feat: updated references from 23ai to 26ai whenever possible, avoiding broken urls and docs

Author: jasperan

README.md

@@ -5,7 +5,7 @@ Welcome to DevRel Labs, a collection of AI solutions and tools built with Oracle
 ## 🚀 New AI Solutions (2024)
 
 ### 1. [Oracle MCP AI Agents](./oracle-mcp-ai-agents/)
-**Building AI Agents with Model Context Protocol (MCP) and Oracle Database 23ai**
+**Building AI Agents with Model Context Protocol (MCP) and Oracle Database 26ai**
 
 - **MCP Integration**: Secure tool calling between LLMs and Oracle Database
 - **Real-time Data Access**: Live business data integration without ETL
@@ -14,7 +14,7 @@ Welcome to DevRel Labs, a collection of AI solutions and tools built with Oracle
 - **Use Cases**: Financial advisor, customer service, business intelligence
 
 ### 2. [Oracle RAG Applications](./oracle-rag-applications/)
-**Building RAG Applications with Oracle Database 23ai**
+**Building RAG Applications with Oracle Database 26ai**
 
 - **Vector Embeddings**: Native vector storage and search
 - **Document Processing**: Support for PDF, DOCX, TXT, and other formats
@@ -34,7 +34,7 @@ Welcome to DevRel Labs, a collection of AI solutions and tools built with Oracle
 ## 🔧 Existing Solutions
 
 ### AI & Machine Learning
-- [Agentic RAG](./agentic_rag/) - AI RAG in a BOX Demo using Oracle Autonomous Database 23ai and Local LLMs
+- [Agentic RAG](./agentic_rag/) - AI RAG in a BOX Demo using Oracle AI Database 26ai and Local LLMs
 - [PlaneLLM](./planeLLM/) - PlaneLLM integration with Oracle Database
 - [Neural Networks Hero](./neural_networks_hero/) - Neural network implementations and tutorials
 
@@ -67,9 +67,8 @@ Each solution includes:
 
 ## 📚 Resources
 
-- [Oracle Database 23ai Documentation](https://docs.oracle.com/en/database/oracle/oracle-database/23ai/)
+- [Oracle AI Database 26ai](https://www.oracle.com/database/ai-native-database-26ai/)
 - [Oracle Cloud Infrastructure](https://www.oracle.com/cloud/)
-- [Oracle AI Documentation](https://docs.oracle.com/en/database/oracle/oracle-database/23ai/ai/)
 
 ## 🤝 Contributing
 

agentic_rag/README.md

@@ -10,7 +10,7 @@ The system has the following features:
 
 - Intelligent query routing
 - PDF processing using Docling for accurate text extraction and chunking
-- Persistent vector storage with Oracle Database 23ai (PDF and Websites)
+- Persistent vector storage with Oracle AI Database 26ai (PDF and Websites)
 - Smart context retrieval and response generation
 - FastAPI-based REST API for document upload and querying
 - Support for both OpenAI-based agents or local, transformer-based agents (`Mistral-7B` by default)
@@ -395,7 +395,7 @@ The system consists of several key components:
 1. **PDF Processor**: we use `docling` to extract and chunk text from PDF documents
 2. **Web Processor**: we use `trafilatura` to extract and chunk text from websites
 3. **GitHub Repository Processor**: we use `gitingest` to extract and chunk text from repositories
-4. **Vector Store**: Manages document embeddings and similarity search using `Oracle Database 23ai` (default) or `ChromaDB` (fallback)
+4. **Vector Store**: Manages document embeddings and similarity search using `Oracle AI Database 26ai` (default) or `ChromaDB` (fallback)
 5. **RAG Agent**: Makes intelligent decisions about query routing and response generation
    - OpenAI Agent: Uses `gpt-4-turbo-preview` for high-quality responses, but requires an OpenAI API key
    - Local Agent: Uses `Mistral-7B` as an open-source alternative

agentic_rag/docs/oracle_db_integration.md

@@ -1,16 +1,16 @@
-# Oracle DB 23ai Integration
+# Oracle AI Database 26ai Integration
 
-The Agentic RAG system now supports Oracle DB 23ai as a vector store backend, providing enhanced performance, scalability, and enterprise-grade database features.
+The Agentic RAG system now supports Oracle AI Database 26ai as a vector store backend, providing enhanced performance, scalability, and enterprise-grade database features.
 
 ## Overview
 
-Oracle Database 23ai is used as the default vector storage system when available, with ChromaDB serving as a fallback option. This integration leverages Oracle's vector database capabilities for efficient semantic search and retrieval.
+Oracle Database 26ai is used as the default vector storage system when available, with ChromaDB serving as a fallback option. This integration leverages Oracle's vector database capabilities for efficient semantic search and retrieval.
 
 ## Requirements
 
 To use the Oracle DB integration, you need:
 
-1. **Oracle Database 23ai**: With vector extensions enabled
+1. **Oracle Database 26ai**: With vector extensions enabled
 2. **Python Packages**:
    - `oracledb`: For database connectivity
    - `sentence-transformers`: For generating embeddings
@@ -42,7 +42,7 @@ The system will automatically look for these credentials in your `config.yaml` f
 
 The system automatically determines which database to use:
 
-1. First tries to connect to Oracle DB 23ai
+1. First tries to connect to Oracle AI Database 26ai
 2. If connection succeeds, uses Oracle for all vector operations
 3. If Oracle DB is unavailable, falls back to ChromaDB
 
@@ -87,15 +87,15 @@ agent = LocalRAGAgent(use_oracle_db=False)
 
 The Gradio web interface displays which database system is active at the top of the page:
 
-- Green banner: Oracle DB 23ai is active
+- Green banner: Oracle AI Database 26ai is active
 - Red banner: ChromaDB is being used (Oracle DB not available)
 
 ## Troubleshooting
 
 If you encounter database connection issues:
 
 1. Verify your Oracle DB credentials and connection string
-2. Check that the Oracle DB 23ai instance is running
+2. Check that the Oracle AI Database 26ai instance is running
 3. Ensure you have the required Python packages installed
 4. Check network connectivity to the database server
 

agentic_rag/gradio_app.py

@@ -50,7 +50,7 @@ def load_config():
 if ORACLE_DB_AVAILABLE:
     try:
         vector_store = OraDBVectorStore()
-        print("Using Oracle DB 23ai for vector storage")
+        print("Using Oracle AI Database 26ai for vector storage")
     except Exception as e:
         print(f"Error initializing Oracle DB: {str(e)}")
         print("Falling back to ChromaDB")
@@ -777,13 +777,13 @@ def create_interface():
         if ORACLE_DB_AVAILABLE and hasattr(vector_store, 'connection'):
             gr.Markdown("""
             <div style="padding: 10px; background-color: #d4edda; color: #155724; border-radius: 5px; margin-bottom: 15px;">
-            ✅ <strong>Oracle DB 23ai</strong> is active and being used for vector storage.
+            ✅ <strong>Oracle AI Database 26ai</strong> is active and being used for vector storage.
             </div>
             """)
         else:
             gr.Markdown("""
             <div style="padding: 10px; background-color: #f8d7da; color: #721c24; border-radius: 5px; margin-bottom: 15px;">
-            ⚠️ <strong>ChromaDB</strong> is being used for vector storage. Oracle DB 23ai is not available.
+            ⚠️ <strong>ChromaDB</strong> is being used for vector storage. Oracle AI Database 26ai is not available.
             </div>
             """)
 

agentic_rag/test_data_lake.py

@@ -253,7 +253,7 @@ def test_event_logging():
 
     print_section("Test Complete")
     print("✅ All event types tested successfully!")
-    print("\nThe data lake is now storing all events in Oracle DB 23ai.")
+    print("\nThe data lake is now storing all events in Oracle AI Database 26ai.")
     print("You can query these events using:")
     print("  - SQL queries directly on the database")
     print("  - REST API endpoints (/events/statistics, /events/{type})")