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Add HCD and DSE 6.9 examples to nav (#585)
* Add HCD and DSE 6.9 examples to nav * Add Atlas extension to local build * Update external links to comply with style guide * add-example-links * move-prereqs-and-add-to-nav --------- Co-authored-by: Mendon Kissling <[email protected]>
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docs/local-preview-playbook.yml

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astra_ui: 'Astra Portal'
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support_url: 'https://support.datastax.com'
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glossary-url: 'https://docs.datastax.com/en/glossary/docs/index.html#'
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# Antora Atlas
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primary-site-url: https://docs.datastax.com/en
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primary-site-manifest-url: https://docs.datastax.com/en/site-manifest.json
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urls:
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latest_version_segment_strategy: redirect:from

docs/modules/ROOT/nav.adoc

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.RAGStack Examples
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* xref:examples:index.adoc[]
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* xref:examples:prerequisites.adoc[]
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* xref:examples:hcd.adoc[]
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* xref:examples:dse-69.adoc[]
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* xref:examples:colbert.adoc[]
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* xref:examples:knowledge-graph.adoc[]
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* xref:examples:knowledge-store.adoc[]

docs/modules/ROOT/pages/changelog.adoc

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For more, see:
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* https://github.com/datastax/ragstack-ai/releases[GitHub releases^]
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* https://github.com/datastax/ragstack-ai/releases[GitHub releases]
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* https://pypi.org/project/ragstack-ai/[PyPI^]
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* https://pypi.org/project/ragstack-ai/[PyPI]
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== `ragstack-ai`@1.0.7 (2024-05-31)
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| <0.2.0,>=0.1.4
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| langchain
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.7/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.7/langchain[==0.1.19]
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| langchain-astradb
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| ==0.3.3
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| <0.2.0,>=0.1.4
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| langchain
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.6/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.6/langchain[==0.1.19]
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| langchain-astradb
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| ==0.3.0
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| <0.2.0,>=0.1.4
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| langchain
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.5/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.5/langchain[==0.1.19]
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| langchain-astradb
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| ==0.3.0
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| <0.2.0,>=0.1.4
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| langchain
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.4/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.4/langchain[==0.1.19]
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| langchain-astradb
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| ==0.3.0
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| <0.2.0,>=0.1.4
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| langchain
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.3/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.3/langchain[==0.1.19]
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| langchain-astradb
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.2/langchain[==0.1.19]{external-link-icon}
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| https://datastax.github.io/ragstack-ai/api_reference/1.0.2/langchain[==0.1.19]
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docs/modules/ROOT/pages/index.adoc

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{company} has been busy helping our customers through the pains of RAG implementation, so the {product} components we've selected have withstood production workloads and stringent testing by our engineering teams for performance, compatibility, and security.
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* {product} leverages the https://python.langchain.com/docs/get_started/introduction[LangChain^] ecosystem and is fully compatible with https://docs.smith.langchain.com/[LangSmith^] for monitoring your AI deployments.
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* {product} leverages the https://python.langchain.com/docs/get_started/introduction[LangChain] ecosystem and is fully compatible with https://docs.smith.langchain.com/[LangSmith] for monitoring your AI deployments.
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* The https://docs.datastax.com/en/astra/astra-db-vector/get-started/quickstart.html[{db-serverless}] database provides the best performance and scalability for RAG applications, in addition to being particularly well-suited to RAG workloads like question answering, semantic search, and semantic caching.
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docs/modules/ROOT/pages/luna-for-ragstack.adoc

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= Luna for RAGStack
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:keywords: Luna, support, expertise, RAGStack, GenAI, Retrieval-Augmented Generation, RAG
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Luna is a subscription to support and expertise at DataStax. It allows you to enjoy all the benefits of the components we support, with the peace of mind knowing you have direct access to the team that authors the majority of the code and supports some of the world's largest deployments.
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Luna is a subscription to support and expertise at DataStax. It allows you to enjoy all the benefits of the components we support, with the peace of mind knowing you have direct access to the team that authors the majority of the code and supports some of the world's largest deployments.
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Adding external components to a GenAI platform to support development and production environments is complex. https://www.datastax.com/products/luna-ragstack[Luna for RAGStack] gives you direct access to experts who specialize in Retrieval-Augmented Generation (RAG) applications.
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Adding external components to a GenAI platform to support development and production environments is complex. https://www.datastax.com/products/luna-ragstack[Luna for RAGStack] gives you direct access to experts who specialize in Retrieval-Augmented Generation (RAG) applications.
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RAGStack includes a RAG implementation, giving you a comprehensive GenAI stack that leverages xref:examples:index.adoc#langchain-astra[LangChain and Astra DB Serverless], xref:examples:index.adoc#llama-astra[LlamaIndex and Astra DB Serverless], and more. RAGStack supports integration with Vertex AI, Gemini, Bedrock, and NVIDIA NeMo embedding microservices, offering a wider array of tested, compatible embeddings and models for your applications.
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To learn more, see the http://www.datastax.com/blog/empowering-enterprise-genai-announcing-luna-for-ragstack[Luna blog].
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== Luna for RAGStack subscription pricing
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Please https://www.datastax.com/contact-us[contact us^] for details about the Luna for RAGStack subscription options and pricing.
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Please https://www.datastax.com/contact-us[contact us] for details about the Luna for RAGStack subscription options and pricing.
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// TODO: update the pricing URL when it's ready just before the launch date.
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// See the https://www.datastax.com/products/luna[Luna packages] page.
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// See the https://www.datastax.com/products/luna[Luna packages] page.
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== What's next?
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docs/modules/default-architecture/pages/loading.adoc

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Start with LangChain for loading your data instead of manually coding your own pipeline.
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LangChain likely already provides the functionalities you need.
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The examples use vector-enabled {db-serverless} database for the vector store and assume you have one available. If not, see xref:ROOT:prerequisites.adoc[].
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The examples use vector-enabled {db-serverless} database for the vector store and assume you have one available. If not, see xref:examples:prerequisites.adoc[].
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== Load PDF from file
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docs/modules/examples/pages/dse-69.adoc

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= RAGStack and DataStax Enterprise (DSE) 6.9 example
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:navtitle: RAGStack with DataStax Enterprise
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. Pull the latest dse-server Docker image and confirm the container is in a running state.
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docs/modules/examples/pages/hcd.adoc

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= RAGStack and Hyper Converged Database (HCD) example
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= RAGStack and Hyper-Converged Database (HCD) example
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:navtitle: RAGStack with Hyper-Converged Database
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. Clone the HCD example repository.
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docs/modules/examples/pages/index.adoc

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| Store external or proprietary data in {db-serverless} and query it to provide more up-to-date LLM responses.
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| https://colab.research.google.com/github/datastax/ragstack-ai/blob/main/examples/notebooks/RAG_with_cassio.ipynb[Open in Colab]
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| xref:rag-with-cassio.adoc[]
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| Use the self-managed Hyper-Converged Database (HCD) as a vector backend for your RAG application.
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| xref:hcd.adoc[]
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| Use DataStax Enterprise as a vector backend for your RAG application.
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| xref:dse-69.adoc[]
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docs/modules/examples/pages/langchain_multimodal_gemini.adoc

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DB Access Token] with Database Administrator permissions.
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* Get your {db-serverless} API Endpoint:
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* Create a https://cloud.google.com/[GCP account^] and a https://cloud.google.com/iam/docs/service-account-overview[service account^]. Download your service account's GCP credentials as a JSON file. For more on service account authorization, see the https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account[GCP documentation^].
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* Create a https://cloud.google.com/[GCP account] and a https://cloud.google.com/iam/docs/service-account-overview[service account]. Download your service account's GCP credentials as a JSON file. For more on service account authorization, see the https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account[GCP documentation].
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See the xref:examples:prerequisites.adoc[] page for more details.
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== Configure {db-serverless} and GCP credentials
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