DA 1153 Autovec Unstructured Data #57
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Caution Notebooks or Frontmatter Files Have Been Modified
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Summary of ChangesHello @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
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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.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
autovec_unstructured/frontmatter.md
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| title: Auto-Vectorization with Couchbase Capella AI Services and LangChain | ||
| short_title: Auto-Vectorization with Couchbase and LangChain | ||
| description: | ||
| - Learn how to use Couchbase Capella's AI Services auto-vectorization feature to automatically convert your data into vector embeddings. |
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convert your unstructured data into vector embeddings
| "query = \"How to setup java SDK?\"\n", | ||
| "results = vector_store.similarity_search(query, k=3)\n", | ||
| "\n", | ||
| "for rank, doc in enumerate(results, start=1):\n", |
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Can you print the score as well? You can use similarity_search_with_score
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For now, to not publish the tutorial as the service is not GA while merging, can you rename this file to something else like frontmatter.md that we can change when we publish?
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its already frontmatter.md
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I meant to name it __frontmatter__.md but it got parsed by markdown so that on merging the tutorial is not published before service is GAed.
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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:
README.mdexplaining prerequisites, installation steps, and a quick start guide for the auto-vectorization tutorial.frontmatter.mdto provide metadata and summary information for the tutorial, including title, description, tags, and estimated duration.Jupyter Notebook Tutorial:
autovec_unstructured.ipynb, a step-by-step notebook covering: