Add Oracle AI Database vector search example using LangChain

examples/vector_databases/oracle_db/README.md

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+# LangChain, OpenAI, and Oracle AI Database for Vector Search
+
+This example shows how to build a semantic search workflow using:
+
+- **OpenAI embeddings** for turning text into vector representations
+- **LangChain’s Oracle vector store integration** for working with vectors through a familiar framework abstraction
+- **Oracle AI Database Vector Search** for storing embeddings and retrieving relevant content with similarity search
+
+If you are looking for a practical starting point for **LangChain + Oracle AI Database**, **OpenAI embeddings with Oracle Vector Search**, or a developer-friendly example of semantic retrieval that integrates directly with data stored in Oracle AI Database, this notebook is designed to be easy to read, run, and adapt.
+
+## Why this stack works well
+
+This combination is useful because each component solves a different part of the workflow:
+
+- **OpenAI embeddings** convert text into semantic vectors that capture meaning beyond keyword matching
+- **LangChain** provides a developer-friendly abstraction for vector stores and retrieval workflows
+- **Oracle AI Database** stores vectors alongside application and relational data, making it easier to build retrieval-enabled applications without introducing a separate vector database
+
+For developers evaluating semantic search patterns, this creates a practical setup that is easy to prototype locally and extend later into larger RAG or agent-based workflows.
+
+## Why use Oracle AI Database with LangChain
+
+Oracle AI Database is a strong fit for LangChain-based vector workflows when you want:
+
+- **vector search and application data in one place**
+- **less infrastructure complexity**, especially for teams already using Oracle
+- **native SQL access to vector operations**
+- **a smoother path from notebook prototype to production architecture**
+- compatibility with **LangChain retrieval patterns**, while keeping Oracle AI Database as the retrieval backbone
+
+This is especially helpful when vector search is not a standalone demo, but one part of a broader enterprise or application stack.
+
+## What you will learn
+
+By working through the notebook, you will learn how to:
+
+- connect to Oracle AI Database from Python
+- configure OpenAI embeddings for semantic search
+- initialize LangChain’s Oracle vector store integration
+- store sample documents and embeddings in Oracle AI Database
+- run vector similarity search against indexed content
+- inspect how LangChain retrieval maps to Oracle AI Database Vector Search
+- use the same pattern as a baseline for larger retrieval or RAG workflows
+
+## Architecture at a glance
+
+The example follows a simple semantic retrieval flow:
+
+1. Load configuration and environment variables
+2. Initialize OpenAI embeddings
+3. Connect to Oracle AI Database
+4. Initialize the LangChain Oracle vector store
+5. Insert sample text documents
+6. Convert the query into an embedding
+7. Run vector similarity search
+8. Return the most relevant matching content
+
+In a larger application, this same pattern can become the retrieval layer behind a RAG pipeline, internal search assistant, or AI agent workflow.
+
+## Prerequisites
+
+You will need:
+
+- **Oracle AI Database** with Vector Search enabled
+- **OpenAI API key**
+- **Python 3.10+**
+- Notebook dependencies installed for this example
+
+## Environment variables
+
+Create a local `.env` file and provide values similar to the following:
+
+```env
+OPENAI_API_KEY=
+ORACLE_USER=PDBADMIN
+ORACLE_PASSWORD=YourPassword
+ORACLE_DSN=localhost:1521/FREEPDB1
+```
+
+## Python dependencies
+Install the required packages using pip:
+
+```bash
+pip install langchain
+pip install langchain-openai
+pip install langchain-oracledb
+pip install oracledb
+pip install python-dotenv
+```
+
+
+## Files in this example
+
+- `oracle_vector_search_langchain.ipynb` — the main notebook demonstrating semantic search with LangChain, OpenAI, and Oracle AI Database
+- `.env` — local configuration file for OpenAI and Oracle connection settings
+
+
+
+## How to run
+
+1. Set your `OPENAI_API_KEY` in the local `.env` file
+2. Make sure your Oracle AI Database instance is running and the connection values are correct
+3. Open `oracle_vector_search_langchain.ipynb`
+4. Run the notebook cells in order
+
+The notebook walks through dependency setup, embedding initialization, Oracle database connection, vector store creation, sample document ingestion, and semantic similarity search.
+
+
+## Expected outcome
+
+By the end of this notebook, you will have a working semantic search example where:
+
+- text is converted into embeddings using OpenAI
+- vectors are stored in Oracle AI Database
+- LangChain interacts with Oracle AI Database through its vector store integration
+- similarity search returns the most relevant documents for a given query
+
+This can serve as a baseline for larger retrieval, RAG, or agent-based applications.
+
+## Next steps
+
+Once this example is working, you can extend it by:
+
+- replacing the sample documents with your own data
+- adding metadata and document chunking
+- using the same retrieval flow in a larger RAG pipeline
+- integrating the notebook logic into an API or application
+
+
+## Documentation
+For additional information, please refer to the documentation:
+
+[![LangChain Oracle Vector Store](https://img.shields.io/badge/LangChain-Oracle%20Vector%20Store-blue)](https://docs.langchain.com/oss/python/integrations/vectorstores/oracle)
+[![LangChain OpenAI Embeddings](https://img.shields.io/badge/LangChain-OpenAI%20Embeddings-green)](https://docs.langchain.com/oss/python/integrations/text_embedding/openai)
+[![Oracle Database Vector Search](https://img.shields.io/badge/Oracle-Database%20Vector%20Search-red)](https://docs.oracle.com/en/database/oracle/oracle-database/26/vecse/)
+