Large language models thrive when powered with clean, curated data. But most of this data is hard to find, hard to work with, and hard to clean. We make it easy.
We start by extracting your data and integrating it into your data pipelines. Our current source connectors include Azure Blob Storage, Amazon S3, Salesforce, SharePoint, Google Cloud Storage, Google Drive, Microsoft OneDrive, Elasticsearch, and OpenSearch.
EXTRACT This step involves extracting natural language and related data from a source document. We can preprocess over 25 different file types, with complexity varying by file type.
PARTITION & STRUCTURE We break documents down into their smallest logical units, called segments, for precise content processing (e.g., titles and body text). This enables accurate cleaning, chunking, and metadata generation. We generate new segment-level metadata for enhanced cleaning and retrieval and render the partitioned results into a normalized vector data.
CLEAN: Data cleaning removes unwanted content, like headers and irrelevant sections, to avoid context pollution. Traditionally requiring laborious custom scripts, InfraHive simplifies this by allowing data scientists to use element metadata to efficiently curate their datasets for chunking and embedding.
CHUNK: We use smart-chunking to group documents into contextually relevant chunks, enhancing RAG application performance by facilitating more precise data retrieval. Unlike traditional chunking techniques that split up a document by a set number of tokens, We applying segmenting techniques from atomic document elements. This method is superior to chunking by character count or punctuation
Vectorizing data chunks enhances Multi-Vector Retrieval systems by providing condensed data representations for efficient query matching. This improves discoverability and context retrieval, particularly for images and tables, by distilling data for answer synthesis. Unlike many preprocessing systems, InfraHive's process can individually extract and utilize document text, images, and tables for this technique.
This step uses models to generate embeddings, vector strings encoding semantic data, enabling semantic-based search beyond keywords, crucial for LLM applications. Developers can experiment with chunking and embeddings to optimize for tasks, considering factors like speed, data specialization, and language complexity. At InfraHive, we support various embedding model hosts, including Hugging Face, AWS Bedrock, and OpenAI.
Upon completion of the pipeline, we can directly integrate this data into LLMs securely and can build AI Copilots which can be deployed independently in your organisation with your knowledge
To succeed and earn your trust, we strive to meet your expectations around our product and you data
