README.md

---
license: other
license_name: copernicus-and-mixed
license_link: https://www.copernicus.eu/en/access-data
language: [en]
pretty_name: Copernicus RAG core (4 stores)
---

# copernicus-rag-core

Full processed core of a 3-tier RAG over **all four Copernicus stores** —
CMEMS (Marine) · CDS (Climate) · ADS (Atmosphere) · EWDS (Early Warning).
Private working dataset: originals as markdown, chunks, ready 768-d embeddings,
prebuilt Qdrant indexes, all linkage sidecars, and the complete pipeline
scripts to rebuild everything from scratch. No PDFs, no images.

Served by the `copernicus-rag` MCP server (12 tools).
Validated 2026-07-23: **50/50 test queries green** across all tiers.

## Tiers

| tier | collection | points | content |
|---|---|---|---|
| L1 discover | `copernicus_docs` | 1,418 | dataset cards, all 4 stores |
| L2 analyze | `marine_docs` | 29,249 | CMEMS PUM/QUID/SQO (807 docs / 306 products) |
| L2 analyze | `cds_docs` | 23,341 | CDS/ADS/EWDS PUG/ATBD (766 docs / 165 datasets) |
| L2 analyze | `eqc_qa` | 1,274 | C3S EQC quality reports (74) |
| L3 method | `publications` | 430,066 | 12,411 parsed papers, dataset-linked |

## Layout

```
originals_md/   cmems/ (813 md) · cds_ads_ewds/ (772) · eqc_reports/ (74)
                notebooks/ (194) · publications_md.tar.gz (11,209 md, MinerU-VLM
                parsed — CC-licensed/PD papers only; 1,202 unlicensed-bronze
                originals removed 2026-07-23, their chunks/embeddings/index
                points remain untouched)
chunks/         per-collection chunks.jsonl + papers.jsonl
embeddings/     *.jsonl.gz — gemini-embedding-2-preview, 768d, L2-norm (ready to load)
indexes/        4 prebuilt embedded-Qdrant dirs (tar.gz) — hybrid dense+BM25,
                publications payloads RELINKED (untar & point the server at them)
metadata/       catalog.json · unified_metadata.json (1,436) · notebooks sidecar ·
                links_by_dataset.json (234 datasets / 8,917 papers / 31,190 links) ·
                publications registry (1,199 DOI) · flagships.json
scripts/        FULL pipeline, per component (see below)
REBUILD.md      full from-scratch rebuild / open-LLM swap guide
```

## Quickstart — plug the RAG database into Qdrant

Two ways, depending on where you want Qdrant to run.

### A) Prebuilt embedded indexes (fastest — no re-compute, no server)

The `indexes/*.tar.gz` are ready-to-serve **embedded-Qdrant** storage dirs
(this is exactly what the MCP server uses). Each tarball unpacks to a `qdrant_db/`:

```bash
pip install "qdrant-client==1.18.0"
hf download dmpantiu/copernicus-rag-core --repo-type dataset \
   --include "indexes/*" --local-dir .
for n in marine_and_cards cds_docs eqc_qa publications; do
  mkdir -p rag/$n && tar xzf indexes/qdrant_$n.tar.gz -C rag/$n
done
```

| dir (after untar) | collections inside | points |
|---|---|---|
| `rag/marine_and_cards/qdrant_db` | `marine_docs` + `copernicus_docs` | 29,249 + 1,418 |
| `rag/cds_docs/qdrant_db` | `cds_docs` | 23,341 |
| `rag/eqc_qa/qdrant_db` | `eqc_qa` | 1,274 |
| `rag/publications/qdrant_db` | `publications` | 430,066 |

```python
from qdrant_client import QdrantClient
c = QdrantClient(path="rag/publications/qdrant_db")   # embedded/local mode
print(c.get_collections())                             # -> publications
print(c.count("publications"))                         # -> 430066
```

Notes:
- Vectors are **named**: `dense` (768-d, cosine, `gemini-embedding-2-preview`,
  L2-normalized) + `sparse` (BM25, IDF modifier) → hybrid dense+sparse queries work
  out of the box. BM25 queries need no embedding model at all
  (`fastembed` `Qdrant/bm25`); dense queries need the same Gemini model
  (or re-embed — see `REBUILD.md`).
- Embedded mode holds a **single-process lock** per dir — one process at a time.
- These dirs are **local-mode storage only**; you cannot mount them into a
  Qdrant docker server. For a server, use option B.
- The MCP server (`scripts/marine_rag/rag_server.py`) expects them at
  `marine_rag/out/qdrant_db`, `deep_docs/qdrant_db`, `eqc_qa/qdrant_db`,
  `pubs_rag/qdrant_db` relative to the repo root.

### B) Full Qdrant server (docker / cloud) — see **`server/GUIDE.md`**

The `server/` folder is a complete, tested deployment kit: `docker-compose.yml`
(Qdrant v1.18) + `load_all.py`, which downloads the prebuilt indexes and streams
all five collections into your server **1:1** — dense + sparse BM25 vectors,
relinked payloads, payload indexes; no embedding model or Gemini key needed.

```bash
export HF_TOKEN=hf_...
hf download dmpantiu/copernicus-rag-core --repo-type dataset \
   --include "server/*" --local-dir . && cd server
pip install -r requirements.txt
docker compose up -d
python load_all.py --url http://localhost:6333
```

Full walkthrough (verification, hybrid/filtered query examples, cloud clusters,
ops & troubleshooting): **`server/GUIDE.md`**.

Note: `embeddings/*.embedded.jsonl.gz` remain the right starting point when you
want to **re-embed with a different model** (see `REBUILD.md`); for a faithful
copy of the validated database, `server/load_all.py` is the path — the raw
embedding files predate the publication↔dataset relink, the indexes carry it.

## Rebuild scripts (`scripts/`)

Everything needed to regenerate this dataset from the originals — or re-embed
with a different model:

```
marine_rag/       CMEMS: clean_md → chunk_docs → batch_orchestrator (embed) →
                  load_qdrant · cards: build_cds_cards → embed_cds_batch →
                  load_copernicus_docs · rag_server.py (the MCP server itself)
deep_docs/        CDS/ADS/EWDS: fetch_parse → chunk_docs → embed_load
eqc_qa/           EQC reports: fetch → parse → chunk → embed → load ·
                  extract_code + merge_notebooks (notebook sidecar)
pubs_rag/         L3: build_corpus_copernicus → chunk_pubs → embed_orchestrator →
                  load_pubs_qdrant → relink_full → build_links_sidecar
meta_harvest/     01–08: upstream metadata harvest, all 4 stores → unified_metadata
publications/     DOI registry + Crossref/OpenAlex/Unpaywall OA-PDF downloaders
notebook_harvest/ CMEMS gallery / INSTAC notebook parsers
run_test_queries.py   the 50-query validation suite (50/50 pass)
build_bundle.py · build_rag_tree.sh   consolidation helpers
```

Order for a cold rebuild: `REBUILD.md` step-by-step; or skip embedding entirely —
`indexes/*.tar.gz` are ready to serve as-is.

## Linkage (baked into indexes + sidecars)

- paper↔dataset: 289 via registry (EQC refs) + ~10.4k via flagship-citation map
  (29 flagship DOIs → verified dataset ids); Qdrant payload `linked_products[]`,
  `flagship_labels[]`, `link_via[]`, `orphan`; serve-time sidecar
  `metadata/links_by_dataset.json` (built by `scripts/pubs_rag/build_links_sidecar.py`)
- notebooks attach to dataset cards via sidecar (`matched_dataset_id == product_id`)
- cards carry `n_linked_publications`, `has_eqc_docs`

## Notes

- Embeddings: `gemini-embedding-2-preview`, RETRIEVAL_DOCUMENT, 768 dim, L2-normalized.
  Query side works with the same model or any 768-d swap after re-embed (see REBUILD.md).
- Licenses (per-paper audit 2026-07-23, OpenAlex×Unpaywall×publisher whitelist,
  see `metadata/publication_licenses.json`): of 12,411 papers — 9,813 CC-BY/SA/PD ·
  1,396 CC-NC/ND · 1,202 no-license/bronze. Full-text originals of the no-license
  group are NOT stored here (removed; chunks and vectors remain). Copernicus service
  documents © respective Copernicus services (free use); harvested notebooks retain
  upstream licenses (incl. some unlicensed training repos). **Keep this repo
  private** — NC/ND full texts and unlicensed notebooks are for internal use.