Minor fixes for haystack and llamaindex #65
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
- azure - claudeAI - cohere - crewAI - crewAi-short-term-memory - huggingface - jinaAI - openrouter-deepseek
- Introduced new tutorials for Retrieval-Augmented Generation (RAG) using LlamaIndex with Couchbase's FTS and GSI. - Updated frontmatter and descriptions to reflect the integration of FTS and GSI capabilities. - Enhanced Jupyter Notebooks with detailed instructions and examples for both FTS and GSI implementations.
|
Caution Notebooks or Frontmatter Files Have Been Modified
21 Notebook Files Modified:
3 Frontmatter Files Modified:
|
Summary of ChangesHello @VirajAgarwal1, 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 primarily focuses on enhancing the accuracy and consistency of documentation within the Highlights
Using Gemini Code AssistThe full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips. Invoking Gemini You can request assistance from Gemini at any point by creating a comment using either
Customization To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a Limitations & Feedback Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here. You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension. Footnotes
|
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Code Review
This pull request introduces a series of minor fixes across multiple Jupyter notebooks and markdown files. The changes primarily involve updating URLs to reflect new file paths, correcting a typo in the llamaindex directory name, and improving the clarity of metadata in frontmatter.md files. Additionally, newlines have been added to some files for better formatting. My review focuses on improving the consistency and readability of the user-facing content. I've suggested capitalizing 'OpenAI' for proper branding in some short titles and using more descriptive link text in a few notebooks.
| "metadata": {}, | ||
| "source": [ | ||
| "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization.\n", | ||
| "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-fts)\n", |
There was a problem hiding this comment.
The link text this. is not very descriptive. Consider using more descriptive text like this tutorial to improve readability and accessibility.
Suggested change
| "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-fts)\n", | |
| "This tutorial shows how to implement a custom memory backend for CrewAI agents using Couchbase's high-performance GSI (Global Secondary Index) vector search. CrewAI agents can retain and recall information across interactions, making them more contextually aware and effective. We'll demonstrate measurable performance improvements with GSI optimization. Alternatively if you want to perform semantic search using the FTS, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-crewai-short-term-memory-couchbase-with-fts)\n", |
| "metadata": {}, | ||
| "source": [ | ||
| "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the FTS index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-using-fts)" | ||
| "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the FTS index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-with-fts/)" |
There was a problem hiding this comment.
The link text this. is not very descriptive. Consider using more descriptive text like this tutorial to improve readability and accessibility.
Suggested change
| "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the FTS index, please take a look at [this.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-with-fts/)" | |
| "In this guide, we will walk you through building a powerful semantic search engine using [Couchbase](https://www.couchbase.com) as the backend database and [CrewAI](https://github.com/crewAIInc/crewAI) for agent-based RAG operations. CrewAI allows us to create specialized agents that can work together to handle different aspects of the RAG workflow, from document retrieval to response generation. This tutorial uses Couchbase's **Global Secondary Index (GSI)** vector search capabilities, which offer high-performance vector search optimized for large-scale applications. This tutorial is designed to be beginner-friendly, with clear, step-by-step instructions that will equip you with the knowledge to create a fully functional semantic search system from scratch. Alternatively if you want to perform semantic search using the FTS index, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-crewai-couchbase-rag-with-fts/)" |
| "- OpenAI for embeddings and text generation\n", | ||
| "\n", | ||
| "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles." | ||
| "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-global-secondary-index)\n", |
There was a problem hiding this comment.
The link text this. is not very descriptive. Consider using more descriptive text like this tutorial to improve readability and accessibility.
Suggested change
| "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-global-secondary-index)\n", | |
| "The system allows users to ask questions about current events and get AI-generated answers based on the latest news articles. Alternatively if you want to perform semantic search using the GSI index, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-global-secondary-index)\n", |
| short_title: "RAG with Openai and Haystack" | ||
| path: "/tutorial-openai-haystack-rag-with-fts" | ||
| title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack using FTS Service" | ||
| short_title: "RAG with Openai and Haystack using FTS" |
There was a problem hiding this comment.
For consistency with the title field and proper branding, Openai should be capitalized to OpenAI.
Suggested change
| short_title: "RAG with Openai and Haystack using FTS" | |
| short_title: "RAG with OpenAI and Haystack using FTS" |
| "- OpenAI for embeddings and text generation\n", | ||
| "\n", | ||
| "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations.\n", | ||
| "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-fts)\n", |
There was a problem hiding this comment.
The link text this. is not very descriptive. Consider using more descriptive text like this tutorial to improve readability and accessibility.
Suggested change
| "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-fts)\n", | |
| "We leverage Couchbase's Global Secondary Index (GSI) vector search capabilities to create and manage vector indexes, enabling efficient semantic search capabilities. GSI provides high-performance vector search with support for both Hyperscale Vector Indexes and Composite Vector Indexes, designed to scale to billions of vectors with low memory footprint and optimized concurrent operations. Alternatively if you want to perform semantic search using the FTS, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-openai-haystack-rag-with-fts)\n", |
| short_title: "RAG with Openai and Haystack" | ||
| path: "/tutorial-openai-haystack-rag-with-global-secondary-index" | ||
| title: "Retrieval-Augmented Generation (RAG) with OpenAI and Haystack with GSI" | ||
| short_title: "RAG with Openai and Haystack with GSI" |
There was a problem hiding this comment.
For consistency with the title field and proper branding, Openai should be capitalized to OpenAI.
Suggested change
| short_title: "RAG with Openai and Haystack with GSI" | |
| short_title: "RAG with OpenAI and Haystack with GSI" |
| "metadata": {}, | ||
| "source": [ | ||
| "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system.\n", | ||
| "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-fts)\n", |
There was a problem hiding this comment.
The link text this. is not very descriptive. Consider using more descriptive text like this tutorial to improve readability and accessibility.
Suggested change
| "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system. Alternatively if you want to perform semantic search using the FTS, please take a look at [this.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-fts)\n", | |
| "This tutorial demonstrates building a high-performance semantic search engine using Couchbase's GSI (Global Secondary Index) vector search and Jina AI for embeddings and language models. We'll show measurable performance improvements with GSI optimization and implement a complete RAG (Retrieval-Augmented Generation) system. Alternatively if you want to perform semantic search using the FTS, please take a look at [this tutorial.](https://developer.couchbase.com/tutorial-jina-couchbase-rag-with-fts)\n", |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
No description provided.