Migrate awsbedrock to new search terminology#91
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- Renamed fts/ to search_based/ - Renamed gsi/ to query_based/ - Updated notebook terminology (FTS -> Search Vector Index, GSI -> Hyperscale and Composite Vector Index) - Updated frontmatter (path, title, tags, alt_paths) - Updated cross-reference links between tutorials
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Caution Notebooks or Frontmatter Files Have Been Modified
3 Notebook Files Modified:
3 Frontmatter Files Modified:
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Summary of ChangesHello @teetangh, 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 focuses on a comprehensive terminology and structural migration for the AWS Bedrock tutorials. The changes ensure that the documentation and example notebooks use the latest search-related terminology, enhancing clarity and consistency across the content. This involves renaming directories, updating content within Jupyter notebooks, and adjusting metadata to reflect the new naming scheme for vector indexes. Highlights
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Code Review
This pull request successfully migrates the awsbedrock tutorials to the new search terminology, including renaming directories from fts to search_based and gsi to query_based. The content within the Jupyter notebooks and frontmatter files has been updated to reflect these changes, such as replacing 'FTS' with 'Search Vector Index' and 'GSI' with 'Hyperscale and Composite Vector Index'.
My review identified a few minor instances in the query_based notebook where the old 'GSI' terminology was missed. I've added comments to address these for consistency. Overall, this is a solid refactoring that improves clarity and aligns with the updated product terminology.
…azon Bedrock - Changed the short title in frontmatter for clarity. - Updated markdown cells in Jupyter notebooks to improve formatting and consistency. - Enhanced introductory sections to better describe the tutorial's purpose and usage. - Ensured all references to Couchbase and Amazon Bedrock are accurate and up-to-date.
…le throughout awsbedrock
…torial - Updated markdown cells in the Jupyter notebook for improved readability and consistency. - Reformatted introductory sections to provide clearer guidance on building a semantic search engine. - Ensured all content is beginner-friendly and accurately describes the tutorial's objectives.
…orial - Enhanced formatting of markdown cells in the Jupyter notebook for better readability. - Added line breaks for improved visual separation of content. - Ensured consistency in the presentation of tutorial instructions and prerequisites.
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Code Review
This pull request effectively migrates the AWS Bedrock tutorials to the new search terminology, renaming fts to search_based and gsi to query_based. The notebooks have been updated to use newer models (Nova Pro), updated dependencies, and improved terminology throughout, which enhances clarity and correctness. The frontmatter for the tutorials has also been updated accordingly. However, a potential N1QL injection vulnerability was identified in the setup_collection function of the new query_based notebook, where user-supplied identifiers are concatenated into queries without proper escaping. This could allow an attacker to execute arbitrary N1QL commands if the notebook is used with untrusted inputs. Additionally, the setup_collection function also has broad exception handling that could silently swallow important errors, making debugging difficult.
…tamps - Revised markdown cells for improved clarity and structure in the Jupyter notebooks. - Updated timestamps in log outputs to reflect recent execution dates. - Enhanced descriptions of embedding creation and caching mechanisms for better understanding of the tutorial's objectives.
Co-authored-by: Viraj Agarwal <91372648+VirajAgarwal1@users.noreply.github.com>
…e model and index type - Revised short titles in frontmatter for clarity. - Updated descriptions to replace references to the Claude language model with Nova Pro. - Enhanced markdown content in the Jupyter notebook for improved readability and structure.
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Summary
Changes