Search that
actually understands.
Hybrid search with sub-300ms latency. Multi-modal RAG for PDFs, images, videos, and web pages. Context-aware reranking that surfaces what matters.
Traditional RAG is broken.
Lost in Chunks
Standard parsing breaks paragraphs midway. AI misses crucial context when relevant information is split across chunk boundaries.
Similarity is not Relevance
Vector cosine similarity indexes synonyms but fails to verify actual semantic relevance, yielding noisy LLM inputs.
Slow at Scale
As repositories grow, dense vector index scans slow down. Sub-second recall becomes impossible without complex database tuning.
RAG, reimagined from scratch.
Semantic Sentence Chunking
Groups sentences dynamically using cosine similarity of adjacent sentence embeddings, preventing split ideas.
LLM Contextual Enrichment
Enriches every chunk with AI-generated summaries, entity lists, and Q&As to improve search matches.
Multi-View Retrieval
Supports vector search (semantic), current truth facts (state), time-based memory logs (episodic), or hybrid unified recall (auto).
const response = await fetch("https://server.memwyre.tech/api/v1/retrieval/search", {
method: "POST",
headers: {
"Authorization": "Bearer bv_sk_your_api_key_here",
"Content-Type": "application/json"
},
body: JSON.stringify({
query: "find error codes for deployment",
view: "auto", // semantic, state, episodic, or auto
top_k: 5
})
});
const results = await response.json();Understand any format.
Context infrastructure for AI agents.
Upgrade your retrieval stack. Get started with sub-300ms semantic RAG queries.
Initialize RAG Engine