Retrieval Augmented Generation (RAG): Unstructured supports end-to-end RAG by continuously hydrating data from the source and transforming it into a canonical structured JSON schema using best-in-class transformation. We perform semantic chunking, multi-modal enrichment and embeddings, writing the data and all relevant metadata to a vector database, enhancing the relevance and accuracy of generated content.
Fine-Tuning Models: Unstructured supplies precise and diverse datasets for fine-tuning models, ensuring that AI systems are tailored to specific tasks and domains with high accuracy.
Pre-Training Models: Unstructured offers vast amounts of varied and representative data, essential for pre-training models to capture a wide range of patterns and knowledge across different domains.
Extract, Transform, Load (ETL): Unstructured simplifies ETL processes by efficiently extracting relevant information from diverse documents, transforming it into structured formats, and seamlessly loading it into target systems.
1) API
For single-batch, production-grade document preprocessing without worrying about any custom code to get started.
Ingest and preprocess complex natural data from any file type or layout to JSON
Features
2) Platform
For enterprises and high-growth companies with large data volumes looking to automatically retrieve, transform, and stage their data for LLMs.
Continuously deliver timely, clean and domain-specific data to your LLM architecture without writing a single line of code. It just works.
Features
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