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* re-organizng ui preview, tailwind improvements
* cherry pick list styles from tailwind typography component and adjust for side effects
* remove commented code
* fix tab issues
* fix support margin
* remove bad merge resolve, fix titles and conum
* bad wrapping selector
* nested list margin fix
* test gh action change
* test another gh action change
* fix image targets
* put back default build-preview-pages.js from antora aside from one small change
* add comment
Copy file name to clipboardExpand all lines: preview-src/asciidoc/images.adoc
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== Block images
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.Optional title
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image::/img/screenshot.png[Screenshot of Astra Portal Home]
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image::../img/screenshot.png[Screenshot of Astra Portal Home]
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=== Sizing and alignment
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.400px width, align="left"
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image::/img/screenshot.png[Screenshot of Astra Portal Home,400,align="left"]
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image::../img/screenshot.png[Screenshot of Astra Portal Home,400,align="left"]
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.400px width, align="right"
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image::/img/screenshot.png[Screenshot of Astra Portal Home,400,align="right"]
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image::../img/screenshot.png[Screenshot of Astra Portal Home,400,align="right"]
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.400px width, align="center"
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image::/img/screenshot.png[Screenshot of Astra Portal Home,400,align="center"]
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image::../img/screenshot.png[Screenshot of Astra Portal Home,400,align="center"]
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=== Float
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[.float-group]
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--
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image:/img/screenshot.png[Screenshot of Astra Portal Home,300,float=right,role=float-gap]
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image:../img/screenshot.png[Screenshot of Astra Portal Home,300,float=right,role=float-gap]
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In AsciiDoc, creating paragraphs is a straightforward process that does not require any special markup. A paragraph can be defined as one or more lines of consecutive text that are logically grouped together. To differentiate between paragraphs, you simply need to insert at least one blank line between them.
In AsciiDoc, creating paragraphs is a straightforward process that does not require any special markup. A paragraph can be defined as one or more lines of consecutive text that are logically grouped together. To differentiate between paragraphs, you simply need to insert at least one blank line between them.
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--
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== Inline images
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Click image:/img/play_circle_FILL0_wght400_GRAD0_opsz24.svg[title=Play] to get the party started.
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Click image:../img/play_circle_FILL0_wght400_GRAD0_opsz24.svg[title=Play] to get the party started.
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Click image:/img/pause_circle_FILL0_wght400_GRAD0_opsz24.svg[title=Pause] when you need a break.
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Click image:../img/pause_circle_FILL0_wght400_GRAD0_opsz24.svg[title=Pause] when you need a break.
Delve into a database optimized for AI-driven tasks.
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Astra Vector is tailored to offer unparalleled precision, speed, and scalability for AI applications, transforming how your applications perceive and interact with data.
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Delve deep into high-dimensional spaces with Astra Vector.
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Engineered for AI-intensive tasks, it offers unparalleled precision, speed, and scalability. Bring complex AI models into production with confidence, leveraging Astra's robust architecture.
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Process, analyze, and act on data as it's generated. Whether you're analyzing user behavior or monitoring systems, Astra Streaming delivers the real-time insights you need.
Copy file name to clipboardExpand all lines: preview-src/page-templates/integrations-detail.adoc
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= LangChain
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:page-layout: tutorial
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:page-icon: preview-src/img/langchain.svg
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:page-icon: preview-src../img/langchain.svg
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:page-toclevels: 1
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:keywords: Machine Learning Frameworks, Embedding Services, Free, Data Warehouses, SDK
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:description: By integrating LangChain with {product}, you can seamlessly utilize the advanced linguistic processing capabilities of LangChain within your database environment.
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This combination enhances vector data analysis, allowing natural language data to be processed, stored, and queried with efficiency.
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.Architecture diagram
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image::/img/placeholder-image.svg["Diagram of LangChain integration architecture"]
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image::../img/placeholder-image.svg["Diagram of LangChain integration architecture"]
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To get started, ensure you have an https://example.com[active LangChain account^,role=external] with the requisite permissions.
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. Open the 'Vector Data' tab.
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. Confirm the appearance of recent vector entries from LangChain.
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Learn how to create a new database, connect to your database, load a set of vector embeddings, and perform a similarity search to find vectors that are close to the one in your query.
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