Accurate data is essential for powering Retrieval Augmented Generation (RAG) workflows with Large Language Models (LLMs). Ingesting high-quality, semantically relevant content into your vector database ensures precise, context-rich responses.
Complex document types like PDFs, Word docs, and presentations often hold valuable business insights. Tools like Unstructured make it easier to extract, clean, and organize this data for LLMs. Now integrated with the KDB.AI vector database, Unstructured streamlines the ingestion of complex documents into RAG pipelines.
Unstructured Features & Functionalities:
- Connectors: Facilitate the ingestion of data from various sources into processing pipelines.
- Partition: Decompose documents into their smallest logical units (e.g., titles, body text) to extract structured content from raw, unstructured documents.
- Clean: Remove irrelevant sections and headers, utilizing element metadata to curate datasets effectively, crucial for maintaining data integrity.
- Extract: Identify and isolate relevant information, including natural language and specific entities within documents.
- Structure: Organize partitioned elements into a normalized JSON format, essential for tasks like enterprise-scale RAG, model fine-tuning, and pretraining.
- Chunk: Implement smart-chunking to group documents into contextually relevant segments for more precise data retrieval.
- Generate Embeddings: Create embeddings using popular models to enhance data utility.
- Destination Connectors: Export processed data to vector databases like KDB.AI for further use.
Check out the sample notebook on GitHub or open the notebook directly in Google Colab and get started with KDB.AI and Unstructured today!