Last week I was teaching a class on snapshot isolation and we discussed the overheads of snapshot isolation. There are three main overheads that you need to be aware of when you enable snapshot isolation read-committed-snapshot on a database. First, each UPDATE/DELETE operation generates a row version. Note, that the row version is not generated when you INSERT a new row but a 14 byte overhead (described below) is added to the inserted row . There is an exceptions to this, but I will skip that discussion here. Second, row versions are created and kept in tempdb that leads to increase space usage and IOs there. Third, the queries run under snapshot isolation or read-committed-snapshot need to traverse the row version chain which may lead to as many logical IOs and potentially as many 'random' physical IOs because row versions for a given row are not necessarily on the same or even contiguous physical page(s). It is interesting to note that if we did not traverse the row versions, the query would have blocked otherwise. So basically, at some expense of IO/CPU, the SQL Server can return the transactional consistent results without incurring blocking. We recommend enabling row versioning based isolation levels only when your application is incurring reader/writer blocking.
One question that came up was on the overhead of row versioning on the data or index row. When you enable snapshot isolation or read-committed-snapshot option on the database, the SQL Server starts adding a 14 byte overhead to each row to keep the following information:
XTS (transaction sequence number). It takes 6 bytes. This is used for marking the XSN that did the DML operation on the row
RID (row identifier) that points to the versioned row. It takes 8 bytes.
But this overhead is only added when an existing row is actually modified or if the database was already enabled for either of these options. Let me give a very simple example.
create table foo..t2_snapshot (c1 int)
go
insert into foo..t2_snapshot values (1)
go
-- show the max rowsize in bytes
select max_record_size_in_bytes
from sys.dm_db_index_physical_stats (db_id('foo'),
object_id('foo.t2_snapshot'),
null, null, 'DETAILED')
This will show a max row size of 11 bytes (note, the 7 bytes are the metadata overhead within the row and other 4 bytes store the integer value). Now, let me enable snapshot isolation on the database foo. Note, this is a metadata operation and it will NOT change the existing rows in the tables to add extra 14 bytes.
alter database foo set allow_snapshot_isolation ON
go
-- you can verify the datbaase state using the following
snapshot_isolation_state from sys.databases where name='foo'
go
Now I run the query
select max_record_size_in_bytes
from sys.dm_db_index_physical_stats (db_id('foo'),
object_id('foo.t2_snapshot'),
null, null, 'DETAILED')
You will notice that the row length still remains 11 bytes. No extra 14 bytes overhead even though the database foo has been enabled for snapshot isolation. Let us now update the data row as follows
update foo..t2_snapshot set c1 = 2
After the update, you will notice the length of the row has become 25 bytes. In other words, there is a 14 byte overhead. Also, the new rows that you insert will have a length of 25 bytes (including row versioning overhead of 14 bytes).There are two interesting observations to be made. First, an update of a fixed length column may lead to page splits because the length of the row has increased. This only happens the first time the row is updated. Second, the increased row length will cause the table size to grow. A typical row size in production is around 300 bytes. So this means that snapshot isolation or read-committed-snapshot will cause 5% growth in the size of the table (in the worst case when all the rows were updated). One point to keep in mind is that new rows inserted after SI/RCSI, will have this 14 byte overhead even though there is no row version to point to.,
Now, if I disable the snapshot isolation and update the same row again, the extra 14 bytes will go away. This is because row versioning has been disabled. I can do the same by rebuilding the index
Thanks
Sunil
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Enriching Data in Azure AI Search Indexing Pipeline Using Azure AI LLMs/SLMs for RAG Apps","id":"message:4358494","revisionNum":4,"repliesCount":0,"author":{"__ref":"User:user:271474"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"conversation":{"__ref":"Conversation:conversation:4358494"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":false},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:4358494"},"teaser":"
Enhance your RAG applications with enriched data context using Azure AI's LLMs/SLMs. This demo shows how to integrate custom skills in the Azure AI Search indexing pipeline, adapting prompts to improve response accuracy. Leverage Azure AI Search's indexing capabilities for efficient data transformations, with practical applications like image captioning and document summarization, tailored for more precise and relevant responses.
","body":"
Retrieval Augmented Generation (RAG) applications often require data preparation processes, including extracting text and images, chunking, and vectorizing data, to enhance chatbot interactions with more insightful responses. Azure AI Search's AI enrichment feature enables these capabilities by allowing advanced data transformations directly within the indexing pipeline. This process includes not only natively supported operations such as optical character recognition (OCR) and integrated vectorization but also supports the addition of personalized logic tailored to specific business needs via custom skills. Organizations can leverage Azure AI Foundry's Large Language Models (LLMs) and Small Language Models (SLMs) to enrich RAG-required data chunks with detailed descriptions, summaries, and metadata. The demo discussed in this blog post showcases an end-to-end implementation of incorporating LLMs/SLMs custom prompts during the data preparation phase to enhance these data chunks, providing a practical guide to adjusting your Azure AI Search index content to meet your RAG apps business needs.
\n
Why Integrate LLMs and SLMs into Azure AI Search Indexing Pipeline?
\n
Adaptable Data Transformation and Customization: Advanced AI models, such as Azure OpenAI's GPT-4o and Microsoft's Phi35-vision, enable businesses to transform data in adaptable ways tailored to specific scenarios. The ability to customize prompts and leverage the Azure AI Model Catalog empowers organizations to tune data transformation processes to meet unique needs. This flexibility supports diverse applications, from image captioning to document summarization, with effectiveness varying by scenario.
\n
Flexible Model Selection: Azure AI Foundry provides a range of models to choose from, allowing users to select the most suitable options based on their data type and transformation requirements.
\n
Key Scenarios and Use Cases
\n
Here are some scenarios where a custom skill calling an LLM/SLM can help you.
\n\n
Image Captioning: Automatically generating descriptive captions for images enhances searchability, particularly useful in industries where technical diagrams need translation into text.
\n
Document Summarization: Summarizing lengthy documents saves time and effort. Concise summaries enable users to quickly grasp key elements, though effectiveness may differ based on document complexity.
\n
Entity Extraction: Extracting key entities from documents augments and enriches search indexes, essential for contexts where metadata classification is crucial.
\n
Metadata Generation: Using models like Phi-3 or Azure OpenAI GPT-4o to generate metadata tags enhances document classification and retrieval.
\n
Image Verbalization: Converting images into descriptive text provides an additional layer of information, helping to improve accessibility and context understanding.
\n\n
\n
Understanding Custom Skills in Azure AI Search
\n
Azure AI Search skills are powerful tools, components of AI enrichment, integrated into the Azure AI Search indexing pipeline, designed to perform various data transformations such as text analysis, image processing, and more. These skills are defined through a skillset, which can include both natively supported skills and custom skills. Custom skills in Azure AI Search allow developers to define targeted tasks for precise data enrichment using their own logic. By creating a skillset, users can utilize natively supported skills and also leverage Azure Functions to deploy custom logic.
\n
One of the key advantages of custom skills is that they enable developers to run their own custom code as part of the ingestion pipeline, while still benefiting from the robust features of the Azure AI Search built-in indexers. This includes support for a variety of data sources, incremental change tracking, and scheduling. This integration allows developers to focus on specific enrichment tasks without having to manage the entire indexing process manually. By using custom skills, developers can efficiently address specific data processing needs and scenarios, leveraging the Azure AI Search platform to create tailored solutions. This approach is more efficient than building a custom indexing pipeline from scratch, as it takes advantage of existing infrastructure and capabilities provided by Azure AI Search, ensuring a seamless and scalable data indexing process.
\nFigure 1 - Demo workflow diagram\n
How to Get Started
\n
Get ready to explore the Azure AI Search with custom skills enabling LLMs/SLMs demo. It demonstrates practical scenarios such as image captioning, document summarization, and entity extraction, offering flexibility through the Azure AI Model Catalog LLMs/SLMs.
\n
This demo also showcases the integration of Azure's built-in indexers, chunking and vectorization to enhance search capabilities. Follow the step-by-step process outlined in the GitHub repo demo sample. Here's a quick overview of the setup process and the features leveraged.
\n
Key Steps
\n\n
Environment Setup: Clone the repository and install dependencies using the provided requirements file.
Configuration: Set environment variables to connect your setup with Azure services. This includes configuring endpoints and API keys for the models you'll use.
Retrieval Augmented Generation (RAG) applications often require data preparation processes, including extracting text and images, chunking, and vectorizing data, to enhance chatbot interactions with more insightful responses. Azure AI Search's AI enrichment feature enables these capabilities by allowing advanced data transformations directly within the indexing pipeline. This process includes not only natively supported operations such as optical character recognition (OCR) and integrated vectorization but also supports the addition of personalized logic tailored to specific business needs via custom skills. Organizations can leverage Azure AI Foundry's Large Language Models (LLMs) and Small Language Models (SLMs) to enrich RAG-required data chunks with detailed descriptions, summaries, and metadata. The demo discussed in this blog post showcases an end-to-end implementation of incorporating LLMs/SLMs custom prompts during the data preparation phase to enhance these data chunks, providing a practical guide to adjusting your Azure AI Search index content to meet your RAG apps business needs.
\n
Why Integrate LLMs and SLMs into Azure AI Search Indexing Pipeline?
\n
Adaptable Data Transformation and Customization: Advanced AI models, such as Azure OpenAI's GPT-4o and Microsoft's Phi35-vision, enable businesses to transform data in adaptable ways tailored to specific scenarios. The ability to customize prompts and leverage the Azure AI Model Catalog empowers organizations to tune data transformation processes to meet unique needs. This flexibility supports diverse applications, from image captioning to document summarization, with effectiveness varying by scenario.
\n
Flexible Model Selection: Azure AI Foundry provides a range of models to choose from, allowing users to select the most suitable options based on their data type and transformation requirements.
\n
Key Scenarios and Use Cases
\n
Here are some scenarios where a custom skill calling an LLM/SLM can help you.
\n\n
Image Captioning: Automatically generating descriptive captions for images enhances searchability, particularly useful in industries where technical diagrams need translation into text.
\n
Document Summarization: Summarizing lengthy documents saves time and effort. Concise summaries enable users to quickly grasp key elements, though effectiveness may differ based on document complexity.
\n
Entity Extraction: Extracting key entities from documents augments and enriches search indexes, essential for contexts where metadata classification is crucial.
\n
Metadata Generation: Using models like Phi-3 or Azure OpenAI GPT-4o to generate metadata tags enhances document classification and retrieval.
\n
Image Verbalization: Converting images into descriptive text provides an additional layer of information, helping to improve accessibility and context understanding.
\n\n
\n
Understanding Custom Skills in Azure AI Search
\n
Azure AI Search skills are powerful tools, components of AI enrichment, integrated into the Azure AI Search indexing pipeline, designed to perform various data transformations such as text analysis, image processing, and more. These skills are defined through a skillset, which can include both natively supported skills and custom skills. Custom skills in Azure AI Search allow developers to define targeted tasks for precise data enrichment using their own logic. By creating a skillset, users can utilize natively supported skills and also leverage Azure Functions to deploy custom logic.
\n
One of the key advantages of custom skills is that they enable developers to run their own custom code as part of the ingestion pipeline, while still benefiting from the robust features of the Azure AI Search built-in indexers. This includes support for a variety of data sources, incremental change tracking, and scheduling. This integration allows developers to focus on specific enrichment tasks without having to manage the entire indexing process manually. By using custom skills, developers can efficiently address specific data processing needs and scenarios, leveraging the Azure AI Search platform to create tailored solutions. This approach is more efficient than building a custom indexing pipeline from scratch, as it takes advantage of existing infrastructure and capabilities provided by Azure AI Search, ensuring a seamless and scalable data indexing process.
\nFigure 1 - Demo workflow diagram\n
How to Get Started
\n
Get ready to explore the Azure AI Search with custom skills enabling LLMs/SLMs demo. It demonstrates practical scenarios such as image captioning, document summarization, and entity extraction, offering flexibility through the Azure AI Model Catalog LLMs/SLMs.
\n
This demo also showcases the integration of Azure's built-in indexers, chunking and vectorization to enhance search capabilities. Follow the step-by-step process outlined in the GitHub repo demo sample. Here's a quick overview of the setup process and the features leveraged.
\n
Key Steps
\n\n
Environment Setup: Clone the repository and install dependencies using the provided requirements file.
Configuration: Set environment variables to connect your setup with Azure services. This includes configuring endpoints and API keys for the models you'll use.
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Enhance your RAG applications with enriched data context using Azure AI's LLMs/SLMs. This demo shows how to integrate custom skills in the Azure AI Search indexing pipeline, adapting prompts to improve response accuracy. Leverage Azure AI Search's indexing capabilities for efficient data transformations, with practical applications like image captioning and document summarization, tailored for more precise and relevant responses.
","introduction":"Enrich your RAG data with AI-generated context using Azure AI Search and Azure AI Foundry SLMs and LLMs. This demo shows how to integrate custom skills into your Azure AI Search indexing pipeline and adapt prompts to improve response accuracy. Leverage Azure AI Search's indexing capabilities for efficient data transformations, with practical applications like image captioning and document summarization, tailored for precise and relevant 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