Update RAG tutorials with Capella Model Service & Couchbase Search Vector Index

[nithishr]

• Updated the folder structure
• Updated tutorial for Retrieval-Augmented Generation (RAG) using LangChain with Couchbase Search Vector Index
• Updated frontmatter files for tutorial to define paths, titles, and descriptions.
• Updated index definition
• Updated Jupyter notebooks demonstrating the RAG pipeline, including setup, model usage, and example queries.

[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.

4 Notebook Files Modified:

Notebook File	Frontmatter Included?
capella-ai/langchain/RAG_with_Couchbase_Capella.ipynb	❌
capella-model-services/haystack/RAG_with_Couchbase_Capella.ipynb	❌
capella-model-services/langchain/search_based/RAG_with_Capella_Model_Services_and_LangChain.ipynb	✅
capella-model-services/llamaindex/RAG_with_Couchbase_Capella.ipynb	❌

1 Frontmatter Files Modified:

Frontmatter File
capella-model-services/langchain/search_based/frontmatter.md
Note: frontmatter will be checked and tested in the Test Frontmatter workflow.

[gemini-code-assist]

Summary of Changes

Hello @nithishr, 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 the Retrieval-Augmented Generation (RAG) tutorials to align with the latest Capella Model Services and Couchbase Search Vector Index capabilities. The changes involve migrating from older AI services to the more current Capella Model Services, introducing a new, comprehensive LangChain-based RAG tutorial, and restructuring the tutorial directories for better organization. These updates ensure the examples are current, accurate, and leverage the most recent features for building intelligent information retrieval systems.

Highlights

• Migration to Capella Model Services: The RAG tutorials have been updated to utilize Capella Model Services for both embedding and large language models, replacing the previous Capella AI Services.
• Updated LangChain RAG Tutorial: A new Jupyter notebook (RAG_with_Capella_Model_Services_and_LangChain.ipynb) has been introduced, demonstrating an end-to-end RAG pipeline with the latest Capella Model Services and Couchbase Search Vector Index. This includes using Mistral-7B-Instruct-v0.3 as the LLM and NVIDIA NeMo Retriever Llama3.2 for embeddings, updated API key handling, and revised guardrail information.
• Refined Folder Structure: The tutorial files have been reorganized into a more logical structure under capella-model-services/langchain/search_based/ to improve navigability and clarity.
• Vector Index Definition Adjustment: The capella_index.json file was updated to reflect a new embedding dimension (2048), aligning with the NVIDIA NeMo Retriever Llama3.2 embedding model used.
• Dependency Updates: Key Python package versions, including datasets, langchain-couchbase, and langchain-openai, have been upgraded to their latest compatible versions.

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[gemini-code-assist]

Code Review

This pull request updates the RAG tutorials to use the new Capella Model Service and Couchbase Search Vector Index. The changes include restructuring folders, updating Jupyter notebooks, and modifying configuration files. My review focuses on improving the new LangChain RAG tutorial notebook by correcting error messages, removing unused code, and enhancing exception handling for better clarity and robustness. I've also pointed out a minor typo in a frontmatter file. Overall, the updates are a great improvement and align the tutorials with the latest services.

[VirajAgarwal1]

lgtm