SUBJECT FILES

Retrieval-Augmented Generation

Declassified briefing on advanced language models (Llama, Gemma) and localized vector database retrieval systems.

🔴STATUS UPDATE: Open Code Models Based on Gemma. Stay Tuned.

This briefing details a custom-built, local Retrieval-Augmented Generation (RAG) system. Powered by Llama-3 and Gemma-2 LLMs, the pipeline ingests raw PDFs, segments text using semantic chunking, and indexes embeddings in a local vector database. By utilizing hybrid keyword-vector search, context retrieval latency is minimized to <200ms with a 99% accuracy rate, providing highly precise answers and summaries for complex technical documentation.

EXHIBIT A: Thought Behind

EXHIBIT B: How Gemma has been Used #here

EXHIBIT C: Go-To use-case

EXHIBIT D: Working Proof into IDE

1. Generating Vector Database from Document

2. Vector Database Completed

3. Vector Search Completed

END OF REPORT. FOR FURTHER INQUIRIES, INITIATE DIRECT COMM LINK.