The Future of AI: Horses for Courses - Task-Specific Models and Content Understanding
Task-specific models are designed to excel at specific use cases, offering highly specialized solutions that can be more efficient and cost-effective than general-purpose models. These models are optimized for particular tasks, resulting in faster performance and lower latency, and they often do not require prompt engineering or fine-tuning.
Unveiling the Next Generation of Table Structure Recognition
In an era where data is abundant, the ability to accurately and efficiently extract structured information like tables from diverse document types is critical. For instance, consider the complexities of a balance sheet with multiple types of assets or an invoice with various charges, both presented in a table format that can be challenging even for humans to interpret. Traditional parsing methods often struggle with the complexity and variability of real-world tables, leading to manual intervention and inefficient workflows. This is because these methods typically rely on rigid rules or predefined templates that fail when encountering variations in layout, formatting, or content, which are common in real-world documents. While the promise of Generative AI and Large Language Models (LLMs) in document understanding is vast, our research in table parsing has revealed a critical insight: for tasks requiring precision in data alignment, such as correctly associating data cells with their respective row and column headers, classical computer vision techniques currently offer superior performance. Generative AI models, despite their powerful contextual understanding, can sometimes exhibit inconsistencies and misalignments in tabular structures, leading to compromised data integrity (Figure 1). Therefore, Azure Document Intelligence (DI) and Content Understanding (CU) leverages an even more robust and proven computer vision algorithms to ensure the foundational accuracy and consistency that enterprises demand. Figure 1: Vision LLMs struggle to accurately recognize table structure, even in simple tables. Our current table recognizer excels at accurately identifying table structures, even those with complex layouts, rotations, or curved shapes. However, it does have its limitations. For example, it occasionally fails to properly delineate a table where the logical boundaries are not visible but must be inferred from the larger document context, making suboptimal inferences. Furthermore, its architectural design makes it challenging to accelerate on modern GPU platforms, impacting its runtime efficiency. Taking these limitations in considerations and building upon our existing foundation, we are introducing the latest advancement in our table structure recognizer. This new version significantly enhances both performance and accuracy, addressing key challenges in document processing. Precise Separation Line Placement We've made significant strides in the precision of separation line placement. While predicting these separation lines might seem deceptively simple, it comes with subtle yet significant challenges. In many real-world documents, these are logical separation lines, meaning they are not always visibly drawn on the page. Instead, their positions are often implied by an array of nuanced visual cues such as table headers/footers, dot filler text, background color changes, and even the spacing and alignment of content within the cells. Figure 2: Visual Comparison of separation line prediction of current and the new version We've developed a novel model architecture that can be trained end-to-end to directly tackle the above challenges. Recognizing the difficulty for humans to consistently label table separation lines, we've devised a training objective that combines Hungarian matching with an adaptive matching weight to correctly align predictions with ground truth even when the latter is noisy. Additionally, we've incorporated a loss function inspired by speech recognition to encourage the model to accurately predict the correct number of separation lines, further enhancing its performance. Our improved algorithms now respect visual cues more effectively, ensuring that separation lines are placed precisely where they belong. This leads to cleaner, more accurate table structures and ultimately, more reliable data extraction. Figure 2 shows the comparison between the current model and the new model on a few examples. Some quantitative results can be found in Table 1. TSR (current, in %) TSR-v2 (next-gen, in %) Segment Precision Recall F1-Score Precision Recall F1-score Latin 90.2 90.7 90.4 94.0 95.7 94.8 Chinese 96.1 95.3 95.7 97.3 96.8 97.0 Japanese 93.5 93.8 93.7 95.1 97.1 96.1 Korean 95.3 95.9 95.6 97.5 97.8 97.7 Table 1: Table structure accuracy measured by cell prediction precision and recall rates at IoU (intersection over union) threshold of 0.5. Tested on in-house test datasets covering four different scripts. A Data-Driven, GPU-Accelerated Design Another innovation in this release is its data-driven, fully GPU-accelerated design. This architectural shift delivers enhanced quality and significantly faster inference speeds, which is critical for processing large volumes of documents. The design carefully considers the trade-off between model capability and latency requirements, prioritizing an architecture that leverages the inherent parallelism of GPUs. This involves favoring highly parallelizable models over serial approaches to maximize GPU utilization. Furthermore, post-processing logic has been minimized to prevent it from becoming a bottleneck. This comprehensive approach has resulted in a drastic reduction in processing latency, from 250ms per image to less than 10ms. Fueling Robustness with Synthetic Data Achieving the high level of accuracy and robustness required for enterprise-grade table recognition demands vast quantities of high-quality training data. To meet this need efficiently, we've strategically incorporated synthetic data into our development pipeline. A few examples can be found in Figure 3. Figure 3: Synthesized tables Synthetic data offers significant advantages: it's cost-effective to generate and provides unparalleled control over the dataset. This allows us to rapidly synthesize diverse and specific table styles, including rare or challenging layouts, which would be difficult and expensive to collect from real-world documents. Crucially, synthetic data comes with perfectly consistent labels. Unlike human annotation, which can introduce variability, synthetic data ensures that our models learn from a flawlessly labeled ground truth, leading to more reliable and precise training outcomes. Summary This latest version of our table structure recognizer enhances critical document understanding capabilities. We've refined separation line placement to better respect visual cues and implied structures, supported by our synthetic data approach for consistent training. This enhancement, in turn, allows users to maintain the table structure as intended, reducing the need for manual post-processing to clean up the structured output. Additionally, a GPU-accelerated, data-driven design delivers both improved quality and faster performance, crucial for processing large document volumes.
Seamlessly Integrating Azure Document Intelligence with Azure API Management (APIM)
Note: For multi-region deployments (e.g., doc-intel-1 in Region 1 and doc-intel-2 in Region 2), use this approach. For resources within the same region, this configuration is redundant. Instead, consolidate traffic into a single resource and, if required, raise a support ticket to request a higher TPS limit. In today’s data-driven world, organizations are increasingly turning to AI for document understanding. Whether it's extracting invoices, contracts, ID cards, or complex forms, Azure Document Intelligence (formerly known as Form Recognizer) provides a robust, AI-powered solution for automated document processing. But what happens when you want to scale, secure, and load balance your document intelligence backend for high availability and enterprise-grade integration? Enter Azure API Management (APIM) — your gateway to efficient, scalable API orchestration. In this blog, we’ll explore how to integrate Azure Document Intelligence with APIM using a load-balanced architecture that works seamlessly with the Document Intelligence SDK — without rewriting your application logic. Azure Doc Intelligence SDKs simplify working with long-running document analysis operations — particularly asynchronous calls — by handling the polling and response parsing under the hood. Why Use API Management with Document Intelligence? While the SDK is great for client-side development, APIM adds essential capabilities for enterprise-scale deployments: 🔐 Security & authentication at the gateway level ⚖️ Load balancing across multiple backend instances 🔁 Circuit breakers, caching, and retries 📊 Monitoring and analytics 🔄 Response rewriting and dynamic routing By routing all SDK and API calls through APIM, you get full control over traffic flow, visibility into usage patterns, and the ability to scale horizontally with multiple Document Intelligence backends. SDK Behavior with Document Intelligence When using the Document Intelligence SDK (e.g., begin_analyze_document), it follows this two-step pattern: POST request to initiate document analysis Polling (GET) request to the operation-location URL until results are ready This is an asynchronous pattern where the SDK expects a polling URL in the response of the POST. If you’re not careful, this polling can bypass APIM — which defeats the purpose of using APIM in the first place. So what do we do? The Smart Rewrite Strategy We use APIM to intercept and rewrite the response from the POST call. POST Flow SDK sends a POST to: https://apim-host/analyze APIM routes the request to one of the backend services: https://doc-intel-backend-1/analyze Backend responds with: operation-location: https://doc-intel-backend-1/operations/123 APIM rewrites this header before returning to the client: operation-location: https://apim-host/operations/poller?backend=doc-intel-backend-1 Now, the SDK will automatically poll APIM, not the backend directly. GET (Polling) Flow Path to be set as /operations/123 in GET operation of APIM SDK polls: https://apim-host/operations/123?backend=doc-intel-backend-1 APIM extracts the query parameter backend=doc-intel-backend-1 APIM dynamically sets the backend URL for this request to: https://doc-intel-backend-1 It forwards the request to: https://doc-intel-backend-1/operations/123 Backend sends the status/result back to APIM → which APIM returns to the SDK. All of this happens transparently to the SDK. Sample policies //Outbound policies for POST - /documentintelligence/documentModels/prebuilt-read:analyze //--------------------------------------------------------------------------------------------------- <!-- - Policies are applied in the order they appear. - Position <base/> inside a section to inherit policies from the outer scope. - Comments within policies are not preserved. --> <!-- Add policies as children to the <inbound>, <outbound>, <backend>, and <on-error> elements --> <policies> <!-- Throttle, authorize, validate, cache, or transform the requests --> <inbound> <base /> </inbound> <!-- Control if and how the requests are forwarded to services --> <backend> <base /> </backend> <!-- Customize the responses --> <outbound> <base /> <set-header name="operation-location" exists-action="override"> <value>@{ // Original operation-location from backend var originalOpLoc = context.Response.Headers.GetValueOrDefault("operation-location", ""); // Encode original URL to pass as query parameter var encoded = System.Net.WebUtility.UrlEncode(originalOpLoc); // Construct APIM URL pointing to poller endpoint with backendUrl var apimUrl = $"https://tstmdapim.azure-api.net/document-intelligent/poller?backendUrl={encoded}"; return apimUrl; }</value> </set-header> </outbound> <!-- Handle exceptions and customize error responses --> <on-error> <base /> </on-error> </policies> //Inbound policies for Get (Note: path for get should be modified - /document-intelligent/poller //---------------------------------------------------------------------------------------------- <!-- - Policies are applied in the order they appear. - Position <base/> inside a section to inherit policies from the outer scope. - Comments within policies are not preserved. --> <!-- Add policies as children to the <inbound>, <outbound>, <backend>, and <on-error> elements --> <policies> <!-- Throttle, authorize, validate, cache, or transform the requests --> <inbound> <base /> <choose> <when condition="@(context.Request.Url.Query.ContainsKey("backendUrl"))"> <set-variable name="decodedUrl" value="@{ var backendUrlEncoded = context.Request.Url.Query.GetValueOrDefault("backendUrl", ""); // Make sure to decode the URL properly, potentially multiple times if needed var decoded = System.Net.WebUtility.UrlDecode(backendUrlEncoded); // Check if it's still encoded and decode again if necessary while (decoded.Contains("%")) { decoded = System.Net.WebUtility.UrlDecode(decoded); } return decoded; }" /> <!-- Log the decoded URL for debugging remove if not needed--> <trace source="Decoded URL">@((string)context.Variables["decodedUrl"])</trace> <send-request mode="new" response-variable-name="backendResponse" timeout="30" ignore-error="false"> <set-url>@((string)context.Variables["decodedUrl"])</set-url> <set-method>GET</set-method> <authentication-managed-identity resource="https://cognitiveservices.azure.com/" /> </send-request> <return-response response-variable-name="backendResponse" /> </when> <otherwise> <return-response> <set-status code="400" reason="Missing backendUrl query parameter" /> <set-body>{"error": "Missing backendUrl query parameter."}</set-body> </return-response> </otherwise> </choose> </inbound> <!-- Control if and how the requests are forwarded to services --> <backend> <base /> </backend> <!-- Customize the responses --> <outbound> <base /> </outbound> <!-- Handle exceptions and customize error responses --> <on-error> <base /> </on-error> </policies> Load Balancing in APIM You can configure multiple backend services in APIM and use built-in load-balancing policies to: Distribute POST requests across multiple Document Intelligence instances Use custom headers or variables to control backend selection Handle failure scenarios with circuit-breakers and retries Reference: Azure API Management backends – Microsoft Learn Sample: Using APIM Circuit Breaker & Load Balancing – Microsoft Community Hub Conclusion By integrating Azure Document Intelligence with Azure API Management native capabilities like Load balancing, rewrite header, authentication, rate limiting policies, organizations can transform their document processing workflows into scalable, secure, and efficient systems.The Future of AI: The paradigm shifts in Generative AI Operations
Dive into the transformative world of Generative AI Operations (GenAIOps) with Microsoft Azure. Discover how businesses are overcoming the challenges of deploying and scaling generative AI applications. Learn about the innovative tools and services Azure AI offers, and how they empower developers to create high-quality, scalable AI solutions. Explore the paradigm shift from MLOps to GenAIOps and see how continuous improvement practices ensure your AI applications remain cutting-edge. Join us on this journey to harness the full potential of generative AI and drive operational excellence.The Future of AI Is: Model Choice - From Structured Process To Seamless Platform
Language models are at the heart of generative AI applications. But in just over a year, we've moved from a handful of model providers to 1M+ community variants and more, resulting in the paradox of choice that ends in decision fatigue. In this blog post, we'll look at how developers can rethink their model selection strategy with a structured decision-making process, and a seamless development platform, to help them. This post is part of the Future of AI series jumpstarted by Marco Casalaina with his post on Exploring Multi-Agent AI Systems.The Future of AI: GraphRAG – A better way to query interlinked documents
All language models are trained on a huge corpus of data. They have some world knowledge and can answer a range of questions about different things. However, due to their probabilistic nature and incomplete world knowledge, especially when it comes to different niches and domains, it’s possible to receive incorrect answers. Retrieval Augmented Generation (RAG) helps augment world knowledge with enterprise-specific references, reducing inaccuracies and inconsistencies in the generated text. How RAG works and improves LLM output In RAG, the corpus of text relevant to your domain is converted into embeddings. Embeddings are created by translating documents into a mathematical form based on their traits, factors, and categories. The resulting vector representation is a long sequence of numbers. The distance between two vectors indicates how closely related they are. Similar objects are positioned closer together in a multi-dimensional embedding space, while less similar objects are positioned farther apart. As the term signifies, RAG consists of three steps – First the relevant vectors related to the query are retrieved (typically from a vector database), then the prompt which is sent to the LLM is augmented with this relevant contextual information, and finally the LLMs generates an answer based on this context and query. Using the RAG approach, developers can extend the factual grounding of the model, improve the relevance, accuracy and quality of the answers generated by the LLMs, and in many cases, refer back to the document snippets which were used in the generation of the answer. RAG has emerged as a powerful approach that combines the strengths of information retrieval and generative models. How GraphRAG builds upon RAG approach Though RAG improves on the LLMs generative capabilities, RAG does sometimes struggle to make sense of concepts and relationships between them when they are spread across documents. Also, as the complexity of data structures grows, there is a need for more advanced systems capable of handling interconnected, multi-faceted information. This is where GraphRAG comes into play. GraphRAG is an advanced version of RAG that utilizes graph-based retrieval mechanisms, enhancing the generation process by capturing richer, more contextual information. GraphRAG improves over vector RAG in the following ways. Enhanced Contextual Understanding with Graphs RAG traditionally uses a flat retrieval system (through embeddings in a vector DB), where it retrieves documents (and relevant document fragments) from a knowledge base based on their relevance to a query. The generative model then uses these retrieved documents to generate a response. While effective, this method can struggle when information is spread across multiple, interconnected documents. GraphRAG, on the other hand, uses graph-based retrieval, which allows it to connect pieces of information across a web of nodes. Each node represents an entity or a concept, and the edges represent the relationships between them. Examples of this could be relations like “is part of,” “is cousin of,” or “is made of.” This structured approach enables GraphRAG to extract and utilize more nuanced, multi-layered contextual information, resulting in more coherent and accurate responses. Improved Knowledge Integration In RAG, the generative model can sometimes produce fragmented or inconsistent outputs when the retrieved documents lack cohesion because of the way the chunking process and embedding vectors work. GraphRAG solves this by using graph databases that can model complex relationships. Graph Databases store both the entities represented by nodes and the relationships connecting them. They make it possible to traverse nodes using relationships between them. By understanding the connections between different pieces of information, GraphRAG can integrate knowledge from diverse sources and provide a more unified and accurate response. For example, if a question involves multiple entities and their interactions (e.g., "How does the supply chain impact product availability during a pandemic?"), GraphRAG can navigate through the interconnected data points, understand their dependencies, and generate a comprehensive answer. Another good example is compliance information for related documents and references to concepts in compliance. Let’s assume you are opening a restaurant and want to know different regulations needed to open a kitchen. Regulations can span fire safety, hygiene, food storage, ingredient sourcing, insurance, and labour guidelines. GraphRAG can work in such a scenario to collect all the references, traversing the relationships between them, giving users a coherent answer spanning a collection of documents. Efficiency and Scalability Another key metric, especially for large, interconnected datasets, is efficiency. RAG requires scanning through multiple documents for relevant content, which can be resource-intensive, especially with vast datasets. GraphRAG’s graph-based structure can efficiently traverse the data by focusing on relevant nodes and relationships, reducing computational overhead. Using GraphRAG intelligently, developers can use a combination of graph traversals of knowledge graphs and vector search to reduce computation and memory overheads. This s better, more intelligent indexing over traditional approaches. Moreover, graphs can be scaled horizontally, allowing for the expansion of knowledge bases without significantly increasing retrieval times. This makes GraphRAG suitable for enterprise-level applications where scalability and performance are critical. Also, when an organization spans many different vertical domains, this helps focus the search. So, you have the advantage both in terms of scalability and performance. GraphRAG Implementation Now that we know the benefits of GraphRAG, let’s implement an approach using GraphRAG. Setup For this demonstration we will use, we will use the GPT-4o as the LLM model in Azure AI Studio and text-embedding-3-small as the embedding model to generate embeddings on the platform. We will use the open source lancedb to store the embeddings and retrieve them for GraphRAG. There are many other models available via the Azure AI model catalog which has a variety of LLMs, SLMs, and embedding models. Let’s now create the deployments using Azure AI Studio for both these models. Next, let’s open a session on WSL to create a virtual env for Python. We will be using the Python package for GraphRAG for this demo. # Create a graphrag directory and change directory to try out this example $ mkdir graphrag $ cd graphrag/ # Install virtualenv package, create a virtual environment called venv_name # & change directory to it. We create a virtual environment so we can safely # install and experiment with package without changing the global Python # environment $ sudo apt-get install python3-virtualenv $ virtualenv -p python3 venv_name $ cd venv_name/ # Activate the virtual environment $ source bin/activate # Next, install the Python GraphRAG package in the virtual environment # created. This will download and install a number of packages and may # take a little time. Amongst other things, it will install the opensource # DataShaper data processing library that allows users to declaratively # express data pipelines, schemas, and related assets using well-defined # schemas $ pip install graphrag For the purposes of this demo, we will use the text of the Mahabharata. The Mahabharata is an epic Indian classical text that is divided into 18 chapters with a multitude of characters. It narrates the events that lead to the Kurukshetra war between two warring clans of cousins – Kauravas and Pandavas and the aftermath of the war. There are more than 100 human characters in the text who interact with each other and are also related to each other in some way. You can read about the epic text here and read about the many characters. We will use one of the translations of the epic text from project Gutenberg which is in the public domain. # Create the directory for input text and download the file using curl and # store it in the input directory. Though this is one document it consists of # many parts. The word count (634955) and line count (58868) in the # example below can be seen using wc commandline utility. $ mkdir -p ./mahabharata/input $ curl curl https://www.gutenberg.org/cache/epub/15474/pg15474.txt -o ./mahabharata/input/book.txt $ wc input/book.txt 58868 634955 3752942 input/book.txt # Next, we will initialize the environment for GraphRAG using the command: $ python -m graphrag.index --init --root ./mahabharata/ This will create a .env file and a settings.yaml file in the mahabharata directory. .env contains the environment variables required to run the GraphRAG pipeline. If the file is edited, a single environment variable will be defined, GRAPHRAG_API_KEY=<API_KEY>. This is the API key for the OpenAI API or Azure OpenAI Service endpoint. This can be replaced with an API key. API keys and other settings can be seen in the screenshot below (red highlight) in Azure AI Studio. In the llm section of settings.yaml, configure the following settings, llm: api_key: ${GRAPHRAG_API_KEY} type: azure_openai_chat # or openai_chat model: gpt-4o model_supports_json: true # recommended if this is available for your model. api_base: https://<your_instance_details>.openai.azure.com api_version: 2024-08-01-preview # please replace with your version deployment_name: gpt-4o In the embeddings section of settings.yaml , configure the following settings, llm: api_key: ${GRAPHRAG_API_KEY} type: azure_openai_embedding model: text-embedding-3-small api_base: https://<your_instance_details>.openai.azure.com api_version: 2024-08-01-preview # please replace with your version deployment_name: text-embedding-3-small Next, run the indexing process as a precursor to creating the embeddings. This will create a log to track the indexing process. This will start the chunking process, create the entities, figure out the relationship between different entities, generate graph relationships between the entities and finally after multiple processing create the final documents to be stored for retrieval in lanceDB. If the process is complete successfully, a message will appear which says, “All workflows completed successfully.” Note, there will be many warnings about deprecation which can be safely ignored - for now. $ python -m graphrag.index --root ./mahabharata/ Now that the embeddings have been created successfully, let's run a couple of queries to see if we can get answers about the characters and the relationships between them. $ python -m graphrag.query --root ./mahabharata --method global "Who is Duryodhana and How is he related to Arjuna?" creating llm client with {'api_key': 'REDACTED,len=32', 'type': "azure_openai_chat", 'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 1.0, 'n': 1, 'request_timeout': 180.0, 'api_base': 'https://graphragdemo-inst.openai.azure.com', 'api_version': '2024-08-01-preview', 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': 'gpt-4o', 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} SUCCESS: Global Search Response: ### Duryodhana: A Central Figure in the Mahabharata Duryodhana is a pivotal character in the Indian epic, the Mahabharata. He is the eldest son of Dhritarashtra and Gandhari, making him the leader of the Kauravas, a group of a hundred brothers [Data: Reports (408, 397, 400, 275, +more)]. Duryodhana is known for his deep-seated enmity towards the Pandavas, particularly Arjuna, and his significant role in the Kurukshetra War, where he stands as a central antagonist [Data: Reports (408, 397, 569, 216, +more)]. ### Relationship with Arjuna Duryodhana and Arjuna are first cousins. Duryodhana is the son of Dhritarashtra, while Arjuna is the son of Pandu. Dhritarashtra and Pandu are brothers, making Duryodhana and Arjuna part of the same Kuru dynasty [Data: Reports (255, 398, 285, 177, 202, +more)]. This familial connection places them in direct conflict over the throne of Hastinapura, leading to the epic battle of Kurukshetra [Data: Reports (399, 216, 406, 440, +more)]. ### Rivalry and Conflict The relationship between Duryodhana and Arjuna is marked by intense rivalry and conflict. Duryodhana's ambition to rule Hastinapura and his enmity towards the Pandavas drive much of the narrative in the Mahabharata. This enmity is particularly highlighted during the Kurukshetra War, where Duryodhana leads the Kauravas against Arjuna and the Pandavas [Data: Reports (408, 397, 273, 202, +more)]. Their rivalry is a central theme in the epic, culminating in numerous battles and deceitful plots, including the infamous game of dice that led to the Pandavas' exile [Data: Reports (398, 255, 400, 256, +more)]. ### Conclusion Duryodhana's character is defined by his leadership of the Kauravas and his antagonistic relationship with the Pandavas, especially Arjuna. Their familial ties and subsequent rivalry form the crux of the Mahabharata's narrative, leading to the monumental conflict of the Kurukshetra War [Data: Reports (408, 397, 569, 216, +more)]. Let’s try another query for another character called Karna. $ python -m graphrag.query --root ./mahabharata --method global "Who is Karna and what are his main relationships?" creating llm client with {'api_key': 'REDACTED,len=32', 'type': "azure_openai_chat", 'model': 'gpt-4o', 'max_tokens': 4000, 'temperature': 0.0, 'top_p': 1.0, 'n': 1, 'request_timeout': 180.0, 'api_base': 'https://graphragdemo-inst.openai.azure.com', 'api_version': '2024-08-01-preview', 'organization': None, 'proxy': None, 'cognitive_services_endpoint': None, 'deployment_name': 'gpt-4o', 'model_supports_json': True, 'tokens_per_minute': 0, 'requests_per_minute': 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': True, 'concurrent_requests': 25} SUCCESS: Global Search Response: ### Karna: A Key Figure in the Mahabharata Karna, also known as the Son of Radha, Vasusena, and Radheya, is a pivotal character in the Indian epic, the Mahabharata. He is renowned for his exceptional martial prowess, unwavering loyalty, and tragic life. Born to Kunti and the Sun God, Surya, Karna's divine heritage endowed him with extraordinary abilities, including natural armor and ear-rings that made him nearly invincible [Data: Reports (373, 198, 465, 502, 155, +more)]. ### Key Relationships #### **Duryodhana** Karna's most significant relationship is with Duryodhana, the leader of the Kauravas. Duryodhana befriends Karna and installs him as the king of Anga, solidifying their bond. This relationship is marked by deep loyalty and mutual support, with Karna vowing to slay Arjuna and supporting Duryodhana in various schemes against the Pandavas [Data: Reports (390, 397, 373, 198, 465, +more)]. Karna's loyalty to Duryodhana is a defining aspect of his character, influencing many of his actions and decisions throughout the epic [Data: Reports (447, 440, 391, 383, 302)]. #### **Kunti** Karna's relationship with his mother, Kunti, is complex and filled with emotional tension. Kunti reveals to Karna that he is her son, born before her marriage to Pandu, which adds a layer of tragedy to his character. Despite this revelation, Karna chooses to remain loyal to Duryodhana and fight against his half-brothers, the Pandavas [Data: Reports (373, 198, 465, 502, 155, +more)]. #### **Arjuna** Karna's rivalry with Arjuna, one of the Pandavas, is a central theme in the Mahabharata. Both warriors are considered equals in skill and valor, and their final confrontation in the Kurukshetra war is one of the epic's most significant events. Karna's enmity with Arjuna is fueled by his loyalty to Duryodhana and his desire to prove his worth [Data: Reports (373, 198, 465, 502, 155, +more)]. #### **Surya** Karna's divine father, Surya, plays a crucial role in his life, often providing guidance and warnings. For instance, Surya forewarns Karna about Indra's intentions to obtain his ear-rings and coat of mail, which are sources of his invincibility [Data: Reports (518, 547, 391, 358, 371)]. #### **Indra** Karna's interactions with Indra, the king of the gods, are also notable. Indra, disguised as a Brahmin, tricks Karna into giving up his ear-rings and armor, which were his sources of invincibility. In return, Indra grants Karna a powerful weapon, the Sakti, which he can use only once [Data: Reports (302, 394)]. ### Conclusion Karna's life is marked by his unwavering loyalty to Duryodhana, his complex relationships with his mother Kunti and his half-brother Arjuna, and his divine heritage. These relationships shape his actions and decisions, making him one of the most compelling and tragic figures in the Mahabharata [Data: Reports (390, 397, 373, 198, 465, +more)]. GraphRAG is able to piece together the relevant bits from different parts of the chapters to offer get us the relationship between the different characters with references (data reports or chunks). In some cases, it can do this over many different chunks of data over a large text. This is a huge improvement over the baseline performance of large language models and baseline vector RAG. In a recent Benchmark paper, it was found that knowledge graphs can improve the accuracy of answers up to 3x (54.2% vs 16.7%). GraphRAG can also be used in applications to make them more scalable and accurate, especially for domain-specific applications. Also, if you are working with many documents such as in data lake or running this is production, I would suggest using Azure AI search as the vector store. The GraphRAG accelerator, More information about GraphRAG and Azure AI Studio is available in the resources below: Resources: Learn more about GraphRAG Build with Azure AI Studio – https://ai.azure.com Review the Azure AI Studio documentation - https://learn.microsoft.com/en-us/azure/ai-studio/ Access Azure AI Studio Learn modules - https://learn.microsoft.com/en-us/training/modules/introduction-to-azure-ai-studio/ Access the Fundamental of Generative AI learning course- https://learn.microsoft.com/en-us/training/modules/fundamentals-generative-ai/ Access the GraphRAG GitHub repository - - https://github.com/microsoft/graphrag/ Use the GraphRAG Solution accelerator - https://github.com/Azure-Samples/graphrag-acceleratorThe Future of AI: Generative AI for...Time Series Forecasting?!? A Look at Nixtla TimeGEN-1
Have you ever wondered how meteorologists predict tomorrow's weather, or how businesses anticipate future sales? These predictions rely on analyzing patterns over time, known as time series forecasts. With advancements in artificial intelligence, forecasting the future has become more accurate and accessible than ever before. Understanding Time Series Forecasting Time series data is a collection of observations recorded at specific time intervals. Examples include daily temperatures, monthly sales figures, or hourly website visitors. By examining this data, we can identify trends and patterns that help us predict future events. Forecasting involves using mathematical models to analyze past data and make informed guesses about what comes next. Traditional Forecasting Methods: ARIMA and Prophet Two of the most popular traditional methods for doing time series forecasting are ARIMA and Prophet. ARIMA, which stands for AutoRegressive Integrated Moving Average, predicts future values based on past data. It involves making the data stationary by removing trends and seasonal effects, then applying statistical techniques. However, ARIMA requires manual setup of parameters like trends and seasonality, which can be complex and time-consuming. It's best suited for simple, one-variable data with minimal seasonal changes. Prophet, a forecasting tool developed by Facebook (now Meta), automatically detects trends, seasonality, and holiday effects in the data, making it more user-friendly than ARIMA. Prophet works well with data that has strong seasonal patterns and doesn't need as much historical data. However, it may struggle with more complex patterns or irregular time intervals. Introducing Nixtla TimeGEN-1: A New Era in Forecasting Nixtla TimeGEN-1 represents a significant advancement in time series forecasting. Unlike traditional models, TimeGEN-1 is a generative pretrained transformer model, much like the GPT models, but rather than working with language, it's specifically designed for time series data. It has been trained on over 100 billion data points from various fields such as finance, weather, energy, and web data. This extensive training allows TimeGEN-1 to handle a wide range of data types and patterns. One of the standout features of TimeGEN-1 is its ability to perform zero-shot inference. This means it can make accurate predictions on new datasets without needing additional training. It can also be fine-tuned on specific datasets for even better accuracy. TimeGEN-1 handles irregular data effortlessly, working with missing timestamps or uneven intervals. Importantly, it doesn't require users to manually specify trends or seasonal components, making it accessible even to those without deep technical expertise. The transformer architecture of TimeGEN-1 enables it to capture complex patterns in data that traditional models might miss. It brings the power of advanced machine learning to time series forecasting – and related tasks like anomaly detection – making the process more efficient and accurate. Real-World Comparison: TimeGEN-1 vs. ARIMA and Prophet To test these claims, I decided to run an experiment to compare the performance of TimeGEN-1 with ARIMA and Prophet. I used a retail dataset where the actual future values were known, which in data science parlance is known as a "backtest." In my dataset, ARIMA struggled to predict future values accurately due to its limitations with complex patterns. Prophet performed better than ARIMA by automatically detecting some patterns, but its predictions still didn't quite hit the mark. TimeGEN-1, however, delivered predictions that closely matched the actual data, significantly outperforming both ARIMA and Prophet. The accuracy of these models was measured using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). TimeGEN-1 had the lowest MAE and RMSE, indicating higher accuracy. This experiment highlights how TimeGEN-1 can provide more precise forecasts, even when compared to established methods. The Team Behind TimeGEN-1: Nixtla Nixtla is a company dedicated to making advanced predictive insights accessible to everyone. It was founded by a team of experts passionate about simplifying forecasting processes while maintaining high accuracy and efficiency. The team includes Max Mergenthaler Canseco, CEO; Azul Garza, CTO; and Cristian Challu, CSO, experts in the forecasting field with extensive experience in machine learning and software engineering.< Their collective goal is to simplify the forecasting process, making powerful tools available to users with varying levels of technical expertise. By integrating TimeGEN-1 into easy-to-use APIs, they ensure that businesses and individuals can leverage advanced forecasting without needing deep machine learning knowledge. The Azure AI Model Catalog TimeGEN-1 is one of the 1700+ models that are now available in the Azure AI model catalog. The model catalog is continuously updated with the latest advancements, like TimeGEN-1, ensuring that users have access to the most cutting-edge tools. Its user-friendly interface makes it easy to navigate and deploy models, and Azure's cloud infrastructure provides the scalability needed to run these models, allowing users to handle large datasets and complex computations efficiently. In the following video, I show how Data Scientists and Developers can build time series forecasting models using data stored in Microsoft Fabric paired with the Nixtla TimeGEN-1 model. The introduction of Nixtla TimeGEN-1 marks a transformative moment in time series forecasting. Whether you're a data scientist, a business owner, or a student interested in AI, TimeGEN-1 opens up new possibilities for understanding and predicting future trends. Explore TimeGEN-1 and thousands of other models through the Azure AI model catalog today!The Future of AI: Power Your Agents with Azure Logic Apps
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