Datasets:

sanjeevafk
/

depthapi_technical_corpus

@@ -4,7 +4,7 @@ This dataset is mixed-license. Downstream consumers must respect the upstream li
 | Upstream License | Documents |
 | --- | ---: |
-| unknown | 5000 |
 ## Policy

 | Upstream License | Documents |
 | --- | ---: |
+| unknown | 3134 |
 ## Policy

README.md CHANGED Viewed

@@ -1,205 +1,59 @@
 ---
 language:
 - en
-license: other
 tags:
 - rag
 - retrieval
 - technical-docs
 - programming
-- code
-- computer-science
-- embeddings
-- chunked
 size_categories:
 - 100K<n<1M
-pretty_name: DepthAPI Technical Corpus
 ---
 # depthapi_technical_corpus
-**260,954 chunks** of curated technical knowledge, purpose-built for hybrid RAG systems, embedding benchmarks, reranker evaluation, and coding agent retrieval experiments.
----
-## Overview
-`depthapi_technical_corpus` is a mixed-license retrieval corpus assembled from technical books, engineering documentation, system design references, coding interview prep material, and post-mortem writeups. It is the backing knowledge store for the [DepthAPI](https://github.com/sanjeevafk/depthapi) hybrid RAG pipeline.
-The corpus is organized into **namespaces** that map to distinct retrieval modes, allowing downstream systems to scope queries to relevant knowledge domains.
----
-## Data Sources
-| Source | Type | Namespace | License |
-|--------|------|-----------|---------|
-| Deep Learning with Python (Chollet) | Book | `default` | Manning fair-use |
-| Designing Data-Intensive Applications (Kleppmann) | Book | `default` | O'Reilly fair-use |
-| System Design resources | Docs / Markdown | `default` | Mixed / CC |
-| CPython Documentation | Official Docs | `default` | PSF-2.0 |
-| Engineering post-mortems | Blog / Web | `default` | CC-BY / Mixed |
-| [Coding Interview University – Cheat Sheets](https://github.com/jwasham/coding-interview-university) | PDF Cheat Sheets | `cs_fundamentals_knowledgeset` | CC BY-SA 4.0 |
-> ⚠️ This is a **mixed-license dataset**. Downstream users must inspect the `upstream_license` field and `SOURCES_MANIFEST.yaml` before redistribution or commercial use.
----
-## Namespaces
-Namespaces act as logical partitions for routing retrieval queries. Each namespace is designed for a different knowledge vertical:
-| Namespace | Purpose | Approx. Chunks |
-|-----------|---------|----------------|
-| `default` | General ML, systems, engineering, and coding knowledge | ~259,800 |
-| `cs_fundamentals_knowledgeset` | CS fundamentals — DS&A, Big-O, OOP, STL, OS, design patterns | ~1,145 |
----
-## Ingestion Pipeline
-The corpus was assembled from multiple upstream source types and normalized through a reproducible local pipeline backed by [Supabase](https://supabase.com) with pgvector.
-### Tools Used
-- **[Scrapling](https://github.com/D4Vinci/Scrapling)** — Stealth web crawler used for live documentation scraping and structured HTML extraction. Handles anti-bot mechanisms and extracts clean text from technical documentation sites.
-- **[opendataloader-pdf](https://github.com/opendataloader-project/opendataloader-pdf)** — PDF-to-Markdown conversion library used for extracting content from book-like and document-style PDF sources. Outputs clean Markdown blocks with layout awareness.
-### Processing Steps
-1. **Extraction** — Raw content pulled from upstream sources via Scrapling (web) or `opendataloader_pdf.convert()` (PDF).
-2. **Noise filtering** — OCR artifacts, page numbers, and garbled text filtered via regex heuristics before chunking.
-3. **Chunking** — Text split semantically via `BaseIngestor.split_text_semantic(chunk_size=800)` with configurable overlap.
-4. **Deduplication** — SHA-256 content hash (`content_hash`) deduplication. Duplicate chunks are skipped on upsert.
-5. **Validation** — Minimum token count and quality threshold enforced before persistence.
-6. **Persistence** — Chunks stored in local Supabase `knowledge_chunks` table with full metadata columns.
-7. **Embedding backfill** — Dense embeddings computed via `BAAI/bge-base-en-v1.5` (768-dim) and stored in the `embedding` pgvector column.
-8. **Export** — Parquet shards generated via `scripts/release/export_to_hf.py` and published to this repository.
----
 ## Schema
-Each row in the dataset represents one chunk:
-| Field | Type | Description |
-|-------|------|-------------|
-| `chunk_id` | `string` | UUID primary key |
-| `source_name` | `string` | Human-readable source title (e.g. `"Deep Learning with Python"`) |
-| `source_url` | `string` | Canonical upstream URL for the source document |
-| `namespace` | `string` | Retrieval namespace / knowledge domain |
-| `upstream_license` | `string` | SPDX license identifier or description |
-| `document_id` | `string` | Groups all chunks belonging to the same source document |
-| `chunk_index` | `int` | Ordinal position of chunk within its document |
-| `content_hash` | `string` | SHA-256 of chunk content — used for deduplication |
-| `content` | `string` | The raw chunk text |
-| `token_count` | `int` | Approximate token count (cl100k_base tokenizer) |
-| `chunker_version` | `string` | Version tag of the chunker that produced this chunk |
-| `retrieved_at` | `string` | ISO-8601 timestamp of when the chunk was ingested |
----
 ## Usage
-### Load the full dataset
-```python
-from datasets import load_dataset
-ds = load_dataset("sanjeevafk/depthapi_technical_corpus", split="train")
-print(ds[0])
-```
-### Stream for large-scale use
-```python
-from datasets import load_dataset
-ds = load_dataset(
-    "sanjeevafk/depthapi_technical_corpus",
-    split="train",
-    streaming=True,
-)
-for row in ds.take(5):
-    print(row["source_name"], "|", row["namespace"], "|", row["content"][:120])
-```
-### Filter by namespace
 ```python
 from datasets import load_dataset
-ds = load_dataset("sanjeevafk/depthapi_technical_corpus", split="train")
-# Get only CS fundamentals chunks (Coding Interview University cheat sheets)
-cs_chunks = ds.filter(lambda x: x["namespace"] == "cs_fundamentals_knowledgeset")
-print(f"CS fundamentals chunks: {len(cs_chunks)}")
-```
-### Filter by source
-```python
-ciu_chunks = ds.filter(
-    lambda x: "Coding Interview University" in (x["source_name"] or "")
-)
 ```
-### Build a simple BM25 retriever
-```python
-from datasets import load_dataset
-from rank_bm25 import BM25Okapi
-ds = load_dataset("sanjeevafk/depthapi_technical_corpus", split="train")
-corpus = [row["content"] for row in ds]
-tokenized = [doc.split() for doc in corpus]
-bm25 = BM25Okapi(tokenized)
-results = bm25.get_top_n("binary search tree time complexity", corpus, n=5)
-for r in results:
-    print(r[:200])
-```
----
 ## Licensing
-This is a **mixed-license dataset**. Each chunk carries its own `upstream_license` value. Downstream users are responsible for verifying license compatibility before use.
-| License | Sources |
-|---------|---------|
-| `CC BY-SA 4.0` | Coding Interview University (CIU) cheat sheets |
-| `PSF-2.0` | CPython documentation |
-| Manning fair-use | Deep Learning with Python, DDIA |
-| Mixed / Unknown | Engineering blogs, post-mortems |
-See `SOURCES_MANIFEST.yaml` (included in this repository) for full per-source provenance.
----
-## Export Workflow
-This dataset is published from the [DepthAPI](https://github.com/sanjeevafk/depthapi) pipeline using `scripts/release/export_to_hf.py`.
-**Incremental update** (recommended after new ingestion runs):
-```bash
-python scripts/release/export_to_hf.py \
-  --hf-repo-id "sanjeevafk/depthapi_technical_corpus" \
-  --append \
-  --commit-message "Add <source> chunks"
-```
-**Full rebuild** (replaces all shards — use after major corpus restructuring):
-```bash
-python scripts/release/export_to_hf.py \
-  --hf-repo-id "sanjeevafk/depthapi_technical_corpus" \
-  --force-rebuild
-```
-A watermark is saved after each export to `data/hf_export/last_export_watermark.json`. The `--append` mode reads this to push only net-new chunks.
----
-## Citation
-If you use this dataset, please cite the upstream sources where applicable. The corpus is provided as-is for research and development use.

 ---
 language:
 - en
 tags:
 - rag
 - retrieval
 - technical-docs
 - programming
 size_categories:
 - 100K<n<1M
 ---
 # depthapi_technical_corpus
+## Summary
+`depthapi_technical_corpus` is a mixed-license retrieval corpus built from technical documentation, books, engineering writeups, and repository-derived reference material. It is intended for open-source RAG systems, embedding benchmarks, reranker evaluation, and enterprise retrieval experiments.
+## Ingestion Process
+The corpus was assembled from multiple upstream source types and normalized through a reproducible local pipeline backed by Supabase.
+- `Scrapling` was used for live technical documentation crawling and structured HTML extraction from documentation websites.
+- [`D4Vinci/Scrapling`](https://github.com/D4Vinci/Scrapling) was used for live technical documentation crawling and structured HTML extraction from documentation websites.
+- [`opendataloader-project/opendataloader-pdf`](https://github.com/opendataloader-project/opendataloader-pdf) was used for PDF extraction when ingesting book-like and document-style technical sources into normalized markdown/text blocks.
+- Source material was then normalized, rechunked deterministically, deduplicated, validated, and exported to parquet for Hugging Face datasets compatibility.
 ## Schema
+Each chunk includes:
+- `chunk_id`
+- `source`
+- `source_url`
+- `upstream_license`
+- `document_id`
+- `chunk_index`
+- `retrieved_at`
+- `chunker_version`
+- `content_hash`
+- `content`
 ## Usage
 ```python
 from datasets import load_dataset
+dataset = load_dataset("sanjeevafk/depthapi_technical_corpus", split="train", streaming=True)
+for row in dataset.take(3):
+    print(row["chunk_id"], row["source"], row["upstream_license"])
 ```
+## Retrieval Benchmark Example
+Use `data/research_corpus/benchmarks/queries.jsonl` with your retriever and score against `qrels.jsonl`.
 ## Licensing
+This is a mixed-license dataset. Downstream users must inspect `upstream_license` and `SOURCES_MANIFEST.yaml` before redistribution or commercial use.

SOURCES_MANIFEST.yaml CHANGED Viewed

@@ -1,12 +1,7 @@
 version: 1
 sources:
-  - source: CPython Docs
-    source_url: file://datasets/cpython/Doc/README.rst
     upstream_license: unknown
     retrieved_at: unknown
-    documents: 4999
-  - source: unknown
-    source_url: unknown
-    upstream_license: unknown
-    retrieved_at: legacy-unknown
-    documents: 1

 version: 1
 sources:
+  - source: OPEA Documentation
+    source_url: file://datasets/opea-docs/CONTRIBUTING.md
     upstream_license: unknown
     retrieved_at: unknown
+    documents: 3134

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