE + reranker sanity gate)
|
||||
- [x] Atomic running on Postgres + pgvector in compose
|
||||
- [x] Canonical tag taxonomy (79 tags, 7 root namespaces)
|
||||
- [x] First Wikipedia import (19 documents, EN+RO, vaccines topic)
|
||||
- [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 dockerized (self-sufficient, taxonomy auto-refresh)
|
||||
- [x] NLI stance vs query in `/v1/gather`
|
||||
- [x] Lint pass contradiction detection (code + smoke test)
|
||||
- [x] Deployment bootstrap script
|
||||
- [ ] Corpus expansion — more topics, more sources (next session)
|
||||
- [ ] Full Lint run on diverse corpus
|
||||
- [ ] Bearer auth on brain_api (when exposing beyond loopback)
|
||||
- [ ] Optional `atomic-web` frontend service
|
||||
|
||||
## Operational notes
|
||||
|
||||
- **Resource footprint**: ~200 MB RAM total across the 3 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.
|
||||
- **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.
|
||||
|
||||
## License & upstream
|
||||
|
||||
DidiBrain itself is private (not open source). It builds on top of
|
||||
[Atomic](https://github.com/kenforthewin/atomic) (MIT). The Atomic
|
||||
upstream clone at `D:\didi_brain\atomic\` is untouched — `git pull`
|
||||
from upstream is always safe and doesn't conflict with anything in
|
||||
this repo.
|
||||
Loading…
Add table
Add a link
Reference in a new issue