README: Qdrant quickstart — prebuilt embedded indexes + server load recipe

README.md

@@ -43,6 +43,90 @@ 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) Into a running Qdrant server (docker / cloud)
+
+Load from `embeddings/*.embedded.jsonl.gz` (text + payload + ready 768-d vectors):
+
+```bash
+docker run -d -p 6333:6333 -v $(pwd)/qdrant_storage:/qdrant/storage qdrant/qdrant
+hf download dmpantiu/copernicus-rag-core --repo-type dataset \
+   --include "embeddings/*" --local-dir .
+```
+
+```python
+import gzip, json, uuid
+from qdrant_client import QdrantClient, models
+
+c = QdrantClient(url="http://localhost:6333")
+c.create_collection("publications",
+    vectors_config={"dense": models.VectorParams(size=768, distance=models.Distance.COSINE)},
+    sparse_vectors_config={"sparse": models.SparseVectorParams(modifier=models.Modifier.IDF)})
+
+buf = []
+for line in gzip.open("embeddings/publications.embedded.jsonl.gz", "rt"):
+    d = json.loads(line)
+    emb = d.pop("embedding")
+    buf.append(models.PointStruct(
+        id=str(uuid.uuid5(uuid.NAMESPACE_DNS, d["chunk_id"])),
+        vector={"dense": emb}, payload=d))
+    if len(buf) >= 512:
+        c.upsert("publications", points=buf); buf = []
+if buf:
+    c.upsert("publications", points=buf)
+```
+
+Same recipe for the other four files (`marine_docs`, `cds_docs`, `eqc_qa`,
+`cards_cds` → collection `copernicus_docs`). To also fill the BM25 `sparse`
+vectors (recommended for hybrid), use the full loaders in `scripts/`
+(`pubs_rag/load_pubs_qdrant.py`, `marine_rag/load_qdrant.py`, …) — they build
+dense+sparse and all payload indexes; switching them from embedded to server
+mode is a one-line change: `QdrantClient(path=...)` → `QdrantClient(url=...)`.
+
 ## Rebuild scripts (`scripts/`)
 
 Everything needed to regenerate this dataset from the originals — or re-embed