DA-1318 Updated the CrewAI Short Term Mem tutorial according to the latest docs

[azaddhirajkumar]

No description provided.

[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.

5 Notebook Files Modified:

Notebook File	Frontmatter Included?
capella-model-services/haystack/RAG_with_Couchbase_Capella.ipynb	❌
capella-model-services/haystack/query_based/RAG_with_Capella_Model_Services_and_Haystack.ipynb	✅
crewai-short-term-memory/gsi/CouchbaseStorage_Demo.ipynb	❌
crewai-short-term-memory/query_based/CouchbaseStorage_Demo.ipynb	✅
crewai-short-term-memory/search_based/CouchbaseStorage_Demo.ipynb	✅

5 Frontmatter Files Modified:

Frontmatter File
capella-model-services/haystack/query_based/frontmatter.md
capella-model-services/haystack/search_based/frontmatter.md
crewai-short-term-memory/gsi/frontmatter.md
crewai-short-term-memory/query_based/frontmatter.md
crewai-short-term-memory/search_based/frontmatter.md
Note: frontmatter will be checked and tested in the Test Frontmatter workflow.

[gemini-code-assist]

Summary of Changes

Hello @azaddhirajkumar, 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 significantly updates and reorganizes the CrewAI Short Term Memory tutorials for Couchbase. It introduces a new, more advanced tutorial showcasing the use of Couchbase's Hyperscale and Composite Vector Indexes for efficient memory storage and retrieval in AI agents. Concurrently, the existing tutorial, which previously used FTS, has been refined and renamed to align with the concept of Search Vector Index, ensuring all examples leverage the most current and performant Couchbase vector search capabilities. These changes aim to provide clearer, more up-to-date guidance for integrating Couchbase as a memory backend for CrewAI.

Highlights

• Tutorial Restructuring: The tutorial structure for CrewAI Short Term Memory with Couchbase has been reorganized, deprecating the GSI-based approach and introducing new 'query_based' and 'search_based' directories to better categorize vector index types.
• New Query-Based Tutorial: A new tutorial (crewai-short-term-memory/query_based/CouchbaseStorage_Demo.ipynb) has been added, demonstrating the implementation of CrewAI short-term memory using Couchbase's Hyperscale and Composite Vector Indexes, complete with performance testing and agent memory demos.
• Search-Based Tutorial Updates: The existing FTS-based tutorial has been updated, renamed to 'search_based', and enhanced with dependency updates, Google Colab setup instructions, and clarified terminology for Search Vector Index.
• Dependency Updates: Key Python package dependencies, specifically 'langchain-couchbase' and 'langchain-openai', have been updated to their latest versions in the 'search_based' tutorial to ensure compatibility and leverage recent improvements.

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[gemini-code-assist]

Code Review

This pull request provides a significant update to the CrewAI short-term memory tutorials, aligning them with the latest documentation by replacing the previous gsi and fts based examples with new query_based (Hyperscale/Composite indexes) and search_based (Search Vector Index) tutorials. The restructuring and content updates are well-executed and improve the overall quality and clarity of the documentation. I've included a few review comments to enhance code robustness, ensure consistency in configuration, and correct a minor inaccuracy in the documentation.

[VirajAgarwal1]

Thanks for the PR. Just a couple of minor changes are needed before merging.

[VirajAgarwal1]

lgtm