From a3907b71147550e760600999e46b8bc8a210d856 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Fri, 30 Aug 2024 10:09:14 -0700 Subject: [PATCH 01/63] updating for AI studio --- .../openai/assistants-quickstart.md | 8 +------- .../openai/includes/assistants-ai-studio.md | 3 +++ .../media/quickstarts/assistant-session.png | Bin 308908 -> 0 bytes .../quickstarts/assistants-playground.png | Bin 221684 -> 0 bytes .../media/quickstarts/assistants-studio.png | Bin 91402 -> 0 bytes .../media/quickstarts/chat-playground.png | Bin 267003 -> 295014 bytes .../use-your-data/bring-your-data-card.png | Bin 134385 -> 359464 bytes 7 files changed, 4 insertions(+), 7 deletions(-) delete mode 100644 articles/ai-services/openai/media/quickstarts/assistant-session.png delete mode 100644 articles/ai-services/openai/media/quickstarts/assistants-playground.png delete mode 100644 articles/ai-services/openai/media/quickstarts/assistants-studio.png diff --git a/articles/ai-services/openai/assistants-quickstart.md b/articles/ai-services/openai/assistants-quickstart.md index bc47d4b69ee..599b2928df1 100644 --- a/articles/ai-services/openai/assistants-quickstart.md +++ b/articles/ai-services/openai/assistants-quickstart.md @@ -18,15 +18,9 @@ recommendations: false Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to your needs through custom instructions and augmented by advanced tools like code interpreter, and custom functions. -::: zone pivot="programming-language-studio" - -[!INCLUDE [Azure OpenAI Studio quickstart](includes/assistants-studio.md)] - -::: zone-end - ::: zone pivot="programming-language-ai-studio" -[!INCLUDE [AI Studio (Preview)](includes/assistants-ai-studio.md)] +[!INCLUDE [AI Studio](includes/assistants-ai-studio.md)] ::: zone-end diff --git a/articles/ai-services/openai/includes/assistants-ai-studio.md b/articles/ai-services/openai/includes/assistants-ai-studio.md index af766e2ef19..9e08c8b912c 100644 --- a/articles/ai-services/openai/includes/assistants-ai-studio.md +++ b/articles/ai-services/openai/includes/assistants-ai-studio.md @@ -22,6 +22,9 @@ author: mrbullwinkle ## Go to the Azure AI Studio (Preview) +> [!NOTE] +> While you can use [Azure OpenAI Studio](https://oai.azure.com/), we recommend using the steps here to use [Azure AI Studio](https://ai.azure.com). AI Studio lets you use Assistants v2 which provides several upgrades such as the [file search](../how-to/file-search.md) tool which is faster and supports more files. + 1. Sign in to [Azure AI Studio](https://ai.azure.com). 1. Go to your project or [create a new project](../../../ai-studio//how-to/create-projects.md) in Azure AI Studio. 1. 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Completions --- articles/ai-services/openai/includes/javascript.md | 13 +++++++++++++ articles/ai-services/openai/quickstart.md | 2 +- 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index c8a423f9171..fc3b8e9a9bd 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -18,13 +18,26 @@ ms.date: 05/20/2024 ## Prerequisites +## [**TypeScript**](#tab/typescript) + +- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) +- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) +- An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). + +> [!div class="nextstepaction"] +> [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&&Product=gpt&Page=quickstart&Section=Prerequisites) + +## [**JavaScript**](#tab/javascript) + - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) +- [TypeScript](https://www.typescriptlang.org/download/) - An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&&Product=gpt&Page=quickstart&Section=Prerequisites) + ## Set up [!INCLUDE [get-key-endpoint](get-key-endpoint.md)] diff --git a/articles/ai-services/openai/quickstart.md b/articles/ai-services/openai/quickstart.md index 9c647ff71ad..fb3345f542d 100644 --- a/articles/ai-services/openai/quickstart.md +++ b/articles/ai-services/openai/quickstart.md @@ -5,7 +5,7 @@ description: Walkthrough on how to get started with Azure OpenAI and make your f #services: cognitive-services manager: nitinme ms.service: azure-ai-openai -ms.custom: devx-track-dotnet, devx-track-python, devx-track-extended-java, devx-track-js, devx-track-go +ms.custom: devx-track-dotnet, devx-track-python, devx-track-extended-java, devx-track-js, devx-track-go, devx-track-ts ms.topic: quickstart author: mrbullwinkle ms.author: mbullwin From aa97b4802e4a0f4b56fb2c82da05dc9fee6d6ddb Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Tue, 3 Sep 2024 14:06:32 -0700 Subject: [PATCH 03/63] ending of tab --- articles/ai-services/openai/includes/javascript.md | 1 + 1 file changed, 1 insertion(+) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index fc3b8e9a9bd..8d873bcff8d 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -37,6 +37,7 @@ ms.date: 05/20/2024 > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&&Product=gpt&Page=quickstart&Section=Prerequisites) +--- ## Set up From f209ebc019d434ad55df4263e70c87dee03a59ac Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Tue, 3 Sep 2024 14:11:22 -0700 Subject: [PATCH 04/63] tabs all the way --- articles/ai-services/openai/includes/javascript.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index 8d873bcff8d..337c17842d4 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -64,6 +64,10 @@ Your app's _package.json_ file will be updated with the dependencies. Open a command prompt where you created the new project, and create a new file named Completion.js. Copy the following code into the Completion.js file. +## [**TypeScript**](#tab/typescript) + +## [**JavaScript**](#tab/javascript) + ```javascript const { AzureOpenAI } = require("openai"); @@ -104,6 +108,8 @@ Run the script with the following command: node.exe Completion.js ``` +--- + ## Output ```output @@ -117,6 +123,11 @@ Microsoft was founded on April 4, 1975. > [!IMPORTANT] > In the previous example we are demonstrating key-based authentication. Once you have tested with key-based authentication successfully, we recommend using the more secure [Microsoft Entra ID](/entra/fundamentals/whatis) for authentication which is demonstrated in the next code sample. Getting started with [Microsoft Entra ID] will require some additional [prerequisites](https://www.npmjs.com/package/@azure/identity). +## [**TypeScript**](#tab/typescript) + + +## [**JavaScript**](#tab/javascript) + ```javascript const { AzureOpenAI } = require("openai"); const { DefaultAzureCredential, getBearerTokenProvider } = require("@azure/identity"); From 838a571aaa1dc9f27aa89333353d86788a6480fc Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Wed, 4 Sep 2024 09:02:29 -0700 Subject: [PATCH 05/63] OpenAI Completions - typescript --- .../ai-services/openai/includes/javascript.md | 126 +++++++++++++++++- 1 file changed, 125 insertions(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index 337c17842d4..592e510270f 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -22,6 +22,7 @@ ms.date: 05/20/2024 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) +- [TypeScript](https://www.typescriptlang.org/download/) - An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). > [!div class="nextstepaction"] @@ -31,7 +32,6 @@ ms.date: 05/20/2024 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) -- [TypeScript](https://www.typescriptlang.org/download/) - An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). > [!div class="nextstepaction"] @@ -66,6 +66,68 @@ Open a command prompt where you created the new project, and create a new file n ## [**TypeScript**](#tab/typescript) +```typescript +import { AzureOpenAI } from "openai"; +import { type Completion } from "openai/resources/index"; +import "dotenv/config"; + +// You will need to set these environment variables or edit the following values +const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; +const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; + +// Azure OpenAI API version and deployment +const apiVersion = "2024-04-01-preview"; +const deployment = "gpt-35-turbo-instruct"; + +// Chat prompt and max tokens +const prompt = ["When was Microsoft founded?"]; +const maxTokens = 128; + +function getClient(): AzureOpenAI { + return new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment }); +} +async function getCompletion( + client: AzureOpenAI, + prompt: string[], + model: string, + max_tokens: number +): Promise { + return await client.completions.create({ + prompt, + model, + max_tokens, + }); +} +async function getChoices(completion: Completion): Promise { + for (const choice of completion.choices) { + console.log(choice.text); + } +} +export async function main() { + console.log("== Get completions Sample =="); + + const client = getClient(); + const completion = await getCompletion(client, prompt, deployment, maxTokens); + await getChoices(completion); +} + +main().catch((err) => { + console.error("Error occurred:", err); +}); +``` + +Build the script with the following command: + +```cmd +tsc +``` + +Run the script with the following command: + +```cmd +node.exe Completion.js +``` + ## [**JavaScript**](#tab/javascript) ```javascript @@ -125,6 +187,68 @@ Microsoft was founded on April 4, 1975. ## [**TypeScript**](#tab/typescript) +```typescript +import { AzureOpenAI } from "openai"; +import { type Completion } from "openai/resources/index"; +import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity"; +import "dotenv/config"; + +// Azure OpenAI API version and deployment +const apiVersion = "2024-04-01-preview"; +const deployment = "gpt-35-turbo-instruct"; + +// Chat prompt and max tokens +const prompt = ["When was Microsoft founded?"]; +const maxTokens = 128; + +function getClient(): AzureOpenAI { + const scope = "https://cognitiveservices.azure.com/.default"; + const azureADTokenProvider = getBearerTokenProvider(new DefaultAzureCredential(), scope); + return new AzureOpenAI({ azureADTokenProvider, deployment, apiVersion }); + +} +async function getCompletion( + client: AzureOpenAI, + prompt: string[], + model: string, + max_tokens: number +): Promise { + return await client.completions.create({ + prompt, + model, + max_tokens, + }); +} +async function getChoices(completion: Completion): Promise { + for (const choice of completion.choices) { + console.log(choice.text); + } +} +export async function main() { + console.log("== Get completions Sample =="); + + const client = getClient(); + const completion = await getCompletion(client, prompt, deployment, maxTokens); + await getChoices(completion); +} + +main().catch((err) => { + console.error("Error occurred:", err); +}); +``` + +Build the script with the following command: + +```cmd +tsc +``` + +Run the script with the following command: + +```cmd +node.exe Completion.js +``` + ## [**JavaScript**](#tab/javascript) From 6474690fa93e4bce733ca0c7717c0702cb878c8a Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Wed, 4 Sep 2024 09:16:49 -0700 Subject: [PATCH 06/63] fix ending marker for tab --- articles/ai-services/openai/includes/javascript.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index 592e510270f..d6a8f2b0e91 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -285,6 +285,8 @@ main().catch((err) => { module.exports = { main }; ``` +--- + > [!NOTE] > If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated. From ca906df7d597fc6171ee0a337d639be73ca08170 Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Wed, 4 Sep 2024 13:44:18 -0700 Subject: [PATCH 07/63] OpenAI Chat quickstart - TypeScript --- .../openai/includes/chatgpt-javascript.md | 130 ++++++++++++++++++ 1 file changed, 130 insertions(+) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index c8ad17b9115..65970feae20 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -18,6 +18,18 @@ ms.date: 05/21/2024 ## Prerequisites +## [**TypeScript**](#tab/typescript) + +- An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) +- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) +- [TypeScript](https://www.typescriptlang.org/download/) +- An Azure OpenAI Service resource with either a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). + +> [!div class="nextstepaction"] +> [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Prerequisites) + +## [**JavaScript**](#tab/javascript) + - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) - An Azure OpenAI Service resource with either a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). @@ -25,6 +37,8 @@ ms.date: 05/21/2024 > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Prerequisites) +--- + ## Set up [!INCLUDE [get-key-endpoint](get-key-endpoint.md)] @@ -52,6 +66,67 @@ Your app's _package.json_ file will be updated with the dependencies. Open a command prompt where you want the new project, and create a new file named ChatCompletion.js. Copy the following code into the ChatCompletion.js file. +## [**TypeScript**](#tab/typescript) + +```typescript +import { AzureOpenAI } from "openai"; +import { type ChatCompletion, type ChatCompletionCreateParamsNonStreaming } from "openai/resources/index"; +import "dotenv/config"; + +// You will need to set these environment variables or edit the following values +const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; +const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; + +const apiVersion = "2024-05-01-preview"; +const deployment = "gpt-4o"; //This must match your deployment name. + +function getClient(): AzureOpenAI { + return new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment }); +} + +function createMessages(): ChatCompletionCreateParamsNonStreaming { + return { + messages: [ + { role: "system", content: "You are a helpful assistant." }, + { role: "user", content: "Does Azure OpenAI support customer managed keys?" }, + { role: "assistant", content: "Yes, customer managed keys are supported by Azure OpenAI?" }, + { role: "user", content: "Do other Azure AI services support this too?" }, + ], + model: "" + } +} +async function getChoices(completion: ChatCompletion): Promise { + for (const choice of completion.choices) { + console.log(choice.message); + } +} +export async function main() { + + const client = getClient(); + const messages = createMessages(); + const result = await client.chat.completions.create(messages); + await getChoices(result); +} + +main().catch((err) => { + console.error("The sample encountered an error:", err); +}); +``` + +Build the script with the following command: + +```cmd +tsc +``` + +Run the script with the following command: + +```cmd +node.exe Completion.js +``` + +## [**JavaScript**](#tab/javascript) + ```javascript const { AzureOpenAI } = require("openai"); @@ -97,6 +172,8 @@ Run the script with the following command: node.exe ChatCompletion.js ``` +--- + ## Output ```output @@ -112,6 +189,56 @@ node.exe ChatCompletion.js > [!IMPORTANT] > In the previous example we are demonstrating key-based authentication. Once you have tested with key-based authentication successfully, we recommend using the more secure [Microsoft Entra ID](/entra/fundamentals/whatis) for authentication which is demonstrated in the next code sample. Getting started with [Microsoft Entra ID] will require some additional [prerequisites](https://www.npmjs.com/package/@azure/identity). +## [**TypeScript**](#tab/typescript) + +```typescript +import { AzureOpenAI } from "openai"; +import { type ChatCompletionCreateParamsNonStreaming, type ChatCompletion } from "openai/resources/index"; +import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity"; +import "dotenv/config"; + +const apiVersion = "2024-05-01-preview"; +const deployment = "gpt-4o"; //This must match your deployment name. + +function getClient(): AzureOpenAI { + const scope = "https://cognitiveservices.azure.com/.default"; + const azureADTokenProvider = getBearerTokenProvider(new DefaultAzureCredential(), scope); + return new AzureOpenAI({ azureADTokenProvider, deployment, apiVersion }); + + } + +function createMessages(): ChatCompletionCreateParamsNonStreaming { + return { + messages: [ + { role: "system", content: "You are a helpful assistant." }, + { role: "user", content: "Does Azure OpenAI support customer managed keys?" }, + { role: "assistant", content: "Yes, customer managed keys are supported by Azure OpenAI?" }, + { role: "user", content: "Do other Azure AI services support this too?" }, + ], + model: "" + } +} +async function getChoices(completion: ChatCompletion): Promise { + for (const choice of completion.choices) { + console.log(choice.message); + } +} +export async function main() { + + const client = getClient(); + const messages = createMessages(); + const result = await client.chat.completions.create(messages); + await getChoices(result); +} + +main().catch((err) => { + console.error("The sample encountered an error:", err); +}); +``` + + +## [**JavaScript**](#tab/javascript) + ```javascript const { AzureOpenAI } = require("openai"); const { DefaultAzureCredential, getBearerTokenProvider } = require("@azure/identity"); @@ -150,6 +277,9 @@ main().catch((err) => { module.exports = { main }; ``` + +--- + > [!NOTE] > If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated. From 3879cf51baa83f0a3faf83c0c02c9b2bd79b539d Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Wed, 11 Sep 2024 09:55:24 -0700 Subject: [PATCH 08/63] after code approval --- .../openai/includes/chatgpt-javascript.md | 144 +++++++++++------- .../ai-services/openai/includes/javascript.md | 69 +++++---- 2 files changed, 133 insertions(+), 80 deletions(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index 65970feae20..88cca22bdc2 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -11,7 +11,7 @@ ms.author: mbullwin ms.date: 05/21/2024 --- -[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) +[Reference documentation](https://platform.openai.com/docs/api-reference/chat) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) > [!NOTE] > This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript). @@ -69,47 +69,61 @@ Open a command prompt where you want the new project, and create a new file name ## [**TypeScript**](#tab/typescript) ```typescript -import { AzureOpenAI } from "openai"; -import { type ChatCompletion, type ChatCompletionCreateParamsNonStreaming } from "openai/resources/index"; import "dotenv/config"; +import { AzureOpenAI } from "openai"; +import type { + ChatCompletion, + ChatCompletionCreateParamsNonStreaming, +} from "openai/resources/index"; // You will need to set these environment variables or edit the following values const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; -const apiVersion = "2024-05-01-preview"; -const deployment = "gpt-4o"; //This must match your deployment name. +// Required Azure OpenAI deployment name and API version +const apiVersion = "2024-07-01-preview"; +const deploymentName = "gpt-4"; //This must match your deployment name. function getClient(): AzureOpenAI { - return new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment }); + return new AzureOpenAI({ + endpoint, + apiKey, + apiVersion, + deployment: deploymentName, + }); } function createMessages(): ChatCompletionCreateParamsNonStreaming { - return { - messages: [ - { role: "system", content: "You are a helpful assistant." }, - { role: "user", content: "Does Azure OpenAI support customer managed keys?" }, - { role: "assistant", content: "Yes, customer managed keys are supported by Azure OpenAI?" }, - { role: "user", content: "Do other Azure AI services support this too?" }, - ], - model: "" - } + return { + messages: [ + { role: "system", content: "You are a helpful assistant." }, + { + role: "user", + content: "Does Azure OpenAI support customer managed keys?", + }, + { + role: "assistant", + content: "Yes, customer managed keys are supported by Azure OpenAI?", + }, + { role: "user", content: "Do other Azure AI services support this too?" }, + ], + model: "", + }; } -async function getChoices(completion: ChatCompletion): Promise { - for (const choice of completion.choices) { - console.log(choice.message); - } +async function printChoices(completion: ChatCompletion): Promise { + for (const choice of completion.choices) { + console.log(choice.message); + } } export async function main() { - - const client = getClient(); - const messages = createMessages(); - const result = await client.chat.completions.create(messages); - await getChoices(result); + const client = getClient(); + const messages = createMessages(); + const result = await client.chat.completions.create(messages); + await printChoices(result); } main().catch((err) => { - console.error("The sample encountered an error:", err); + console.error("The sample encountered an error:", err); }); ``` @@ -192,47 +206,69 @@ node.exe ChatCompletion.js ## [**TypeScript**](#tab/typescript) ```typescript -import { AzureOpenAI } from "openai"; -import { type ChatCompletionCreateParamsNonStreaming, type ChatCompletion } from "openai/resources/index"; -import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity"; +import { + DefaultAzureCredential, + getBearerTokenProvider, +} from "@azure/identity"; import "dotenv/config"; +import { AzureOpenAI } from "openai"; +import type { + ChatCompletion, + ChatCompletionCreateParamsNonStreaming, +} from "openai/resources/index"; -const apiVersion = "2024-05-01-preview"; -const deployment = "gpt-4o"; //This must match your deployment name. +// You will need to set these environment variables or edit the following values +const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; + +// Required Azure OpenAI deployment name and API version +const apiVersion = "2024-07-01-preview"; +const deploymentName = "gpt-4o"; //This must match your deployment name. function getClient(): AzureOpenAI { - const scope = "https://cognitiveservices.azure.com/.default"; - const azureADTokenProvider = getBearerTokenProvider(new DefaultAzureCredential(), scope); - return new AzureOpenAI({ azureADTokenProvider, deployment, apiVersion }); - - } + const scope = "https://cognitiveservices.azure.com/.default"; + const azureADTokenProvider = getBearerTokenProvider( + new DefaultAzureCredential(), + scope + ); + return new AzureOpenAI({ + endpoint, + azureADTokenProvider, + deployment: deploymentName, + apiVersion, + }); +} function createMessages(): ChatCompletionCreateParamsNonStreaming { - return { - messages: [ - { role: "system", content: "You are a helpful assistant." }, - { role: "user", content: "Does Azure OpenAI support customer managed keys?" }, - { role: "assistant", content: "Yes, customer managed keys are supported by Azure OpenAI?" }, - { role: "user", content: "Do other Azure AI services support this too?" }, - ], - model: "" - } + return { + messages: [ + { role: "system", content: "You are a helpful assistant." }, + { + role: "user", + content: "Does Azure OpenAI support customer managed keys?", + }, + { + role: "assistant", + content: "Yes, customer managed keys are supported by Azure OpenAI?", + }, + { role: "user", content: "Do other Azure AI services support this too?" }, + ], + model: "", + }; } -async function getChoices(completion: ChatCompletion): Promise { - for (const choice of completion.choices) { - console.log(choice.message); - } +async function printChoices(completion: ChatCompletion): Promise { + for (const choice of completion.choices) { + console.log(choice.message); + } } export async function main() { - - const client = getClient(); - const messages = createMessages(); - const result = await client.chat.completions.create(messages); - await getChoices(result); + const client = getClient(); + const messages = createMessages(); + const result = await client.chat.completions.create(messages); + await printChoices(result); } main().catch((err) => { - console.error("The sample encountered an error:", err); + console.error("The sample encountered an error:", err); }); ``` diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index d6a8f2b0e91..140e3d14899 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -11,7 +11,7 @@ ms.author: mbullwin ms.date: 05/20/2024 --- -[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) +[Reference documentation](https://platform.openai.com/docs/api-reference/completions) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) > [!NOTE] > This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript). @@ -67,38 +67,42 @@ Open a command prompt where you created the new project, and create a new file n ## [**TypeScript**](#tab/typescript) ```typescript +import "dotenv/config"; import { AzureOpenAI } from "openai"; import { type Completion } from "openai/resources/index"; -import "dotenv/config"; // You will need to set these environment variables or edit the following values const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; -// Azure OpenAI API version and deployment -const apiVersion = "2024-04-01-preview"; -const deployment = "gpt-35-turbo-instruct"; +// Required Azure OpenAI deployment name and API version +const apiVersion = "2024-07-01-preview"; +const deploymentName = "gpt-35-turbo-instruct"; // Chat prompt and max tokens const prompt = ["When was Microsoft founded?"]; const maxTokens = 128; function getClient(): AzureOpenAI { - return new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment }); + return new AzureOpenAI({ + endpoint, + apiKey, + apiVersion, + deployment: deploymentName, + }); } async function getCompletion( client: AzureOpenAI, prompt: string[], - model: string, max_tokens: number ): Promise { - return await client.completions.create({ + return client.completions.create({ prompt, - model, + model: "", max_tokens, }); } -async function getChoices(completion: Completion): Promise { +async function printChoices(completion: Completion): Promise { for (const choice of completion.choices) { console.log(choice.text); } @@ -107,8 +111,8 @@ export async function main() { console.log("== Get completions Sample =="); const client = getClient(); - const completion = await getCompletion(client, prompt, deployment, maxTokens); - await getChoices(completion); + const completion = await getCompletion(client, prompt, maxTokens); + await printChoices(completion); } main().catch((err) => { @@ -188,14 +192,20 @@ Microsoft was founded on April 4, 1975. ## [**TypeScript**](#tab/typescript) ```typescript +import { + DefaultAzureCredential, + getBearerTokenProvider, +} from "@azure/identity"; +import "dotenv/config"; import { AzureOpenAI } from "openai"; import { type Completion } from "openai/resources/index"; -import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity"; -import "dotenv/config"; -// Azure OpenAI API version and deployment -const apiVersion = "2024-04-01-preview"; -const deployment = "gpt-35-turbo-instruct"; +// You will need to set these environment variables or edit the following values +const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; + +// Required Azure OpenAI deployment name and API version +const apiVersion = "2024-07-01-preview"; +const deploymentName = "gpt-35-turbo-instruct"; // Chat prompt and max tokens const prompt = ["When was Microsoft founded?"]; @@ -203,23 +213,29 @@ const maxTokens = 128; function getClient(): AzureOpenAI { const scope = "https://cognitiveservices.azure.com/.default"; - const azureADTokenProvider = getBearerTokenProvider(new DefaultAzureCredential(), scope); - return new AzureOpenAI({ azureADTokenProvider, deployment, apiVersion }); - + const azureADTokenProvider = getBearerTokenProvider( + new DefaultAzureCredential(), + scope + ); + return new AzureOpenAI({ + endpoint, + azureADTokenProvider, + deployment: deploymentName, + apiVersion, + }); } async function getCompletion( client: AzureOpenAI, prompt: string[], - model: string, max_tokens: number ): Promise { - return await client.completions.create({ + return client.completions.create({ prompt, - model, + model: "", max_tokens, }); } -async function getChoices(completion: Completion): Promise { +async function printChoices(completion: Completion): Promise { for (const choice of completion.choices) { console.log(choice.text); } @@ -228,13 +244,14 @@ export async function main() { console.log("== Get completions Sample =="); const client = getClient(); - const completion = await getCompletion(client, prompt, deployment, maxTokens); - await getChoices(completion); + const completion = await getCompletion(client, prompt, maxTokens); + await printChoices(completion); } main().catch((err) => { console.error("Error occurred:", err); }); + ``` Build the script with the following command: From 41f30abb937eba91e71f93b4058504a25c34cdc8 Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:01 -0700 Subject: [PATCH 09/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index 88cca22bdc2..9c18644f4e6 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -23,7 +23,7 @@ ms.date: 05/21/2024 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) - [TypeScript](https://www.typescriptlang.org/download/) -- An Azure OpenAI Service resource with either a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). +- An Azure OpenAI Service resource with a `gpt-35-turbo` , `gpt-4` , `gpt-4o`, or `gpt-4o-mini` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Prerequisites) From 21bf5996d11f0572f3ee8ca9ce460b063c3c6c9a Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:12 -0700 Subject: [PATCH 10/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index 9c18644f4e6..e7f98248155 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -82,7 +82,7 @@ const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; // Required Azure OpenAI deployment name and API version const apiVersion = "2024-07-01-preview"; -const deploymentName = "gpt-4"; //This must match your deployment name. +const deploymentName = "gpt-4o-mini"; //This must match your deployment name. function getClient(): AzureOpenAI { return new AzureOpenAI({ From d893dd1f4654519d3db8d956c81a559e3280e509 Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:19 -0700 Subject: [PATCH 11/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index e7f98248155..c66b1049a67 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -81,7 +81,7 @@ const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; // Required Azure OpenAI deployment name and API version -const apiVersion = "2024-07-01-preview"; +const apiVersion = "2024-08-01-preview"; const deploymentName = "gpt-4o-mini"; //This must match your deployment name. function getClient(): AzureOpenAI { From 4e42ea12592a6d5fd727de2c4e46b0dc605fc19f Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:27 -0700 Subject: [PATCH 12/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index c66b1049a67..8134075d879 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -221,7 +221,7 @@ import type { const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; // Required Azure OpenAI deployment name and API version -const apiVersion = "2024-07-01-preview"; +const apiVersion = "2024-08-01-preview"; const deploymentName = "gpt-4o"; //This must match your deployment name. function getClient(): AzureOpenAI { From e88555a38ad33b19cf95c271e502b32a048e53b5 Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:33 -0700 Subject: [PATCH 13/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index 8134075d879..2d9bf15e60f 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -222,7 +222,7 @@ const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; // Required Azure OpenAI deployment name and API version const apiVersion = "2024-08-01-preview"; -const deploymentName = "gpt-4o"; //This must match your deployment name. +const deploymentName = "gpt-4o-mini"; //This must match your deployment name. function getClient(): AzureOpenAI { const scope = "https://cognitiveservices.azure.com/.default"; From d803a45f364dae39c6caff79b0741f52cc1667f1 Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:46 -0700 Subject: [PATCH 14/63] Update articles/ai-services/openai/includes/javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index 140e3d14899..36d6891fb53 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -76,7 +76,7 @@ const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; const apiKey = process.env["AZURE_OPENAI_API_KEY"] || ""; // Required Azure OpenAI deployment name and API version -const apiVersion = "2024-07-01-preview"; +const apiVersion = "2024-08-01-preview"; const deploymentName = "gpt-35-turbo-instruct"; // Chat prompt and max tokens From 0a2fd50cf5ce849326feff8b75b5dd29cf5b842f Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Fri, 13 Sep 2024 08:53:52 -0700 Subject: [PATCH 15/63] Update articles/ai-services/openai/includes/javascript.md Co-authored-by: Michael <31510320+mrbullwinkle@users.noreply.github.com> --- articles/ai-services/openai/includes/javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index 36d6891fb53..fb9d47f0a70 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -204,7 +204,7 @@ import { type Completion } from "openai/resources/index"; const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || ""; // Required Azure OpenAI deployment name and API version -const apiVersion = "2024-07-01-preview"; +const apiVersion = "2024-08-01-preview"; const deploymentName = "gpt-35-turbo-instruct"; // Chat prompt and max tokens From dba73028c43ea1eb50d4ae604ca54dccad48c90c Mon Sep 17 00:00:00 2001 From: Facundo Santiago Date: Sat, 14 Sep 2024 20:25:49 +0000 Subject: [PATCH 16/63] feat: SDKs --- .../ai-studio/how-to/develop/llama-index.md | 203 ++ .../how-to/develop/model-inference-chat.md | 2073 +++++++++++++++++ articles/ai-studio/toc.yml | 6 + 3 files changed, 2282 insertions(+) create mode 100644 articles/ai-studio/how-to/develop/llama-index.md create mode 100644 articles/ai-studio/how-to/develop/model-inference-chat.md diff --git a/articles/ai-studio/how-to/develop/llama-index.md b/articles/ai-studio/how-to/develop/llama-index.md new file mode 100644 index 00000000000..06435b2941b --- /dev/null +++ b/articles/ai-studio/how-to/develop/llama-index.md @@ -0,0 +1,203 @@ +--- +title: Develop application with LlamaIndex and Azure AI studio +titleSuffix: Azure AI Studio +description: This article explains how to use LlamaIndex with models deployed in Azure AI studio to build advance intelligent applications +manager: nitinme +ms.service: azure-ai-studio +ms.topic: how-to +ms.date: 9/14/2024 +ms.reviewer: fasantia +ms.author: eur +author: eric-urban +--- + +# Develop application with LlamaIndex and Azure AI studio + +In this article, you learn how to use [`llama-index`](https://github.com/run-llama/llama_index) with models deployed from the Azure AI model catalog deployed to Azure AI studio. + +## Prerequisites + +To run this tutorial you need: + +1. An [Azure subscription](https://azure.microsoft.com). +2. An Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). +3. A model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. + + * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). + +4. A Python environment. + + +## Install dependencies + +Ensure you have `llama-index` installed: + +```bash +pip install llama-index +``` + +Models deployed to Azure AI studio or Azure Machine Learning can be used with LlamaIndex in two ways: + +- **Using the Azure AI model inference API:** All models deployed to Azure AI studio and Azure Machine Learning support the Azure AI model inference API, which offers a common set of functionalities that can be used for most of the models in the catalog. The benefit of this API is that, since it's the same for all the models, changing from one to another is as simple as changing the model deployment being use. No further changes are required in the code. When working with `llama-index`, install the extensions `llama-index-llms-azure-inference` and `llama-index-embeddings-azure-inference`. + +- **Using the model's provider specific API:** Some models, like OpenAI, Cohere, or Mistral, offer their own set of APIs and extensions for `llama-index`. Those extensions may include specific functionalities that the model support and hence are suitable if you want to exploit them. When working with `llama-index`, install the extension specific for the model you want to use, like `llama-index-llms-openai` or `llama-index-llms-cohere`. + + +In this example, we are working with the Azure AI model inference API, hence we install the following packages: + +```bash +pip install -U llama-index-llms-azure-inference +pip install -U llama-index-embeddings-azure-inference +``` + +## Configure the environment + +To use LLMs deployed in Azure AI studio you need the endpoint and credentials to connect to it. The parameter `model_name` is not required for endpoints serving a single model, like Managed Online Endpoints. Follow this steps to get the information you need from the model you want to use: + +1. Go to the [Azure AI studio](https://ai.azure.com/). +2. Go to deployments and select the model you have deployed as indicated in the prerequisites. +3. Copy the endpoint URL and the key. + +> [!TIP] +> If your model was deployed with Microsoft Entra ID support, you don't need a key. + +In this scenario, we have placed both the endpoint URL and key in the following environment variables: + +```bash +export AZURE_INFERENCE_ENDPOINT="" +export AZURE_INFERENCE_CREDENTIAL="" +``` + +Once configured, create a client to connect to the endpoint: + +```python +import os +from llama_index.llms.azure_inference import AzureAICompletionsModel + +llm = AzureAICompletionsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=os.environ["AZURE_INFERENCE_CREDENTIAL"], +) +``` + +Alternatively, if you endpoint support Microsoft Entra ID, you can use the following code to create the client: + +```python +from azure.identity import DefaultAzureCredential + +llm = AzureAICompletionsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=DefaultAzureCredential(), +) +``` + +> [!NOTE] +> > Note: When using Microsoft Entra ID, make sure that the endpoint was deployed with that authentication method and that you have the required permissions to invoke it. + +If you are planning to use asynchronous calling, it's a best practice to use the asynchronous version for the credentials: + +```python +from azure.identity.aio import ( + DefaultAzureCredential as DefaultAzureCredentialAsync, +) + +llm = AzureAICompletionsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=DefaultAzureCredentialAsync(), +) +``` + +### Inference parameters + +You can configure how inference in performed for all the operations that are using this client by setting extra parameters. This helps avoid indicating them on each call you make to the model. + +```python +llm = AzureAICompletionsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=os.environ["AZURE_INFERENCE_CREDENTIAL"], + temperature=0.0, + model_kwargs={"top_p": 1.0}, +) +``` + +For parameters extra parameters that are not supported by the Azure AI model inference API but that are available in the underlying model, you can use the `model_extras` argument. In the following example, the parameter `safe_prompt`, only available for Mistral models, is being passed. + +```python +llm = AzureAICompletionsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=os.environ["AZURE_INFERENCE_CREDENTIAL"], + temperature=0.0, + model_kwargs={"model_extras": {"safe_prompt": True}}, +) +``` + +## Use LLMs models + +Use the `chat` endpoint for chat instruction models. The `complete` method is still available for model of type `chat-completions`. On those cases, your input text is converted to a message with `role="user"`. + +```python +from llama_index.core.llms import ChatMessage + +messages = [ + ChatMessage( + role="system", content="You are a pirate with colorful personality." + ), + ChatMessage(role="user", content="Hello"), +] + +response = llm.chat(messages) +print(response) +``` + +You can stream the outputs also: + +```python +response = llm.stream_chat(messages) +for r in response: + print(r.delta, end="") +``` + +## Use embeddings models + +In the same way you create an LLM client, you can connect to an embedding model. In the following example, we are setting again the environment variable to now point to an embeddings model: + +```bash +export AZURE_INFERENCE_ENDPOINT="" +export AZURE_INFERENCE_CREDENTIAL="" +``` + +Then create the client: + +```python +from llama_index.embeddings.azure_inference import AzureAIEmbeddingsModel + +embed_model = AzureAIEmbeddingsModel( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=os.environ['AZURE_INFERENCE_CREDENTIAL'], +) +``` + +## Configure the models used by your code + +You can use the LLM or embeddings model client individually in the code you develop with LlamaIndex or you can configure the entire session using the `Settings` options. Configuring the session has the advantage that then all your code will use the same models for all the operations. + +```python +from llama_index.core import Settings + +Settings.llm = llm +Settings.embed_model = embed_model +``` + +However, there are scenarios where you want to use a general model for most of the operations but an specific one for a given task. On those cases, it's useful to set the LLM or embedding model your are using for each LlamaIndex construct. In the following example, we set an specific model: + +```python +from llama_index.core.evaluation import RelevancyEvaluator + +relevancy_evaluator = RelevancyEvaluator(llm=llm) +``` + +In general, you will use a combination of both strategies. + +## Related content + +* [How to get started with Azure AI SDKs](sdk-overview.md) diff --git a/articles/ai-studio/how-to/develop/model-inference-chat.md b/articles/ai-studio/how-to/develop/model-inference-chat.md new file mode 100644 index 00000000000..95a5afc175c --- /dev/null +++ b/articles/ai-studio/how-to/develop/model-inference-chat.md @@ -0,0 +1,2073 @@ +--- +title: Develop with Azure AI Model Inference API with Azure AI Studio +titleSuffix: Azure AI Studio +description: This article explains how to use models deployed in Azure AI Studio and consume them using the Azure AI model inference API in the language of your choice. +ms.service: azure-ai-studio +manager: scottpolly +ms.topic: how-to +ms.date: 09/14/2024 +ms.reviewer: fasantia +ms.author: eur +author: eric-urba +ms.custom: references_regions, generated +zone_pivot_groups: azure-ai-model-catalog-samples-chat +--- + +# Develop with Azure AI Model Inference API to consume chat models in the language of your choice + +[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)] + +In this example, you learn how to use the Azure AI Model inference API to consume chat models deployed in Azure AI Studio using the programming language of your choice. + + +::: zone pivot="programming-language-python" + +## Prerequisites + +You need the following prerequisites: + +1. An [Azure subscription](https://azure.microsoft.com). +2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). +3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. + + * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). + +4. Python 3.8 or later installed, including pip. +5. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). +6. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. +7. The Azure AI inference package installed with the following command: + + ```bash + pip install azure-ai-inference + ``` + +Read more about the [Azure AI inference package and reference](https://aka.ms/azsdk/azure-ai-inference/python/reference). + +## Work with chat completions + +In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. + +### Create a client to consume the model + +First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. + + +```python +import os +from azure.ai.inference import ChatCompletionsClient +from azure.core.credentials import AzureKeyCredential + +client = ChatCompletionsClient( + endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], + credential=AzureKeyCredential(os.environ["AZURE_INFERENCE_CREDENTIAL"]), +) +``` + +### Get the model's capabilities + +The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: + + +```python +model_info = client.get_model_info() +``` + +The response is as follows: + + +```python +print("Model name:", model_info.model_name) +print("Model type:", model_info.model_type) +print("Model provider name:", model_info.model_provider_name) +``` + +```console +Model name: Mistral-Large +Model type: chat-completions +Model provider name: MistralAI +``` + +### Create a chat completion request + +The following example shows how you can create a basic chat completions request to the model. + +```python +from azure.ai.inference.models import SystemMessage, UserMessage + +response = client.complete( + messages=[ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="How many languages are in the world?"), + ], +) +``` + +The response is as follows, where you can see the model's usage statistics: + + +```python +print("Response:", response.choices[0].message.content) +print("Model:", response.model) +print("Usage:") +print("\tPrompt tokens:", response.usage.prompt_tokens) +print("\tTotal tokens:", response.usage.total_tokens) +print("\tCompletion tokens:", response.usage.completion_tokens) +``` + +```console +Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. +Model: Mistral-Large +Usage: + Prompt tokens: 19 + Total tokens: 91 + Completion tokens: 72 +``` + +Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. + +#### Stream content + +By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. + +You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. + + +```python +result = client.complete( + messages=[ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="How many languages are in the world?"), + ], + temperature=0, + top_p=1, + max_tokens=2048, + stream=True, +) +``` + +To stream completions, set `stream=True` when you call the model. + +To visualize the output, define a helper function to print the stream. + +```python +def print_stream(result): + """ + Prints the chat completion with streaming. + """ + import time + for update in result: + if update.choices: + print(update.choices[0].delta.content, end="") +``` + +You can visualize how streaming generates content: + + +```python +print_stream(result) +``` + +#### Explore more parameters supported by the inference client + +Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). + +```python +from azure.ai.inference.models import ChatCompletionsResponseFormatText + +response = client.complete( + messages=[ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="How many languages are in the world?"), + ], + presence_penalty=0.1, + frequency_penalty=0.8, + max_tokens=2048, + stop=["<|endoftext|>"], + temperature=0, + top_p=1, + response_format={ "type": ChatCompletionsResponseFormatText() }, +) +``` + +> [!TIP] +> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. + +If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). + +#### Create JSON outputs + +For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. + + +```python +from azure.ai.inference.models import ChatCompletionsResponseFormatJSON + +response = client.complete( + messages=[ + SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using." + " the following format: { ""answer"": ""response"" }."), + UserMessage(content="How many languages are in the world?"), + ], + response_format={ "type": ChatCompletionsResponseFormatJSON() } +) +``` + +### Pass extra parameters to the model + +The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. + +Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. + + +```python +response = client.complete( + messages=[ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="How many languages are in the world?"), + ], + model_extras={ + "logprobs": True + } +) +``` + +To learn more about the extra parameters that you can pass to the model, see its documentation. + +### Use tools + +Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. + +The following code example creates a tool definition that is able to look from flight information from two different cities. + + +```python +from azure.ai.inference.models import FunctionDefinition, ChatCompletionsFunctionToolDefinition + +flight_info = ChatCompletionsFunctionToolDefinition( + function=FunctionDefinition( + name="get_flight_info", + description="Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + parameters={ + "type": "object", + "properties": { + "origin_city": { + "type": "string", + "description": "The name of the city where the flight originates", + }, + "destination_city": { + "type": "string", + "description": "The flight destination city", + }, + }, + "required": ["origin_city", "destination_city"], + }, + ) +) + +tools = [flight_info] +``` + +In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. + + +```python +def get_flight_info(loc_origin: str, loc_destination: str): + return { + "info": f"There are no flights available from {loc_origin} to {loc_destination}. You should take a train, specially if it helps to reduce CO2 emissions." + } +``` + +> [!NOTE] +> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. + +Prompt the model to book flights with the help of this function: + + +```python +messages = [ + SystemMessage( + content="You are a helpful assistant that help users to find information about traveling, how to get" + " to places and the different transportations options. You care about the environment and you" + " always have that in mind when answering inqueries.", + ), + UserMessage( + content="When is the next flight from Miami to Seattle?", + ), +] + +response = client.complete( + messages=messages, tools=tools, tool_choice="auto" +) +``` + +You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. + + +```python +response_message = response.choices[0].message +tool_calls = response_message.tool_calls + +print("Finish reason:", response.choices[0].finish_reason) +print("Tool call:", tool_calls) +``` + +To continue, append this message to the chat history: + + +```python +messages.append( + response_message +) +``` + +Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. + + +```python +import json +from azure.ai.inference.models import ToolMessage + +for tool_call in tool_calls: + + # Get the tool details: + + function_name = tool_call.function.name + function_args = json.loads(tool_call.function.arguments.replace("\'", "\"")) + tool_call_id = tool_call.id + + print(f"Calling function `{function_name}` with arguments {function_args}") + + # Call the function defined above using `locals()`, which returns the list of all functions + # available in the scope as a dictionary. Notice that this is just done as a simple way to get + # the function callable from its string name. Then we can call it with the corresponding + # arguments. + + callable_func = locals()[function_name] + function_response = callable_func(**function_args) + + print("->", function_response) + + # Once we have a response from the function and its arguments, we can append a new message to the chat + # history. Notice how we are telling to the model that this chat message came from a tool: + + messages.append( + ToolMessage( + tool_call_id=tool_call_id, + content=json.dumps(function_response) + ) + ) +``` + +View the response from the model: + + +```python +response = client.complete( + messages=messages, + tools=tools, +) +``` + +### Apply content safety + +The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. + +The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. + + +```python +from azure.ai.inference.models import AssistantMessage, UserMessage, SystemMessage + +try: + response = client.complete( + messages=[ + SystemMessage(content="You are an AI assistant that helps people find information."), + UserMessage(content="Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."), + ] + ) + + print(response.choices[0].message.content) + +except HttpResponseError as ex: + if ex.status_code == 400: + response = ex.response.json() + if isinstance(response, dict) and "error" in response: + print(f"Your request triggered an {response['error']['code']} error:\n\t {response['error']['message']}") + else: + raise + raise +``` + +> [!TIP] +> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). + +> [!NOTE] +> Azure AI content safety is only available for models deployed as serverless API endpoints. + +## Use chat completions with images + +Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: + +> [!IMPORTANT] +> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. + +To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): + + +```python +from urllib.request import urlopen, Request +import base64 + +image_url = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg" +image_format = "jpeg" + +request = Request(image_url, headers={"User-Agent": "Mozilla/5.0"}) +image_data = base64.b64encode(urlopen(request).read()).decode("utf-8") +data_url = f"data:image/{image_format};base64,{image_data}" +``` + +Visualize the image: + + +```python +import requests +import IPython.display as Disp + +Disp.Image(requests.get(image_url).content) +``` + +:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: + +Now, create a chat completion request with the image: + + +```python +from azure.ai.inference.models import TextContentItem, ImageContentItem, ImageUrl +response = client.complete( + messages=[ + SystemMessage("You are a helpful assistant that can generate responses based on images."), + UserMessage(content=[ + TextContentItem(text="Which conclusion can be extracted from the following chart?"), + ImageContentItem(image=ImageUrl(url=data_url)) + ]), + ], + temperature=0, + top_p=1, + max_tokens=2048, +) +``` + +The response is as follows, where you can see the model's usage statistics: + + +```python +print(f"{response.choices[0].message.role}:\n\t{response.choices[0].message.content}\n") +print("Model:", response.model) +print("Usage:") +print("\tPrompt tokens:", response.usage.prompt_tokens) +print("\tCompletion tokens:", response.usage.completion_tokens) +print("\tTotal tokens:", response.usage.total_tokens) +``` + +```console +ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. +Model: Mistral-Large +Usage: + Prompt tokens: 2380 + Completion tokens: 126 + Total tokens: 2506 +``` + +::: zone-end + + +::: zone pivot="programming-language-javascript" + +You need the following prerequisites: + +1. An [Azure subscription](https://azure.microsoft.com). +2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). +3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. + + * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). + +4. LTS versions of `Node.js` with `npm`. +5. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). +6. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. +7. The Azure Inference library for JavaScript installed with the following command: + + ```bash + npm install @azure-rest/ai-inference + ``` + +## Work with chat completions + +In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. + +### Create a client to consume the model + +First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. + + +```javascript +import ModelClient from "@azure-rest/ai-inference"; +import { isUnexpected } from "@azure-rest/ai-inference"; +import { AzureKeyCredential } from "@azure/core-auth"; + +const client = new ModelClient( + process.env.AZURE_INFERENCE_ENDPOINT, + new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL) +); +``` + +### Get the model's capabilities + +The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: + + +```javascript +var model_info = await client.path("/info").get() +``` + +The response is as follows: + + +```javascript +console.log("Model name: ", model_info.body.model_name) +console.log("Model type: ", model_info.body.model_type) +console.log("Model provider name: ", model_info.body.model_provider_name) +``` + +```console +Model name: Mistral-Large +Model type: chat-completions +Model provider name: MistralAI +``` + +### Create a chat completion request + +The following example shows how you can create a basic chat completions request to the model. + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant" }, + { role: "user", content: "How many languages are in the world?" }, +]; + +var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + } +}); +``` + +The response is as follows, where you can see the model's usage statistics: + + +```javascript +if (isUnexpected(response)) { + throw response.body.error; +} + +console.log("Response: ", response.body.choices[0].message.content); +console.log("Model: ", response.body.model); +console.log("Usage:"); +console.log("\tPrompt tokens:", response.body.usage.prompt_tokens); +console.log("\tTotal tokens:", response.body.usage.total_tokens); +console.log("\tCompletion tokens:", response.body.usage.completion_tokens); +``` + +```console +Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. +Model: Mistral-Large +Usage: + Prompt tokens: 19 + Total tokens: 91 + Completion tokens: 72 +``` + +Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. + +#### Stream content + +By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. + +You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. + + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant" }, + { role: "user", content: "How many languages are in the world?" }, +]; + +var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + } +}).asNodeStream(); +``` + +To stream completions, use `.asNodeStream()` when you call the model. + +You can visualize how streaming generates content: + + +```javascript +var stream = response.body; +if (!stream) { + stream.destroy(); + throw new Error(`Failed to get chat completions with status: ${response.status}`); +} + +if (response.status !== "200") { + throw new Error(`Failed to get chat completions: ${response.body.error}`); +} + +var sses = createSseStream(stream); + +for await (const event of sses) { + if (event.data === "[DONE]") { + return; + } + for (const choice of (JSON.parse(event.data)).choices) { + console.log(choice.delta?.content ?? ""); + } +} +``` + +#### Explore more parameters supported by the inference client + +Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant" }, + { role: "user", content: "How many languages are in the world?" }, +]; + +var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + presence_penalty: "0.1", + frequency_penalty: "0.8", + max_tokens: 2048, + stop: ["<|endoftext|>"], + temperature: 0, + top_p: 1, + response_format: { type: "text" }, + } +}); +``` + +> [!TIP] +> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. + +If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). + +#### Create JSON outputs + +For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. + + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant that always generate responses in JSON format, using." + + " the following format: { \"answer\": \"response\" }." }, + { role: "user", content: "How many languages are in the world?" }, +]; + +var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + response_format: { type: "json_object" } + } +}); +``` + +### Pass extra parameters to the model + +The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. + +Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. + + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant" }, + { role: "user", content: "How many languages are in the world?" }, +]; + +var response = await client.path("/chat/completions").post({ + headers: { + "extra-params": "pass-through" + }, + body: { + messages: messages, + logprobs: true + } +}); +``` + +To learn more about the extra parameters that you can pass to the model, see its documentation. + +### Use tools + +Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. + +The following code example creates a tool definition that is able to look from flight information from two different cities. + + +```javascript +const flight_info = { + name: "get_flight_info", + description: "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + parameters: { + type: "object", + properties: { + origin_city: { + type: "string", + description: "The name of the city where the flight originates", + }, + destination_city: { + type: "string", + description: "The flight destination city", + }, + }, + required: ["origin_city", "destination_city"], + }, +} + +const tools = [ + { + type: "function", + function: flight_info, + }, +]; +``` + +In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. + + +```javascript +function get_flight_info(loc_origin, loc_destination) { + return { + info: "There are no flights available from " + loc_origin + " to " + loc_destination + ". You should take a train, specially if it helps to reduce CO2 emissions." + } +} +``` + +> [!NOTE] +> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. + +Prompt the model to book flights with the help of this function: + + +```javascript +var result = await client.path("/chat/completions").post({ + body: { + messages: messages, + tools: tools, + tool_choice: "auto" + } +}); +``` + +You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. + + +```javascript +const response_message = response.body.choices[0].message; +const tool_calls = response_message.tool_calls; + +console.log("Finish reason: " + response.body.choices[0].finish_reason); +console.log("Tool call: " + tool_calls); +``` + +To continue, append this message to the chat history: + + +```javascript +messages.push(response_message); +``` + +Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. + + +```javascript +function applyToolCall({ function: call, id }) { + // Get the tool details: + const tool_params = JSON.parse(call.arguments); + console.log("Calling function " + call.name + " with arguments " + tool_params); + + // Call the function defined above using `window`, which returns the list of all functions + // available in the scope as a dictionary. Notice that this is just done as a simple way to get + // the function callable from its string name. Then we can call it with the corresponding + // arguments. + const function_response = tool_params.map(window[call.name]); + console.log("-> " + function_response); + + return function_response +} + +for (const tool_call of tool_calls) { + var tool_response = tool_call.apply(applyToolCall); + + messages.push( + { + role: "tool", + tool_call_id: tool_call.id, + content: tool_response + } + ); +} +``` + +View the response from the model: + + +```javascript +var result = await client.path("/chat/completions").post({ + body: { + messages: messages, + tools: tools, + } +}); +``` + +### Apply content safety + +The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. + +The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. + + +```javascript +try { + var messages = [ + { role: "system", content: "You are an AI assistant that helps people find information." }, + { role: "user", content: "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." }, + ]; + + var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + } + }); + + console.log(response.body.choices[0].message.content); +} +catch (error) { + if (error.status_code == 400) { + var response = JSON.parse(error.response._content); + if (response.error) { + console.log(`Your request triggered an ${response.error.code} error:\n\t ${response.error.message}`); + } + else + { + throw error; + } + } +} +``` + +> [!TIP] +> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). + +> [!NOTE] +> Azure AI content safety is only available for models deployed as serverless API endpoints. + +## Use chat completions with images + +Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: + +> [!IMPORTANT] +> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. + +To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): + + +```javascript +const image_url = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg"; +const image_format = "jpeg"; + +const response = await fetch(image_url, { headers: { "User-Agent": "Mozilla/5.0" } }); +const image_data = await response.arrayBuffer(); +const image_data_base64 = Buffer.from(image_data).toString("base64"); +const data_url = `data:image/${image_format};base64,${image_data_base64}`; +``` + +Visualize the image: + + +```javascript +const img = document.createElement("img"); +img.src = data_url; +document.body.appendChild(img); +``` + +:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: + +Now, create a chat completion request with the image: + + +```javascript +var messages = [ + { role: "system", content: "You are a helpful assistant that can generate responses based on images." }, + { role: "user", content: + [ + { type: "text", text: "Which conclusion can be extracted from the following chart?" }, + { type: "image_url", image: + { + url: data_url + } + } + ] + } +]; + +var response = await client.path("/chat/completions").post({ + body: { + messages: messages, + temperature: 0, + top_p: 1, + max_tokens: 2048, + } +}); +``` + +The response is as follows, where you can see the model's usage statistics: + + +```javascript +console.log(response.body.choices[0].message.role + ": " + response.body.choices[0].message.content); +console.log("Model:", response.body.model); +console.log("Usage:"); +console.log("\tPrompt tokens:", response.body.usage.prompt_tokens); +console.log("\tCompletion tokens:", response.body.usage.completion_tokens); +console.log("\tTotal tokens:", response.body.usage.total_tokens); +``` + +```console +ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. +Model: Mistral-Large +Usage: + Prompt tokens: 2380 + Completion tokens: 126 + Total tokens: 2506 +``` + +::: zone-end + + +::: zone pivot="programming-language-csharp" + +You need the following prerequisites: + +1. An [Azure subscription](https://azure.microsoft.com). +2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). +3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. + + * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). + +4. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). +5. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. + +6. The Azure AI inference library installed with the following command: + + ```dotnetcli + dotnet add package Azure.AI.Inference --prerelease + ``` + +7. You can also authenticate with Microsoft Entra ID (formerly Azure Active Directory). To use credential providers provided with the Azure SDK, install the `Azure.Identity` package: + + ```dotnetcli + dotnet add package Azure.Identity + ``` + +Import the following namespaces: + + +```csharp +using Azure; +using Azure.Identity; +using Azure.AI.Inference; +``` + +This example also uses the following namespaces but you may not always need them: + + +```csharp +using System.Text.Json; +using System.Text.Json.Serialization; +using System.Reflection; +``` + +## Work with chat completions + +In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. + +### Create a client to consume the model + +First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. + + +```csharp +ChatCompletionsClient client = new ChatCompletionsClient( + new Uri(Environment.GetEnvironmentVariable("AZURE_INFERENCE_ENDPOINT")), + new AzureKeyCredential(Environment.GetEnvironmentVariable("AZURE_INFERENCE_CREDENTIAL")) +); +``` + +### Get the model's capabilities + +The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: + + +```csharp +Response modelInfo = client.GetModelInfo(); +``` + +The response is as follows: + + +```csharp +Console.WriteLine($"Model name: {modelInfo.Value.ModelName}"); +Console.WriteLine($"Model type: {modelInfo.Value.ModelType}"); +Console.WriteLine($"Model provider name: {modelInfo.Value.ModelProviderName}"); +``` + +```console +Model name: Mistral-Large +Model type: chat-completions +Model provider name: MistralAI +``` + +### Create a chat completion request + +The following example shows how you can create a basic chat completions request to the model. + +```csharp +ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() +{ + Messages = { + new ChatRequestSystemMessage("You are a helpful assistant."), + new ChatRequestUserMessage("How many languages are in the world?") + }, +}; + +Response response = client.Complete(requestOptions); +``` + +The response is as follows, where you can see the model's usage statistics: + + +```csharp +Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); +Console.WriteLine($"Model: {response.Value.Model}"); +Console.WriteLine("Usage:"); +Console.WriteLine($"\tPrompt tokens: {response.Value.Usage.PromptTokens}"); +Console.WriteLine($"\tTotal tokens: {response.Value.Usage.TotalTokens}"); +Console.WriteLine($"\tCompletion tokens: {response.Value.Usage.CompletionTokens}"); +``` + +```console +Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. +Model: Mistral-Large +Usage: + Prompt tokens: 19 + Total tokens: 91 + Completion tokens: 72 +``` + +Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. + +#### Stream content + +By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. + +You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. + + +```csharp +static async Task StreamMessageAsync(ChatCompletionsClient client) +{ + ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() + { + Messages = { + new ChatRequestSystemMessage("You are a helpful assistant."), + new ChatRequestUserMessage("How many languages are in the world? Write an essay about it.") + }, + MaxTokens=4096 + }; + + StreamingResponse streamResponse = await client.CompleteStreamingAsync(requestOptions); + + await PrintStream(streamResponse); +} +``` + +To stream completions, use `CompleteStreamingAsync` method when you call the model. Notice that in this example we the call is wrapped in an asynchronous method. + +To visualize the output, define an asynchronous method to print the stream in the console. + +```csharp +static async Task PrintStream(StreamingResponse response) +{ + await foreach (StreamingChatCompletionsUpdate chatUpdate in response) + { + if (chatUpdate.Role.HasValue) + { + Console.Write($"{chatUpdate.Role.Value.ToString().ToUpperInvariant()}: "); + } + if (!string.IsNullOrEmpty(chatUpdate.ContentUpdate)) + { + Console.Write(chatUpdate.ContentUpdate); + } + } +} +``` + +You can visualize how streaming generates content: + + +```csharp +StreamMessageAsync(client).GetAwaiter().GetResult(); +``` + +#### Explore more parameters supported by the inference client + +Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). + +```csharp +requestOptions = new ChatCompletionsOptions() +{ + Messages = { + new ChatRequestSystemMessage("You are a helpful assistant."), + new ChatRequestUserMessage("How many languages are in the world?") + }, + PresencePenalty = 0.1f, + FrequencyPenalty = 0.8f, + MaxTokens = 2048, + StopSequences = { "<|endoftext|>" }, + Temperature = 0, + NucleusSamplingFactor = 1, + ResponseFormat = new ChatCompletionsResponseFormatText() +}; + +response = client.Complete(requestOptions); +Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); +``` + +> [!TIP] +> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. + +If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). + +#### Create JSON outputs + +For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. + + +```csharp +requestOptions = new ChatCompletionsOptions() +{ + Messages = { + new ChatRequestSystemMessage( + "You are a helpful assistant that always generate responses in JSON format, " + + "using. the following format: { \"answer\": \"response\" }." + ), + new ChatRequestUserMessage( + "How many languages are in the world?" + ) + }, + ResponseFormat = new ChatCompletionsResponseFormatJSON() +}; + +response = client.Complete(requestOptions); +Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); +``` + +### Pass extra parameters to the model + +The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. + +Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. + + +```csharp +requestOptions = new ChatCompletionsOptions() +{ + Messages = { + new ChatRequestSystemMessage("You are a helpful assistant."), + new ChatRequestUserMessage("How many languages are in the world?") + }, + AdditionalProperties = { { "logprobs", BinaryData.FromString("true") } }, +}; + +response = client.Complete(requestOptions, extraParams: ExtraParameters.PassThrough); +Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); +``` + +To learn more about the extra parameters that you can pass to the model, see its documentation. + +### Use tools + +Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. + +The following code example creates a tool definition that is able to look from flight information from two different cities. + + +```csharp +FunctionDefinition flightInfoFunction = new FunctionDefinition("getFlightInfo") +{ + Description = "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + Parameters = BinaryData.FromObjectAsJson(new + { + Type = "object", + Properties = new + { + origin_city = new + { + Type = "string", + Description = "The name of the city where the flight originates" + }, + destination_city = new + { + Type = "string", + Description = "The flight destination city" + } + } + }, + new JsonSerializerOptions() { PropertyNamingPolicy = JsonNamingPolicy.CamelCase } + ) +}; + +ChatCompletionsFunctionToolDefinition getFlightTool = new ChatCompletionsFunctionToolDefinition(flightInfoFunction); +``` + +In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. + + +```csharp +static string getFlightInfo(string loc_origin, string loc_destination) +{ + return JsonSerializer.Serialize(new + { + info = $"There are no flights available from {loc_origin} to {loc_destination}. You " + + "should take a train, specially if it helps to reduce CO2 emissions." + }); +} +``` + +> [!NOTE] +> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. + +Prompt the model to book flights with the help of this function: + + +```csharp +var chatHistory = new List(){ + new ChatRequestSystemMessage( + "You are a helpful assistant that help users to find information about traveling, " + + "how to get to places and the different transportations options. You care about the" + + "environment and you always have that in mind when answering inqueries." + ), + new ChatRequestUserMessage("When is the next flight from Miami to Seattle?") + }; + +requestOptions = new ChatCompletionsOptions(chatHistory); +requestOptions.Tools.Add(getFlightTool); +requestOptions.ToolChoice = ChatCompletionsToolChoice.Auto; + +response = client.Complete(requestOptions); +``` + +You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. + + +```csharp +var responseMenssage = response.Value.Choices[0].Message; +var toolsCall = responseMenssage.ToolCalls; + +Console.WriteLine($"Finish reason: {response.Value.Choices[0].FinishReason}"); +Console.WriteLine($"Tool call: {toolsCall[0].Id}"); +``` + +To continue, append this message to the chat history: + + +```csharp +requestOptions.Messages.Add(new ChatRequestAssistantMessage(response.Value.Choices[0].Message)); +``` + +Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. + + +```csharp +foreach (ChatCompletionsToolCall tool in toolsCall) +{ + if (tool is ChatCompletionsFunctionToolCall functionTool) + { + // Get the tool details: + string callId = functionTool.Id; + string toolName = functionTool.Name; + string toolArgumentsString = functionTool.Arguments; + Dictionary toolArguments = JsonSerializer.Deserialize>(toolArgumentsString); + + // Here you have to call the function defined. In this particular example we use + // reflection to find the method we definied before in an static class called + // `ChatCompletionsExamples`. Using reflection allows us to call a function + // by string name. Notice that this is just done for demonstration purposes as a + // simple way to get the function callable from its string name. Then we can call + // it with the corresponding arguments. + + var flags = BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Static; + string toolResponse = (string)typeof(ChatCompletionsExamples).GetMethod(toolName, flags).Invoke(null, toolArguments.Values.Cast().ToArray()); + + Console.WriteLine("->", toolResponse); + requestOptions.Messages.Add(new ChatRequestToolMessage(toolResponse, callId)); + } + else + throw new Exception("Unsupported tool type"); +} +``` + +View the response from the model: + + +```csharp +response = client.Complete(requestOptions); +``` + +### Apply content safety + +The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. + +The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. + + +```csharp +try +{ + requestOptions = new ChatCompletionsOptions() + { + Messages = { + new ChatRequestSystemMessage("You are an AI assistant that helps people find information."), + new ChatRequestUserMessage( + "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." + ), + }, + }; + + response = client.Complete(requestOptions); + Console.WriteLine(response.Value.Choices[0].Message.Content); +} +catch (RequestFailedException ex) +{ + if (ex.ErrorCode == "content_filter") + { + Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}"); + } + else + { + throw; + } +} +``` + +> [!TIP] +> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). + +> [!NOTE] +> Azure AI content safety is only available for models deployed as serverless API endpoints. + +## Use chat completions with images + +Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: + +> [!IMPORTANT] +> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. + +To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): + + +```csharp +string imageUrl = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg"; +string imageFormat = "jpeg"; +HttpClient httpClient = new HttpClient(); +httpClient.DefaultRequestHeaders.Add("User-Agent", "Mozilla/5.0"); +byte[] imageBytes = httpClient.GetByteArrayAsync(imageUrl).Result; +string imageBase64 = Convert.ToBase64String(imageBytes); +string dataUrl = $"data:image/{imageFormat};base64,{imageBase64}"; +``` + +Visualize the image: + +:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: + +Now, create a chat completion request with the image: + + +```csharp +ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() +{ + Messages = { + new ChatRequestSystemMessage("You are an AI assistant that helps people find information."), + new ChatRequestUserMessage([ + new ChatMessageTextContentItem("Which conclusion can be extracted from the following chart?"), + new ChatMessageImageContentItem(new Uri(dataUrl)) + ]), + }, + MaxTokens=2048, +}; + +var response = client.Complete(requestOptions); +Console.WriteLine(response.Value.Choices[0].Message.Content); +``` + +The response is as follows, where you can see the model's usage statistics: + + +```csharp +Console.WriteLine($"{response.Value.Choices[0].Message.Role}: {response.Value.Choices[0].Message.Content}"); +Console.WriteLine($"Model: {response.Value.Model}"); +Console.WriteLine("Usage:"); +Console.WriteLine($"\tPrompt tokens: {response.Value.Usage.PromptTokens}"); +Console.WriteLine($"\tTotal tokens: {response.Value.Usage.TotalTokens}"); +Console.WriteLine($"\tCompletion tokens: {response.Value.Usage.CompletionTokens}"); +``` + +```console +ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. +Model: Mistral-Large +Usage: + Prompt tokens: 2380 + Completion tokens: 126 + Total tokens: 2506 +``` + +::: zone-end + + +::: zone pivot="programming-language-rest" + +Models deployed with the [Azure AI model inference API](https://aka.ms/azureai/modelinference) can be consumed using any REST client. To use the REST client, you need the following prerequisites: + +1. An [Azure subscription](https://azure.microsoft.com). +2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). +3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. + + * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). + +4. To construct the requests, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name`` is your unique model deployment host name and `your-azure-region`` is the Azure region where the model is deployed (for example, eastus2). +5. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. + +## Work with chat completions + +In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. + +### Create a client to consume the model + +First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. + +### Get the model's capabilities + +The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: + +```http +GET /info HTTP/1.1 +Host: +Authorization: Bearer +Content-Type: application/json +``` + +The response is as follows: + + +```json +{ + "model_name": "Mistral-Large", + "model_type": "chat-completions", + "model_provider_name": "MistralAI" +} +``` + +### Create a chat completion request + +The following example shows how you can create a basic chat completions request to the model. + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "How many languages are in the world?" + } + ] +} +``` + +The response is as follows, where you can see the model's usage statistics: + + +```json +{ + "id": "0a1234b5de6789f01gh2i345j6789klm", + "object": "chat.completion", + "created": 1718726686, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.", + "tool_calls": null + }, + "finish_reason": "stop", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 19, + "total_tokens": 91, + "completion_tokens": 72 + } +} +``` + +Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. + +#### Stream content + +By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. + +You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "How many languages are in the world?" + } + ], + "stream": true, + "temperature": 0, + "top_p": 1, + "max_tokens": 2048 +} +``` + +You can visualize how streaming generates content: + + +```json +{ + "id": "23b54589eba14564ad8a2e6978775a39", + "object": "chat.completion.chunk", + "created": 1718726371, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "delta": { + "role": "assistant", + "content": "" + }, + "finish_reason": null, + "logprobs": null + } + ] +} +``` + +The last message in the stream has `finish_reason` set, indicating the reason for the generation process to stop. + + +```json +{ + "id": "23b54589eba14564ad8a2e6978775a39", + "object": "chat.completion.chunk", + "created": 1718726371, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "delta": { + "content": "" + }, + "finish_reason": "stop", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 19, + "total_tokens": 91, + "completion_tokens": 72 + } +} +``` + +#### Explore more parameters supported by the inference client + +Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "How many languages are in the world?" + } + ], + "presence_penalty": 0.1, + "frequency_penalty": 0.8, + "max_tokens": 2048, + "stop": ["<|endoftext|>"], + "temperature" :0, + "top_p": 1, + "response_format": { "type": "text" } +} +``` + + +```json +{ + "id": "0a1234b5de6789f01gh2i345j6789klm", + "object": "chat.completion", + "created": 1718726686, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.", + "tool_calls": null + }, + "finish_reason": "stop", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 19, + "total_tokens": 91, + "completion_tokens": 72 + } +} +``` + +> [!TIP] +> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. + +If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). + +#### Create JSON outputs + +For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that always generate responses in JSON format, using the following format: { \"answer\": \"response\" }" + }, + { + "role": "user", + "content": "How many languages are in the world?" + } + ], + "response_format": { "type": "json_object" } +} +``` + + +```json +{ + "id": "0a1234b5de6789f01gh2i345j6789klm", + "object": "chat.completion", + "created": 1718727522, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "{\"answer\": \"There are approximately 7,117 living languages in the world today, according to the latest estimates. However, this number can vary as some languages become extinct and others are newly discovered or classified.\"}", + "tool_calls": null + }, + "finish_reason": "stop", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 39, + "total_tokens": 87, + "completion_tokens": 48 + } +} +``` + +### Pass extra parameters to the model + +The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. + +Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. + +```http +POST /chat/completions HTTP/1.1 +Host: +Authorization: Bearer +Content-Type: application/json +extra-parameters: pass-through +``` + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "How many languages are in the world?" + } + ], + "logprobs": true +} +``` + +To learn more about the extra parameters that you can pass to the model, see its documentation. + +### Use tools + +Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. + +The following code example creates a tool definition that is able to look from flight information from two different cities. + + +```json +{ + "type": "function", + "function": { + "name": "get_flight_info", + "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + "parameters": { + "type": "object", + "properties": { + "origin_city": { + "type": "string", + "description": "The name of the city where the flight originates" + }, + "destination_city": { + "type": "string", + "description": "The flight destination city" + } + }, + "required": [ + "origin_city", + "destination_city" + ] + } + } +} +``` + +In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. + +> [!NOTE] +> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. + +Prompt the model to book flights with the help of this function: + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that help users to find information about traveling, how to get to places and the different transportations options. You care about the environment and you always have that in mind when answering inqueries" + }, + { + "role": "user", + "content": "When is the next flight from Miami to Seattle?" + } + ], + "tool_choice": "auto", + "tools": [ + { + "type": "function", + "function": { + "name": "get_flight_info", + "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + "parameters": { + "type": "object", + "properties": { + "origin_city": { + "type": "string", + "description": "The name of the city where the flight originates" + }, + "destination_city": { + "type": "string", + "description": "The flight destination city" + } + }, + "required": [ + "origin_city", + "destination_city" + ] + } + } + } + ] +} +``` + +You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. + + +```json +{ + "id": "0a1234b5de6789f01gh2i345j6789klm", + "object": "chat.completion", + "created": 1718726007, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "abc0dF1gh", + "type": "function", + "function": { + "name": "get_flight_info", + "arguments": "{\"origin_city\": \"Miami\", \"destination_city\": \"Seattle\"}", + "call_id": null + } + } + ] + }, + "finish_reason": "tool_calls", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 190, + "total_tokens": 226, + "completion_tokens": 36 + } +} +``` + +To continue, append this message to the chat history: + +Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. + +View the response from the model: + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that help users to find information about traveling, how to get to places and the different transportations options. You care about the environment and you always have that in mind when answering inqueries" + }, + { + "role": "user", + "content": "When is the next flight from Miami to Seattle?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "id": "abc0DeFgH", + "type": "function", + "function": { + "name": "get_flight_info", + "arguments": "{\"origin_city\": \"Miami\", \"destination_city\": \"Seattle\"}", + "call_id": null + } + } + ] + }, + { + "role": "tool", + "content": "{ \"info\": \"There are no flights available from Miami to Seattle. You should take a train, specially if it helps to reduce CO2 emissions.\" }", + "tool_call_id": "abc0DeFgH" + } + ], + "tool_choice": "auto", + "tools": [ + { + "type": "function", + "function": { + "name": "get_flight_info", + "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", + "parameters":{ + "type": "object", + "properties": { + "origin_city": { + "type": "string", + "description": "The name of the city where the flight originates" + }, + "destination_city": { + "type": "string", + "description": "The flight destination city" + } + }, + "required": ["origin_city", "destination_city"] + } + } + } + ] +} +``` + +### Apply content safety + +The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. + +The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. + + +```json +{ + "messages": [ + { + "role": "system", + "content": "You are an AI assistant that helps people find information." + }, + { + "role": "user", + "content": "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." + } + ] +} +``` + + +```json +{ + "error": { + "message": "The response was filtered due to the prompt triggering Microsoft's content management policy. Please modify your prompt and retry.", + "type": null, + "param": "prompt", + "code": "content_filter", + "status": 400 + } +} +``` + +> [!TIP] +> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). + +> [!NOTE] +> Azure AI content safety is only available for models deployed as serverless API endpoints. + +## Use chat completions with images + +Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: + +> [!IMPORTANT] +> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. + +To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): + +> [!TIP] +> You will need to construct the data URL using an scripting or programming language. This tutorial use [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`. + +Visualize the image: + +:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: + +Now, create a chat completion request with the image: + + +```json +{ + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Which peculiar conclusion about LLMs and SLMs can be extracted from the following chart?" + }, + { + "type": "image_url", + "image_url": { + "url": "data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ..." + } + } + ] + } + ], + "temperature": 0, + "top_p": 1, + "max_tokens": 2048 +} +``` + +The response is as follows, where you can see the model's usage statistics: + + +```json +{ + "id": "0a1234b5de6789f01gh2i345j6789klm", + "object": "chat.completion", + "created": 1718726686, + "model": "Mistral-Large", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there may be other factors at play that influence a model's performance.", + "tool_calls": null + }, + "finish_reason": "stop", + "logprobs": null + } + ], + "usage": { + "prompt_tokens": 2380, + "completion_tokens": 126, + "total_tokens": 2506 + } +} +``` + +::: zone-end + +## Related content + + +* [Azure AI Model Inference API](../reference/reference-model-inference-api.md) +* [Deploy models as serverless APIs](deploy-models-serverless.md) +* [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) +* [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) +* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) diff --git a/articles/ai-studio/toc.yml b/articles/ai-studio/toc.yml index b6d2e560dbc..0f0338cfaf3 100644 --- a/articles/ai-studio/toc.yml +++ b/articles/ai-studio/toc.yml @@ -216,6 +216,12 @@ items: href: how-to/develop/vscode.md - name: Start with an AI template href: how-to/develop/ai-template-get-started.md + - name: Develop with Azure AI Model Inference API + items: + - name: Use chat models with the Azure AI Model Inference API + href: how-to/develop/model-inference-chat.md + - name: Develop with LlamaIndex and Azure AI studio + href: how-to/develop/llama-index.md - name: Trace your application with prompt flow href: how-to/develop/trace-local-sdk.md displayName: code,sdk From 54531b87f0d22646d97499a8a589fdc9165fe9f0 Mon Sep 17 00:00:00 2001 From: "Dina Berry (MSFT)" Date: Tue, 17 Sep 2024 14:24:25 -0700 Subject: [PATCH 17/63] Update articles/ai-services/openai/includes/chatgpt-javascript.md Co-authored-by: Minh-Anh Phan <111523473+minhanh-phan@users.noreply.github.com> --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index 2d9bf15e60f..af62fc21a69 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -23,7 +23,7 @@ ms.date: 05/21/2024 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true) - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule) - [TypeScript](https://www.typescriptlang.org/download/) -- An Azure OpenAI Service resource with a `gpt-35-turbo` , `gpt-4` , `gpt-4o`, or `gpt-4o-mini` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). +- An Azure OpenAI Service resource with a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=JAVASCRIPT&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Prerequisites) From 23e2c0552090220e3b9f3098e569f85cd717f3ee Mon Sep 17 00:00:00 2001 From: PatrickFarley Date: Wed, 18 Sep 2024 00:09:32 -0400 Subject: [PATCH 18/63] add tony's draft --- .../how-to/improve-performance.md | 66 +++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 articles/ai-services/content-safety/how-to/improve-performance.md diff --git a/articles/ai-services/content-safety/how-to/improve-performance.md b/articles/ai-services/content-safety/how-to/improve-performance.md new file mode 100644 index 00000000000..75ff396520e --- /dev/null +++ b/articles/ai-services/content-safety/how-to/improve-performance.md @@ -0,0 +1,66 @@ +--- +title: "Improve performance in Azure AI Content Safety" +titleSuffix: Azure AI services +description: tbd +#services: cognitive-services +author: PatrickFarley +manager: nitinme +ms.service: azure-ai-content-safety +ms.topic: how-to +ms.date: 09/18/2024 +ms.author: pafarley +--- + +# Improve performance in Azure AI Content Safety + +This playbook provides a step-by-step guide for users of Azure AI Content Safety on how to handle false positives and false negatives effectively. False positives occur when the system incorrectly identifies non-harmful content as harmful, while false negatives occur when harmful content is not flagged. Properly addressing these instances ensures the integrity and reliability of the content moderation process, including responsible generative AI deployment. + +## Review and verification + +Conduct an initial assessment to determine whether the flagged content is indeed a false positive or false negative. This can involve: +- Checking the context of the flagged content. +- Comparing the flagged content against the content safety risk categories and severity definitions. + - If you are using content safety in Azure OpenAI, go here + - If you are using the Azure AI Content Safety standalone API, go here for harm categories and here for Prompt Shields + +## Customize your severity settings + +If your assessment confirms that this is indeed a false positive or false negative, you can try customizing your severity settings to mitigate the issue before reaching out to Microsoft. + +### Azure AI Content Safety standalone API users + +If you are using the Azure AI Content Safety standalone API , try experimenting by setting the severity threshold at different levels for [harm categories](/azure/ai-services/content-safety/concepts/harm-categories?tabs=definitions) based on API output. Alternatively, if you prefer the no-code approeach, you can try out those settings in [content safety studio](https://contentsafety.cognitive.azure.com/) or Azure AI Studio’s [content safety page](https://ai.azure.com/explore/contentsafety). Instructions can be found [here](/azure/ai-studio/quickstarts/content-safety?tabs=moderate-text-content). + +In addition to adjusting the severity levels for false negatives, you can also use blocklists. More information on using blocklists for text moderation can be found in [Use blocklists for text moderation](/azure/ai-services/content-safety/how-to/use-blocklist?tabs=windows%2Crest). + +## Azure AI Studio Content Filtering users + +Read the [Configurability](/azure/ai-studio/concepts/content-filtering#configurability-preview) documentation, as some content filtering configurations may require approval through the process mentioned there. + +Follow the steps in the documentation to update configurations to handle false positives or negatives: [Azure AI Studio content filtering](/azure/ai-studio/concepts/content-filtering#create-a-content-filter). + +In addition to adjusting the severity levels for false negatives, you can also use blocklists. Detailed instruction can be found here: [Azure AI Studio content filtering](/azure/ai-studio/concepts/content-filtering#use-a-blocklist-as-a-filter). + +## Create a custom category based on your own RAI policy + +Sometimes, you might need a custom category to ensure the filtering aligns with your specific Responsible AI policy, as pre-built categories or content filtering may not suffice. You may require an entirely new content category. + +Refer to the [custom categories documentation](/azure/ai-services/content-safety/custom-category) to build your own categories with the Azure AI Content Safety standalone API. + +We are currently working on integrating the custom categories feature into Azure OpenAI and AI Studio, which will be available soon. + +## Document issues and send feedback to Azure + +If, after you’ve exhausted all the steps mentioned above, the false positives or negatives cannot be resolved by Azure AI Content Safety, it is likely a policy definition or model issue that needs further attention. + +Document the details of the false positives and/or false negatives by providing the following information: +- Description of the flagged content. +- Context in which the content was posted. +- Reason given by Azure AI Content Safety for the flagging. +- Explanation of why the content is a false positive or negative. +- Any adjustments already attempted in severity settings or custom categories. +- Screenshots or logs of the flagged content and system responses. + +This documentation will help in escalating the issue to the appropriate teams for resolution. + +Send the feedback to our Azure CSS by following the instructions [here](tbd). \ No newline at end of file From fa1420f040c19b7b431c1d95014fd5ee79a52e75 Mon Sep 17 00:00:00 2001 From: Facundo Santiago Date: Wed, 18 Sep 2024 18:59:00 +0000 Subject: [PATCH 19/63] clearnup --- .../ai-studio/how-to/develop/llama-index.md | 47 +- .../how-to/develop/model-inference-chat.md | 2073 ----------------- .../ai-studio/how-to/develop/sdk-overview.md | 5 +- articles/ai-studio/toc.yml | 4 - 4 files changed, 26 insertions(+), 2103 deletions(-) delete mode 100644 articles/ai-studio/how-to/develop/model-inference-chat.md diff --git a/articles/ai-studio/how-to/develop/llama-index.md b/articles/ai-studio/how-to/develop/llama-index.md index 06435b2941b..49718a6ed1c 100644 --- a/articles/ai-studio/how-to/develop/llama-index.md +++ b/articles/ai-studio/how-to/develop/llama-index.md @@ -11,44 +11,41 @@ ms.author: eur author: eric-urban --- -# Develop application with LlamaIndex and Azure AI studio +# Develop applications with LlamaIndex and Azure AI studio -In this article, you learn how to use [`llama-index`](https://github.com/run-llama/llama_index) with models deployed from the Azure AI model catalog deployed to Azure AI studio. +In this article, you learn how to use [LlamaIndex](https://github.com/run-llama/llama_index) with models deployed from the Azure AI model catalog deployed to Azure AI studio. -## Prerequisites - -To run this tutorial you need: +Models deployed to Azure AI studio can be used with LlamaIndex in two ways: -1. An [Azure subscription](https://azure.microsoft.com). -2. An Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). -3. A model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. +- **Using the Azure AI model inference API:** All models deployed to Azure AI studio support the Azure AI model inference API, which offers a common set of functionalities that can be used for most of the models in the catalog. The benefit of this API is that, since it's the same for all the models, changing from one to another is as simple as changing the model deployment being use. No further changes are required in the code. When working with LlamaIndex, install the extensions `llama-index-llms-azure-inference` and `llama-index-embeddings-azure-inference`. - * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). +- **Using the model's provider specific API:** Some models, like OpenAI, Cohere, or Mistral, offer their own set of APIs and extensions for LlamaIndex. Those extensions may include specific functionalities that the model support and hence are suitable if you want to exploit them. When working with `llama-index`, install the extension specific for the model you want to use, like `llama-index-llms-openai` or `llama-index-llms-cohere`. -4. A Python environment. +In this example, we are working with the Azure AI model inference API. +## Prerequisites -## Install dependencies - -Ensure you have `llama-index` installed: - -```bash -pip install llama-index -``` +To run this tutorial you need: -Models deployed to Azure AI studio or Azure Machine Learning can be used with LlamaIndex in two ways: +1. An [Azure subscription](https://azure.microsoft.com). +2. An Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](../how-to/create-azure-ai-resource). +3. A model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like `cohere-embed-v3-multilingual`. -- **Using the Azure AI model inference API:** All models deployed to Azure AI studio and Azure Machine Learning support the Azure AI model inference API, which offers a common set of functionalities that can be used for most of the models in the catalog. The benefit of this API is that, since it's the same for all the models, changing from one to another is as simple as changing the model deployment being use. No further changes are required in the code. When working with `llama-index`, install the extensions `llama-index-llms-azure-inference` and `llama-index-embeddings-azure-inference`. + * You can follow the instructions at [Deploy models as serverless APIs](../how-to/deploy-models-serverless). -- **Using the model's provider specific API:** Some models, like OpenAI, Cohere, or Mistral, offer their own set of APIs and extensions for `llama-index`. Those extensions may include specific functionalities that the model support and hence are suitable if you want to exploit them. When working with `llama-index`, install the extension specific for the model you want to use, like `llama-index-llms-openai` or `llama-index-llms-cohere`. +4. Python 3.8 or later installed, including pip. +5. LlamaIndex installed. You can do it with: + ```bash + pip install llama-index + ``` -In this example, we are working with the Azure AI model inference API, hence we install the following packages: +6. In this example, we are working with the Azure AI model inference API, hence we install the following packages: -```bash -pip install -U llama-index-llms-azure-inference -pip install -U llama-index-embeddings-azure-inference -``` + ```bash + pip install -U llama-index-llms-azure-inference + pip install -U llama-index-embeddings-azure-inference + ``` ## Configure the environment diff --git a/articles/ai-studio/how-to/develop/model-inference-chat.md b/articles/ai-studio/how-to/develop/model-inference-chat.md deleted file mode 100644 index 95a5afc175c..00000000000 --- a/articles/ai-studio/how-to/develop/model-inference-chat.md +++ /dev/null @@ -1,2073 +0,0 @@ ---- -title: Develop with Azure AI Model Inference API with Azure AI Studio -titleSuffix: Azure AI Studio -description: This article explains how to use models deployed in Azure AI Studio and consume them using the Azure AI model inference API in the language of your choice. -ms.service: azure-ai-studio -manager: scottpolly -ms.topic: how-to -ms.date: 09/14/2024 -ms.reviewer: fasantia -ms.author: eur -author: eric-urba -ms.custom: references_regions, generated -zone_pivot_groups: azure-ai-model-catalog-samples-chat ---- - -# Develop with Azure AI Model Inference API to consume chat models in the language of your choice - -[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)] - -In this example, you learn how to use the Azure AI Model inference API to consume chat models deployed in Azure AI Studio using the programming language of your choice. - - -::: zone pivot="programming-language-python" - -## Prerequisites - -You need the following prerequisites: - -1. An [Azure subscription](https://azure.microsoft.com). -2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). -3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. - - * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). - -4. Python 3.8 or later installed, including pip. -5. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). -6. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. -7. The Azure AI inference package installed with the following command: - - ```bash - pip install azure-ai-inference - ``` - -Read more about the [Azure AI inference package and reference](https://aka.ms/azsdk/azure-ai-inference/python/reference). - -## Work with chat completions - -In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. - -### Create a client to consume the model - -First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. - - -```python -import os -from azure.ai.inference import ChatCompletionsClient -from azure.core.credentials import AzureKeyCredential - -client = ChatCompletionsClient( - endpoint=os.environ["AZURE_INFERENCE_ENDPOINT"], - credential=AzureKeyCredential(os.environ["AZURE_INFERENCE_CREDENTIAL"]), -) -``` - -### Get the model's capabilities - -The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: - - -```python -model_info = client.get_model_info() -``` - -The response is as follows: - - -```python -print("Model name:", model_info.model_name) -print("Model type:", model_info.model_type) -print("Model provider name:", model_info.model_provider_name) -``` - -```console -Model name: Mistral-Large -Model type: chat-completions -Model provider name: MistralAI -``` - -### Create a chat completion request - -The following example shows how you can create a basic chat completions request to the model. - -```python -from azure.ai.inference.models import SystemMessage, UserMessage - -response = client.complete( - messages=[ - SystemMessage(content="You are a helpful assistant."), - UserMessage(content="How many languages are in the world?"), - ], -) -``` - -The response is as follows, where you can see the model's usage statistics: - - -```python -print("Response:", response.choices[0].message.content) -print("Model:", response.model) -print("Usage:") -print("\tPrompt tokens:", response.usage.prompt_tokens) -print("\tTotal tokens:", response.usage.total_tokens) -print("\tCompletion tokens:", response.usage.completion_tokens) -``` - -```console -Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. -Model: Mistral-Large -Usage: - Prompt tokens: 19 - Total tokens: 91 - Completion tokens: 72 -``` - -Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. - -#### Stream content - -By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. - -You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. - - -```python -result = client.complete( - messages=[ - SystemMessage(content="You are a helpful assistant."), - UserMessage(content="How many languages are in the world?"), - ], - temperature=0, - top_p=1, - max_tokens=2048, - stream=True, -) -``` - -To stream completions, set `stream=True` when you call the model. - -To visualize the output, define a helper function to print the stream. - -```python -def print_stream(result): - """ - Prints the chat completion with streaming. - """ - import time - for update in result: - if update.choices: - print(update.choices[0].delta.content, end="") -``` - -You can visualize how streaming generates content: - - -```python -print_stream(result) -``` - -#### Explore more parameters supported by the inference client - -Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). - -```python -from azure.ai.inference.models import ChatCompletionsResponseFormatText - -response = client.complete( - messages=[ - SystemMessage(content="You are a helpful assistant."), - UserMessage(content="How many languages are in the world?"), - ], - presence_penalty=0.1, - frequency_penalty=0.8, - max_tokens=2048, - stop=["<|endoftext|>"], - temperature=0, - top_p=1, - response_format={ "type": ChatCompletionsResponseFormatText() }, -) -``` - -> [!TIP] -> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. - -If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). - -#### Create JSON outputs - -For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. - - -```python -from azure.ai.inference.models import ChatCompletionsResponseFormatJSON - -response = client.complete( - messages=[ - SystemMessage(content="You are a helpful assistant that always generate responses in JSON format, using." - " the following format: { ""answer"": ""response"" }."), - UserMessage(content="How many languages are in the world?"), - ], - response_format={ "type": ChatCompletionsResponseFormatJSON() } -) -``` - -### Pass extra parameters to the model - -The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. - -Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. - - -```python -response = client.complete( - messages=[ - SystemMessage(content="You are a helpful assistant."), - UserMessage(content="How many languages are in the world?"), - ], - model_extras={ - "logprobs": True - } -) -``` - -To learn more about the extra parameters that you can pass to the model, see its documentation. - -### Use tools - -Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. - -The following code example creates a tool definition that is able to look from flight information from two different cities. - - -```python -from azure.ai.inference.models import FunctionDefinition, ChatCompletionsFunctionToolDefinition - -flight_info = ChatCompletionsFunctionToolDefinition( - function=FunctionDefinition( - name="get_flight_info", - description="Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - parameters={ - "type": "object", - "properties": { - "origin_city": { - "type": "string", - "description": "The name of the city where the flight originates", - }, - "destination_city": { - "type": "string", - "description": "The flight destination city", - }, - }, - "required": ["origin_city", "destination_city"], - }, - ) -) - -tools = [flight_info] -``` - -In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. - - -```python -def get_flight_info(loc_origin: str, loc_destination: str): - return { - "info": f"There are no flights available from {loc_origin} to {loc_destination}. You should take a train, specially if it helps to reduce CO2 emissions." - } -``` - -> [!NOTE] -> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. - -Prompt the model to book flights with the help of this function: - - -```python -messages = [ - SystemMessage( - content="You are a helpful assistant that help users to find information about traveling, how to get" - " to places and the different transportations options. You care about the environment and you" - " always have that in mind when answering inqueries.", - ), - UserMessage( - content="When is the next flight from Miami to Seattle?", - ), -] - -response = client.complete( - messages=messages, tools=tools, tool_choice="auto" -) -``` - -You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. - - -```python -response_message = response.choices[0].message -tool_calls = response_message.tool_calls - -print("Finish reason:", response.choices[0].finish_reason) -print("Tool call:", tool_calls) -``` - -To continue, append this message to the chat history: - - -```python -messages.append( - response_message -) -``` - -Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. - - -```python -import json -from azure.ai.inference.models import ToolMessage - -for tool_call in tool_calls: - - # Get the tool details: - - function_name = tool_call.function.name - function_args = json.loads(tool_call.function.arguments.replace("\'", "\"")) - tool_call_id = tool_call.id - - print(f"Calling function `{function_name}` with arguments {function_args}") - - # Call the function defined above using `locals()`, which returns the list of all functions - # available in the scope as a dictionary. Notice that this is just done as a simple way to get - # the function callable from its string name. Then we can call it with the corresponding - # arguments. - - callable_func = locals()[function_name] - function_response = callable_func(**function_args) - - print("->", function_response) - - # Once we have a response from the function and its arguments, we can append a new message to the chat - # history. Notice how we are telling to the model that this chat message came from a tool: - - messages.append( - ToolMessage( - tool_call_id=tool_call_id, - content=json.dumps(function_response) - ) - ) -``` - -View the response from the model: - - -```python -response = client.complete( - messages=messages, - tools=tools, -) -``` - -### Apply content safety - -The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. - -The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. - - -```python -from azure.ai.inference.models import AssistantMessage, UserMessage, SystemMessage - -try: - response = client.complete( - messages=[ - SystemMessage(content="You are an AI assistant that helps people find information."), - UserMessage(content="Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills."), - ] - ) - - print(response.choices[0].message.content) - -except HttpResponseError as ex: - if ex.status_code == 400: - response = ex.response.json() - if isinstance(response, dict) and "error" in response: - print(f"Your request triggered an {response['error']['code']} error:\n\t {response['error']['message']}") - else: - raise - raise -``` - -> [!TIP] -> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). - -> [!NOTE] -> Azure AI content safety is only available for models deployed as serverless API endpoints. - -## Use chat completions with images - -Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: - -> [!IMPORTANT] -> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. - -To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): - - -```python -from urllib.request import urlopen, Request -import base64 - -image_url = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg" -image_format = "jpeg" - -request = Request(image_url, headers={"User-Agent": "Mozilla/5.0"}) -image_data = base64.b64encode(urlopen(request).read()).decode("utf-8") -data_url = f"data:image/{image_format};base64,{image_data}" -``` - -Visualize the image: - - -```python -import requests -import IPython.display as Disp - -Disp.Image(requests.get(image_url).content) -``` - -:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: - -Now, create a chat completion request with the image: - - -```python -from azure.ai.inference.models import TextContentItem, ImageContentItem, ImageUrl -response = client.complete( - messages=[ - SystemMessage("You are a helpful assistant that can generate responses based on images."), - UserMessage(content=[ - TextContentItem(text="Which conclusion can be extracted from the following chart?"), - ImageContentItem(image=ImageUrl(url=data_url)) - ]), - ], - temperature=0, - top_p=1, - max_tokens=2048, -) -``` - -The response is as follows, where you can see the model's usage statistics: - - -```python -print(f"{response.choices[0].message.role}:\n\t{response.choices[0].message.content}\n") -print("Model:", response.model) -print("Usage:") -print("\tPrompt tokens:", response.usage.prompt_tokens) -print("\tCompletion tokens:", response.usage.completion_tokens) -print("\tTotal tokens:", response.usage.total_tokens) -``` - -```console -ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. -Model: Mistral-Large -Usage: - Prompt tokens: 2380 - Completion tokens: 126 - Total tokens: 2506 -``` - -::: zone-end - - -::: zone pivot="programming-language-javascript" - -You need the following prerequisites: - -1. An [Azure subscription](https://azure.microsoft.com). -2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). -3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. - - * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). - -4. LTS versions of `Node.js` with `npm`. -5. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). -6. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. -7. The Azure Inference library for JavaScript installed with the following command: - - ```bash - npm install @azure-rest/ai-inference - ``` - -## Work with chat completions - -In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. - -### Create a client to consume the model - -First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. - - -```javascript -import ModelClient from "@azure-rest/ai-inference"; -import { isUnexpected } from "@azure-rest/ai-inference"; -import { AzureKeyCredential } from "@azure/core-auth"; - -const client = new ModelClient( - process.env.AZURE_INFERENCE_ENDPOINT, - new AzureKeyCredential(process.env.AZURE_INFERENCE_CREDENTIAL) -); -``` - -### Get the model's capabilities - -The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: - - -```javascript -var model_info = await client.path("/info").get() -``` - -The response is as follows: - - -```javascript -console.log("Model name: ", model_info.body.model_name) -console.log("Model type: ", model_info.body.model_type) -console.log("Model provider name: ", model_info.body.model_provider_name) -``` - -```console -Model name: Mistral-Large -Model type: chat-completions -Model provider name: MistralAI -``` - -### Create a chat completion request - -The following example shows how you can create a basic chat completions request to the model. - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant" }, - { role: "user", content: "How many languages are in the world?" }, -]; - -var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - } -}); -``` - -The response is as follows, where you can see the model's usage statistics: - - -```javascript -if (isUnexpected(response)) { - throw response.body.error; -} - -console.log("Response: ", response.body.choices[0].message.content); -console.log("Model: ", response.body.model); -console.log("Usage:"); -console.log("\tPrompt tokens:", response.body.usage.prompt_tokens); -console.log("\tTotal tokens:", response.body.usage.total_tokens); -console.log("\tCompletion tokens:", response.body.usage.completion_tokens); -``` - -```console -Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. -Model: Mistral-Large -Usage: - Prompt tokens: 19 - Total tokens: 91 - Completion tokens: 72 -``` - -Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. - -#### Stream content - -By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. - -You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. - - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant" }, - { role: "user", content: "How many languages are in the world?" }, -]; - -var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - } -}).asNodeStream(); -``` - -To stream completions, use `.asNodeStream()` when you call the model. - -You can visualize how streaming generates content: - - -```javascript -var stream = response.body; -if (!stream) { - stream.destroy(); - throw new Error(`Failed to get chat completions with status: ${response.status}`); -} - -if (response.status !== "200") { - throw new Error(`Failed to get chat completions: ${response.body.error}`); -} - -var sses = createSseStream(stream); - -for await (const event of sses) { - if (event.data === "[DONE]") { - return; - } - for (const choice of (JSON.parse(event.data)).choices) { - console.log(choice.delta?.content ?? ""); - } -} -``` - -#### Explore more parameters supported by the inference client - -Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant" }, - { role: "user", content: "How many languages are in the world?" }, -]; - -var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - presence_penalty: "0.1", - frequency_penalty: "0.8", - max_tokens: 2048, - stop: ["<|endoftext|>"], - temperature: 0, - top_p: 1, - response_format: { type: "text" }, - } -}); -``` - -> [!TIP] -> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. - -If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). - -#### Create JSON outputs - -For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. - - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant that always generate responses in JSON format, using." - + " the following format: { \"answer\": \"response\" }." }, - { role: "user", content: "How many languages are in the world?" }, -]; - -var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - response_format: { type: "json_object" } - } -}); -``` - -### Pass extra parameters to the model - -The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. - -Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. - - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant" }, - { role: "user", content: "How many languages are in the world?" }, -]; - -var response = await client.path("/chat/completions").post({ - headers: { - "extra-params": "pass-through" - }, - body: { - messages: messages, - logprobs: true - } -}); -``` - -To learn more about the extra parameters that you can pass to the model, see its documentation. - -### Use tools - -Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. - -The following code example creates a tool definition that is able to look from flight information from two different cities. - - -```javascript -const flight_info = { - name: "get_flight_info", - description: "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - parameters: { - type: "object", - properties: { - origin_city: { - type: "string", - description: "The name of the city where the flight originates", - }, - destination_city: { - type: "string", - description: "The flight destination city", - }, - }, - required: ["origin_city", "destination_city"], - }, -} - -const tools = [ - { - type: "function", - function: flight_info, - }, -]; -``` - -In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. - - -```javascript -function get_flight_info(loc_origin, loc_destination) { - return { - info: "There are no flights available from " + loc_origin + " to " + loc_destination + ". You should take a train, specially if it helps to reduce CO2 emissions." - } -} -``` - -> [!NOTE] -> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. - -Prompt the model to book flights with the help of this function: - - -```javascript -var result = await client.path("/chat/completions").post({ - body: { - messages: messages, - tools: tools, - tool_choice: "auto" - } -}); -``` - -You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. - - -```javascript -const response_message = response.body.choices[0].message; -const tool_calls = response_message.tool_calls; - -console.log("Finish reason: " + response.body.choices[0].finish_reason); -console.log("Tool call: " + tool_calls); -``` - -To continue, append this message to the chat history: - - -```javascript -messages.push(response_message); -``` - -Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. - - -```javascript -function applyToolCall({ function: call, id }) { - // Get the tool details: - const tool_params = JSON.parse(call.arguments); - console.log("Calling function " + call.name + " with arguments " + tool_params); - - // Call the function defined above using `window`, which returns the list of all functions - // available in the scope as a dictionary. Notice that this is just done as a simple way to get - // the function callable from its string name. Then we can call it with the corresponding - // arguments. - const function_response = tool_params.map(window[call.name]); - console.log("-> " + function_response); - - return function_response -} - -for (const tool_call of tool_calls) { - var tool_response = tool_call.apply(applyToolCall); - - messages.push( - { - role: "tool", - tool_call_id: tool_call.id, - content: tool_response - } - ); -} -``` - -View the response from the model: - - -```javascript -var result = await client.path("/chat/completions").post({ - body: { - messages: messages, - tools: tools, - } -}); -``` - -### Apply content safety - -The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. - -The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. - - -```javascript -try { - var messages = [ - { role: "system", content: "You are an AI assistant that helps people find information." }, - { role: "user", content: "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." }, - ]; - - var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - } - }); - - console.log(response.body.choices[0].message.content); -} -catch (error) { - if (error.status_code == 400) { - var response = JSON.parse(error.response._content); - if (response.error) { - console.log(`Your request triggered an ${response.error.code} error:\n\t ${response.error.message}`); - } - else - { - throw error; - } - } -} -``` - -> [!TIP] -> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). - -> [!NOTE] -> Azure AI content safety is only available for models deployed as serverless API endpoints. - -## Use chat completions with images - -Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: - -> [!IMPORTANT] -> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. - -To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): - - -```javascript -const image_url = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg"; -const image_format = "jpeg"; - -const response = await fetch(image_url, { headers: { "User-Agent": "Mozilla/5.0" } }); -const image_data = await response.arrayBuffer(); -const image_data_base64 = Buffer.from(image_data).toString("base64"); -const data_url = `data:image/${image_format};base64,${image_data_base64}`; -``` - -Visualize the image: - - -```javascript -const img = document.createElement("img"); -img.src = data_url; -document.body.appendChild(img); -``` - -:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: - -Now, create a chat completion request with the image: - - -```javascript -var messages = [ - { role: "system", content: "You are a helpful assistant that can generate responses based on images." }, - { role: "user", content: - [ - { type: "text", text: "Which conclusion can be extracted from the following chart?" }, - { type: "image_url", image: - { - url: data_url - } - } - ] - } -]; - -var response = await client.path("/chat/completions").post({ - body: { - messages: messages, - temperature: 0, - top_p: 1, - max_tokens: 2048, - } -}); -``` - -The response is as follows, where you can see the model's usage statistics: - - -```javascript -console.log(response.body.choices[0].message.role + ": " + response.body.choices[0].message.content); -console.log("Model:", response.body.model); -console.log("Usage:"); -console.log("\tPrompt tokens:", response.body.usage.prompt_tokens); -console.log("\tCompletion tokens:", response.body.usage.completion_tokens); -console.log("\tTotal tokens:", response.body.usage.total_tokens); -``` - -```console -ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. -Model: Mistral-Large -Usage: - Prompt tokens: 2380 - Completion tokens: 126 - Total tokens: 2506 -``` - -::: zone-end - - -::: zone pivot="programming-language-csharp" - -You need the following prerequisites: - -1. An [Azure subscription](https://azure.microsoft.com). -2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). -3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. - - * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). - -4. The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2). -5. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. - -6. The Azure AI inference library installed with the following command: - - ```dotnetcli - dotnet add package Azure.AI.Inference --prerelease - ``` - -7. You can also authenticate with Microsoft Entra ID (formerly Azure Active Directory). To use credential providers provided with the Azure SDK, install the `Azure.Identity` package: - - ```dotnetcli - dotnet add package Azure.Identity - ``` - -Import the following namespaces: - - -```csharp -using Azure; -using Azure.Identity; -using Azure.AI.Inference; -``` - -This example also uses the following namespaces but you may not always need them: - - -```csharp -using System.Text.Json; -using System.Text.Json.Serialization; -using System.Reflection; -``` - -## Work with chat completions - -In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. - -### Create a client to consume the model - -First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. - - -```csharp -ChatCompletionsClient client = new ChatCompletionsClient( - new Uri(Environment.GetEnvironmentVariable("AZURE_INFERENCE_ENDPOINT")), - new AzureKeyCredential(Environment.GetEnvironmentVariable("AZURE_INFERENCE_CREDENTIAL")) -); -``` - -### Get the model's capabilities - -The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: - - -```csharp -Response modelInfo = client.GetModelInfo(); -``` - -The response is as follows: - - -```csharp -Console.WriteLine($"Model name: {modelInfo.Value.ModelName}"); -Console.WriteLine($"Model type: {modelInfo.Value.ModelType}"); -Console.WriteLine($"Model provider name: {modelInfo.Value.ModelProviderName}"); -``` - -```console -Model name: Mistral-Large -Model type: chat-completions -Model provider name: MistralAI -``` - -### Create a chat completion request - -The following example shows how you can create a basic chat completions request to the model. - -```csharp -ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() -{ - Messages = { - new ChatRequestSystemMessage("You are a helpful assistant."), - new ChatRequestUserMessage("How many languages are in the world?") - }, -}; - -Response response = client.Complete(requestOptions); -``` - -The response is as follows, where you can see the model's usage statistics: - - -```csharp -Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); -Console.WriteLine($"Model: {response.Value.Model}"); -Console.WriteLine("Usage:"); -Console.WriteLine($"\tPrompt tokens: {response.Value.Usage.PromptTokens}"); -Console.WriteLine($"\tTotal tokens: {response.Value.Usage.TotalTokens}"); -Console.WriteLine($"\tCompletion tokens: {response.Value.Usage.CompletionTokens}"); -``` - -```console -Response: As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred. -Model: Mistral-Large -Usage: - Prompt tokens: 19 - Total tokens: 91 - Completion tokens: 72 -``` - -Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. - -#### Stream content - -By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. - -You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. - - -```csharp -static async Task StreamMessageAsync(ChatCompletionsClient client) -{ - ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() - { - Messages = { - new ChatRequestSystemMessage("You are a helpful assistant."), - new ChatRequestUserMessage("How many languages are in the world? Write an essay about it.") - }, - MaxTokens=4096 - }; - - StreamingResponse streamResponse = await client.CompleteStreamingAsync(requestOptions); - - await PrintStream(streamResponse); -} -``` - -To stream completions, use `CompleteStreamingAsync` method when you call the model. Notice that in this example we the call is wrapped in an asynchronous method. - -To visualize the output, define an asynchronous method to print the stream in the console. - -```csharp -static async Task PrintStream(StreamingResponse response) -{ - await foreach (StreamingChatCompletionsUpdate chatUpdate in response) - { - if (chatUpdate.Role.HasValue) - { - Console.Write($"{chatUpdate.Role.Value.ToString().ToUpperInvariant()}: "); - } - if (!string.IsNullOrEmpty(chatUpdate.ContentUpdate)) - { - Console.Write(chatUpdate.ContentUpdate); - } - } -} -``` - -You can visualize how streaming generates content: - - -```csharp -StreamMessageAsync(client).GetAwaiter().GetResult(); -``` - -#### Explore more parameters supported by the inference client - -Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). - -```csharp -requestOptions = new ChatCompletionsOptions() -{ - Messages = { - new ChatRequestSystemMessage("You are a helpful assistant."), - new ChatRequestUserMessage("How many languages are in the world?") - }, - PresencePenalty = 0.1f, - FrequencyPenalty = 0.8f, - MaxTokens = 2048, - StopSequences = { "<|endoftext|>" }, - Temperature = 0, - NucleusSamplingFactor = 1, - ResponseFormat = new ChatCompletionsResponseFormatText() -}; - -response = client.Complete(requestOptions); -Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); -``` - -> [!TIP] -> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. - -If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). - -#### Create JSON outputs - -For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. - - -```csharp -requestOptions = new ChatCompletionsOptions() -{ - Messages = { - new ChatRequestSystemMessage( - "You are a helpful assistant that always generate responses in JSON format, " + - "using. the following format: { \"answer\": \"response\" }." - ), - new ChatRequestUserMessage( - "How many languages are in the world?" - ) - }, - ResponseFormat = new ChatCompletionsResponseFormatJSON() -}; - -response = client.Complete(requestOptions); -Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); -``` - -### Pass extra parameters to the model - -The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. - -Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. - - -```csharp -requestOptions = new ChatCompletionsOptions() -{ - Messages = { - new ChatRequestSystemMessage("You are a helpful assistant."), - new ChatRequestUserMessage("How many languages are in the world?") - }, - AdditionalProperties = { { "logprobs", BinaryData.FromString("true") } }, -}; - -response = client.Complete(requestOptions, extraParams: ExtraParameters.PassThrough); -Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}"); -``` - -To learn more about the extra parameters that you can pass to the model, see its documentation. - -### Use tools - -Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. - -The following code example creates a tool definition that is able to look from flight information from two different cities. - - -```csharp -FunctionDefinition flightInfoFunction = new FunctionDefinition("getFlightInfo") -{ - Description = "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - Parameters = BinaryData.FromObjectAsJson(new - { - Type = "object", - Properties = new - { - origin_city = new - { - Type = "string", - Description = "The name of the city where the flight originates" - }, - destination_city = new - { - Type = "string", - Description = "The flight destination city" - } - } - }, - new JsonSerializerOptions() { PropertyNamingPolicy = JsonNamingPolicy.CamelCase } - ) -}; - -ChatCompletionsFunctionToolDefinition getFlightTool = new ChatCompletionsFunctionToolDefinition(flightInfoFunction); -``` - -In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. - - -```csharp -static string getFlightInfo(string loc_origin, string loc_destination) -{ - return JsonSerializer.Serialize(new - { - info = $"There are no flights available from {loc_origin} to {loc_destination}. You " + - "should take a train, specially if it helps to reduce CO2 emissions." - }); -} -``` - -> [!NOTE] -> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. - -Prompt the model to book flights with the help of this function: - - -```csharp -var chatHistory = new List(){ - new ChatRequestSystemMessage( - "You are a helpful assistant that help users to find information about traveling, " + - "how to get to places and the different transportations options. You care about the" + - "environment and you always have that in mind when answering inqueries." - ), - new ChatRequestUserMessage("When is the next flight from Miami to Seattle?") - }; - -requestOptions = new ChatCompletionsOptions(chatHistory); -requestOptions.Tools.Add(getFlightTool); -requestOptions.ToolChoice = ChatCompletionsToolChoice.Auto; - -response = client.Complete(requestOptions); -``` - -You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. - - -```csharp -var responseMenssage = response.Value.Choices[0].Message; -var toolsCall = responseMenssage.ToolCalls; - -Console.WriteLine($"Finish reason: {response.Value.Choices[0].FinishReason}"); -Console.WriteLine($"Tool call: {toolsCall[0].Id}"); -``` - -To continue, append this message to the chat history: - - -```csharp -requestOptions.Messages.Add(new ChatRequestAssistantMessage(response.Value.Choices[0].Message)); -``` - -Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. - - -```csharp -foreach (ChatCompletionsToolCall tool in toolsCall) -{ - if (tool is ChatCompletionsFunctionToolCall functionTool) - { - // Get the tool details: - string callId = functionTool.Id; - string toolName = functionTool.Name; - string toolArgumentsString = functionTool.Arguments; - Dictionary toolArguments = JsonSerializer.Deserialize>(toolArgumentsString); - - // Here you have to call the function defined. In this particular example we use - // reflection to find the method we definied before in an static class called - // `ChatCompletionsExamples`. Using reflection allows us to call a function - // by string name. Notice that this is just done for demonstration purposes as a - // simple way to get the function callable from its string name. Then we can call - // it with the corresponding arguments. - - var flags = BindingFlags.Instance | BindingFlags.Public | BindingFlags.NonPublic | BindingFlags.Static; - string toolResponse = (string)typeof(ChatCompletionsExamples).GetMethod(toolName, flags).Invoke(null, toolArguments.Values.Cast().ToArray()); - - Console.WriteLine("->", toolResponse); - requestOptions.Messages.Add(new ChatRequestToolMessage(toolResponse, callId)); - } - else - throw new Exception("Unsupported tool type"); -} -``` - -View the response from the model: - - -```csharp -response = client.Complete(requestOptions); -``` - -### Apply content safety - -The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. - -The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. - - -```csharp -try -{ - requestOptions = new ChatCompletionsOptions() - { - Messages = { - new ChatRequestSystemMessage("You are an AI assistant that helps people find information."), - new ChatRequestUserMessage( - "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." - ), - }, - }; - - response = client.Complete(requestOptions); - Console.WriteLine(response.Value.Choices[0].Message.Content); -} -catch (RequestFailedException ex) -{ - if (ex.ErrorCode == "content_filter") - { - Console.WriteLine($"Your query has trigger Azure Content Safety: {ex.Message}"); - } - else - { - throw; - } -} -``` - -> [!TIP] -> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). - -> [!NOTE] -> Azure AI content safety is only available for models deployed as serverless API endpoints. - -## Use chat completions with images - -Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: - -> [!IMPORTANT] -> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. - -To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): - - -```csharp -string imageUrl = "https://news.microsoft.com/source/wp-content/uploads/2024/04/The-Phi-3-small-language-models-with-big-potential-1-1900x1069.jpg"; -string imageFormat = "jpeg"; -HttpClient httpClient = new HttpClient(); -httpClient.DefaultRequestHeaders.Add("User-Agent", "Mozilla/5.0"); -byte[] imageBytes = httpClient.GetByteArrayAsync(imageUrl).Result; -string imageBase64 = Convert.ToBase64String(imageBytes); -string dataUrl = $"data:image/{imageFormat};base64,{imageBase64}"; -``` - -Visualize the image: - -:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: - -Now, create a chat completion request with the image: - - -```csharp -ChatCompletionsOptions requestOptions = new ChatCompletionsOptions() -{ - Messages = { - new ChatRequestSystemMessage("You are an AI assistant that helps people find information."), - new ChatRequestUserMessage([ - new ChatMessageTextContentItem("Which conclusion can be extracted from the following chart?"), - new ChatMessageImageContentItem(new Uri(dataUrl)) - ]), - }, - MaxTokens=2048, -}; - -var response = client.Complete(requestOptions); -Console.WriteLine(response.Value.Choices[0].Message.Content); -``` - -The response is as follows, where you can see the model's usage statistics: - - -```csharp -Console.WriteLine($"{response.Value.Choices[0].Message.Role}: {response.Value.Choices[0].Message.Content}"); -Console.WriteLine($"Model: {response.Value.Model}"); -Console.WriteLine("Usage:"); -Console.WriteLine($"\tPrompt tokens: {response.Value.Usage.PromptTokens}"); -Console.WriteLine($"\tTotal tokens: {response.Value.Usage.TotalTokens}"); -Console.WriteLine($"\tCompletion tokens: {response.Value.Usage.CompletionTokens}"); -``` - -```console -ASSISTANT: The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there might be other factors at play that influence a model's performance. -Model: Mistral-Large -Usage: - Prompt tokens: 2380 - Completion tokens: 126 - Total tokens: 2506 -``` - -::: zone-end - - -::: zone pivot="programming-language-rest" - -Models deployed with the [Azure AI model inference API](https://aka.ms/azureai/modelinference) can be consumed using any REST client. To use the REST client, you need the following prerequisites: - -1. An [Azure subscription](https://azure.microsoft.com). -2. Create an Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/create-azure-ai-resource). -3. Deploy one model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference). In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like Cohere Embed V3. - - * You can follow the instructions at [Deploy models as serverless APIs](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless). - -4. To construct the requests, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name`` is your unique model deployment host name and `your-azure-region`` is the Azure region where the model is deployed (for example, eastus2). -5. Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string. - -## Work with chat completions - -In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat. - -### Create a client to consume the model - -First, create the client to consume the model. The following code uses an endpoint URL and key that are stored in environment variables. - -### Get the model's capabilities - -The `/info` route returns information about the model that is deployed to the endpoint. Return the model's information by calling the following method: - -```http -GET /info HTTP/1.1 -Host: -Authorization: Bearer -Content-Type: application/json -``` - -The response is as follows: - - -```json -{ - "model_name": "Mistral-Large", - "model_type": "chat-completions", - "model_provider_name": "MistralAI" -} -``` - -### Create a chat completion request - -The following example shows how you can create a basic chat completions request to the model. - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "How many languages are in the world?" - } - ] -} -``` - -The response is as follows, where you can see the model's usage statistics: - - -```json -{ - "id": "0a1234b5de6789f01gh2i345j6789klm", - "object": "chat.completion", - "created": 1718726686, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.", - "tool_calls": null - }, - "finish_reason": "stop", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 19, - "total_tokens": 91, - "completion_tokens": 72 - } -} -``` - -Inspect the `usage` section in the response to see the number of tokens used for the prompt, the total number of tokens generated, and the number of tokens used for the completion. - -#### Stream content - -By default, the completions API returns the entire generated content in a single response. If you're generating long completions, waiting for the response can take many seconds. - -You can _stream_ the content to get it as it's being generated. Streaming content allows you to start processing the completion as content becomes available. This mode returns an object that streams back the response as [data-only server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events). Extract chunks from the delta field, rather than the message field. - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "How many languages are in the world?" - } - ], - "stream": true, - "temperature": 0, - "top_p": 1, - "max_tokens": 2048 -} -``` - -You can visualize how streaming generates content: - - -```json -{ - "id": "23b54589eba14564ad8a2e6978775a39", - "object": "chat.completion.chunk", - "created": 1718726371, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "delta": { - "role": "assistant", - "content": "" - }, - "finish_reason": null, - "logprobs": null - } - ] -} -``` - -The last message in the stream has `finish_reason` set, indicating the reason for the generation process to stop. - - -```json -{ - "id": "23b54589eba14564ad8a2e6978775a39", - "object": "chat.completion.chunk", - "created": 1718726371, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "delta": { - "content": "" - }, - "finish_reason": "stop", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 19, - "total_tokens": 91, - "completion_tokens": 72 - } -} -``` - -#### Explore more parameters supported by the inference client - -Explore other parameters that you can specify in the inference client. For a full list of all the supported parameters and their corresponding documentation, see [Azure AI Model Inference API reference](https://aka.ms/azureai/modelinference). - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "How many languages are in the world?" - } - ], - "presence_penalty": 0.1, - "frequency_penalty": 0.8, - "max_tokens": 2048, - "stop": ["<|endoftext|>"], - "temperature" :0, - "top_p": 1, - "response_format": { "type": "text" } -} -``` - - -```json -{ - "id": "0a1234b5de6789f01gh2i345j6789klm", - "object": "chat.completion", - "created": 1718726686, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "As of now, it's estimated that there are about 7,000 languages spoken around the world. However, this number can vary as some languages become extinct and new ones develop. It's also important to note that the number of speakers can greatly vary between languages, with some having millions of speakers and others only a few hundred.", - "tool_calls": null - }, - "finish_reason": "stop", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 19, - "total_tokens": 91, - "completion_tokens": 72 - } -} -``` - -> [!TIP] -> Some models may not support JSON output formatting. You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON. - -If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model). - -#### Create JSON outputs - -For models that support JSON outputs, set `response_format` to `json_object` to enable JSON mode and guarantee that the message the model generates is valid JSON. You must also instruct the model to produce JSON yourself via a system or user message. Also, the message content might be partially cut off if `finish_reason="length"`, which indicates that the generation exceeded `max_tokens` or that the conversation exceeded the max context length. - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant that always generate responses in JSON format, using the following format: { \"answer\": \"response\" }" - }, - { - "role": "user", - "content": "How many languages are in the world?" - } - ], - "response_format": { "type": "json_object" } -} -``` - - -```json -{ - "id": "0a1234b5de6789f01gh2i345j6789klm", - "object": "chat.completion", - "created": 1718727522, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "{\"answer\": \"There are approximately 7,117 living languages in the world today, according to the latest estimates. However, this number can vary as some languages become extinct and others are newly discovered or classified.\"}", - "tool_calls": null - }, - "finish_reason": "stop", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 39, - "total_tokens": 87, - "completion_tokens": 48 - } -} -``` - -### Pass extra parameters to the model - -The Azure AI Model Inference API allows you to pass extra parameters to the model. The following code example shows how to pass the extra parameter `logprobs` to the model. - -Before you pass extra parameters to the Azure AI model inference API, make sure your model supports those extra parameters. When the request is made to the underlying model, the header `extra-parameters` is passed to the model with the value `pass-through`. This value tells the endpoint to pass the extra parameters to the model. Use of extra parameters with the model doesn't guarantee that the model can actually handle them. Read the model's documentation to understand which extra parameters are supported. - -```http -POST /chat/completions HTTP/1.1 -Host: -Authorization: Bearer -Content-Type: application/json -extra-parameters: pass-through -``` - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "How many languages are in the world?" - } - ], - "logprobs": true -} -``` - -To learn more about the extra parameters that you can pass to the model, see its documentation. - -### Use tools - -Certain models support the use of tools, which can be an extraordinary resource when you need to offload specific tasks from the language model and instead rely on a more deterministic system or even a different language model. The Azure AI Model Inference API allows you to define tools in the following way. - -The following code example creates a tool definition that is able to look from flight information from two different cities. - - -```json -{ - "type": "function", - "function": { - "name": "get_flight_info", - "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - "parameters": { - "type": "object", - "properties": { - "origin_city": { - "type": "string", - "description": "The name of the city where the flight originates" - }, - "destination_city": { - "type": "string", - "description": "The flight destination city" - } - }, - "required": [ - "origin_city", - "destination_city" - ] - } - } -} -``` - -In this example, the function's output is that there are no flights available for the selected route, but the user should consider taking a train. - -> [!NOTE] -> Certain models may require a tool's responses to be a valid JSON content formatted as a string. In this case, we are using them just to illustrate the concept. - -Prompt the model to book flights with the help of this function: - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant that help users to find information about traveling, how to get to places and the different transportations options. You care about the environment and you always have that in mind when answering inqueries" - }, - { - "role": "user", - "content": "When is the next flight from Miami to Seattle?" - } - ], - "tool_choice": "auto", - "tools": [ - { - "type": "function", - "function": { - "name": "get_flight_info", - "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - "parameters": { - "type": "object", - "properties": { - "origin_city": { - "type": "string", - "description": "The name of the city where the flight originates" - }, - "destination_city": { - "type": "string", - "description": "The flight destination city" - } - }, - "required": [ - "origin_city", - "destination_city" - ] - } - } - } - ] -} -``` - -You can inspect the response to find out if a tool needs to be called. Inspect the finish reason to determine if the tool should be called. Remember that multiple tool types can be indicated. This example demonstrates a tool of type `function`. - - -```json -{ - "id": "0a1234b5de6789f01gh2i345j6789klm", - "object": "chat.completion", - "created": 1718726007, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "", - "tool_calls": [ - { - "id": "abc0dF1gh", - "type": "function", - "function": { - "name": "get_flight_info", - "arguments": "{\"origin_city\": \"Miami\", \"destination_city\": \"Seattle\"}", - "call_id": null - } - } - ] - }, - "finish_reason": "tool_calls", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 190, - "total_tokens": 226, - "completion_tokens": 36 - } -} -``` - -To continue, append this message to the chat history: - -Now, it's time to call the appropriate function to handle the tool call. The following code snippet iterates over all the tool calls indicated in the response and calls the corresponding function with the appropriate parameters. The response is also appended to the chat history. - -View the response from the model: - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant that help users to find information about traveling, how to get to places and the different transportations options. You care about the environment and you always have that in mind when answering inqueries" - }, - { - "role": "user", - "content": "When is the next flight from Miami to Seattle?" - }, - { - "role": "assistant", - "content": "", - "tool_calls": [ - { - "id": "abc0DeFgH", - "type": "function", - "function": { - "name": "get_flight_info", - "arguments": "{\"origin_city\": \"Miami\", \"destination_city\": \"Seattle\"}", - "call_id": null - } - } - ] - }, - { - "role": "tool", - "content": "{ \"info\": \"There are no flights available from Miami to Seattle. You should take a train, specially if it helps to reduce CO2 emissions.\" }", - "tool_call_id": "abc0DeFgH" - } - ], - "tool_choice": "auto", - "tools": [ - { - "type": "function", - "function": { - "name": "get_flight_info", - "description": "Returns information about the next flight between two cities. This includes the name of the airline, flight number and the date and time of the next flight", - "parameters":{ - "type": "object", - "properties": { - "origin_city": { - "type": "string", - "description": "The name of the city where the flight originates" - }, - "destination_city": { - "type": "string", - "description": "The flight destination city" - } - }, - "required": ["origin_city", "destination_city"] - } - } - } - ] -} -``` - -### Apply content safety - -The Azure AI model inference API supports [Azure AI content safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI content safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. - -The following example shows how to handle events when the model detects harmful content in the input prompt and content safety is enabled. - - -```json -{ - "messages": [ - { - "role": "system", - "content": "You are an AI assistant that helps people find information." - }, - { - "role": "user", - "content": "Chopping tomatoes and cutting them into cubes or wedges are great ways to practice your knife skills." - } - ] -} -``` - - -```json -{ - "error": { - "message": "The response was filtered due to the prompt triggering Microsoft's content management policy. Please modify your prompt and retry.", - "type": null, - "param": "prompt", - "code": "content_filter", - "status": 400 - } -} -``` - -> [!TIP] -> To learn more about how you can configure and control Azure AI content safety settings, check the [Azure AI content safety documentation](https://aka.ms/azureaicontentsafety). - -> [!NOTE] -> Azure AI content safety is only available for models deployed as serverless API endpoints. - -## Use chat completions with images - -Some models can reason across text and images and generate text completions based on both kinds of input. In this section, you explore the capabilities for vision in a chat fashion: - -> [!IMPORTANT] -> Take into account the amount of images that the model support, both at each turn in the chat conversation and in the entire context. - -To see this capability, download an image and encode the information as `base64` string. The resulting data should be inside of a [data URL](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/Data_URLs): - -> [!TIP] -> You will need to construct the data URL using an scripting or programming language. This tutorial use [this sample image](../media/how-to/sdks/small-language-models-chart-example.jpg) in JPEG format. A data URL has a format as follows: `data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ...`. - -Visualize the image: - -:::image type="content" source="../media/how-to/sdks/small-language-models-chart-example.jpg" alt-text="A chart displaying the relative capabilities between large language models and small language models." lightbox="../media/how-to/sdks/small-language-models-chart-example.jpg"::: - -Now, create a chat completion request with the image: - - -```json -{ - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "Which peculiar conclusion about LLMs and SLMs can be extracted from the following chart?" - }, - { - "type": "image_url", - "image_url": { - "url": "data:image/jpg;base64,0xABCDFGHIJKLMNOPQRSTUVWXYZ..." - } - } - ] - } - ], - "temperature": 0, - "top_p": 1, - "max_tokens": 2048 -} -``` - -The response is as follows, where you can see the model's usage statistics: - - -```json -{ - "id": "0a1234b5de6789f01gh2i345j6789klm", - "object": "chat.completion", - "created": 1718726686, - "model": "Mistral-Large", - "choices": [ - { - "index": 0, - "message": { - "role": "assistant", - "content": "The chart illustrates that larger models tend to perform better in quality, as indicated by their size in billions of parameters. However, there are exceptions to this trend, such as Phi-3-medium and Phi-3-small, which outperform smaller models in quality. This suggests that while larger models generally have an advantage, there may be other factors at play that influence a model's performance.", - "tool_calls": null - }, - "finish_reason": "stop", - "logprobs": null - } - ], - "usage": { - "prompt_tokens": 2380, - "completion_tokens": 126, - "total_tokens": 2506 - } -} -``` - -::: zone-end - -## Related content - - -* [Azure AI Model Inference API](../reference/reference-model-inference-api.md) -* [Deploy models as serverless APIs](deploy-models-serverless.md) -* [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md) -* [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md) -* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace) diff --git a/articles/ai-studio/how-to/develop/sdk-overview.md b/articles/ai-studio/how-to/develop/sdk-overview.md index 844caba6f59..97b143ea34b 100644 --- a/articles/ai-studio/how-to/develop/sdk-overview.md +++ b/articles/ai-studio/how-to/develop/sdk-overview.md @@ -15,7 +15,7 @@ author: eric-urban # Overview of the Azure AI SDKs -Microsoft offers a variety of packages that you can use for building generative AI applications in the cloud. In most applications, you need to use a combination of packages to manage and use various Azure services that provide AI functionality. We also offer integrations with open-source libraries like LangChain and mlflow for use with Azure. In this article we'll give an overview of the main services and SDKs you can use with Azure AI Studio. +Microsoft offers a variety of packages that you can use for building generative AI applications in the cloud. In most applications, you need to use a combination of packages to manage and use various Azure services that provide AI functionality. We also offer integrations with open-source libraries like LangChain and MLflow for use with Azure. In this article we'll give an overview of the main services and SDKs you can use with Azure AI Studio. For building generative AI applications, we recommend using the following services and SDKs: * [Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) for the hub and project infrastructure used in AI Studio to organize your work into projects, manage project artifacts (data, evaluation runs, traces), fine-tune & deploy models, and connect to external services and resources. @@ -54,6 +54,9 @@ Azure AI services Prompt flow * [Prompt flow SDK](https://microsoft.github.io/promptflow/how-to-guides/quick-start.html) +Agentic frameworks: +* [LlamaIndex](llama-index.md) + ## Related content - [Get started building a chat app using the prompt flow SDK](../../quickstarts/get-started-code.md) diff --git a/articles/ai-studio/toc.yml b/articles/ai-studio/toc.yml index 6a08dda89d1..8dd35cedabf 100644 --- a/articles/ai-studio/toc.yml +++ b/articles/ai-studio/toc.yml @@ -218,10 +218,6 @@ items: href: how-to/develop/vscode.md - name: Start with an AI template href: how-to/develop/ai-template-get-started.md - - name: Develop with Azure AI Model Inference API - items: - - name: Use chat models with the Azure AI Model Inference API - href: how-to/develop/model-inference-chat.md - name: Develop with LlamaIndex and Azure AI studio href: how-to/develop/llama-index.md - name: Trace your application with prompt flow From b45e28e053370013a080a67ea8c0873d273b5ddd Mon Sep 17 00:00:00 2001 From: Facundo Santiago Date: Wed, 18 Sep 2024 19:05:39 +0000 Subject: [PATCH 20/63] fix --- articles/ai-studio/how-to/develop/llama-index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/articles/ai-studio/how-to/develop/llama-index.md b/articles/ai-studio/how-to/develop/llama-index.md index 49718a6ed1c..f722e1bd39f 100644 --- a/articles/ai-studio/how-to/develop/llama-index.md +++ b/articles/ai-studio/how-to/develop/llama-index.md @@ -28,10 +28,10 @@ In this example, we are working with the Azure AI model inference API. To run this tutorial you need: 1. An [Azure subscription](https://azure.microsoft.com). -2. An Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](../how-to/create-azure-ai-resource). +2. An Azure AI hub resource as explained at [How to create and manage an Azure AI Studio hub](../how-to/create-azure-ai-resource.md). 3. A model supporting the [Azure AI model inference API](https://aka.ms/azureai/modelinference) deployed. In this example we use a `Mistral-Large` deployment, but use any model of your preference. For using embeddings capabilities in LlamaIndex, you need an embedding model like `cohere-embed-v3-multilingual`. - * You can follow the instructions at [Deploy models as serverless APIs](../how-to/deploy-models-serverless). + * You can follow the instructions at [Deploy models as serverless APIs](../how-to/deploy-models-serverless.md). 4. Python 3.8 or later installed, including pip. 5. LlamaIndex installed. You can do it with: From 816485bd6bf209eb93884998c056afa2af6af9f5 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Wed, 18 Sep 2024 12:07:02 -0700 Subject: [PATCH 21/63] zone pivots --- articles/ai-services/openai/assistants-quickstart.md | 6 +++--- zone-pivots/zone-pivot-groups.yml | 2 -- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/articles/ai-services/openai/assistants-quickstart.md b/articles/ai-services/openai/assistants-quickstart.md index 599b2928df1..4fc98f0f10f 100644 --- a/articles/ai-services/openai/assistants-quickstart.md +++ b/articles/ai-services/openai/assistants-quickstart.md @@ -6,9 +6,9 @@ manager: nitinme ms.service: azure-ai-openai ms.custom: devx-track-python, devx-track-dotnet, devx-track-extended-java, devx-track-js ms.topic: quickstart -author: mrbullwinkle -ms.author: mbullwin -ms.date: 07/18/2024 +author: aahill +ms.author: aahi +ms.date: 09/18/2024 zone_pivot_groups: openai-quickstart-assistants recommendations: false --- diff --git a/zone-pivots/zone-pivot-groups.yml b/zone-pivots/zone-pivot-groups.yml index 4b7d1d854cd..8622cf1ce28 100644 --- a/zone-pivots/zone-pivot-groups.yml +++ b/zone-pivots/zone-pivot-groups.yml @@ -119,8 +119,6 @@ groups: title: Programming languages prompt: Choose your preferred usage method pivots: - - id: programming-language-studio - title: Azure OpenAI Studio - id: programming-language-ai-studio title: AI Studio (Preview) - id: programming-language-csharp From 39225569b380e07fd51c0d3417a57506a6a9d0e9 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Wed, 18 Sep 2024 12:46:10 -0700 Subject: [PATCH 22/63] prereqs --- .../ai-services/openai/includes/connect-your-data-studio.md | 4 ++++ articles/ai-services/openai/use-your-data-quickstart.md | 6 ++++++ 2 files changed, 10 insertions(+) diff --git a/articles/ai-services/openai/includes/connect-your-data-studio.md b/articles/ai-services/openai/includes/connect-your-data-studio.md index 1fb53c5a9de..9b44eba63a9 100644 --- a/articles/ai-services/openai/includes/connect-your-data-studio.md +++ b/articles/ai-services/openai/includes/connect-your-data-studio.md @@ -21,6 +21,10 @@ Navigate to [Azure OpenAI Studio](https://oai.azure.com/) and sign-in with crede :::image type="content" source="../media/use-your-data/bring-your-data-card.png" alt-text="A screenshot of the Azure OpenAI Studio landing page." lightbox="../media/use-your-data/bring-your-data-card.png"::: +1. In the **Chat playground**, Select **Add your data** and then **Add a data source** + + :::image type="content" source="../media/use-your-data/chat-playground.png" alt-text="A screenshot of the chat playground in OpenAI Studio." lightbox="../media/use-your-data/chat-playground.png"::: + 1. In the pane that appears, select **Upload files (preview)** under **Select data source**. Azure OpenAI needs both a storage resource and a search resource to access and index your data. > [!TIP] diff --git a/articles/ai-services/openai/use-your-data-quickstart.md b/articles/ai-services/openai/use-your-data-quickstart.md index 6e1fe636c24..659809bf108 100644 --- a/articles/ai-services/openai/use-your-data-quickstart.md +++ b/articles/ai-services/openai/use-your-data-quickstart.md @@ -79,6 +79,12 @@ In this quickstart, you can use your own data with Azure OpenAI models. Using Az ::: zone-end +The following resources: +- Azure OpenAI +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=OVERVIEW&Pillar=AOAI&Product=ownData&Page=quickstart&Section=Prerequisites) From 669df36119163c0d920510313937d1cc86caafdd Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Wed, 18 Sep 2024 13:06:56 -0700 Subject: [PATCH 23/63] prereqs --- .../media/use-your-data/chat-playground.png | Bin 0 -> 251366 bytes .../openai/use-your-data-quickstart.md | 87 +++++++++--------- 2 files changed, 41 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zE)yT3v4W8=bA9ohp5#1YnJ6jZu?(u%QD47A;_ zM-;`~Z=`qob2}pJdS+WfJBij&-WmBV&En$re}%N0#U+YtX`H%x43OBq6XI-K;PcP^Ai@ zFTvqLu%H=^|8ozN2@(U-eiF`d6w|0 zz(c-xr|Br!I5#6pYnL+MWimH1qH290-gaYNir;_`p}`0@M8|dJ08BFI zRg~;8E5ZKc9wdQ9OOlh%z#^cy)tQNdlKMh`TliqhVR}YNlGU1hNEm1wh=|#MG#2Wc@l65Jb@6xRB3+ zR}D>uy_SY3vno?*ATm|F_ z;0B*moh&1na!~2Bhj(X37G1{CTdcBS%ns1w+lF;YAEmfqDF%wHMo%mgZKxH)*h59l zboqt;O8jR;MU$oo6NN>olb9Y-%xh@Q zpd`#OHm*sCerVa&P#IhPexwCsQ89UE^%1haZMgcHBnJb$Kpnl5a6K(7Qg^p?^LMzaZN+S0u4K4CyuvX z9puFx5f5JiCEgBA6X3N#)Y)CjRMqw6(Cat)I&Q0JpX}B8LvJJ}h&tsR6BU)7wOW7( z263q*H+Md3L*=I+Hs [!div class="nextstepaction"] +> [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=OVERVIEW&Pillar=AOAI&Product=ownData&Page=quickstart&Section=Prerequisites) -[Reference documentation](https://platform.openai.com/docs/api-reference/chat) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) + +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] + +[!INCLUDE [Studio quickstart](includes/use-your-data-studio.md)] ::: zone-end -::: zone pivot="programming-language-python" +::: zone pivot="programming-language-csharp" -[Reference](https://platform.openai.com/docs/api-reference?lang=python) | [Source code](https://github.com/openai/openai-python) | [Package (pypi)](https://pypi.org/project/openai/) | [Samples](https://github.com/openai/openai-cookbook/) +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] -These links reference the OpenAI API for Python. There's no Azure-specific OpenAI Python SDK. [Learn how to switch between the OpenAI services and Azure OpenAI services](/azure/ai-services/openai/how-to/switching-endpoints). +[!INCLUDE [Csharp quickstart](includes/use-your-data-dotnet.md)] ::: zone-end -::: zone pivot="programming-language-go" +::: zone pivot="programming-language-spring" -[Reference](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go) | [Source code](https://github.com/Azure/azure-sdk-for-go) | [Package (Go)](https://pkg.go.dev/github.com/azure/azure-dev) | [Samples](https://github.com/azure-samples/azure-sdk-for-go-samples) +[Source code](https://github.com/spring-projects-experimental/spring-ai)| [Source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) | [Sample](https://github.com/rd-1-2022/ai-azure-retrieval-augmented-generation) + +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] + +[!INCLUDE [Spring quickstart](includes/use-your-data-spring.md)] ::: zone-end -In this quickstart, you can use your own data with Azure OpenAI models. Using Azure OpenAI's models on your data can provide you with a powerful conversational AI platform that enables faster and more accurate communication. +::: zone pivot="programming-language-javascript" ## Prerequisites @@ -59,6 +74,7 @@ In this quickstart, you can use your own data with Azure OpenAI models. Using Az - Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. + #### [JavaScript](#tab/javascript) - An Azure subscription - Create one for free. @@ -71,51 +87,22 @@ In this quickstart, you can use your own data with Azure OpenAI models. Using Az --- -::: zone pivot="programming-language-javascript" - - - -- [Long Term Support (LTS) versions of Node.js](https://github.com/nodejs/release#release-schedule) - -::: zone-end - -The following resources: -- Azure OpenAI -- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) -- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) -- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). - -> [!div class="nextstepaction"] -> [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=OVERVIEW&Pillar=AOAI&Product=ownData&Page=quickstart&Section=Prerequisites) +[Reference documentation](https://platform.openai.com/docs/api-reference/chat) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] -::: zone pivot="programming-language-studio" - -[!INCLUDE [Studio quickstart](includes/use-your-data-studio.md)] - -::: zone-end - -::: zone pivot="programming-language-csharp" - -[!INCLUDE [Csharp quickstart](includes/use-your-data-dotnet.md)] - -::: zone-end - -::: zone pivot="programming-language-spring" - -[!INCLUDE [Spring quickstart](includes/use-your-data-spring.md)] +[!INCLUDE [JavaScript quickstart](includes/use-your-data-javascript.md)] ::: zone-end -::: zone pivot="programming-language-javascript" +::: zone pivot="programming-language-python" -[!INCLUDE [JavaScript quickstart](includes/use-your-data-javascript.md)] +[Reference](https://platform.openai.com/docs/api-reference?lang=python) | [Source code](https://github.com/openai/openai-python) | [Package (pypi)](https://pypi.org/project/openai/) | [Samples](https://github.com/openai/openai-cookbook/) -::: zone-end +These links reference the OpenAI API for Python. There's no Azure-specific OpenAI Python SDK. [Learn how to switch between the OpenAI services and Azure OpenAI services](/azure/ai-services/openai/how-to/switching-endpoints). -::: zone pivot="programming-language-python" +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] [!INCLUDE [Python quickstart](includes/use-your-data-python.md)] @@ -123,18 +110,26 @@ The following resources: ::: zone pivot="programming-language-powershell" +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] + [!INCLUDE [PowerShell quickstart](includes/use-your-data-powershell.md)] ::: zone-end ::: zone pivot="programming-language-go" +[Reference](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go) | [Source code](https://github.com/Azure/azure-sdk-for-go) | [Package (Go)](https://pkg.go.dev/github.com/azure/azure-dev) | [Samples](https://github.com/azure-samples/azure-sdk-for-go-samples) + +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] + [!INCLUDE [Go quickstart](includes/use-your-data-go.md)] ::: zone-end ::: zone pivot="rest-api" +[!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] + [!INCLUDE [REST API quickstart](includes/use-your-data-rest.md)] ::: zone-end From bf9c1d23945985ca2a9d521abda427920fcbc3d0 Mon Sep 17 00:00:00 2001 From: Dina Berry Date: Thu, 19 Sep 2024 07:37:42 -0700 Subject: [PATCH 24/63] Remove ref docs --- articles/ai-services/openai/includes/chatgpt-javascript.md | 2 +- articles/ai-services/openai/includes/javascript.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/articles/ai-services/openai/includes/chatgpt-javascript.md b/articles/ai-services/openai/includes/chatgpt-javascript.md index af62fc21a69..1b0573c117d 100644 --- a/articles/ai-services/openai/includes/chatgpt-javascript.md +++ b/articles/ai-services/openai/includes/chatgpt-javascript.md @@ -11,7 +11,7 @@ ms.author: mbullwin ms.date: 05/21/2024 --- -[Reference documentation](https://platform.openai.com/docs/api-reference/chat) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) +[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) > [!NOTE] > This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript). diff --git a/articles/ai-services/openai/includes/javascript.md b/articles/ai-services/openai/includes/javascript.md index fb9d47f0a70..7b4714dd135 100644 --- a/articles/ai-services/openai/includes/javascript.md +++ b/articles/ai-services/openai/includes/javascript.md @@ -11,7 +11,7 @@ ms.author: mbullwin ms.date: 05/20/2024 --- -[Reference documentation](https://platform.openai.com/docs/api-reference/completions) | [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) +[Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples) > [!NOTE] > This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript). From 4d6fd922712399522bdf613dd97abc813752fc10 Mon Sep 17 00:00:00 2001 From: Sheri Gilley Date: Thu, 19 Sep 2024 09:39:53 -0500 Subject: [PATCH 25/63] freshness what is azure ml --- ...overview-what-is-azure-machine-learning.md | 20 ++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/articles/machine-learning/overview-what-is-azure-machine-learning.md b/articles/machine-learning/overview-what-is-azure-machine-learning.md index 37165f84211..fa870a9a16c 100644 --- a/articles/machine-learning/overview-what-is-azure-machine-learning.md +++ b/articles/machine-learning/overview-what-is-azure-machine-learning.md @@ -8,12 +8,13 @@ ms.topic: overview author: sdgilley ms.author: sgilley ms.reviewer: saoh -ms.date: 01/29/2024 +ms.date: 09/18/2024 ms.custom: - build-2023 - build-2023-dataai - ignite-2023 adobe-target: true +# Customer intent: Learn about Azure Machine Learning --- # What is Azure Machine Learning? @@ -60,7 +61,8 @@ As you're refining the model and collaborating with others throughout the rest o [Machine Learning studio](https://ml.azure.com) offers multiple authoring experiences depending on the type of project and the level of your past ML experience, without having to install anything. -* **Notebooks**: Write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio. +* **Notebooks**: Write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio. Or, open the notebooks in [VS Code](how-to-work-in-vs-code-remote.md), on the web or on your desktop. + * **Visualize run metrics**: Analyze and optimize your experiments with visualization. :::image type="content" source="media/how-to-log-view-metrics/metrics.png" alt-text="Screenshot that shows metrics for a training run."::: @@ -69,6 +71,18 @@ As you're refining the model and collaborating with others throughout the rest o * **Automated machine learning UI**: Learn how to create [automated ML experiments](tutorial-first-experiment-automated-ml.md) with an easy-to-use interface. * **Data labeling**: Use Machine Learning data labeling to efficiently coordinate [image labeling](how-to-create-image-labeling-projects.md) or [text labeling](how-to-create-text-labeling-projects.md) projects. +## Work with LLMs and Generative AI + +Azure Machine Learning includes tools to help you build Generative AI applications powered by Large Language Models (LLMs). The solution includes a model catalog, prompt flow, and a suite of tools to streamline the development cycle of AI applications. + +### Model catalog + +The model catalog in Azure Machine Learning studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models from model providers such as Azure OpenAI service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model. + +### Prompt flow + +Azure Machine Learning prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). Prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications. + ## Enterprise-readiness and security Machine Learning integrates with the Azure cloud platform to add security to ML projects. @@ -207,7 +221,7 @@ Machine Learning also includes features for monitoring and auditing: If you use Apache Airflow, the [airflow-provider-azure-machinelearning](https://github.com/Azure/airflow-provider-azure-machinelearning) package is a provider that enables you to submit workflows to Azure Machine Learning from Apache AirFlow. -## Next steps +## Related content Start using Azure Machine Learning: From 771d9f9ef92e4be21c4971479748d07cfd50f594 Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Thu, 19 Sep 2024 08:45:39 -0700 Subject: [PATCH 26/63] resource links --- .../openai/use-your-data-quickstart.md | 54 ++++++++++++++++++- 1 file changed, 53 insertions(+), 1 deletion(-) diff --git a/articles/ai-services/openai/use-your-data-quickstart.md b/articles/ai-services/openai/use-your-data-quickstart.md index 2aeba3f4832..504f9ebc8e0 100644 --- a/articles/ai-services/openai/use-your-data-quickstart.md +++ b/articles/ai-services/openai/use-your-data-quickstart.md @@ -24,7 +24,7 @@ In this quickstart, you can use your own data with Azure OpenAI models. Using Az ## Prerequisites The following resources: -- Azure OpenAI +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). @@ -41,6 +41,15 @@ The following resources: ::: zone pivot="programming-language-csharp" +## Prerequisites + +- The following resources: + - [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +- The [.NET 8 SDK](https://dotnet.microsoft.com/download/dotnet/8.0) + [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] [!INCLUDE [Csharp quickstart](includes/use-your-data-dotnet.md)] @@ -51,6 +60,14 @@ The following resources: [Source code](https://github.com/spring-projects-experimental/spring-ai)| [Source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai) | [Sample](https://github.com/rd-1-2022/ai-azure-retrieval-augmented-generation) +## Prerequisites + +- The following resources: + - Azure OpenAI + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] [!INCLUDE [Spring quickstart](includes/use-your-data-spring.md)] @@ -98,6 +115,14 @@ The following resources: ::: zone pivot="programming-language-python" +## Prerequisites + +- The following resources: + - Azure OpenAI + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + [Reference](https://platform.openai.com/docs/api-reference?lang=python) | [Source code](https://github.com/openai/openai-python) | [Package (pypi)](https://pypi.org/project/openai/) | [Samples](https://github.com/openai/openai-cookbook/) These links reference the OpenAI API for Python. There's no Azure-specific OpenAI Python SDK. [Learn how to switch between the OpenAI services and Azure OpenAI services](/azure/ai-services/openai/how-to/switching-endpoints). @@ -110,6 +135,15 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ::: zone pivot="programming-language-powershell" + +## Prerequisites + +- The following resources: + - Azure OpenAI + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] [!INCLUDE [PowerShell quickstart](includes/use-your-data-powershell.md)] @@ -118,6 +152,15 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ::: zone pivot="programming-language-go" + +## Prerequisites + +- The following resources: + - Azure OpenAI + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + [Reference](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go) | [Source code](https://github.com/Azure/azure-sdk-for-go) | [Package (Go)](https://pkg.go.dev/github.com/azure/azure-dev) | [Samples](https://github.com/azure-samples/azure-sdk-for-go-samples) [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] @@ -128,6 +171,15 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ::: zone pivot="rest-api" + +## Prerequisites + +- The following resources: + - Azure OpenAI + - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) + - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) + - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] [!INCLUDE [REST API quickstart](includes/use-your-data-rest.md)] From 29d6036d706a9a1d1abe5457028eb1e44a9cd8de Mon Sep 17 00:00:00 2001 From: Aaron Hill Date: Thu, 19 Sep 2024 08:54:20 -0700 Subject: [PATCH 27/63] prereqs --- .../openai/use-your-data-quickstart.md | 74 +++++++++++-------- 1 file changed, 44 insertions(+), 30 deletions(-) diff --git a/articles/ai-services/openai/use-your-data-quickstart.md b/articles/ai-services/openai/use-your-data-quickstart.md index 504f9ebc8e0..c144b8d6b6d 100644 --- a/articles/ai-services/openai/use-your-data-quickstart.md +++ b/articles/ai-services/openai/use-your-data-quickstart.md @@ -28,6 +28,8 @@ The following resources: - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. > [!div class="nextstepaction"] > [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=OVERVIEW&Pillar=AOAI&Product=ownData&Page=quickstart&Section=Prerequisites) @@ -43,11 +45,13 @@ The following resources: ## Prerequisites -- The following resources: - - [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. - The [.NET 8 SDK](https://dotnet.microsoft.com/download/dotnet/8.0) [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] @@ -62,11 +66,13 @@ The following resources: ## Prerequisites -- The following resources: - - Azure OpenAI - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] @@ -117,11 +123,13 @@ The following resources: ## Prerequisites -- The following resources: - - Azure OpenAI - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. [Reference](https://platform.openai.com/docs/api-reference?lang=python) | [Source code](https://github.com/openai/openai-python) | [Package (pypi)](https://pypi.org/project/openai/) | [Samples](https://github.com/openai/openai-cookbook/) @@ -138,11 +146,13 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ## Prerequisites -- The following resources: - - Azure OpenAI - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] @@ -155,11 +165,13 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ## Prerequisites -- The following resources: - - Azure OpenAI - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. [Reference](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go) | [Source code](https://github.com/Azure/azure-sdk-for-go) | [Package (Go)](https://pkg.go.dev/github.com/azure/azure-dev) | [Samples](https://github.com/azure-samples/azure-sdk-for-go-samples) @@ -174,11 +186,13 @@ These links reference the OpenAI API for Python. There's no Azure-specific OpenA ## Prerequisites -- The following resources: - - Azure OpenAI - - [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) - - [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) - - An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). +The following resources: +- [Azure OpenAI](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) +- [Azure Blob Storage](https://portal.azure.com/#create/Microsoft.StorageAccount-ARM) +- [Azure AI Search](https://portal.azure.com/#create/Microsoft.Search) +- An [Azure OpenAI resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesOpenAI) deployed in a [supported region and with a supported model](./concepts/use-your-data.md#regional-availability-and-model-support). + - Be sure that you're assigned at least the [Cognitive Services Contributor](./how-to/role-based-access-control.md#cognitive-services-contributor) role for the Azure OpenAI resource. +- Download the example data from [GitHub](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/openai/contoso_benefits_document_example.pdf) if you don't have your own data. [!INCLUDE [Connect your data to OpenAI](includes/connect-your-data-studio.md)] From fd85b7d778b90b50a31ab879d71cc79b76ed32df Mon Sep 17 00:00:00 2001 From: Heidi Steen <1612796+HeidiSteen@users.noreply.github.com> Date: Thu, 19 Sep 2024 09:06:22 -0700 Subject: [PATCH 28/63] September freshness pass --- ...cognitive-search-common-errors-warnings.md | 45 +++++++++---------- .../cognitive-search-predefined-skills.md | 8 ++-- articles/search/query-lucene-syntax.md | 6 +-- articles/search/samples-python.md | 42 ++++++----------- articles/search/search-filters.md | 18 +++++--- articles/search/search-what-is-data-import.md | 12 ++--- ...ector-search-how-to-generate-embeddings.md | 21 ++++++--- articles/search/vector-search-ranking.md | 4 +- articles/search/vector-store.md | 10 ++--- 9 files changed, 81 insertions(+), 85 deletions(-) diff --git a/articles/search/cognitive-search-common-errors-warnings.md b/articles/search/cognitive-search-common-errors-warnings.md index eab329fce38..d5721593cf4 100644 --- a/articles/search/cognitive-search-common-errors-warnings.md +++ b/articles/search/cognitive-search-common-errors-warnings.md @@ -9,8 +9,8 @@ ms.author: heidist ms.service: cognitive-search ms.custom: - ignite-2023 -ms.topic: conceptual -ms.date: 03/27/2024 +ms.topic: reference +ms.date: 09/19/2024 --- # Troubleshooting common indexer errors and warnings in Azure AI Search @@ -32,29 +32,28 @@ Warnings don't stop indexing, but they do indicate conditions that could result To verify an indexer status and identify errors in the Azure portal, follow the steps below: -1. Navigate to the Azure portal and locate your AI Search service. -1. Once you're in the AI Search service, click on the 'Indexers' tab. -1. From the list of indexers, identify the specific indexer you wish to verify. -1. Under the 'Execution History' column, click on the 'Status' hyperlink associated with the selected indexer. -1. If there's an error, hover over the error message. A pane will appear on the right side of your screen displaying detailed information about the error. +1. Sign in to the [Azure portal](https://portal.azure.com/) and [find your search service](https://portal.azure.com/#blade/HubsExtension/BrowseResourceBlade/resourceType/Microsoft.Search%2FsearchServices). -## Transient errors +1. On the left, expand **Search Management** > **Indexers** and select an indexer. -For various reasons, such as transient network communication interruptions, timeouts from long-running processes, or specific document nuances, it's common to encounter transient errors or warnings during indexer runs. However, these errors are temporary and should be resolved in subsequent indexer runs. +1. Under **Execution History**, select the status. All statuses, including Success, have details about the execution. -To manage these errors effectively, it is recommended [putting your indexer on a schedule](search-howto-schedule-indexers.md), for instance, to run every five minutes. This means the next run will commence five minutes after the completion of the first run, adhering to the [maximum runtime limit](search-limits-quotas-capacity.md#indexer-limits). Regularly scheduled runs help to rectify any transient errors or warnings swiftly. +1. If there's an error, hover over the error message. A pane appears on the right side of your screen displaying detailed information about the error. -If you notice an error persisting over multiple indexer runs, it's likely not a transient issue. In such cases, refer to the list below for potential solutions. Please note, always ensure your indexing schedule aligns with the limitations outlined in our indexer limits guide. +## Transient errors +For various reasons, such as transient network communication interruptions, timeouts from long-running processes, or specific document nuances, it's common to encounter transient errors or warnings during indexer runs. However, these errors are temporary and should be resolved in subsequent indexer runs. -## Error properties +To manage these errors effectively, we recommend [putting your indexer on a schedule](search-howto-schedule-indexers.md), for instance, to run every five minutes, where the next run commences five minutes after completing the first run, adhering to the [maximum runtime limit](search-limits-quotas-capacity.md#indexer-limits) on your service. Regularly scheduled runs help rectify transient errors or warnings. -Beginning with API version `2019-05-06`, item-level Indexer errors and warnings are structured to provide increased clarity around causes and next steps. They contain the following properties: +If an error persists over multiple indexer runs, it's likely not a transient issue. In such cases, refer to the list below for potential solutions. + +## Error properties | Property | Description | Example | | --- | --- | --- | -| Key | The document ID of the document impacted by the error or warning. | `https://.blob.core.windows.net/jfk-1k/docid-32112954.pdf`| -| Name | The operation name describing where the error or warning occurred. This is generated by the following structure: `[category]`.`[subcategory]`.`[resourceType]`.`[resourceName]` | `DocumentExtraction.azureblob.myBlobContainerName` `Enrichment.WebApiSkill.mySkillName` `Projection.SearchIndex.OutputFieldMapping.myOutputFieldName` `Projection.SearchIndex.MergeOrUpload.myIndexName` `Projection.KnowledgeStore.Table.myTableName` | +| Key | The ID of the document impacted by the error or warning. | Azure Storage example, where the default ID is the metadata storage path: `https://.blob.core.windows.net/jfk-1k/docid-32112954.pdf`| +| Name | The operation causing the error or warning. This is generated by the following structure: `[category]`.`[subcategory]`.`[resourceType]`.`[resourceName]` | `DocumentExtraction.azureblob.myBlobContainerName` `Enrichment.WebApiSkill.mySkillName` `Projection.SearchIndex.OutputFieldMapping.myOutputFieldName` `Projection.SearchIndex.MergeOrUpload.myIndexName` `Projection.KnowledgeStore.Table.myTableName` | | Message | A high-level description of the error or warning. | `Could not execute skill because the Web Api request failed.` | | Details | Specific information that might be helpful in diagnosing the issue, such as the WebApi response if executing a custom skill failed. | `link-cryptonyms-list - Error processing the request record : System.ArgumentNullException: Value cannot be null. Parameter name: source at System.Linq.Enumerable.All[TSource](IEnumerable 1 source, Func 2 predicate) at Microsoft.CognitiveSearch.WebApiSkills.JfkWebApiSkills. ...rest of stack trace...` | | DocumentationLink | A link to relevant documentation with detailed information to debug and resolve the issue. This link will often point to one of the below sections on this page. | `https://go.microsoft.com/fwlink/?linkid=2106475` | @@ -95,8 +94,8 @@ Indexer read the document from the data source, but there was an issue convertin | The document key is missing | `Document key cannot be missing or empty` | Ensure all documents have valid document keys. The document key is determined by setting the 'key' property as part of the [index definition](/rest/api/searchservice/indexes/create#request-body). Indexers emit this error when the property flagged as the 'key' can't be found on a particular document. | | The document key is invalid | `Invalid document key. Keys can only contain letters, digits, underscore (_), dash (-), or equal sign (=). ` | Ensure all documents have valid document keys. Review [Indexing Blob Storage](search-howto-indexing-azure-blob-storage.md) for more details. If you're using the blob indexer, and your document key is the `metadata_storage_path` field, make sure that the indexer definition has a [base64Encode mapping function](search-indexer-field-mappings.md?tabs=rest#base64encode-function) with `parameters` equal to `null`, instead of the path in plain text. | | The document key is invalid | `Document key cannot be longer than 1024 characters` | Modify the document key to meet the validation requirements. | -| Could not apply field mapping to a field | `Could not apply mapping function 'functionName' to field 'fieldName'. Array cannot be null. Parameter name: bytes` | Double check the [field mappings](search-indexer-field-mappings.md) defined on the indexer, and compare with the data of the specified field of the failed document. It might be necessary to modify the field mappings or the document data. | -| Could not read field value | `Could not read the value of column 'fieldName' at index 'fieldIndex'. A transport-level error has occurred when receiving results from the server. (provider: TCP Provider, error: 0 - An existing connection was forcibly closed by the remote host.)` | These errors are typically due to unexpected connectivity issues with the data source's underlying service. Try running the document through your indexer again later. | +| Couldn't apply field mapping to a field | `Could not apply mapping function 'functionName' to field 'fieldName'. Array cannot be null. Parameter name: bytes` | Double check the [field mappings](search-indexer-field-mappings.md) defined on the indexer, and compare with the data of the specified field of the failed document. It might be necessary to modify the field mappings or the document data. | +| Couldn't read field value | `Could not read the value of column 'fieldName' at index 'fieldIndex'. A transport-level error has occurred when receiving results from the server. (provider: TCP Provider, error: 0 - An existing connection was forcibly closed by the remote host.)` | These errors are typically due to unexpected connectivity issues with the data source's underlying service. Try running the document through your indexer again later. | @@ -189,7 +188,7 @@ The document was read and processed, but the indexer couldn't add it to the sear | Trouble connecting to the target index (that persists after retries) because the service is under other load, such as querying or indexing. | Failed to establish connection to update index. Search service is under heavy load. | [Scale up your search service](search-capacity-planning.md) | Search service is being patched for service update, or is in the middle of a topology reconfiguration. | Failed to establish connection to update index. Search service is currently down/Search service is undergoing a transition. | Configure service with at least three replicas for 99.9% availability per [SLA documentation](https://azure.microsoft.com/support/legal/sla/search/v1_0/) | Failure in the underlying compute/networking resource (rare) | Failed to establish connection to update index. An unknown failure occurred. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state. -| An indexing request made to the target index wasn't acknowledged within a timeout period due to network issues. | Could not establish connection to the search index in a timely manner. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state. Additionally, try lowering the indexer [batch size](/rest/api/searchservice/indexers/create#indexingparameters) if this error condition persists. +| An indexing request made to the target index wasn't acknowledged within a timeout period due to network issues. | Couldn't establish connection to the search index in a timely manner. | Configure indexers to [run on a schedule](search-howto-schedule-indexers.md) to pick up from a failed state. Additionally, try lowering the indexer [batch size](/rest/api/searchservice/indexers/create#indexingparameters) if this error condition persists. @@ -225,9 +224,9 @@ This error occurs when the indexer is attempting to [project data into a knowled | Reason | Details/Example | Resolution | | --- | --- | --- | -| Could not update projection blob `'blobUri'` in container `'containerName'` |The specified container doesn't exist. | The indexer checks if the specified container has been previously created and will create it if necessary, but it only performs this check once per indexer run. This error means that something deleted the container after this step. To resolve this error, try this: leave your storage account information alone, wait for the indexer to finish, and then rerun the indexer. | -| Could not update projection blob `'blobUri'` in container `'containerName'` |Unable to write data to the transport connection: An existing connection was forcibly closed by the remote host. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further. | -| Could not update row `'projectionRow'` in table `'tableName'` | The server is busy. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further. | +| Couldn't update projection blob `'blobUri'` in container `'containerName'` |The specified container doesn't exist. | The indexer checks if the specified container has been previously created and will create it if necessary, but it only performs this check once per indexer run. This error means that something deleted the container after this step. To resolve this error, try this: leave your storage account information alone, wait for the indexer to finish, and then rerun the indexer. | +| Couldn't update projection blob `'blobUri'` in container `'containerName'` |Unable to write data to the transport connection: An existing connection was forcibly closed by the remote host. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further. | +| Couldn't update row `'projectionRow'` in table `'tableName'` | The server is busy. | This is expected to be a transient failure with Azure Storage and thus should be resolved by rerunning the indexer. If you encounter this error consistently, file a [support ticket](https://portal.azure.com/#create/Microsoft.Support) so it can be investigated further. | @@ -237,7 +236,7 @@ Skill execution failed because the call to Azure AI services was throttled. Typi ## `Error: Expected IndexAction metadata` -An 'Expected IndexAction metadata' error means when the indexer attempted to read the document to identify what action should be taken, it did not find any corresponding metadata on the document. Typically, this error occurs when the indexer has an annotation cache added or removed without resetting the indexer. To address this, you should [reset and rerun the indexer](search-howto-run-reset-indexers.md). +An 'Expected IndexAction metadata' error means when the indexer attempted to read the document to identify what action should be taken, it didn't find any corresponding metadata on the document. Typically, this error occurs when the indexer has an annotation cache added or removed without resetting the indexer. To address this, you should [reset and rerun the indexer](search-howto-run-reset-indexers.md). @@ -379,7 +378,7 @@ Output field mappings that reference non-existent/null data will produce warning | Reason | Details/Example | Resolution | | --- | --- | --- | -| Cannot iterate over non-array | "Cannot iterate over non-array `/document/normalized_images/0/imageCelebrities/0/detail/celebrities`." | This error occurs when the output isn't an array. If you think the output should be an array, check the indicated output source field path for errors. For example, you might have a missing or extra `*` in the source field name. It's also possible that the input to this skill is null, resulting in an empty array. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section. | +| Can't iterate over non-array | "Cannot iterate over non-array `/document/normalized_images/0/imageCelebrities/0/detail/celebrities`." | This error occurs when the output isn't an array. If you think the output should be an array, check the indicated output source field path for errors. For example, you might have a missing or extra `*` in the source field name. It's also possible that the input to this skill is null, resulting in an empty array. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section. | | Unable to select `0` in non-array | "Unable to select `0` in non-array `/document/pages`." | This could happen if the skills output doesn't produce an array and the output source field name has array index or `*` in its path. Double check the paths provided in the output source field names and the field value for the indicated field name. Find similar details in [Skill Input was Invalid](cognitive-search-common-errors-warnings.md#warning-skill-input-was-invalid) section. | diff --git a/articles/search/cognitive-search-predefined-skills.md b/articles/search/cognitive-search-predefined-skills.md index c7d75fc4910..6d338f47cb2 100644 --- a/articles/search/cognitive-search-predefined-skills.md +++ b/articles/search/cognitive-search-predefined-skills.md @@ -8,19 +8,19 @@ ms.service: cognitive-search ms.custom: - ignite-2023 - build-2024 -ms.topic: conceptual -ms.date: 10/28/2023 +ms.topic: concept-article +ms.date: 09/19/2024 --- # Skills for extra processing during indexing (Azure AI Search) -This article describes the skills provided with Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. +This article describes the skills in Azure AI Search that you can include in a [skillset](cognitive-search-working-with-skillsets.md) to access external processing. A *skill* provides an atomic operation that transforms content in some way. Often, it's an operation that recognizes or extracts text, but it can also be a utility skill that reshapes the enrichments that are already created. Typically, the output is text-based so that it can be used in [full text search](search-lucene-query-architecture.md) or vectors used in [vector search](vector-search-overview.md). Skills are organized into categories: -* A *built-in skill* wraps API calls to an Azure resource, where the inputs, outputs, and processing steps are well understood. For skills that call an Azure AI resource, the connection is made over the internal network. For skills that call Azure OpenAI, you provide the connection information that the search service uses to connect to the resource. A small quantity of processing is non-billable, but at larger volumes, processing is billable. Built-in skills are based on pretrained models from Microsoft, which means you can't train the model using your own training data. +* A *built-in skill* wraps API calls to an Azure AI resource, where the inputs, outputs, and processing steps are well understood. For skills that call an Azure AI resource, the connection is made over the internal network. For skills that call Azure OpenAI, you provide the connection information that the search service uses to connect to the resource. A small quantity of processing is non-billable, but at larger volumes, processing is billable. Built-in skills are based on pretrained models from Microsoft, which means you can't train the model using your own training data. * A *custom skill* provides custom code that executes externally to the search service. It's accessed through a URI. Custom code is often made available through an Azure function app. To attach an open-source or third-party vectorization model, use a custom skill. diff --git a/articles/search/query-lucene-syntax.md b/articles/search/query-lucene-syntax.md index 64c17e985ba..ba1c3ef24fa 100644 --- a/articles/search/query-lucene-syntax.md +++ b/articles/search/query-lucene-syntax.md @@ -9,13 +9,13 @@ ms.author: beloh ms.service: cognitive-search ms.custom: - ignite-2023 -ms.topic: conceptual -ms.date: 02/22/2024 +ms.topic: concept-article +ms.date: 09/19/2024 --- # Lucene query syntax in Azure AI Search -When creating queries in Azure AI Search, you can opt for the full [Lucene Query Parser](https://lucene.apache.org/core/6_6_1/queryparser/org/apache/lucene/queryparser/classic/package-summary.html) syntax for specialized query forms: wildcard, fuzzy search, proximity search, regular expressions. Much of the Lucene Query Parser syntax is [implemented intact in Azure AI Search](search-lucene-query-architecture.md), except for *range searches, which are constructed through **`$filter`** expressions. +When creating queries in Azure AI Search, you can opt for the full [Lucene Query Parser](https://lucene.apache.org/core/6_6_1/queryparser/org/apache/lucene/queryparser/classic/package-summary.html) syntax for specialized query forms: wildcard, fuzzy search, proximity search, regular expressions. Much of the Lucene Query Parser syntax is [implemented intact in Azure AI Search](search-lucene-query-architecture.md), except for *range searches*, which are constructed through **`$filter`** expressions. To use full Lucene syntax, set the queryType to `full` and pass in a query expression patterned for wildcard, fuzzy search, or one of the other query forms supported by the full syntax. In REST, query expressions are provided in the **`search`** parameter of a [Search Documents (REST API)](/rest/api/searchservice/documents/search-post) request. diff --git a/articles/search/samples-python.md b/articles/search/samples-python.md index 8ece27e447a..3dddcf8e8a6 100644 --- a/articles/search/samples-python.md +++ b/articles/search/samples-python.md @@ -12,7 +12,7 @@ ms.custom: - devx-track-python - ignite-2023 ms.topic: conceptual -ms.date: 08/16/2024 +ms.date: 09/19/2024 --- # Python samples for Azure AI Search @@ -28,21 +28,9 @@ Learn about the Python code samples that demonstrate the functionality and workf ## SDK samples -Code samples from the Azure SDK development team demonstrate API usage. You can find these samples in [**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub. - -| Samples | Description | -|---------|-------------| -| [Authenticate](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_authentication.py) | Demonstrates how to configure a client and authenticate to the service. | -| [Index Create-Read-Update-Delete operations](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_index_crud_operations.py) | Demonstrates how to create, update, get, list, and delete [search indexes](search-what-is-an-index.md). | -| [Indexer Create-Read-Update-Delete operations](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_indexers_operations.py) | Demonstrates how to create, update, get, list, reset, and delete [indexers](search-indexer-overview.md). | -| [Search indexer data sources](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_indexer_datasource_skillset.py) | Demonstrates how to create, update, get, list, and delete indexer data sources, required for indexer-based indexing of [supported Azure data sources](search-indexer-overview.md#supported-data-sources). | -| [Synonyms](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_synonym_map_operations.py) | Demonstrates how to create, update, get, list, and delete [synonym maps](search-synonyms.md). | -| [Load documents](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_crud_operations.py) | Demonstrates how to upload or merge documents into an index in a [data import](search-what-is-data-import.md) operation. | -| [Simple query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_simple_query.py) | Demonstrates how to set up a [basic query](search-query-overview.md). | -| [Filter query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_filter_query.py) | Demonstrates setting up a [filter expression](search-filters.md). | -| [Facet query](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_facet_query.py) | Demonstrates working with [facets](search-faceted-navigation.md). | -| [Semantic ranking sample](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_semantic_search.py) | Shows you how to configure semantic ranking in an index and invoke semantic queries. | -| [Vector search](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/samples/sample_vector_search.py) | Demonstrates how to get embeddings from a description field and then send vector queries against the data. | +Code samples from the Azure SDK development team demonstrate API usage. + +You can find these samples in [**azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples**](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/search/azure-search-documents/samples) on GitHub. ## Doc samples @@ -50,24 +38,20 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M | Samples | Article | |---------|---------| -| [quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. | -| [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranking. | +| [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | Source code for the Python portion of [How to build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md).| +| [Quickstart](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart) | Source code for the Python portion of [Quickstart: Full text search using the Azure SDKs](search-get-started-text.md). This article covers the basic workflow for creating, loading, and querying a search index using sample data. | +| [Quickstart-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-RAG) | Source code for the Python portion of [Quickstart: Generative search (RAG) with grounding data from Azure AI Search](search-get-started-rag.md). | +| [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | Source code for the Python portion of [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md). It shows the index schema and query request for invoking semantic ranking. | | [bulk-insert](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/bulk-insert) | Source code for the Python example of how to [use the push APIs](search-how-to-load-search-index.md) to upload and index documents. | -| [azure-functions](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. | +| [azure-function-search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/azure-function-search) | Source code for the Python example of an Azure function that sends queries to a search service. You can substitute this Python version of the `api` code used in the [Add search to web sites](tutorial-csharp-overview.md) C# sample. | ## Demos -A demo repo provides proof-of-concept source code for examples or scenarios shown in demonstrations. Demo solutions aren't designed for adaptation by customers. +A demo repo provides proof-of-concept source code for examples or scenarios shown in demonstrations. -| Repository | Description | -|------------|-------------| -| [azure-search-vector-python-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/basic-vector-workflow/azure-search-vector-python-sample.ipynb) | Uses the **azure.search.documents** library in the Azure SDK for Python to create, load, and query a vector index. | -| [azure-search-integrated-vectorization-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/integrated-vectorization/azure-search-integrated-vectorization-sample.ipynb) | Extends the vector indexing workflow to include integrated data chunking and embedding. | -| [azure-search-custom-vectorization-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/custom-vectorizer/azure-search-custom-vectorization-sample.ipynb) | Demonstrates custom vectorization. | -| [azure-search-vector-python-huggingface-model-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/hugging-face/azure-search-vector-python-huggingface-model-sample.ipynb) | Hugging Face integration. | -| [azure-search-vector-python-langchain-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/langchain/azure-search-vector-python-langchain-sample.ipynb) | LangChain integration. | -| [azure-search-vector-python-llamaindex-sample.ipynb](https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/community-integration/llamaindex/azure-search-vector-python-llamaindex-sample.ipynb) | Llamaindex integration. | -| [azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md) | ChatGPT + Enterprise data with Azure OpenAI Python code showing how to use Azure AI Search with the large language models in Azure OpenAI. For background, see this Tech Community blog post: [Revolutionize your Enterprise Data with ChatGPT](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087). | +[**azure-search-vector-samples**](https://github.com/Azure/azure-search-vector-samples/blob/main/README.md) on GitHub provides a comprehensive collection of samples organized by scenario or technology. + +We also recommend [**azure-search-openai-demo**](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/README.md). This is a ChatGPT-like experience over enterprise data with Azure OpenAI Python code showing how to use Azure AI Search with the large language models in Azure OpenAI. For background, see this Tech Community blog post: [Revolutionize your Enterprise Data with ChatGPT](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087). | ## Other samples diff --git a/articles/search/search-filters.md b/articles/search/search-filters.md index 7306ec54c6b..64a89d8f129 100644 --- a/articles/search/search-filters.md +++ b/articles/search/search-filters.md @@ -7,18 +7,20 @@ manager: nitinme author: HeidiSteen ms.author: heidist ms.service: cognitive-search -ms.topic: conceptual -ms.date: 02/22/2024 +ms.topic: concept-article +ms.date: 09/19/2024 ms.custom: - devx-track-csharp - ignite-2023 --- -# Filters in text queries +# Filters in keyword search -A *filter* provides value-based criteria for including or excluding content before query execution. For example, including or excluding documents based on dates, locations, or language. Filters are specified on individual fields. A field definition must be attributed as "filterable" if you want to use it in filter expressions. +A *filter* provides value-based criteria for including or excluding content before query execution for keyword search, or before or after query execution for vector search. Filters are applied to nonvector fields, but can be used in vector search if documents include nonvector fields. For example, for indexes organized around chunked content, you might have parent-level fields or metadata fields that can be filtered. -A filter is specified using [OData filter expression syntax](search-query-odata-filter.md). In contrast with full text search, a filter succeeds only if the match is exact. +This article explains filtering for keyword search. For more information about vectors, see [Add a filter in a vector query](vector-search-filters.md). + +A filter is specified using [OData filter expression syntax](search-query-odata-filter.md). In contrast with keyword and vector search, a filter succeeds only if the match is exact. ## When to use a filter @@ -42,7 +44,9 @@ At query time, a filter parser accepts criteria as input, converts the expressio Filtering occurs in tandem with search, qualifying which documents to include in downstream processing for document retrieval and relevance scoring. When paired with a search string, the filter effectively reduces the recall set of the subsequent search operation. When used alone (for example, when the query string is empty where `search=*`), the filter criteria is the sole input. -## Defining filters +## How filters are defined + +Filters apply to alphanumeric content on fields that are attributed as `filterable`. Filters are OData expressions, articulated in the [filter syntax](search-query-odata-filter.md) supported by Azure AI Search. @@ -112,7 +116,7 @@ The following examples illustrate several usage patterns for filter scenarios. F In the REST API, filterable is *on* by default for simple fields. Filterable fields increase index size; be sure to set `"filterable": false` for fields that you don't plan to actually use in a filter. For more information about settings for field definitions, see [Create Index](/rest/api/searchservice/indexes/create). -In the .NET SDK, the filterable is *off* by default. You can make a field filterable by setting the [IsFilterable property](/dotnet/api/azure.search.documents.indexes.models.searchfield.isfilterable) of the corresponding [SearchField](/dotnet/api/azure.search.documents.indexes.models.searchfield) object to `true`. In the next example, the attribute is set on the `Rating` property of a model class that maps to the index definition. +In the Azure SDKs, filterable is *off* by default. You can make a field filterable by setting the [IsFilterable property](/dotnet/api/azure.search.documents.indexes.models.searchfield.isfilterable) of the corresponding [SearchField](/dotnet/api/azure.search.documents.indexes.models.searchfield) object to `true`. In the next example, the attribute is set on the `Rating` property of a model class that maps to the index definition. ```csharp [SearchField(IsFilterable = true, IsSortable = true, IsFacetable = true)] diff --git a/articles/search/search-what-is-data-import.md b/articles/search/search-what-is-data-import.md index c8985a852ce..64f29f5cad1 100644 --- a/articles/search/search-what-is-data-import.md +++ b/articles/search/search-what-is-data-import.md @@ -9,9 +9,10 @@ ms.author: heidist ms.service: cognitive-search ms.custom: - ignite-2023 -ms.topic: conceptual -ms.date: 01/17/2024 +ms.topic: concept-article +ms.date: 09/17/2024 --- + # Data import in Azure AI Search In Azure AI Search, queries execute over user-owned content that's loaded into a [search index](search-what-is-an-index.md). This article describes the two basic workflows for populating an index: *push* your data into the index programmatically, or *pull* in the data using a [search indexer](search-indexer-overview.md). @@ -19,7 +20,7 @@ In Azure AI Search, queries execute over user-owned content that's loaded into a Both approaches load documents from an external data source. Although you can create an empty index, it's not queryable until you add the content. > [!NOTE] -> If [AI enrichment](cognitive-search-concept-intro.md) is a solution requirement, you must use the pull model (indexers) to load an index. Skillsets are attached to an indexer and don't run independently. +> If [AI enrichment](cognitive-search-concept-intro.md) or [integrated vectorization](vector-search-integrated-vectorization.md) are solution requirements, you must use the pull model (indexers) to load an index. Skillsets are attached to indexers and don't run independently. ## Pushing data to an index @@ -77,7 +78,8 @@ The pull model uses *indexers* connecting to a supported data source, automatica + [Azure Files (preview)](search-file-storage-integration.md) + [Azure Cosmos DB](search-howto-index-cosmosdb.md) + [Azure SQL Database, SQL Managed Instance, and SQL Server on Azure VMs](search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md) -+ [SharePoint in Microsoft 365 (preview)](search-howto-index-sharepoint-online.md) ++ [OneLake files and shortcuts](search-how-to-index-onelake-files.md) ++ [SharePoint Online (preview)](search-howto-index-sharepoint-online.md) You can use third-party connectors, developed and maintained by Microsoft partners. For more information and links, see [Data source gallery](search-data-sources-gallery.md). @@ -87,7 +89,7 @@ Indexers connect an index to a data source (usually a table, view, or equivalent Use the following tools and APIs for indexer-based indexing: -+ [Import data wizard in the Azure portal](search-import-data-portal.md) ++ [Import data wizard or Import and vectorize data wizard](search-import-data-portal.md) + REST APIs: [Create Indexer (REST)](/rest/api/searchservice/indexers/create), [Create Data Source (REST)](/rest/api/searchservice/data-sources/create), [Create Index (REST)](/rest/api/searchservice/indexes/create) + Azure SDK for .NET: [SearchIndexer](/dotnet/api/azure.search.documents.indexes.models.searchindexer), [SearchIndexerDataSourceConnection](/dotnet/api/azure.search.documents.indexes.models.searchindexerdatasourceconnection), [SearchIndex](/dotnet/api/azure.search.documents.indexes.models.searchindex), + Azure SDK for Python: [SearchIndexer](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindexer), [SearchIndexerDataSourceConnection](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindexerdatasourceconnection), [SearchIndex](/python/api/azure-search-documents/azure.search.documents.indexes.models.searchindex), diff --git a/articles/search/vector-search-how-to-generate-embeddings.md b/articles/search/vector-search-how-to-generate-embeddings.md index 34cd4f93637..8ed3bf6e151 100644 --- a/articles/search/vector-search-how-to-generate-embeddings.md +++ b/articles/search/vector-search-how-to-generate-embeddings.md @@ -9,12 +9,12 @@ ms.service: cognitive-search ms.custom: - ignite-2023 ms.topic: how-to -ms.date: 08/05/2024 +ms.date: 09/19/2024 --- # Generate embeddings for search queries and documents -Azure AI Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any embedding model, but this article assumes Azure OpenAI embedding models. +Azure AI Search doesn't host vectorization models, so one of your challenges is creating embeddings for query inputs and outputs. You can use any supported embedding model, but this article assumes Azure OpenAI embedding models for the steps. We recommend [integrated vectorization](vector-search-integrated-vectorization.md), which provides built-in data chunking and vectorization. Integrated vectorization takes a dependency on indexers, skillsets, and built-in or custom skills that point to a model that executes externally from Azure AI Search. @@ -30,13 +30,13 @@ If you want to handle data chunking and vectorization yourself, we provide demos ## Create resources in the same region -If you want resources in the same region, start with: +Integrated vectorization requires resoruces to be in the same region: -1. [Check regions for a text embedding model](/azure/ai-services/openai/concepts/models#standard-and-global-standard-deployment-model-quota). +1. [Check regions for a text embedding model](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability). 1. [Find the same region for Azure AI Search](search-region-support.md). -1. To support hybrid queries that include [semantic ranking](semantic-how-to-query-request.md), or if you want to try machine learning model integration using a [custom skill](cognitive-search-custom-skill-interface.md) in an [AI enrichment pipeline](cognitive-search-concept-intro.md), note the Azure AI Search regions that provide those features. +1. To support hybrid queries that include [semantic ranking](semantic-how-to-query-request.md), or if you want to try machine learning model integration using a [custom skill](cognitive-search-custom-skill-interface.md) in an [AI enrichment pipeline](cognitive-search-concept-intro.md), select an Azure AI Search region that provides those features. ## Generate an embedding for an improvised query @@ -64,12 +64,18 @@ Output is a vector array of 1,536 dimensions. ## Tips and recommendations for embedding model integration -+ **Identify use cases**: Evaluate the specific use cases where embedding model integration for vector search features can add value to your search solution. This can include matching image content with text content, cross-lingual searches, or finding similar documents. ++ **Identify use cases**: Evaluate the specific use cases where embedding model integration for vector search features can add value to your search solution. This can include multimodal or matching image content with text content, multilingual search, or similarity search. + + **Design a chunking strategy**: Embedding models have limits on the number of tokens they can accept, which introduces a data chunking requirement for large files. For more information, see [Chunk large documents for vector search solutions](vector-search-how-to-chunk-documents.md). -+ **Optimize cost and performance**: Vector search can be resource-intensive and is subject to maximum limits, so consider only vectorizing the fields that contain semantic meaning. [Reduce vector size]() so that you can store more vectors for the same price. + ++ **Optimize cost and performance**: Vector search can be resource-intensive and is subject to maximum limits, so consider only vectorizing the fields that contain semantic meaning. [Reduce vector size](vector-search-how-to-configure-compression-storage.md) so that you can store more vectors for the same price. + + **Choose the right embedding model:** Select an appropriate model for your specific use case, such as word embeddings for text-based searches or image embeddings for visual searches. Consider using pretrained models like **text-embedding-ada-002** from OpenAI or **Image Retrieval** REST API from [Azure AI Computer Vision](/azure/ai-services/computer-vision/how-to/image-retrieval). + + **Normalize Vector lengths**: Ensure that the vector lengths are normalized before storing them in the search index to improve the accuracy and performance of similarity search. Most pretrained models already are normalized but not all. + + **Fine-tune the model**: If needed, fine-tune the selected model on your domain-specific data to improve its performance and relevance to your search application. + + **Test and iterate**: Continuously test and refine your embedding model integration to achieve the desired search performance and user satisfaction. ## Next steps @@ -77,3 +83,4 @@ Output is a vector array of 1,536 dimensions. + [Understanding embeddings in Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings) + [Learn how to generate embeddings](/azure/ai-services/openai/how-to/embeddings?tabs=console) + [Tutorial: Explore Azure OpenAI Service embeddings and document search](/azure/ai-services/openai/tutorials/embeddings?tabs=command-line) ++ [Tutorial: Choose a model (RAG solutions in Azure AI Search)](tutorial-rag-build-solution-models.md) diff --git a/articles/search/vector-search-ranking.md b/articles/search/vector-search-ranking.md index 8514d8e22fa..c182384df05 100644 --- a/articles/search/vector-search-ranking.md +++ b/articles/search/vector-search-ranking.md @@ -8,8 +8,8 @@ ms.author: jlembicz ms.service: cognitive-search ms.custom: - ignite-2023 -ms.topic: conceptual -ms.date: 08/05/2024 +ms.topic: concept-article +ms.date: 09/19/2024 --- # Relevance in vector search diff --git a/articles/search/vector-store.md b/articles/search/vector-store.md index f9b51460cd9..3e6fbf39703 100644 --- a/articles/search/vector-store.md +++ b/articles/search/vector-store.md @@ -8,15 +8,15 @@ ms.author: robertlee ms.service: cognitive-search ms.custom: - ignite-2023 -ms.topic: conceptual -ms.date: 02/14/2024 +ms.topic: concept-article +ms.date: 09/19/2024 --- # Vector storage in Azure AI Search Azure AI Search provides vector storage and configurations for [vector search](vector-search-overview.md) and [hybrid search](hybrid-search-overview.md). Support is implemented at the field level, which means you can combine vector and nonvector fields in the same search corpus. -Vectors are stored in a search index. Use the [Create Index REST API](/rest/api/searchservice/indexes/create-or-update) or an equivalent Azure SDK method to [create the vector store](vector-search-how-to-create-index.md). +Vectors are stored in a search index. Use the [Create Index REST API](/rest/api/searchservice/indexes/create) or an equivalent Azure SDK method to [create the vector store](vector-search-how-to-create-index.md). Considerations for vector storage include the following points: @@ -54,7 +54,7 @@ Vector fields are distinguished by their data type and vector-specific propertie } ``` -Vector fields are of type `Collection(Edm.Single)`. +Vector fields have [specific data types](/rest/api/searchservice/supported-data-types#edm-data-types-for-vector-fields). Currently, `Collection(Edm.Single)` is the most common, but using narrow data types can save on storage. Vector fields must be searchable and retrievable, but they can't be filterable, facetable, or sortable, or have analyzers, normalizers, or synonym map assignments. @@ -146,7 +146,7 @@ Here's a screenshot showing search results in [Search Explorer](search-explorer. ## Physical structure and size -In Azure AI Search, the physical structure of an index is largely an internal implementation. You can access its schema, load and query its content, monitor its size, and manage capacity, but the clusters themselves (inverted and vector indexes), and other files and folders) are managed internally by Microsoft. +In Azure AI Search, the physical structure of an index is largely an internal implementation. You can access its schema, load and query its content, monitor its size, and manage capacity, but the clusters themselves (inverted and vector indexes), and other files and folders are managed internally by Microsoft. The size and substance of an index is determined by: From a2a84f5777fc54553fceabed5d6a4269ba172b05 Mon Sep 17 00:00:00 2001 From: mrbullwinkle <31510320+mrbullwinkle@users.noreply.github.com> Date: Thu, 19 Sep 2024 12:23:47 -0400 Subject: [PATCH 29/63] update --- .../openai/includes/chatgpt-studio.md | 75 ++++-------------- .../media/quickstarts/chat-view-code.png | Bin 0 -> 109544 bytes .../quickstarts/chatgpt-playground-load.png | Bin 210136 -> 173933 bytes .../media/quickstarts/chatgpt-playground.png | Bin 136519 -> 171069 bytes 4 files changed, 15 insertions(+), 60 deletions(-) create mode 100644 articles/ai-services/openai/media/quickstarts/chat-view-code.png diff --git a/articles/ai-services/openai/includes/chatgpt-studio.md b/articles/ai-services/openai/includes/chatgpt-studio.md index 394d07079cc..257a672175b 100644 --- a/articles/ai-services/openai/includes/chatgpt-studio.md +++ b/articles/ai-services/openai/includes/chatgpt-studio.md @@ -6,19 +6,13 @@ description: Walkthrough on how to get started with Azure OpenAI and make your f manager: nitinme ms.service: azure-ai-openai ms.topic: include -ms.date: 03/01/2023 +ms.date: 09/19/2024 --- ## Prerequisites - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true). -- An Azure OpenAI Service resource with either the `gpt-35-turbo` or the `gpt-4` models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). - -> [!div class="nextstepaction"] -> [I ran into an issue with the prerequisites.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=STUDIO&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Prerequisites) - -> [!TIP] -> Try out the new unified [Azure AI Studio (preview)](../../../ai-studio/what-is-ai-studio.md) which brings together capabilities from across multiple Azure AI services. +- An Azure OpenAI Service resource with either `gpt-4o` or the `gpt-4o-mini` models deployed. We recommend using standard or global standard model [deployment tpyes](../how-to/deployment-types.md) for initial exploration. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md). ## Go to Azure OpenAI Studio @@ -34,82 +28,43 @@ Start exploring OpenAI capabilities with a no-code approach through the Azure Op :::image type="content" source="../media/quickstarts/chatgpt-playground-load.png" alt-text="Screenshot of the Chat playground page." lightbox="../media/quickstarts/chatgpt-playground-load.png"::: -### Assistant setup +### Setup -You can use the **Assistant setup** dropdown to select a few pre-loaded **System message** examples to get started. +You can use the **Prompt samples* dropdown to select a few pre-loaded **System message** examples to get started. **System messages** give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, tell it what it should and shouldn't answer, and tell it how to format responses. -**Add few-shot examples** allows you to provide conversational examples that are used by the model for [in-context learning](../concepts/prompt-engineering.md#basics). - At any time while using the Chat playground you can select **View code** to see Python, curl, and json code samples pre-populated based on your current chat session and settings selections. You can then take this code and write an application to complete the same task you're currently performing with the playground. ### Chat session -Selecting the **Send** button sends the entered text to the completions API and the results are returned back to the text box. +Selecting the **Enter** button or selecting the right arrow icon sends the entered text to the chat completions API and the results are returned back to the text box. Select the **Clear chat** button to delete the current conversation history. -### Settings +### Key settings | **Name** | **Description** | |:--------------------|:-------------------------------------------------------------------------------| | Deployments | Your deployment name that is associated with a specific model. | +| Add your data | +| Parameters | Custom parameters that alter the model responses. When you are starting out we recommend to stick with the defaults for most parameters | | Temperature | Controls randomness. Lowering the temperature means that the model produces more repetitive and deterministic responses. Increasing the temperature results in more unexpected or creative responses. Try adjusting temperature or Top P but not both. | -| Max length (tokens) | Set a limit on the number of tokens per model response. The API supports a maximum of 4096 tokens shared between the prompt (including system message, examples, message history, and user query) and the model's response. One token is roughly four characters for typical English text.| -| Top probabilities | Similar to temperature, this controls randomness but uses a different method. Lowering Top P narrows the model’s token selection to likelier tokens. Increasing Top P lets the model choose from tokens with both high and low likelihood. Try adjusting temperature or Top P but not both.| -| Multi-turn conversations | Select the number of past messages to include in each new API request. This helps give the model context for new user queries. Setting this number to 10 results in five user queries and five system responses.| +| Max response (tokens) | Set a limit on the number of tokens per model response. The API supports a maximum of 4096 tokens shared between the prompt (including system message, examples, message history, and user query) and the model's response. One token is roughly four characters for typical English text.| +| Top p | Similar to temperature, this controls randomness but uses a different method. Lowering Top P narrows the model’s token selection to likelier tokens. Increasing Top P lets the model choose from tokens with both high and low likelihood. Try adjusting temperature or Top P but not both.| | Stop sequences | Stop sequence make the model end its response at a desired point. The model response ends before the specified sequence, so it won't contain the stop sequence text. For GPT-35-Turbo, using `<|im_end|>` ensures that the model response doesn't generate a follow-up user query. You can include as many as four stop sequences.| -### Show panels - -By default there are three panels: assistant setup, chat session, and settings. **Show panels** allows you to add, remove, and rearrange the panels. If you ever close a panel and need to get it back, use **Show panels** to restore the lost panel. - -## Start a chat session - -1. From the assistant setup drop-down, select **Xbox customer support agent** -2. You'll be prompted asking if you want to update the system message, select **Continue**. -3. In the chat session pane, enter the following question: "I'm interested in buying a new Xbox", and select **Send**. -4. You'll receive a response similar to: +## View code - :::image type="content" source="../media/quickstarts/xbox.png" alt-text="Screenshot of a first question and answer in playground." lightbox="../media/quickstarts/xbox.png"::: +Once you have experimented with chatting with the model select the ** View Code** button. This will give you a replay of the code behind your entire conversation so far: -5. Enter a follow-up question like: "which models support 4K?" - - :::image type="content" source="../media/quickstarts/models.png" alt-text="Screenshot of follow-up question and answer in playground." lightbox="../media/quickstarts/models.png"::: - -6. Now that you have a basic conversation select **View code** from under **Assistant setup** and you'll have a replay of the code behind the entire conversation so far: - -```python -#Note: The openai-python library support for Azure OpenAI is in preview. -import os -import openai -openai.api_type = "azure" -openai.api_base = "https://docs-test-001.openai.azure.com/" -openai.api_version = "2024-02-01" -openai.api_key = os.getenv("OPENAI_API_KEY") - -response = openai.ChatCompletion.create( - engine="gpt-35-turbo", # replace this value with the deployment name you chose when you deployed the associated model. - messages = [{"role":"system","content":"You are an Xbox customer support agent whose primary goal is to help users with issues they are experiencing with their Xbox devices. You are friendly and concise. You only provide factual answers to queries, and do not provide answers that are not related to Xbox."},{"role":"user","content":"How much is a PS5?"},{"role":"assistant","content":"I apologize, but I do not have information about the prices of other gaming devices such as the PS5. My primary focus is to assist with issues regarding Xbox devices. Is there a specific issue you are having with your Xbox device that I may be able to help with?"},{"role":"user","content":"I am interested in buying an Xbox?"},{"role":"assistant","content":"Great! There are several Xbox models available in the market, including the Xbox Series X, Xbox Series S, and Xbox One. The Xbox Series X is the latest and most powerful console, while the Xbox Series S is a more affordable option that still offers great performance. The Xbox One is the previous generation console, but it still has a large library of games available to play.\n\nBefore purchasing an Xbox, you may want to consider factors such as your budget, the types of games you want to play, and whether you want to play games in 4K resolution. Once you have decided on a console, you can purchase it from a retailer or directly from the Xbox website. Let me know if you have any other questions or concerns!"},{"role":"user","content":"Which models are 4K?"},{"role":"assistant","content":"Both the Xbox Series X and Xbox One X support 4K resolution gaming. The Xbox Series S, on the other hand, supports up to 1440p resolution gaming, but it can upscale games to 4K. It's worth noting that in order to take advantage of 4K gaming, you'll need a 4K TV or monitor that supports HDR (High Dynamic Range) and HDMI 2.0 or higher."}], - temperature=0, - max_tokens=350, - top_p=0.95, - frequency_penalty=0, - presence_penalty=0, - stop=None) -``` - -> [!div class="nextstepaction"] -> [I ran into an issue with the playground.](https://microsoft.qualtrics.com/jfe/form/SV_0Cl5zkG3CnDjq6O?PLanguage=STUDIO&Pillar=AOAI&Product=Chatgpt&Page=quickstart&Section=Set-up) +:::image type="content" source="../media/quickstarts/chat-view-code.png" alt-text="Screenshot of the Chat playground page." lightbox="../media/quickstarts/chat-view-code.png"::: ### Understanding the prompt structure -If you examine the sample from **View code** you'll notice some unique tokens that weren't part of a typical GPT completion call. GPT-35-Turbo was trained to use special tokens to delineate different parts of the prompt. Content is provided to the model in between `<|im_start|>` and `<|im_end|>` tokens. The prompt begins with a system message that can be used to prime the model by including context or instructions for the model. After that, the prompt contains a series of messages between the user and the assistant. - -The assistant's response to the prompt will then be returned below the `<|im_start|>assistant` token and will end with `<|im_end|>` denoting that the assistant has finished its response. You can also use the **Show raw syntax** toggle button to display these tokens within the chat session panel. +If you examine the sample from **View code** you'll notice that the conversation is broken into three distinct roles `system`, `user`, `assistant`. Each time you message the model the entire conversation history up to that point is resent. When using the chat completions API the model has no true memory of what you have sent to it in the past so you provide the conversation history for context to allow the model to respond properly. -The [GPT-35-Turbo & GPT-4 how-to guide](../how-to/chatgpt.md) provides an in-depth introduction into the new prompt structure and how to use the `gpt-35-turbo` model effectively. +The [Chat completions how-to guide](../how-to/chatgpt.md) provides an in-depth introduction into the new prompt structure and how to use chat completions models effectively. 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