Add Weaviate Enterprise RAG Agent

Summary

Add an enterprise RAG example using Weaviate for hybrid retrieval, metadata filtering, and cited answer generation.

Proposed location

rag_apps/weaviate_enterprise_rag_agent/

What to build

Create a runnable RAG application that indexes sample enterprise documents such as policies, product docs, incident runbooks, and support knowledge articles in Weaviate.

The agent should support hybrid keyword/vector search, metadata filters, reranking or relevance scoring, and grounded answers with citations. It should demonstrate why Weaviate is useful for production-style RAG workflows, while following the repository's existing Nebius Token Factory configuration pattern for model calls.

Acceptance criteria

• Includes a README.md based on .github/README_TEMPLATE.md.
• Includes sample enterprise-style documents and ingestion scripts.
• Uses Weaviate for vector or hybrid retrieval.
• Supports metadata filtering by document type, team, or source.
• Produces answers with citations to source files or chunks.
• Documents local setup using Weaviate Cloud or a local Weaviate instance.
• Includes .env.example with no secrets and configurable model/provider settings.
• Adds one catalog entry to the root README.md under RAG Applications.

Contribution notes

Please keep this to one self-contained project and one pull request. Link the pull request with Closes #<issue-number>.