Chonkie is an open-source data ingestion system designed to prepare data for AI applications. It cleans, chunks, and makes data AI-ready, aiming to provide models with the precise information needed for accurate answers.

Key Features:

• Ingests various data sources including TXT, PDF, and Code.
• Cleans data by adding punctuation, removing PII, and standardizing format.
• Splits data into AI-ready, meaningful chunks for optimal retrieval.
• Enriches chunks with embeddings, summaries, topics, labels, and other metadata.
• Establishes secure connections with vector databases like Chroma, Qdrant, and Turbopuffer.
• Ports chunks to any format or destination.
• Open-source under the MIT license.

Use Cases:

• Building context for AI models to generate accurate answers.
• AI Chat applications requiring factual and efficient responses.
• Eliminating hallucinations in AI outputs.
• Reducing token usage (up to 90% less).
• Including citations in AI answers.

Benefits:

• Up to 10x faster inference for AI models.
• Eliminates hallucinations in AI-generated content.
• Significantly reduces token usage (up to 90%).
• Enables inclusion of citations in every AI answer.
• Provides a robust and flexible data preparation pipeline for AI.