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README.md

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+---
+license: apache-2.0
+language:
+  - en
+pretty_name: Telco-Retrieve Chunks — 3GPP Rel-19 Vector Database Grid
+size_categories:
+  - n<1K
+task_categories:
+  - feature-extraction
+  - sentence-similarity
+  - text-retrieval
+tags:
+  - telecom
+  - telecommunications
+  - 3gpp
+  - rag
+  - retrieval-augmented-generation
+  - vector-database
+  - chromadb
+  - embeddings
+  - bm25
+  - benchmark
+---
+
+# Telco-Retrieve Chunks — 3GPP Rel-19 Vector Database Grid
+
+**120 pre-built retrieval indexes** over the 3GPP Release-19 corpus — one per
+`(embedding model × chunking strategy × enrichment strategy)` configuration. Each is a complete
+**ChromaDB** dense index plus a parallel **SQLite FTS5 (BM25)** index, ready to query with no
+re-ingestion.
+
+Part of the **[GSMA Open Telco AI](https://www.open-telco.ai/)** initiative. Companion to
+[`GSMA/telco-retrieve-QnA`](https://huggingface.co/datasets/GSMA/telco-retrieve-QnA) (the evaluation
+questions) and the [`open-telco-rag`](https://github.com/Znbne/telco-retrieve) pipeline that reads
+these indexes directly.
+
+> **~1.05 TB total.** Individual configs range from ~1 GB to ~25 GB. Download the one config you
+> need, not the repo.
+
+## Dataset Summary
+
+Ingesting a large standards corpus into a vector store is the expensive, non-reproducible step in a
+RAG benchmark. This dataset ships that step's output for **every** point in a 120-cell grid so that
+retrieval and generation can be evaluated identically across configurations, on any machine, with
+no GPU and no ingestion run.
+
+Each configuration was produced by `open-telco-rag ingest 3gpp` and pushed with `push-hf`.
+
+### The grid (6 × 5 × 4 = 120)
+
+| Axis | Values |
+|---|---|
+| **Embedding model** (6) | `minilm` (all-MiniLM-L6-v2, 384d) · `mpnet` (all-mpnet-base-v2, 768d) · `e5` (intfloat/e5-large-v2, 1024d) · `bge` (BAAI/bge-large-en-v1.5, 1024d) · `otel-109m` (OTel-Embedding-109M, telecom fine-tuned, 768d) · `otel-0.6b` (OTel-Embedding-0.6B, telecom fine-tuned, 1024d) |
+| **Chunking strategy** (5) | `text_baseline` (1024 char / 200 overlap) · `sliding_window_tokens` (512 tok / 50) · `parent_child` (2048 parent / 512 child) · `hierarchical_markdown` (2048 / 200) · `lumber_chunker` (LLM semantic chunking, `t<theta>`) |
+| **Enrichment strategy** (4) | `none` · `metadata_tagging` · `acronym_expansion` · `llm_metadata` (LLM-generated per-chunk metadata) |
+
+## Repository Structure
+
+One folder per configuration, named:
+
+```
+3gpp-r19_<model>_<chunking>_<enrichment>_<params>/
+```
+
+Examples:
+- `3gpp-r19_bge_text_baseline_none_c1024_o200/`
+- `3gpp-r19_otel-0.6b_parent_child_llm_metadata_p2048_c512/`
+- `3gpp-r19_minilm_lumber_chunker_none_t550/`
+
+Inside each folder:
+
+| Path | What |
+|---|---|
+| `chroma.sqlite3` | ChromaDB metadata + document store |
+| `<uuid>/data_level0.bin`, `header.bin`, `length.bin`, `link_lists.bin`, `index_metadata.pickle` | HNSW dense index for the collection |
+| `<collection>_fts.db` (`-wal`, `-shm`) | SQLite FTS5 BM25 index (parallel keyword search) |
+| `_ingest_complete.json` | Per-config ingestion manifest — `complete`, `files_total`, `files_processed`, `skipped_files`, `error_files`, `chunks_stored`, `timestamp` |
+
+**Dataset viewer note:** Hugging Face auto-builds the viewer from the 120 `_ingest_complete.json`
+files, so it shows a **120-row ingestion-manifest table**, *not* the chunk text. The chunk text
+lives inside `chroma.sqlite3` / the FTS DBs and is reached through the retrieval library.
+
+## Usage
+
+```bash
+pip install "open-telco-rag[local-embed]"
+
+# pull one configuration
+open-telco-rag pull-hf GSMA/telco-retrieve-chunks \
+  3gpp-r19_bge_parent_child_llm_metadata_p2048_c512 --local-dir ./db
+
+# query it — dense, BM25, or hybrid — no ingestion needed
+open-telco-rag search "handover procedure for conditional PSCell change" \
+  --source-type 3gpp --db-path ./db --top-k 5
+
+# or run the full evaluation against GSMA/telco-retrieve-QnA
+open-telco-rag evaluate 3gpp --db-path ./db
+```
+
+Raw access without the library: open `chroma.sqlite3` with the `chromadb` client, or query
+`<collection>_fts.db` with any SQLite client (FTS5 `MATCH`).
+
+## Source Data & Curation
+
+- **Corpus:** 3GPP specifications, Release 19 (all series), the same underlying text as
+  `GSMA/telco-retrieve-QnA`. Sourced via the TSpec-LLM dataset and the 3GPP spec archive
+  (`3gpp.org/ftp/Specs/archive`).
+- **Pipeline:** `open-telco-rag` — per-SDO source loader → clean → chunk (per strategy) → enrich
+  (per strategy) → embed (per model) → ChromaStore + SQLite FTS5. See the
+  [`open-telco-rag`](https://github.com/Znbne/telco-retrieve) README for stage details.
+- **Curation rationale:** a fully-crossed grid lets an evaluation isolate the effect of each axis
+  (embedding model, chunking, enrichment) on retrieval quality, holding the corpus and question set
+  fixed — which is the point of the companion `GSMA/telco-retrieve-QnA` benchmark.
+
+## Considerations & Limitations
+
+- **Scale.** ~1.05 TB. Pull individual config folders; never clone the whole repo.
+- **Single corpus / single release.** 3GPP Rel-19 only. No other SDOs, no other releases.
+- **Point-in-time.** Reflects Rel-19 spec text as ingested in August 2026.
+- **Reproducibility, not novelty.** These are derived indexes; the value is identical retrieval
+  state across configs, not new source data.
+- **Format lock-in.** ChromaDB + SQLite-FTS5 on-disk formats; readable via `open-telco-rag` or the
+  `chromadb` / `sqlite3` clients, not as plain Parquet/JSONL.
+- **`.db-wal` / `.db-shm`** sidecar files may be present; SQLite recreates them on open, so a
+  consumer can ignore them.
+
+## Licensing
+
+- **Index artifacts and manifests:** **Apache-2.0**, consistent with the `open-telco-rag` toolkit.
+  *(Set here as `apache-2.0`; change if GSMA requires a different licence for this repo.)*
+- **Stored chunk text** (inside `chroma.sqlite3` / the FTS DBs) consists of excerpts of 3GPP
+  specifications. That text remains subject to 3GPP's copyright and redistribution terms and those
+  of the upstream source (the TSpec-LLM dataset / the 3GPP spec archive). Downstream users are
+  responsible for compliance.