Retrieval Augmented Generation
LLMs are trained on world knowledge – not your enterprise knowledge. Our RAG pipeline processes your enterprise knowledge into language(embeddings) that LLMs understand. You can deploy it in your cloud environment and thereby reduce the time and effort involved in building one from scratch. Leverage our experience to tackle issues with scaling, security or document complexity for this foundational component of your LLM stack.
Ingest data from any source
- Ability to ingest files from any document repository e.g. cloud storage, email etc.
- Ingest content from websites, YouTube, podcasts etc.
- Ability for users to upload files from chatbot / LLM applications
- Manage ingested files via UI ( file manager )
- Parallelization of ingestion process – file uploads, chunking and embedding
- Defenders against malicious content in files and websites
- Scheduling data ingestion jobs with built-in state preservation and retry mechanisms
Intelligent Parsing
- Structure input from documents (text, tables, infographs etc.) into chunks that are optimized for LLMs
- All text document formats supported e.g. pdf, pptx, docx, html etc.
- Support accurate extraction of tables, ppt slides, contracts, emails, forms etc.
- Multi-modal processing – reliable descriptions of images and infographics
- Unique excel parsers for key business artifacts e.g. P&L, Balance Sheet etc.
- Extensible architecture for custom use cases, audio and video content
Index and Search
- Embed chunks and store in a vector database of your choice
- Retrieve the most relevant chunks for any user querry
- Ability to integrate with any embedding model and vector database
- Azure AI Search, PGVector and Milvus integrations ready for deployment
- Embedding model optimized for vector service after extensive testing
- Ability to customize metadata definition per index and enable filtering during search