DA-924: Mistral AI Smolagents and Pydantic

[AayushTyagi1]

Smolagents
Pydantic
Mistral AI added

Updated 3 Tutorials

[gemini-code-assist]

Summary of Changes

Hello @AayushTyagi1, 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 enhances the vector search tutorials for Mistral AI, PydanticAI, and Smolagents by introducing comprehensive examples and documentation for Couchbase's Global Secondary Index (GSI). This refactoring provides users with clearer guidance on implementing high-performance vector search, contrasting it with the existing Full-Text Search (FTS) methods, and offering detailed insights into GSI's benefits and configuration for scalable AI applications.

Highlights

• Tutorial Restructuring: The existing Mistral AI, PydanticAI, and Smolagents tutorials have been reorganized into 'fts/' (Full-Text Search) and new 'gsi/' (Global Secondary Index) subdirectories to clearly separate different vector search implementations.
• GSI Vector Search Integration: New tutorials and associated files have been added for Mistral AI, PydanticAI, and Smolagents to demonstrate and utilize Couchbase's Global Secondary Index (GSI) for optimized vector search operations.
• Documentation Updates: Frontmatter files ('frontmatter.md') for both FTS and GSI versions have been updated with specific titles, paths, and descriptions to clearly distinguish between the two vector search approaches and their benefits.
• Environment Configuration: New '.env.sample' files are introduced in the 'gsi/' directories to provide specific environment variable configurations required for the GSI-based examples.
• Notebook Enhancements: Jupyter notebooks ('.ipynb') have been updated with introductory text linking to alternative GSI/FTS tutorials, and minor Python environment details (version, kernel name) were adjusted for consistency.

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[Copilot]

Pull Request Overview

This pull request introduces comprehensive tutorials for integrating Mistral AI, smolagents, and PydanticAI with Couchbase's vector search capabilities, supporting both Full-Text Search (FTS) and Global Secondary Index (GSI) implementations.

Key Changes:

• Added tutorials for smolagents and PydanticAI agent frameworks with Couchbase RAG systems using GSI and FTS indexes
• Introduced Mistral AI integration examples demonstrating embedding generation and vector search with both GSI and FTS
• Updated existing FTS tutorials with cross-references to new GSI alternatives

Reviewed Changes

Copilot reviewed 16 out of 21 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
smolagents/gsi/*	New GSI-based RAG tutorial using smolagents framework with performance comparisons
smolagents/fts/*	Updated FTS tutorial with reference to GSI alternative
pydantic_ai/gsi/*	New GSI-based RAG tutorial using PydanticAI with performance analysis
pydantic_ai/fts/*	Updated FTS tutorial with cross-reference to GSI version
mistralai/gsi/*	New Mistral AI integration tutorial using GSI indexes
mistralai/fts/*	Updated Mistral AI FTS tutorial with GSI alternative reference

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[gemini-code-assist]

Code Review

This pull request introduces new tutorials for integrating Mistral AI, PydanticAI, and Smolagents with Couchbase, leveraging both Full-Text Search (FTS) and Global Secondary Index (GSI) for vector search. The changes involve renaming existing FTS-based tutorial files and creating new GSI-based versions, along with corresponding .env.sample and frontmatter.md files. The new GSI tutorials provide detailed explanations and performance comparisons, which is a valuable addition. There are a few minor issues related to file formatting and duplicated tags that should be addressed for consistency and clarity.