Q-RAG Context Engine
A retrieval pipeline designed to minimize LLM hallucinations while dramatically reducing prompt context size.
The Context Retrieval Architecture
Matches query text vectors against candidate memory nodes in our index database.
Applies cross-encoders to evaluate exact situational context, filtering out low-scoring noise.
Pulls active user preferences and long-term habits to wrap around the matched segments.
Injects the clean context block into the prompt template immediately before LLM generation.
Sub-100ms Query Resolution
End-to-end traversal latency ranges between 85ms (projected, internal benchmarks) and 120ms (p99 target).
92% Prompt Context Savings
Removes redundant threads to compress typical 40K histories into a 2K token summary (roadmap target).
Retrieval Implementation Code
import PayQIA from "payqia";
const client = new PayQIA({ apiKey: process.env.PAYQIA_API_KEY });
// Query the RAG context pipeline
const { context, latency } = await client.search.execute({
q: "What user preferences were active on this project?",
containerTag: "user_session_4821",
rerank: true
});
console.log(`[QIA] Retrieval latency: ${latency}ms`); // e.g. ~85ms (projected)Read the Context Compression Research
Explore the mathematical decay formulas and semantic parsing theories on the Haqikos Research portal.