DA 1153 Autovec Unstructured Data

[giriraj-singh-couchbase]

This pull request introduces a new tutorial for using Couchbase Capella's AI Services auto-vectorization feature with LangChain, focusing on unstructured data workflows—especially data stored in S3 buckets. The changes add comprehensive documentation and a runnable Jupyter notebook that walks users through deploying models, configuring workflows, importing unstructured data, and performing semantic vector search with LangChain.

The most important changes are:

Documentation and Tutorial Content:

• Added a detailed README.md explaining prerequisites, installation steps, and a quick start guide for the auto-vectorization tutorial.
• Added frontmatter.md to provide metadata and summary information for the tutorial, including title, description, tags, and estimated duration.

Jupyter Notebook Tutorial:

• Introduced autovec_unstructured.ipynb, a step-by-step notebook covering:
  • Capella cluster and model deployment.
  • Setting up access control and API keys for AI models.
  • Creating and configuring auto-vectorization workflows to import and process unstructured data from S3.
  • Performing semantic search using vector embeddings and LangChain integration, with code samples and explanations.

[github-actions]

Caution

Notebooks or Frontmatter Files Have Been Modified

• Please ensure that a frontmatter.md file is accompanying the notebook file, and that the frontmatter is up to date.
• These changes will be published to the developer portal tutorials only if frontmatter.md is included.
• Proofread all changes before merging, as changes to notebook and frontmatter content will update the developer tutorial.

1 Notebook Files Modified:

Notebook File	Frontmatter Included?
autovec_unstructured/autovec_unstructured.ipynb	✅

1 Frontmatter Files Modified:

Frontmatter File
autovec_unstructured/frontmatter.md
Note: frontmatter will be checked and tested in the Test Frontmatter workflow.

[gemini-code-assist]

Summary of Changes

Hello @giriraj-singh-couchbase, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a new, comprehensive tutorial designed to educate users on leveraging Couchbase Capella's AI Services for auto-vectorization of unstructured data sourced from S3 buckets. The tutorial provides a practical, step-by-step guide, including a Jupyter notebook, to set up and execute workflows that transform raw data into vector embeddings, enabling advanced semantic search capabilities through LangChain integration. The primary goal is to empower users to efficiently manage and query unstructured data within the Capella ecosystem.

Highlights

• New Tutorial Introduction: This pull request introduces a new tutorial demonstrating Couchbase Capella's AI Services auto-vectorization feature for unstructured data, specifically focusing on data stored in S3 buckets.
• LangChain Integration: The tutorial showcases how to integrate with LangChain for performing semantic vector search on the auto-vectorized data.
• Comprehensive Documentation: A detailed README.md and frontmatter.md have been added to provide prerequisites, installation steps, and metadata for the tutorial.
• Interactive Jupyter Notebook: A runnable Jupyter notebook (autovec_unstructured.ipynb) is included, guiding users through Capella cluster and model deployment, access control setup, configuring auto-vectorization workflows for S3 data, and executing semantic search.

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[gemini-code-assist]

Code Review

This pull request introduces a new tutorial for using Couchbase Capella's AI Services auto-vectorization feature. While the tutorial is comprehensive, there are several areas that need improvement. Critically, the Jupyter notebook references images that are not included in the pull request, which will prevent users from following the visual steps. There are also significant structural issues, such as incorrect section numbering and confusing instructions that reference incorrect data sources. Additionally, there are opportunities to improve code quality by removing unused imports, using environment variables for credentials to promote security best practices, and fixing minor typos and grammatical errors. Addressing these points will greatly improve the quality and usability of the tutorial.