feat(examples): add Dozer + LLM + Vector DB + LangChain banking advisor sample

[open5588]

/claim #1690

Summary

Adds a complete, working sample (examples/llm-banking-advisor) demonstrating how Dozer, Vector Databases (ChromaDB / semantic embeddings), LangChain, and LLMs work together to power a hyper-personalized credit card recommendation and banking chatbot (#1690).

Architecture & Features

1. Multi-Dataset Dozer Streaming Pipeline (dozer-config.yaml):
   • Ingests customer_profiles, transactions, and credit_card_products datasets.
   • Uses Dozer streaming SQL to join customer demographics with real-time category-level transaction spend (travel_spend, dining_spend, groceries_spend, shopping_spend, entertainment_spend, total_spend, txn_count) into materialized customer_profile and card_catalog views.
2. Dozer API + LangChain + Vector RAG Integration (dozer_client.py, vector_rag.py, guardrails.py, advisor.py, app.py):
   • Queries live Dozer REST endpoints (http://localhost:8080/customer_profile/query, /card_catalog/query) with automatic local SQL emulation fallback for zero-setup offline testing.
   • Enforces financial underwriting guardrails (minimum age, FICO score, annual income, and risk tolerance).
   • Ranks eligible credit card products using semantic vector similarity (Chroma / normalized embeddings) combined with customer category spend alignment, and synthesizes grounded recommendations via LangChain (ChatOpenAI when OPENAI_API_KEY is set, or deterministic grounded explainer fallback).
3. Docker Compose & Automated Test Suite (docker-compose.yml, .env.example, tests/):
   • Includes docker-compose.yml for launching Dozer and ChromaDB together, plus 19 unit and integration tests (python3 -m unittest discover -s tests -v).

Fixes #1690

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