The Synthesis Archive
Agentic System
Azure
Vectorless RAG – Explainable AI Knowledge Assistant with BM25 Retrieval.
Lightweight AI knowledge assistant using BM25 retrieval and structured search without vector databases.
Response
Speed Improvement: 2x Faster
Infrastructure
Cost Reduction: 50%
Search
Accuracy: High Precision BM25
Visual Manifest.
The Obstacle
Architectural Friction.
Traditional RAG systems rely heavily on vector embeddings and vector databases, increasing infrastructure complexity, operational cost, and latency while reducing explainability in production AI systems.
The Synthesis
The Protocol.
Vectorless architecture using BM25 for keyword-based retrieval Structured knowledge storage using SQL databases LLM integration for contextual response generation Faster response time with reduced infrastructure overhead Explainable and deterministic response flow
Technical Blueprint.
Uses Elasticsearch/OpenSearch BM25 ranking for fast and accurate keyword-based search without vector embeddings.
Stores and retrieves information using SQL databases and structured knowledge systems for consistency and control.
Integrates OpenAI/OpenRouter APIs to generate contextual and human-like responses.
Eliminates vector database overhead to reduce infrastructure costs and improve response speed.
Deterministic retrieval provides traceable and understandable AI response generation.
Technology Stack
Python
FastAPI
React
Tailwind CSS
Elasticsearch
OpenSearch
PostgreSQL
MySQL
Redis
NginxPerformance Pillars
Security
Secure authentication and authorization
Scale
Scalable architecture for growth
Efficiency
Optimized for performance