Enterprise LLM Data Ingestion Pipeline
Build the preprocessing infrastructure to ingest, chunk, clean, and embed millions of internal company documents for LLM training.
fig 1 — llm training data pipeline
VOLUME
1B+
Tokens Processed
EFFICIENCY
Regex
Optimized Cleaning
QUALITY
0 PII
Leakage Guarantee
SCALE
1M+
Documents Handled
What You'll Build
A complete LLM training data pipeline — from raw web pages to training-ready datasets published on HuggingFace.
Production Web Crawler
Async crawler with rate limiting, robots.txt compliance, PDF extraction, and retry logic for web-scale collection
Quality & Safety Pipeline
MinHash deduplication, PII masking with regex + NER, perplexity scoring, and toxicity detection filters
Multi-Tokenizer Engine
BPE tokenization with tiktoken and sentencepiece, efficient sequence packing for GPT-4 and LLaMA formats
HuggingFace Distribution
Export training-ready datasets with validation splits, dataset cards, lineage tracking, and S3/Hub publishing
Curriculum
4 parts, each with a clear checkpoint. Build incrementally, test as you go.
Technical Standards
Production patterns you'll implement across the data pipeline.
Process billion-token corpora with Ray/Dask distributed computing and efficient Parquet columnar storage
Regex-optimized cleaning with NER-backed PII masking — zero personally identifiable information in output
MinHash/LSH near-duplicate detection at 100K docs/minute throughput with configurable similarity thresholds
Environment Setup
Spin up the pipeline stack and run your first end-to-end data processing job.
# Clone the project & launch data pipeline$ git clone https://github.com/aide-hub/llm-dataforge.git$ cd llm-dataforge# Start Ray cluster + MinHash index + storage$ docker-compose -f docker-compose.pipeline.yml up -d# Run the full training data pipeline$ python -m pipeline run \$ --crawl "https://example.com/sitemap.xml" \$ --dedup minhash --tokenizer tiktoken \$ --export huggingface --dataset "my-org/corpus-v1"
Tech Stack
Prerequisites
- Python 3.10+ (async/await, generators, data structures)
- Data processing basics (pandas, working with large files)
- Distributed computing concepts (Spark/Dask helpful)
- ML fundamentals (embeddings, tokenization concepts)
Related Learning Path
Master LLM pipeline concepts — tokenization, embeddings, prompt engineering, and production deployment patterns.
LLM Pipeline Learning PathNew to LLM pipelines? Read the complete guide covering data collection, deduplication, tokenization, and sequence packing.
What is an LLM Pipeline? — Full GuideReady to build LLM data pipelines?
Start with Part 1: Data Collection & Crawling