Azure OpenAI: GPT-5-Codex Availability?
Greetings everyone! I just wanted to see if there's any word as to when/if https://openai.com/index/introducing-upgrades-to-codex/ will make it's way to the AI Foundry. It was released on September 15th, 2025, but I have no idea how long Azure tends to follow behind OpenAI's releases. It doesn't really seem like there's any source of information to view whenever new models drop as to what Azure is going to do with them, if any. Any conversation around this would be helpful and appreciated, thanks!Multi-agent systems on Azure: identity, monitoring, and security guardrails
I wrote this piece because I know security concerns around AI agents are one of the main things holding many companies back from getting started. There is a lot of excitement around what agents can do on Azure, especially as multi-agent systems become more practical to build. But for many teams, the real hesitation starts when questions come up around trust, identity, permissions, monitoring, and what happens when something goes wrong. This PDF is my attempt to break that down in a practical way, from an Azure architect’s perspective: what multi-agent systems are, where they can fail, and which security layers matter most if you want to build them responsibly in Azure. It is based on hands-on architecture experience, Microsoft guidance, and recent security thinking around agentic systems. Read it on https://medium.com/@SCSA_MJ/multi-agent-systems-on-azure-identity-monitoring-and-security-guardrails-b8b7c82a0c57:
Using artificial intelligence to verify document compliance
Organizations of all domains and sizes are actively exploring ways to leverage Artificial intelligence and infuse it into it's business. There are several business challenges in which AI technologies have already made a significant impact to organization's bottom lines; one of these challenges is in the domain of legal document review, processing, and compliance. Any business that regularly reviews and processes legal documents (e.g. financial services, professional services, legal firms) are inundated with both open contracts and repositories of previously executed agreements, all of which have historically been managed by humans. Though humans may bring the required domain expertise their ability to review dense and lengthy legal agreements is manual, slow, and subject to human error. Efforts to modernize these operations began with documents being digitized (i.e. contracts either originating as a digital form or being uploaded via .pdf, post-execution). The next opportunity to innovate in the legal document domain now includes processing these digitized documents through AI services to extract key dates, phrases, or contract terms and create rules to identify outliers or point out terms/conditions for further review. As a note, humans are still involved in the document compliance process but further down the value chain where their abilities to reason and leverage their domain expertise is required. Whether it’s a vendor agreement that must include an arbitration clause, or a loan document requiring specific disclosures, ensuring the presence of these clauses may prove vital in reducing the legal exposure for an organization. With AI, we can now automate much of the required analysis and due diligence that takes place before a legal agreement is ever signed. From classical algorithms like cosine similarity to advanced reasoning using large language models (LLMs), Microsoft Azure offers powerful tools that enable AI solutions that can compare documents and validate their contents. Attached is a link to an Azure AI Document Compliance Proof of Concept-Toolkit. This repo will help rapidly build AI-powered document compliance proof-of-concepts. It leverages multiple similarity-analysis techniques to verify that legal, financial or other documents include all required clauses—and expose them via a simple REST API. Key Features of the Document Compliance PoC toolkit: Clause Verification - Detect and score the presence of required clauses in any document. Multi-Technique Similarity - Compare documents using TF-IDF, cosine similarity over embeddings, and more. Modular Architecture - Swap in your preferred NLP models or similarity algorithms with minimal changes. Extensible Examples - Sample configs and test documents to help you get started in minutes. Please note, this repo is in active development, the API and UI are not operationalAzure OpenAI Service - Features Overview and Key Concepts
Azure artificial intelligence services including a variety services related to language and language processing (speech recognition, speech formation, translations), text recognition, and image and character recognition. What is Azure OpenAI Service? Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series. Azure OpenAI Model Azure OpenAI provides access to many different models, grouped by family and capability. A model family typically associates models by their intended task. Azure OpenAI Service Model capabilities Each model family has a series of models that are further distinguished by capability. These capabilities are typically identified by names, and the alphabetical order of these names generally signifies the relative capability and cost of that model within a given model family. Azure OpenAI models fall into a few main families: GPT-4: A set of models that improve on GPT-3.5 and can understand as well as generate natural language and code. GPT-3.5: A set of models that improve on GPT-3 and can understand as well as generate natural language and code. Embeddings: A set of models that can convert text into numerical vector form to facilitate text similarity. DALL-E: A series of models that can generate original images from natural language. Key concepts: Prompts & Completion The completions endpoint is the core component of the API service. This API provides access to the model's text-in, text-out interface. Users simply need to provide an input prompt containing the English text command, and the model will generate a text completion. Token Azure OpenAI processes text by breaking it down into tokens. Tokens can be words or just chunks of characters. For example, the word “hamburger” gets broken up into the tokens “ham”, “bur” and “ger” The total number of tokens processed in a given request depends on the length of your input, output and request parameters. The quantity of tokens being processed will also affect your response latency and throughput for the models. Resource Azure OpenAI is a new product offering on Azure. You can get started with Azure OpenAI the same way as any other Azure product where you create a resource, or instance of the service, in your Azure Subscription. You can read more about Azure's resource management design. Deployment Once you create an Azure OpenAI Resource, you must deploy a model before you can start making API calls and generating text. This action can be done using the Deployment APIs. These APIs allow you to specify the model you wish to use. In-context learning The models used by Azure OpenAI use natural language instructions and examples provided during the generation call to identify the task being asked and skill required. When you use this approach, the first part of the prompt includes natural language instructions and/or examples of the specific task desired. The model then completes the task by predicting the most probable next piece of text. This technique is known as "in-context" learning. There are three main approaches for in-context learning: Few-shot: In this case, a user includes several examples in the call prompt that demonstrate the expected answer format and content. One-shot: This case is the same as the few-shot approach except only one example is provided. Zero-shot: In this case, no examples are provided to the model and only the task request is provided. Model The service provides users access to several different models. Each model provides a different capability and price point. GPT-4 models are the latest available models. Due to high demand access to this model series is currently only available by request. The GPT-3 base models are known as Davinci, Curie, Babbage, and Ada in decreasing order of capability and increasing order of speed. The Codex series of models is a descendant of GPT-3 and has been trained on both natural language and code to power natural language to code use cases. Use cases: GPT 3.5 Generating natural language for chatbots and virtual assistants with awareness of the previous history of chat Power chatbots that can handle customer inquiries, provide assistance, and converse but doesn’t have memory of conversations Automatically summarize lengthy texts Assist writers by suggesting synonyms, correcting grammar and spelling errors, and even generating entire sentences or paragraphs Help researchers by quickly processing large amounts of data and generating insights, summaries, and visualizations to aid in analysis Generate good quality code based on natural language Use cases: GPT 4.0 Generating and understanding natural language for customer service interactions, chatbots, and virtual assistants – doesn’t have memory of conversations Generating high-quality code for programming languages based on natural language input. Providing accurate translations between languages Improving text summarization and content generation Provides for multi-modal interaction (text and images) Substantial reduction in Hallucinations Consistency between different runs is high Multi-Modal Transformer Architecture Multi-modal models combine text and other types of input (such as graphics, images etc.) and are more task-specific. One multi-modal model in the collection has not been pre-trained in the same self-supervised manner. These models have performed state-of-the-art tasks, including visual question answering, image captioning, and speech recognition. Pricing Pricing will be based on the pay-as-you-go consumption model with a price per unit for each model, which is similar to other Azure Cognitive Services pricing models. Language models Image models Fine-tuned models Embedding models DALL-E Image Generation Editing an image Creating variations of image Embedding models The embedding is an information dense representation of the semantic meaning of a piece of text. Microsoft currently offers three families of Embeddings models for different functionalities: Similarity embedding: are good at capturing semantic similarity between two or more pieces of text. Text search embedding: help measure whether long documents are relevant to a short query. Code search embedding: are useful for embedding code snippets and embedding natural language search queries.CS in eDiscovery
Very curious to hear about plans to integrate CS into content search and Adv. eDiscovery--especially for setting up query-based eDiscovery holds early in a case (but also within review sets later). When it comes, it's going to be a quantum leap in document recall compared to standard keyword queries.
Searching pdf files using Natural Language
I'm new to all the Azure services and have been tasked with finding a solution to search a collection of pdf files using natural language queries. One use case would be to search resumés by saying (or entering) something like. "Find me all the candidates who have worked for XYZ company within the last 5 years". It looks like Azure Cognitive Search could be very useful for this. I've created a search service, and resource and data source etc and have successfully done a simple text search of my file. The resulting JSON looks like it did a good identifying all the various text concepts (names and places etc). I'm overwhelmed with all the various services that are available. If anyone can give a little newbie guidances as to which direction to go it would be greatly appreciated and would save me insane amounts of time running down rabbit holes looking for the correct services to use for my purposes. Thanks.
