Python
forms the domain or execution core of the solution.
Top technology
RAG and semantic search
Make company knowledge safely usable through retrieval-augmented generation and hybrid search. General Informatics covers analysis, target architecture, implementation, integration, quality assurance and production-oriented operations from one accountable partner.
Architecture and stack
The most common technology chain for RAG and semantic search
Technology is selected according to the problem, operating model and existing landscape—not fashion. The following chain is a frequent and sensible starting point.
Python →PostgreSQL/pgvector →OpenSearch →LLM →Docker →Object Storage
PostgreSQL/pgvector
provides persistent data, state or integration information reliably.
OpenSearch
connects components with the wider system landscape through typed, testable contracts.
LLM
isolates runtime and dependencies for reproducible deployments.
Docker
handles caching, asynchronous processing or horizontal decoupling.
Object Storage
makes releases, observability and secure operation repeatable.
Architecture decisions
- Build versus buy and provider dependencies
- Synchronous request versus asynchronous job
- Tenant separation and data isolation
- Error handling, retries and idempotency
- Scaling, cost and performance budget
- Observability, audit and recovery
Technical quality goals
Clear interfaces, limited coupling, traceable data flows, reproducible environments, automated tests and a documented recovery path. Lighthouse-sensitive frontends use lean assets, server-side metadata and controlled third-party scripts.
Demand and keyword cluster
Important search and project situations
The 30 priority phrases are distributed across the hub and detail pages. Each page shows only the relevant subset so content and search intent stay focused.
RAG and semantic search securityRAG and semantic search performance optimizationRAG and semantic search costRAG and semantic search projectRAG and semantic search custom softwareRAG and semantic search interfaceRAG and semantic search API integration
Project CTA
Get a concrete assessment for RAG and semantic search
Describe the current system, target, required interfaces and current blockers. General Informatics turns this into a technical first assessment with sensible phases, risks and next steps.