1. Problem: The Hallucination Dilemma
Standard large language models hallucinate false facts when queried about proprietary domain knowledge. Conversely, traditional full-text search fails to comprehend semantic synonyms, conceptual relationships, or user intent.
Vaxa AI bridges this divide with an end-to-end Retrieval-Augmented Generation (RAG) architecture that enforces strict factual attribution by anchoring LLM outputs directly to verifiable source chunks.
2. Architecture & Tech Stack
Vaxa AI Neural Pipeline
[Document Ingestion (PDF, Markdown, HTML, APIs)]
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[Semantic Chunking & Metadata Enrichment]
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[Dual Encoder Vectorization (Dense + Sparse Embeddings)]
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[Vector Store (Qdrant / Milvus) + Knowledge Graph]
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[Hybrid Query: Dense Similarity + BM25 Keyword Search]
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[Cross-Encoder Reranking & LLM Synthesis]
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[Verified Output with Direct Inline Footnotes]
Python & FastAPI
Qdrant Vector DB
Hugging Face Transformers
Redis Cache
React & Tailwind UI
3. Key Capabilities
- Reciprocal Rank Fusion (RRF): Combines lexical precision (exact serial codes, part numbers) with semantic nuance (natural language intent).
- Fact Verification Guardrails: Every claim generated by the model must cite a specific chunk ID or the system explicitly flags uncertainty.
- Zero Data Leakage: Strict enterprise tenant isolation ensuring multi-user organizations maintain absolute privacy over proprietary knowledge repositories.