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Copy file name to clipboardExpand all lines: README.md
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## Description
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The **Oracle AI Optimizer and Toolkit** (the **AI Optimizer**) provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (GenAI) combined with Retrieval-Augmented Generation (RAG) capabilities. By integrating **Oracle Database 23ai** AI Vector Search, the Sandbox enables users to enhance existing Large Language Models (LLMs) through RAG.
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The **Oracle AI Optimizer and Toolkit** (the **AI Optimizer**) provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (GenAI) combined with Retrieval-Augmented Generation (RAG) capabilities. By integrating **Oracle Database 23ai** AI VectorSearch and SelectAI, the Sandbox enables users to enhance existing Large Language Models (LLMs) through RAG.
Copy file name to clipboardExpand all lines: docs/content/_index.md
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@@ -13,7 +13,7 @@ Licensed under the Universal Permissive License v1.0 as shown at http://oss.orac
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spell-checker:ignore streamlit, genai, venv
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The {{< full_app_ref >}} provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (**GenAI**) combined with Retrieval-Augmented Generation (**RAG**) capabilities. By integrating Oracle Database AI Vector Search, the {{< short_app_ref >}} enables users to enhance existing Large Language Models (**LLM**s) through **RAG**. This method significantly improves the performance and accuracy of AI models, helping to avoid common issues such as knowledge cutoff and hallucinations.
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The {{< full_app_ref >}} provides a streamlined environment where developers and data scientists can explore the potential of Generative Artificial Intelligence (**GenAI**) combined with Retrieval-Augmented Generation (**RAG**) capabilities. By integrating Oracle Database AI VectorSearch and SelectAI, the {{< short_app_ref >}} enables users to enhance existing Large Language Models (**LLM**s) through **RAG**. This method significantly improves the performance and accuracy of AI models, helping to avoid common issues such as knowledge cutoff and hallucinations.
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- **GenAI**: Powers the generation of text, images, or other data based on prompts using pre-trained **LLM**s.
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- **RAG**: Enhances **LLM**s by retrieving relevant, real-time information allowing models to provide up-to-date and accurate responses.
Copy file name to clipboardExpand all lines: docs/content/client/configuration/db_config.md
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## Database User
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A database user is required to store the embeddings, used for **RAG**, into the Oracle Database. A non-privileged user with a *non-SYSTEM tablespace* should be used for this purpose. Use the below syntax as an example of creating a new user:
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A database user is required to store the embeddings, used for **RAG**, into the Oracle Database. A non-privileged user with a *non-SYSTEM tablespace* should be used for this purpose. Use the below syntax as an __example__ of creating a new user with least privileges:
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```sql
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CREATEUSER "DEMO" IDENTIFIED BY MYCOMPLEXSECRET
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ALTERUSER"DEMO" QUOTA UNLIMITED ON DATA;
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```
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Replace "DEMO" as required.
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If running on a supported database for [SelectAI](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/select-ai.html) and want to use the feature, grant the following additional privileges and open appropriate ACLs:
format("allow dynamic-group %s to use generative-ai-family in compartment id %s", oci_identity_dynamic_group.resource_dynamic_group.name, local.compartment_ocid),
format("allow any-user to manage autonomous-database-family in compartment id %s where all {request.principal.type = 'workload', request.principal.namespace = 'oracle-database-operator-system', request.principal.service_account = 'default', request.principal.cluster_id = '%s'}", var.compartment_id, oci_containerengine_cluster.default_cluster.id),
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format("allow any-user to read objectstorage-namespaces in compartment id %s where all {request.principal.type = 'workload', request.principal.service_account = 'default', request.principal.cluster_id = '%s'}", var.compartment_id, oci_containerengine_cluster.default_cluster.id),
format("allow any-user to manage waf-family in compartment id %s where all {request.principal.type = 'workload', request.principal.namespace = 'native-ingress-controller-system', request.principal.service_account = 'oci-native-ingress-controller', request.principal.cluster_id = '%s'}", var.compartment_id, oci_containerengine_cluster.default_cluster.id),
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format("allow any-user to read cluster-family in compartment id %s where all {request.principal.type = 'workload', request.principal.namespace = 'native-ingress-controller-system', request.principal.service_account = 'oci-native-ingress-controller', request.principal.cluster_id = '%s'}", var.compartment_id, oci_containerengine_cluster.default_cluster.id),
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format("allow any-user to use tag-namespaces in compartment id %s where all {request.principal.type = 'workload', request.principal.namespace = 'native-ingress-controller-system', request.principal.service_account = 'oci-native-ingress-controller', request.principal.cluster_id = '%s'}", var.compartment_id, oci_containerengine_cluster.default_cluster.id),
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format("allow dynamic-group %s to manage repos in compartment id %s", oci_identity_dynamic_group.node_dynamic_group.name, var.compartment_id),
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format("allow dynamic-group %s to manage repos in compartment id %s", var.dynamic_group, var.compartment_id),
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