---
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

```bash
pip install datasets
```

### Load Dataset

```python
from datasets import load_dataset

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

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

---

## Example Use Cases

### 1. Semantic Search

```python
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

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

Use with:

* FAISS
* Qdrant
* Pinecone

---

### 3. Fine-tuning

Convert to instruction format:

```json
{
  "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

```python
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.

---