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data-platform/data-science/oracle-data-science/RAG-Wikipedia/LICENSE

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+Copyright (c) 2025 Oracle and/or its affiliates.
+
+The Universal Permissive License (UPL), Version 1.0
+
+Subject to the condition set forth below, permission is hereby granted to any
+person obtaining a copy of this software, associated documentation and/or data
+(collectively the "Software"), free of charge and under any and all copyright
+rights in the Software, and any and all patent rights owned or freely
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+Works (as defined below), to deal in both
+
+(a) the Software, and
+(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+one is included with the Software (each a "Larger Work" to which the Software
+is contributed by such licensors),
+
+without restriction, including without limitation the rights to copy, create
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+use, sell, offer for sale, import, export, have made, and have sold the
+Software and the Larger Work(s), and to sublicense the foregoing rights on
+either these or other terms.
+
+This license is subject to the following condition:
+The above copyright notice and either this complete permission notice or at
+a minimum a reference to the UPL must be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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data-platform/data-science/oracle-data-science/RAG-Wikipedia/README.md

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+# RAG Wikipedia
+This package provides an end-to-end example of performing vector search and Retrieval-Augmented Generation (RAG) from an OCI Data Science Notebook Session using Wikipedia's Artificial Intelligence page.
+
+Reviewed: 2025.11.27
+
+# When to use this asset?
+
+Use this asset as support material when wanting to showcase Oracle AI Vector Search within Oracle AI Database 26ai. The asset includes explanations along with advanced RAG techniques, including:
+
+1. Comparing rule-based and semantic-based text splitters
+2. Connecting to the Oracle AI Database 26ai and creating vector-enabled tables
+3. Running hybrid search (keyword + vector similarity)
+4. Applying rerankers to improve retrieved context
+
+# Prerequisites
+
+To use this package, you need:
+- Basic Python knowledge
+- Access to an OCI Data Science Notebook Session
+- An Oracle AI Database 26ai or 23ai with Vector Search enabled
+- Required IAM permissions for Data Science and Database access
+- A configured database wallet or secure connection details
+
+# Environment
+
+Run the examples in a Jupyter Notebook inside an OCI Data Science Notebook Session.
+
+# License
+
+Copyright (c) 2025 Oracle and/or its affiliates.
+
+Licensed under the Universal Permissive License (UPL), Version 1.0.
+
+See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/main/LICENSE) for more details.