From 41aada0a4ac5fcff3a37efd926591b45e6ef9afe Mon Sep 17 00:00:00 2001 From: Alvin Bryan Date: Fri, 27 Sep 2024 17:51:00 +0100 Subject: [PATCH 1/7] first draft --- .../feature-flag-tutorials/use-cases/ai.md | 187 ++++++++++++++++++ .../use-cases/sveltekit-chatbot.png | Bin 0 -> 61474 bytes 2 files changed, 187 insertions(+) create mode 100644 website/docs/feature-flag-tutorials/use-cases/ai.md create mode 100644 website/docs/feature-flag-tutorials/use-cases/sveltekit-chatbot.png diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md new file mode 100644 index 000000000000..4ea7907385c0 --- /dev/null +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -0,0 +1,187 @@ +--- +title: How to use feature flags with AI +slug: /feature-flag-tutorials/use-cases/ai +--- + +Many tech companies today are implementing some form of generative AI with LLMs into their products. This is usually a chatbot or a content generator. + +The main way to interact with LLMs today is via a set of APIs, usually either OpenAI, Anthropic or aggregators like Groq. All the APIs usually have similar parameters, like: + +- `model`: The model and the specific version of the model to use. +- `prompt`: The prompt to give to the LLM. + +Because all these models vary a lot in their capabilities, chances are you'll be testing multiple models, and multiple versions of the same model, and multiple prompts. + +This is where feature flags are super useful. And this is what we'll be doing in this tutorial. + +We'll be starting with a basic chatbot. I know building a chatbot is a total cliché at this point, but it's a small, self-contained example that everyone knows how to interact with. You can take this example and apply it to any other use case where you need to interact with an LLM via an API. + +First, clone the repo: + +```sh +git clone https://github.com/alvinometric/feature-flags-ai +cd feature-flags-ai +``` + +Install the dependencies + +```sh +npm install +``` + +Run the app + +```sh +npm run dev +``` + +And you should see something like this: + +![chatbot UI](./sveltekit-chatbot.png) + +This is a simple Sveltekit app with a chatbot UI. It doesn't really matter if you've never used Sveltekit before. It's similar to Next.js or Nuxt or SolidStart, where your file structure defines your routes. + +The most important file for this tutorial is the `src/routes/api/chat/+server.js` file. + +It creates an API endpoint at `/api/chat`. When your frontend sends a POST request to `/api/chat`, this is the code that handles it. + +```javascript +import { createOpenAI } from "@ai-sdk/openai"; +import { streamText } from "ai"; +import { env } from "$env/dynamic/private"; + +const openai = createOpenAI({ + apiKey: env.OPENAI_API_KEY ?? "", +}); + +const variants = { + variant1: { + model: "gpt-3.5-turbo", + max_tokens: 4096, + temperature: 1, + }, + variant2: { + model: "gpt-4-turbo", + max_tokens: 4096, + temperature: 1, + }, + variant3: { + model: "gpt-4-vision-preview", + max_tokens: 4096, + temperature: 1, + }, +}; + +export const POST = async ({ request }) => { + const { messages } = await request.json(); + const variant = variants["variant1"]; + const result = await streamText({ + model: openai(variant.model), + messages, + max_tokens: variant.max_tokens, + temperature: variant.temperature, + }); + return result.toDataStreamResponse(); +}; +``` + +This file is doing a few key things: + +1. It sets up our OpenAI client with an API key. +2. It defines different AI model configurations in the `variants` object. +3. The `POST` function handles incoming chat requests. + +When a request comes in, it: + +- Extracts the messages from the request body +- Selects a variant (currently hardcoded to 'variant1') +- Uses the OpenAI API to generate a response +- Streams the response back to the client + +The `streamText` function part of some utilities provided by Vercel's AI SDK, which helps deal with real-time streaming of the AI's responses and other AI-related tasks. + +## Creating a feature flag with AI variants + +Instead of hardcoding `variant1`, we want to use feature flags to dynamically choose which AI model to use. This will let us easily switch between models, test different configurations, or even do some A/B testing. + +To implement this, we'll need to: + +1. Set up a feature flag provider (we'll use Unleash). +2. Replace our static `variants` selection with feature flag calls. +3. Use the feature flag in our code to determine which AI model and settings to use for each request. + +## 2. Install a local feature flag provider + +In this section, we’ll install Unleash, run the instance locally, log in, and create a feature flag. If you prefer, you can use other tools instead of Unleash, but you’ll need to update the code accordingly. The basic steps will probably be the same. + +Use Git to clone the Unleash repository and Docker to build and run it. Open a terminal window and run the following commands: + +```sh +git clone https://github.com/unleash/unleash.git +cd unleash +docker compose up -d +``` + +You will now have Unleash installed onto your machine and running in the background. You can access this instance in your web browser at [http://localhost:4242](http://localhost:4242). + +Log in to the platform using these credentials: + +``` +Username: admin +Password: unleash4all +``` + +Click the ‘New feature flag’ button to create a new feature flag. + + + +### Adding AI feature flags + +Now let's go back to the code and grab our AI config from the feature flag that we just created. + +First, install the Unleash Node.js client: + +```sh +npm install unleash-client +``` + +Now, let's modify our `+server.js` file to use Unleash: + +```javascript +import { initialize } from "unleash-client"; +import { createOpenAI } from "@ai-sdk/openai"; +import { streamText } from "ai"; +import { env } from "$env/dynamic/private"; + +const openai = createOpenAI({ + apiKey: env.OPENAI_API_KEY ?? "", +}); + +const unleash = initialize({ + url: "http://localhost:4242/api/", + appName: "my-ai-app", + customHeaders: { Authorization: env.UNLEASH_API_KEY ?? "" }, +}); + +export const POST = async ({ request }) => { + const { messages } = await request.json(); + + // Get the feature flag variant + const variant = unleash.getVariant("gpt-versions"); + + const result = await streamText({ + model: openai(variant.model), + messages, + max_tokens: variant.max_tokens, + temperature: variant.temperature, + }); + + return result.toDataStreamResponse(); +}; +``` + +This setup uses the Unleash client to fetch the value of a feature flag called `gpt-versions`. + +Now, instead of hardcoding 'variant1', we're dynamically choosing the AI model based on the feature flag variant. + +This setup gives us a ton of flexibility. Want to roll out GPT-4 to 10% of your users? Easy. Need to quickly switch everyone back to GPT-3.5 because of a bug? No problem. 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Updating the nav --- website/docs/feature-flag-tutorials/use-cases/ai.md | 4 ++-- website/sidebars.js | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index 4ea7907385c0..e619826b5452 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -110,7 +110,7 @@ To implement this, we'll need to: 2. Replace our static `variants` selection with feature flag calls. 3. Use the feature flag in our code to determine which AI model and settings to use for each request. -## 2. Install a local feature flag provider +### Install a local feature flag provider In this section, we’ll install Unleash, run the instance locally, log in, and create a feature flag. If you prefer, you can use other tools instead of Unleash, but you’ll need to update the code accordingly. The basic steps will probably be the same. @@ -135,7 +135,7 @@ Click the ‘New feature flag’ button to create a new feature flag. -### Adding AI feature flags +## Querying AI feature flags Now let's go back to the code and grab our AI config from the feature flag that we just created. diff --git a/website/sidebars.js b/website/sidebars.js index c56c65608a5a..b8e957e42a80 100644 --- a/website/sidebars.js +++ b/website/sidebars.js @@ -112,6 +112,11 @@ module.exports = { label: 'Gradual Rollout', id: 'feature-flag-tutorials/use-cases/gradual-rollout', }, + { + type: 'doc', + label: 'Feature flags for AI', + id: 'feature-flag-tutorials/use-cases/ai', + }, ], }, { From 5ba0646244d58d04af077e93edd6aed36ee29b10 Mon Sep 17 00:00:00 2001 From: Alvin Bryan Date: Wed, 16 Oct 2024 19:10:29 +0100 Subject: [PATCH 3/7] Added missing steps --- .../feature-flag-tutorials/use-cases/ai.md | 40 +++++++++++++++++- .../use-cases/model-variant.png | Bin 0 -> 135092 bytes 2 files changed, 38 insertions(+), 2 deletions(-) create mode 100644 website/docs/feature-flag-tutorials/use-cases/model-variant.png diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index e619826b5452..90a0037f5b2a 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -102,7 +102,7 @@ The `streamText` function part of some utilities provided by Vercel's AI SDK, wh ## Creating a feature flag with AI variants -Instead of hardcoding `variant1`, we want to use feature flags to dynamically choose which AI model to use. This will let us easily switch between models, test different configurations, or even do some A/B testing. +Instead of hardcoding `variant1`, we want to use feature flags to dynamically choose which AI model to use. This will let us easily switch between models, test different configurations, or even do some A/B testing to see which model performs better for which task. To implement this, we'll need to: @@ -133,7 +133,39 @@ Password: unleash4all Click the ‘New feature flag’ button to create a new feature flag. - +![Create a new feature flag](../ruby/new-ff.png) + +Create a new feature flag called `gpt-version`. + +After that, and this the most important part, we need to add a variant to our feature flag. + +What we'll do is hold all the model configurations in the feature flag variants. + +So, we can take these model configurations: + +```javascript +const variants = { + variant1: { + model: "gpt-3.5-turbo", + max_tokens: 4096, + temperature: 1, + }, + variant2: { + model: "gpt-4-turbo", + max_tokens: 4096, + temperature: 1, + }, + variant3: { + model: "gpt-4-vision-preview", + max_tokens: 4096, + temperature: 1, + }, +}; +``` + +And add them into Unleash. + +![a variant with parameters for an OpenAI model](./model-variant.png) ## Querying AI feature flags @@ -185,3 +217,7 @@ This setup uses the Unleash client to fetch the value of a feature flag called ` Now, instead of hardcoding 'variant1', we're dynamically choosing the AI model based on the feature flag variant. This setup gives us a ton of flexibility. Want to roll out GPT-4 to 10% of your users? Easy. Need to quickly switch everyone back to GPT-3.5 because of a bug? No problem. You can do all of this from your Unleash dashboard without touching your code. + +## Conclusion + +Thanks for following along! 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zuTyq<%GnISno|S2i3>j^YCK3pcUlfL5S=D=oH5IO|=F1HEx=eNz|J?yyR=+#+a%X}DL(`v! z$D;~&u|cH@O6DZVUHX}H$Jw`b@z+qsafib0kKFm4kwuMLnYPDy7F->9rc|d^a8Z)8 zz^;$0^K%NYEb|(t`RJ3p8 z>D-_5d1WTsYKR1<_8QcSg$Y8B{l4of#wge#?Vo2M4I)O!X5PFoxIgLx?D-kexBV$+ zu=9VoW=oL^txe-X$Q7M`7d-N?mBfgxU`B1YjgIo{2lm5MnX}% JO4Kmu{{Vi`-%|hp literal 0 HcmV?d00001 From 957254bc636a9fda603d82d6fd9c039b547e6ee5 Mon Sep 17 00:00:00 2001 From: Alvin Bryan <107407814+alvinometric@users.noreply.github.com> Date: Wed, 16 Oct 2024 19:18:29 +0100 Subject: [PATCH 4/7] Apply suggestions from code review Co-authored-by: Melinda Fekete --- .../feature-flag-tutorials/use-cases/ai.md | 38 +++++++++---------- website/sidebars.js | 2 +- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index 90a0037f5b2a..53dce616e41e 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -3,7 +3,7 @@ title: How to use feature flags with AI slug: /feature-flag-tutorials/use-cases/ai --- -Many tech companies today are implementing some form of generative AI with LLMs into their products. This is usually a chatbot or a content generator. +Many tech companies today are integrating generative AI with large language models (LLMs) into their products, often in the form of chatbots or content generators. The main way to interact with LLMs today is via a set of APIs, usually either OpenAI, Anthropic or aggregators like Groq. All the APIs usually have similar parameters, like: @@ -12,9 +12,9 @@ The main way to interact with LLMs today is via a set of APIs, usually either Op Because all these models vary a lot in their capabilities, chances are you'll be testing multiple models, and multiple versions of the same model, and multiple prompts. -This is where feature flags are super useful. And this is what we'll be doing in this tutorial. +This is where feature flags are super useful. They allow you to easily switch between different configurations. In this tutorial, we’ll explore how to use feature flags with AI models. -We'll be starting with a basic chatbot. I know building a chatbot is a total cliché at this point, but it's a small, self-contained example that everyone knows how to interact with. You can take this example and apply it to any other use case where you need to interact with an LLM via an API. +We'll start with a basic chatbot. I know building a chatbot is a total cliché at this point, but it's a small, self-contained example that everyone knows how to interact with. You can take this example and apply it to any other use case where you need to interact with an LLM via an API. First, clone the repo: @@ -23,27 +23,27 @@ git clone https://github.com/alvinometric/feature-flags-ai cd feature-flags-ai ``` -Install the dependencies +Install the dependencies: ```sh npm install ``` -Run the app +Run the app: ```sh npm run dev ``` -And you should see something like this: +You should now see a chatbot UI, like this: ![chatbot UI](./sveltekit-chatbot.png) -This is a simple Sveltekit app with a chatbot UI. It doesn't really matter if you've never used Sveltekit before. It's similar to Next.js or Nuxt or SolidStart, where your file structure defines your routes. +This is a simple Sveltekit app with a chatbot interface. If you’re not familiar with SvelteKit, it works similarly to frameworks like Next.js, Nuxt, or SolidStart, where your file structure defines the routing. The most important file for this tutorial is the `src/routes/api/chat/+server.js` file. -It creates an API endpoint at `/api/chat`. When your frontend sends a POST request to `/api/chat`, this is the code that handles it. +It creates an API endpoint at `/api/chat`. When your frontend sends a POST request to `/api/chat`, this is the code that handles the request. ```javascript import { createOpenAI } from "@ai-sdk/openai"; @@ -87,16 +87,16 @@ export const POST = async ({ request }) => { This file is doing a few key things: -1. It sets up our OpenAI client with an API key. -2. It defines different AI model configurations in the `variants` object. -3. The `POST` function handles incoming chat requests. +1. Sets up our OpenAI client with an API key. +2. Defines different AI model configurations in the `variants` object. +3. Handles incoming chat requests with the `POST` function. When a request comes in, it: -- Extracts the messages from the request body -- Selects a variant (currently hardcoded to 'variant1') -- Uses the OpenAI API to generate a response -- Streams the response back to the client +- Extracts the messages from the request body. +- Selects a variant (currently hardcoded to 'variant1'). +- Uses the OpenAI API to generate a response. +- Streams the response back to the client. The `streamText` function part of some utilities provided by Vercel's AI SDK, which helps deal with real-time streaming of the AI's responses and other AI-related tasks. @@ -107,12 +107,12 @@ Instead of hardcoding `variant1`, we want to use feature flags to dynamically ch To implement this, we'll need to: 1. Set up a feature flag provider (we'll use Unleash). -2. Replace our static `variants` selection with feature flag calls. +2. Replace our static variant selection with feature flag calls. 3. Use the feature flag in our code to determine which AI model and settings to use for each request. ### Install a local feature flag provider -In this section, we’ll install Unleash, run the instance locally, log in, and create a feature flag. If you prefer, you can use other tools instead of Unleash, but you’ll need to update the code accordingly. The basic steps will probably be the same. +In this section, we’ll install Unleash, run the instance locally, log in, and create a feature flag. If you prefer, you can use other tools instead of Unleash, but you’ll need to update the code accordingly. Use Git to clone the Unleash repository and Docker to build and run it. Open a terminal window and run the following commands: @@ -122,7 +122,7 @@ cd unleash docker compose up -d ``` -You will now have Unleash installed onto your machine and running in the background. You can access this instance in your web browser at [http://localhost:4242](http://localhost:4242). +You now have Unleash installed on your machine and running in the background. You can access this instance in your web browser at [http://localhost:4242](http://localhost:4242). Log in to the platform using these credentials: @@ -131,7 +131,7 @@ Username: admin Password: unleash4all ``` -Click the ‘New feature flag’ button to create a new feature flag. +Click **New feature flag** to create a new feature flag. ![Create a new feature flag](../ruby/new-ff.png) diff --git a/website/sidebars.js b/website/sidebars.js index b8e957e42a80..b00fedd146fc 100644 --- a/website/sidebars.js +++ b/website/sidebars.js @@ -114,7 +114,7 @@ module.exports = { }, { type: 'doc', - label: 'Feature flags for AI', + label: 'Feature Flags for AI', id: 'feature-flag-tutorials/use-cases/ai', }, ], From ffaf42eb5e0be1facd0de728e07e64b61a32aef8 Mon Sep 17 00:00:00 2001 From: Alvin Bryan Date: Wed, 16 Oct 2024 19:22:02 +0100 Subject: [PATCH 5/7] Copy edits --- website/docs/feature-flag-tutorials/use-cases/ai.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index 53dce616e41e..621735e3aad9 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -5,12 +5,12 @@ slug: /feature-flag-tutorials/use-cases/ai Many tech companies today are integrating generative AI with large language models (LLMs) into their products, often in the form of chatbots or content generators. -The main way to interact with LLMs today is via a set of APIs, usually either OpenAI, Anthropic or aggregators like Groq. All the APIs usually have similar parameters, like: +The main way to interact with LLMs today is via a set of APIs, usually either OpenAI, Anthropic or aggregators like Groq. Most of these APIs have similar parameters, like: - `model`: The model and the specific version of the model to use. - `prompt`: The prompt to give to the LLM. -Because all these models vary a lot in their capabilities, chances are you'll be testing multiple models, and multiple versions of the same model, and multiple prompts. +Given how much these models vary in their capabilities, chances are you'll be testing multiple models, multiple versions of the same model, and multiple prompts. This is where feature flags are super useful. They allow you to easily switch between different configurations. In this tutorial, we’ll explore how to use feature flags with AI models. @@ -98,7 +98,7 @@ When a request comes in, it: - Uses the OpenAI API to generate a response. - Streams the response back to the client. -The `streamText` function part of some utilities provided by Vercel's AI SDK, which helps deal with real-time streaming of the AI's responses and other AI-related tasks. +The `streamText` function is one of the utilities provided by Vercel's AI SDK. It helps deal with the real-time streaming of the AI's responses. ## Creating a feature flag with AI variants From 726903eef307317598c42ce19ba1f2a1fb3ed073 Mon Sep 17 00:00:00 2001 From: Alvin Bryan <107407814+alvinometric@users.noreply.github.com> Date: Thu, 17 Oct 2024 09:39:57 +0100 Subject: [PATCH 6/7] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Nuno Góis Co-authored-by: Melinda Fekete --- website/docs/feature-flag-tutorials/use-cases/ai.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index 621735e3aad9..5623d09eef00 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -39,7 +39,7 @@ You should now see a chatbot UI, like this: ![chatbot UI](./sveltekit-chatbot.png) -This is a simple Sveltekit app with a chatbot interface. If you’re not familiar with SvelteKit, it works similarly to frameworks like Next.js, Nuxt, or SolidStart, where your file structure defines the routing. +This is a simple SvelteKit app with a chatbot interface. If you’re not familiar with SvelteKit, it works similarly to frameworks like Next.js, Nuxt, or SolidStart, where your file structure defines the routing. The most important file for this tutorial is the `src/routes/api/chat/+server.js` file. @@ -131,13 +131,11 @@ Username: admin Password: unleash4all ``` -Click **New feature flag** to create a new feature flag. +Click **New feature flag** to create a new feature flag, called `gpt-version`. -![Create a new feature flag](../ruby/new-ff.png) -Create a new feature flag called `gpt-version`. -After that, and this the most important part, we need to add a variant to our feature flag. +After that, and this is the most important part, we need to add a variant to our feature flag. What we'll do is hold all the model configurations in the feature flag variants. From 1a9657a2c1df3ce996f9683a09bfd0cb0fa77080 Mon Sep 17 00:00:00 2001 From: Alvin Bryan Date: Thu, 17 Oct 2024 19:35:56 +0100 Subject: [PATCH 7/7] Updated the text based on feedback from the reviews --- .../feature-flag-tutorials/use-cases/ai.md | 46 ++++++++++++------ .../use-cases/model-variant.png | Bin 135092 -> 279179 bytes 2 files changed, 30 insertions(+), 16 deletions(-) diff --git a/website/docs/feature-flag-tutorials/use-cases/ai.md b/website/docs/feature-flag-tutorials/use-cases/ai.md index 5623d09eef00..10596d0a47f1 100644 --- a/website/docs/feature-flag-tutorials/use-cases/ai.md +++ b/website/docs/feature-flag-tutorials/use-cases/ai.md @@ -3,7 +3,9 @@ title: How to use feature flags with AI slug: /feature-flag-tutorials/use-cases/ai --- -Many tech companies today are integrating generative AI with large language models (LLMs) into their products, often in the form of chatbots or content generators. +Hello, + +Like many people in lots of tech companies today, you might be playing with generative AI and large language models (LLMs). You might even be integrating these AI technologies into your company's products, probably in the form of chatbots or content generators. The main way to interact with LLMs today is via a set of APIs, usually either OpenAI, Anthropic or aggregators like Groq. Most of these APIs have similar parameters, like: @@ -29,6 +31,12 @@ Install the dependencies: npm install ``` +Copy the `.env.example` file to a new `.env` file, and add your OpenAI API key to it: + +```sh +OPENAI_API_KEY=sk-... +``` + Run the app: ```sh @@ -98,16 +106,18 @@ When a request comes in, it: - Uses the OpenAI API to generate a response. - Streams the response back to the client. -The `streamText` function is one of the utilities provided by Vercel's AI SDK. It helps deal with the real-time streaming of the AI's responses. +The `streamText` function is one of the utilities provided by [Vercel's AI SDK](https://sdk.vercel.ai/). It helps deal with the real-time streaming of the AI's responses. ## Creating a feature flag with AI variants -Instead of hardcoding `variant1`, we want to use feature flags to dynamically choose which AI model to use. This will let us easily switch between models, test different configurations, or even do some A/B testing to see which model performs better for which task. +Instead of hardcoding `variant1`, we want to use feature flags to dynamically choose which AI model to use. This will let us easily switch between models, test different configurations, or even do some A/B testing to see which model performs better for which task. And we can do this without having to redeploy our app. + +We can also disable it altogether if the upstream API stops working, or we run out of credits. To implement this, we'll need to: 1. Set up a feature flag provider (we'll use Unleash). -2. Replace our static variant selection with feature flag calls. +2. Replace our static objects with dynamic feature flag variants. 3. Use the feature flag in our code to determine which AI model and settings to use for each request. ### Install a local feature flag provider @@ -131,15 +141,11 @@ Username: admin Password: unleash4all ``` -Click **New feature flag** to create a new feature flag, called `gpt-version`. - - +Click **New feature flag** to create a new feature flag, called `ai-model`. After that, and this is the most important part, we need to add a variant to our feature flag. -What we'll do is hold all the model configurations in the feature flag variants. - -So, we can take these model configurations: +Add a strategy to the feature flag in the `development` environment, in that strategy, create a variant for each of these model configurations: ```javascript const variants = { @@ -161,7 +167,7 @@ const variants = { }; ``` -And add them into Unleash. +What we'll do is move all the model configurations from the code to the feature flag variants. ![a variant with parameters for an OpenAI model](./model-variant.png) @@ -197,7 +203,7 @@ export const POST = async ({ request }) => { const { messages } = await request.json(); // Get the feature flag variant - const variant = unleash.getVariant("gpt-versions"); + const variant = unleash.getVariant("ai-model"); const result = await streamText({ model: openai(variant.model), @@ -210,12 +216,20 @@ export const POST = async ({ request }) => { }; ``` -This setup uses the Unleash client to fetch the value of a feature flag called `gpt-versions`. +This setup uses the Unleash client to fetch the value of a feature flag called `ai-model`. + +Now, instead of hardcoding `variant1`, we're dynamically choosing the AI model based on the feature flag variant. -Now, instead of hardcoding 'variant1', we're dynamically choosing the AI model based on the feature flag variant. +This setup gives us a ton of flexibility. -This setup gives us a ton of flexibility. Want to roll out GPT-4 to 10% of your users? Easy. Need to quickly switch everyone back to GPT-3.5 because of a bug? No problem. You can do all of this from your Unleash dashboard without touching your code. +Do you want to roll out GPT-4 to 10% of your users? Easy. Need to quickly switch everyone back to GPT-3.5 because of a bug? No problem. + +You can do all of this from your Unleash dashboard without touching your code, and without needing to redeploy. ## Conclusion -Thanks for following along! You've learned one way to use feature flags to interact with AI models. +Thanks for following along! + +In this guide, we covered how to use feature flags to help you manage AI models. + +That approach lets you switch between different model configurations, experiment with variations, and even roll out updates without needing to touch your code or redeploy. 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