What Open Source RAG can do for Modern Enterprises

Follow this guide to leverage your enterprise data with a self-hosted AI assistant, powered by the semantic search capabilities of open source vector databases.

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In today's fast-paced enterprise environment, the volume of internal documentation-from support tickets and confluence pages to technical specifications and project wikis-grows exponentially. For knowledge workers, this deluge of information presents a significant challenge.

Traditional, keyword-based search engines are often too rigid and brittle to navigate this unstructured sea of data.

A search for 'server performance issue' may fail to find a document titled 'optimising backend latency', despite the two topics being semantically identical. The inability to quickly access relevant, up-to-date information represents a critical bottleneck for productivity and innovation.

This challenge has a modern, robust solution: retrievalaugmented generation (RAG). RAG is an AI framework that fundamentally shifts how large language models (LLMs) operate. Instead of relying solely on their pretrained knowledge, which can be outdated or contain inaccuracies, RAG systems first retrieve information from a specified external knowledge base before generating a response. This approach provides a 'factual grounding' for the LLM, effectively mitigating the notorious problem of AI 'hallucinations'. The resulting answers are not only more accurate but can also be directly linked to their source documents, a feature that significantly enhances user trust and transparency.