Datasets:

nuhmanpk
/

dev-knowledge-base

license: mit
dataset_info:
  features:
    - name: title
      dtype: string
    - name: source
      dtype: string
    - name: url
      dtype: string
    - name: category
      dtype: string
    - name: language
      dtype: string
    - name: content
      dtype: string
    - name: chunk_id
      dtype: int64
    - name: chunk_length
      dtype: int64
    - name: last_updated
      dtype: string
  splits:
    - name: train
      num_bytes: 401051216
      num_examples: 426107
    - name: test
      num_bytes: 941198
      num_examples: 1000
  download_size: 180107389
  dataset_size: 401992414
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
task_categories:
  - question-answering
  - summarization
  - text-generation
language:
  - en
tags:
  - code
pretty_name: 'DevBase '
size_categories:
  - 100K<n<1M

Dev Knowledge Base (Programming Documentation Dataset)

A large-scale, structured dataset of programming documentation collected from official sources across languages, frameworks, tools, and AI ecosystems.

Do Follow me on Github: https://github.com/nuhmanpk

Overview

This dataset contains cleaned and structured documentation content scraped from official developer docs across multiple domains such as:

Programming languages
Frameworks (frontend, backend)
DevOps & infrastructure tools
Databases
Machine learning & AI libraries

All content is chunked (~800 characters) and optimized for:

Retrieval-Augmented Generation (RAG)
Developer copilots
Code assistants
Semantic search

Dataset Structure

Each row represents a chunk of documentation.

Column	Description
title	Page title or endpoint
source	Source name (e.g., react, python, fastapi)
url	Original documentation URL
category	Type (language, framework, database, etc.)
language	Programming language
content	Cleaned text chunk
chunk_id	Chunk index within page
chunk_length	Character length
last_updated	Timestamp

Sources Included

Languages

python, javascript, typescript, go, rust, java, csharp, dart, swift, kotlin

Frontend & Frameworks

react, nextjs, vue, nuxt, svelte, sveltekit, angular, astro, qwik, solidjs

Backend & APIs

fastapi, django, flask, express, nestjs, hono, elysia

Runtime & Tooling

nodejs, deno, bun, vite, webpack, turborepo, nx, pnpm, biome

UI Libraries

tailwind, shadcn_ui, chakra_ui, mui

Mobile & Desktop

react_native, expo, flutter, tauri, electron

Machine Learning & AI

numpy, pandas, pytorch, tensorflow, scikit_learn, xgboost, lightgbm transformers, langchain, llamaindex, openai, vllm, ollama, haystack mastra, pydantic_ai, langfuse, mcp

Databases

postgresql, mysql, sqlite, mongodb, redis, supabase, firebase planetscale, neon, convex, drizzle_orm, qdrant, turso

DevOps & Infrastructure

docker, kubernetes, terraform, ansible github_actions, gitlab_ci, git, opentelemetry, inngest, temporal

Other

claude_agent_sdk

Full crawl configuration available here:

Chunk Distribution

Example distribution after cleaning and removing Zig:

Source	Chunks
python	~15,000
javascript	~4,000
go	~8,000
react	~3,000
nextjs	~4,000
docker	~4,000
kubernetes	~14,000
transformers	~14,000
firebase	~300,000
redis	~17,000
git	~14,000
flutter	~14,000
supabase	~10,000

Total: millions of chunks across 80+ sources

How to Use (Hugging Face)

Install

pip install datasets

Load Dataset

from datasets import load_dataset

dataset = load_dataset("nuhmanpk/dev-knowledge-base")

print(dataset["train"][0])

Example Use Cases

1. Semantic Search

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("all-MiniLM-L6-v2")

docs = [x["content"] for x in dataset["train"][:1000]]
embeddings = model.encode(docs)

query = "how to build api with fastapi"
q_emb = model.encode([query])

scores = np.dot(embeddings, q_emb.T).squeeze()
print(docs[scores.argmax()])

2. RAG Pipeline

User Query → Embed → Vector DB → Retrieve → LLM → Answer

Use with:

FAISS
Qdrant
Pinecone

3. Fine-tuning

Convert to instruction format:

{
  "instruction": "Explain JWT authentication",
  "input": "",
  "output": "<documentation chunk>"
}

4. Developer Chatbot

Build:

AI coding assistant
StackOverflow-style search
Internal dev knowledge system

Data Processing Pipeline

Async crawling with rate limiting
HTML parsing (BeautifulSoup)
Navigation/content filtering
Chunking (~800 chars)
Cleaning & binary removal

Crawler implementation:

Limitations

Some duplicate content may exist
Chunk-level context only (not full pages)
No semantic labeling yet
Some sources larger than others

Future Improvements

Deduplication
Better chunking (semantic splitting)
Q/A generation
Code extraction
Metadata enrichment

License

This dataset is built from publicly available documentation. Refer to individual sources for licensing.

Author

https://github.com/nuhmanpk

Quick Example

from datasets import load_dataset

ds = load_dataset("nuhmanpk/dev-knowledge-base")

for row in ds["train"].select(range(3)):
    print(row["source"], "→", row["content"][:150])

Summary

A large, structured, and practical dataset for building developer-focused AI systems from code assistants to full RAG pipelines.