Postmaster

Composite Vector DBs. No More Plain Old RAGs

1. Context Assumption: In RAG systems, contextless questions like "What is the tallest mountain?" may default to Mount Everest, assuming it to be the most likely answer. However, if the context is North America,this response is erroneous.
2. Question Decomposition: In RAG systems, not effectively breaking down complex questions into sub-questions can result in partial or incorrect answers, missing critical details.
3. Knowledge Base Expansion: As the knowledge base grows, responses may become less accurate due to noise and ambiguity in retrieval.
4. Hallucination Risk: When the knowledge base lacks an answer, RAG systems may generate hallucinations.
5. Fragmented Knowledge Integration: RAG might struggle when relevant information is scattered within the same knowledge base. For example, answering “How many medical schools offer free tuition?” may require combining details from admissions policies, financial aid data, and school descriptions. Traditional RAG systems might return incomplete or inconsistent answers instead of a precise response.

Build Knowledge Bases - Post Knowledge Chunks, and Get Accurate, Context-Aware Responses

Postmaster leverages a composite vector database to bring efficiency and precision to knowledge retrieval. Unlike traditional RAG (Retrieval-Augmented Generation) systems that often introduce noise and inaccuracies, Postmaster uses composite vectors to capture and store knowledge in a context-aware manner. By posting knowledge chunks, whether they are text, images, or other data formats, the system generates composite vectors that represent the core meaning and relationships within the knowledge.

This approach eliminates many of the issues associated with traditional RAG systems, such as context assumptions, question decomposition failures, hallucination risks and fragmented knowledge integration. With Postmaster, users can trust the system to return accurate, relevant, and contextually precise answers without the noise and limitations of traditional methods.