financial and retail app work

financial/README.md

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+Write up a demo script using the format and style in the attached docx file but for the following workshop.... 
+"
+This financial application and its corresponding workshop are aimed at both financial line of business experts and developers.
+It is intended to allow the two personas to have a common ground and shared understanding of the possibilities and finer details of both the business solutions and the development architecture, features, etc. involved in them.
+
+Each lab/part of the application describes various aspects including... 
+- business process
+- dev/tech involved
+- existing companies that use such solutions
+- differentiators
+- low level details of code, tests, and comparisons with other solutions/vendors
+- brief video walkthrough
+
+The workshop in addition goes into further details on...
+- migration
+- scaling and sizing
+- expert contacts in product management, etc. 
+
+Introduction
+The application used is a full-stack, microservices based architecture, using all of the latest and most popular developer technologies.
+The frontend is written predominantly in React.
+The mid-tier is written in various languages and platforms such as Java and Spring Boot, React and Node, Python, .NET, Go, Rust, and WASM
+Oracle Database serves as the backend, however, Oracle database is far from just a storage mechanism as you will see as you go through the labs.
+A number or Oracle and Oracle database technologies are used.
+
+Lab 1 
+Process: FinTech/Bank APIs: Access/use financial data or processes from APIs.  Display the largest changes in portfolio
+Tech Used: Oracle Rest Data Services (ORDS)
+Reference: Bank Of India
+Differentiators: Create APIs from data an processes in under a minute
+Low-level details: Comparison of speed with other API creation methods as well as the advantage of ORDS
+
+Lab 2 
+Process: DevOps: Kubernetes, Microservices, and Observability
+Tech Used: Oracle Backend For Microservices and AI, OpenTelemetry, Grafan
+Reference: LOLC
+Differentiators: Simplified management of Kubernetes and microservices, one of a kind trace exporter in the database, giving the ability to trace *into* the database that no other vendor has, as well as metrics and log exports - all exporters accept SQL for the most advanced querying of data.
+Low-level details: Realize the amount of architecture that is automated and the convenience, and time saving time-to-mark advantages
+
+Lab 3 
+Process: Create and Query Accounts: Create with MongoDB API, query with SQL
+Tech Used: MongoDB API adapter, JSON Duality
+Reference: Santander
+Differentiators: Use JSON Duality for seamless SQL querying of the same data. No other database can do this.
+Low-level details: Instigate crash and notice transactionality of Oracle Database for JSON and relational.
+
+Lab 4
+Process: Transfer funds between banks
+Tech Used: Spring Boot, MicroTx, Lock-free reservations
+Reference: U of Naples
+Differentiators: The only database that provides auto-compensating sagas (microservice transactions) and highest throughput for hotspots. Simplified development (~80% less code)
+Low-level details: Instigate crash and notice automatic recovery that is possible and the huge amount of error-prone code that would be required otherwise.
+
+Lab 5 
+Process: Credit card purchases, fraud, and money laundering
+Tech Used: Credit card purchases are conducted using Oracle Globally Distributed Database. 
+Fraud detection and visualization is conducted using OML4Py (Python) and Spatial.  
+Money Laundering is detected using Oracle Graph. 
+Events are sent using Knative Eventing and CloudEvents.
+Reference: AMEX
+Differentiators: 
+Low-level details:
+
+Lab 6 
+Process: Transfer to brokerage accounts
+Tech Used: Kafka and TxEventQ
+Reference: FSGUB
+Differentiators: 
+Low-level details: Instigate crash and notice message duplication, message loss, data duplication, and additional code required when using Kafka with Postgres and MongoDB that is automatically and transactionally handled when using Kafka with Oracle Database.
+
+Lab 7 
+Process: Stock ticker and buy/sell stock
+Tech Used: TrueCache, Polyglot (Java, JS, Python, .NET, Go, Rust, PL/SQL)
+Reference: NYSE
+Differentiators: Unlike Redis, True Cache uses SQL, not a proprietary API
+Low-level details:
+
+Lab 8 
+Process: Personalized Financial Insights
+Tech Used: Vector Search, AI Agents, and MCP
+Reference: Merrill Lynch
+Differentiators: Access data securely from Oracle Database hub. Even using JavaScript and Java from within the database to make MCP AI Agent calls
+Low-level details:
+
+Lab 9 Speak with your financial data
+Process: Access/use financial data or processes from APIs.  Display the largest changes in portfolio
+Tech Used: NL2SQL/Select AI, Vector Search, Oracle AI Explorer, Speech AI
+Reference: various call centers
+Differentiators:
+Low-level details:
+""

financial/ai-agents/.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual Environment
+venv/
+env/
+.env
+
+# IDE
+.vscode/
+.idea/
+
+# Gradio
+.gradio/
+
+# Generated files
+embeddings/
+chroma_db/
+docs/*.json
+
+# Distribution / packaging
+dist/
+build/
+*.egg-info/
+
+# Logs
+*.log