[goddrivenbullet]

Summary

Adds a self-contained Dozer + LLM + Vector Database + LangChain sample in examples/dozer-llm-langchain-sample/ demonstrating a real-time Banking & Credit Card AI Advisor (#1690).

Architecture & Implementation

• Dozer Real-Time Feature & Aggregation Pipeline (dozer-config.yaml + src/dozer_client.py):
  • Ingests customers, credit_cards, and transactions tables and computes real-time per-customer category spend aggregations (customer_spending_summary via Dozer SQL GROUP BY customer_id, category), exposing low-latency REST (:8080) and gRPC (:50051) endpoints.
  • DozerFeatureStoreClient queries live Dozer REST endpoints (POST /{endpoint}/query) with automatic local CSV fallback for offline/CI test runs.
• Unstructured Vector Knowledge Base (knowledge_base/credit_card_terms.md + src/vector_store.py):
  • Indexes unstructured credit card benefits, travel protection rules, lounge access perks, and underwriting criteria for cosine similarity vector search.
• LangChain Grounded Underwriting & Recommendation Engine (src/advisor.py):
  • Combines live customer credit score, income, utilization, and real-time category spend from Dozer with semantic vector hits from the knowledge base.
  • Enforces underwriting eligibility (min_credit_score, min_annual_income, max_utilization_pct), computes exact annualized reward value (net_ongoing_annual_value_usd and net_first_year_value_usd), and synthesizes grounded recommendations via LangChain (ChatOpenAI when OPENAI_API_KEY is configured, or deterministic grounded synthesis offline).

Verification

python3 -m unittest discover -s examples/dozer-llm-langchain-sample/tests -v
# Ran 5 tests in 0.014s - OK

Fixes #1690
/claim #1690

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