README.md

metadata

language:
  - en
tags:
  - rag
  - retrieval
  - technical-docs
  - programming
  - depthapi
size_categories:
  - 10K<n<100K

DepthAPI Technical Corpus

Overview

The DepthAPI Technical Corpus is a curated, high-quality retrieval corpus designed for modern RAG (Retrieval-Augmented Generation) systems. It features clean, aggressively normalized technical documentation, code snippets, engineering post-mortems, and system design literature.

This dataset was explicitly built to serve as the local ground-truth for the DepthAPI project.

About the DepthAPI Project

DepthAPI is an enterprise-grade, declarative RAG pipeline built with asynchronous concurrency and Supabase Vector embeddings. It aims to systematize RAG data ingestion by moving away from hardcoded scripts to a configuration-driven architecture, ensuring maximum throughput, observability, and resilience.

Key features of DepthAPI include:

• Plugin-based Architecture: Easily extendable ingestion strategies via declarative configurations.
• Async Concurrency: High-throughput processing using bounded, multi-document orchestration.
• Namespace Isolation: Precise control over data organization within the vector store.
• Idempotency: Safe, hash-based upserts to avoid duplication and state corruption.

Included Data Sources

The corpus aggregates multiple highly-valued technical domains, explicitly isolated into distinct namespaces:

1. OPEA Documentation (OPEA Documentation): 104 detailed technical documents covering the Open Platform for Enterprise AI ecosystem.
2. 30 Seconds of Code (code_snippets): 2,784 programming snippets and algorithms chunked for precise code retrieval.
3. System Design Primer (system_design): Core principles of scalable system architecture and design.
4. Engineering Post-Mortems (Technical Corpus - Postmortems & Archived): Incident reports and technical retrospectives.
5. DepthAPI Trusted Corpus: Curated deep learning tutorials, FastAPI templates, and SQL guides.

Ingestion Pipeline Details

The corpus is powered by the DepthAPI Declarative Ingestion Pipeline, moving from manual scripts to a config.yaml driven approach.

During the ingestion of this corpus, we heavily utilized the following open-source extraction libraries:

• Scrapling: Used for live technical documentation crawling and structured HTML extraction from documentation websites.
• opendataloader-pdf: Used for PDF extraction when ingesting book-like and document-style technical sources into normalized markdown/text blocks.

Key features of the pipeline used to create this dataset:

• Declarative Sources: Ingestion configs declare LocalDirSource with strict glob matching.
• Concurrent Processing: Processed using ConcurrentPipelineOrchestrator via asynchronous worker pools.
• Semantic Chunking: Processed via SemanticChunker (v2) with strict token boundaries and overlap for contextual continuity.
• Middleware Extraction: Handled through custom middleware like CodeSnippetExtractor to isolate raw code blocks and attach precise metadata.
• Idempotent Upserts: Deduplicated using SHA-256 content hashing natively within Supabase pgvector tables (knowledge_chunks).

Data Schema

Each exported Parquet shard normalizes the Supabase relational schema into a flat, Hugging Face compatible format:

• chunk_id: Unique identifier for the chunk.
• source: The raw file or origin name.
• source_url: A unique URI or file path pointing back to the specific resource.
• upstream_license: Licensing information associated with the document.
• document_id: Relational ID linking chunks back to their parent document.
• chunk_index: The sequential order of the chunk in the original text to maintain narrative context.
• content: The cleaned, ready-to-embed text block.
• content_hash: Deterministic SHA-256 hash used for idempotent ingestion.
• collection_name: The logical namespace under which the chunk was isolated (e.g., code_snippets).

Usage

from datasets import load_dataset

dataset = load_dataset("sanjeevafk/depthapi_technical_corpus", split="train", streaming=True)
for row in dataset.take(3):
    print(row["collection_name"], "-", row["source_url"])

Licensing & Governance

This is a mixed-license dataset. Downstream users must dynamically inspect the upstream_license field per chunk and consult the accompanying SOURCES_MANIFEST.yaml before redistribution or commercial application.