From eb243df727ca38585b7f4777759e95921548b8b3 Mon Sep 17 00:00:00 2001 From: Jacques Date: Thu, 3 Apr 2025 20:30:08 +0100 Subject: [PATCH] Update --- .github/workflows/deploy.yml | 43 + .gitignore | 2 + docs/.gitignore | 2 + docs/app/[[...mdxPath]]/page.jsx | 23 + docs/app/layout.jsx | 47 + docs/content/_meta.js | 15 + docs/content/guides/_meta.js | 6 + docs/content/guides/models.mdx | 87 + docs/content/guides/overview.mdx | 62 + docs/content/guides/structured_outputs.mdx | 220 + docs/content/guides/text_generation.mdx | 204 + docs/content/index.mdx | 56 + docs/content/providers/_meta.js | 5 + docs/content/providers/anthropic.mdx | 160 + docs/content/providers/openai.mdx | 178 + docs/content/providers/openrouter.mdx | 193 + docs/mdx-components.js | 12 + docs/next.config.mjs | 17 + docs/package-lock.json | 6893 ++++++++++++++++++++ docs/package.json | 21 + src/ai_sdk/anthropic/__init__.py | 4 +- src/ai_sdk/openai/__init__.py | 4 +- src/ai_sdk/openrouter/__init__.py | 4 +- 23 files changed, 8252 insertions(+), 6 deletions(-) create mode 100644 .github/workflows/deploy.yml create mode 100644 docs/.gitignore create mode 100644 docs/app/[[...mdxPath]]/page.jsx create mode 100644 docs/app/layout.jsx create mode 100644 docs/content/_meta.js create mode 100644 docs/content/guides/_meta.js create mode 100644 docs/content/guides/models.mdx create mode 100644 docs/content/guides/overview.mdx create mode 100644 docs/content/guides/structured_outputs.mdx create mode 100644 docs/content/guides/text_generation.mdx create mode 100644 docs/content/index.mdx create mode 100644 docs/content/providers/_meta.js create mode 100644 docs/content/providers/anthropic.mdx create mode 100644 docs/content/providers/openai.mdx create mode 100644 docs/content/providers/openrouter.mdx create mode 100644 docs/mdx-components.js create mode 100644 docs/next.config.mjs create mode 100644 docs/package-lock.json create mode 100644 docs/package.json diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000..2d88187 --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,43 @@ +name: Deploy to GitHub Pages + +on: + push: + branches: + - main + paths: + - 'docs/**' + +jobs: + deploy: + runs-on: ubuntu-latest + permissions: + contents: write + defaults: + run: + working-directory: docs + + steps: + - uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: '20' + cache: 'npm' + cache-dependency-path: docs/package-lock.json + + - name: Install dependencies + run: npm ci + + - name: Build + run: npm run build + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./docs/dist + force_orphan: true + user_name: 'github-actions[bot]' + user_email: 'github-actions[bot]@users.noreply.github.com' + commit_message: 'Deploy documentation to GitHub Pages' \ No newline at end of file diff --git a/.gitignore b/.gitignore index c50bc18..53319da 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,5 @@ __pycache__/ .pytest_cache **.egg-info dist/ +.venv/ +node_modules/ \ No newline at end of file diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000..01f41fa --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,2 @@ +.next/ +.DS_Store diff --git a/docs/app/[[...mdxPath]]/page.jsx b/docs/app/[[...mdxPath]]/page.jsx new file mode 100644 index 0000000..72db16f --- /dev/null +++ b/docs/app/[[...mdxPath]]/page.jsx @@ -0,0 +1,23 @@ +import { generateStaticParamsFor, importPage } from 'nextra/pages' +import { useMDXComponents as getMDXComponents } from '../../mdx-components' + +export const generateStaticParams = generateStaticParamsFor('mdxPath') + +export async function generateMetadata(props) { + const params = await props.params + const { metadata } = await importPage(params.mdxPath) + return metadata +} + +const Wrapper = getMDXComponents().wrapper + +export default async function Page(props) { + const params = await props.params + const result = await importPage(params.mdxPath) + const { default: MDXContent, toc, metadata } = result + return ( + + + + ) +} diff --git a/docs/app/layout.jsx b/docs/app/layout.jsx new file mode 100644 index 0000000..5ca0fa4 --- /dev/null +++ b/docs/app/layout.jsx @@ -0,0 +1,47 @@ +import { Footer, Layout, Navbar } from 'nextra-theme-docs' +import { Banner, Head } from 'nextra/components' +import { getPageMap } from 'nextra/page-map' +import 'nextra-theme-docs/style.css' + +export const metadata = { + // Define your metadata here + // For more information on metadata API, see: https://nextjs.org/docs/app/building-your-application/optimizing/metadata +} + +const navbar = ( + AI SDK - Python} + // ... Your additional navbar options + /> +) +const footer =
MIT {new Date().getFullYear()} © jverre.
+ +export default async function RootLayout({ children }) { + return ( + + + {/* Your additional tags should be passed as `children` of `` element */} + + + + {children} + + + + ) +} \ No newline at end of file diff --git a/docs/content/_meta.js b/docs/content/_meta.js new file mode 100644 index 0000000..0cf63ca --- /dev/null +++ b/docs/content/_meta.js @@ -0,0 +1,15 @@ +import { GitHubIcon } from 'nextra/icons' + +export default { + index: { + title: 'AI SDK', + type: 'doc' + }, + guides: 'Guides', + providers: 'Providers' +} + +// Custom component for italicized text +function Italic({ children, ...props }) { + return {children} +} \ No newline at end of file diff --git a/docs/content/guides/_meta.js b/docs/content/guides/_meta.js new file mode 100644 index 0000000..091077c --- /dev/null +++ b/docs/content/guides/_meta.js @@ -0,0 +1,6 @@ +export default { + overview: 'Overview', + models: 'Models and Providers', + text_generation: 'Text Generation', + structured_outputs: 'Structured Outputs', +} \ No newline at end of file diff --git a/docs/content/guides/models.mdx b/docs/content/guides/models.mdx new file mode 100644 index 0000000..0b5bce8 --- /dev/null +++ b/docs/content/guides/models.mdx @@ -0,0 +1,87 @@ +import { Tabs } from 'nextra/components' + +# Models and Providers + +The Python AI SDK allowss you to call many different LLM models through a unified interface which makes it easy +to switch between models. + +The following providers are currently supported: + +* [OpenAI](https://platform.openai.com/docs/guides/text) +* [Anthropic](https://docs.anthropic.com/en/docs/welcome) +* [OpenRouter](https://openrouter.ai/docs/quickstart) + +## Using a model + +In order to use a model from a specific provider, you will first need to import the provider and then initialize the model which +can then be used within the `generate_text` and `generate_object` functions. + + + + ```python + from ai_sdk import generate_text + from ai_sdk.openai import openai + + response = generate_text( + model=openai("gpt-4o"), + prompt="Hello, world!" + ) + + print(response.text) + ``` + + + ```python + from ai_sdk import generate_text + from ai_sdk.anthropic import anthropic + + response = generate_text( + model=anthropic("claude-3-5-sonnet-20240620"), + prompt="Hello, world!" + ) + + print(response.text) + ``` + + + ```python + from ai_sdk import generate_text + from ai_sdk.openrouter import openrouter + + response = generate_text( + model=openrouter("openai/gpt-4o"), + prompt="Hello, world!" + ) + + print(response.text) + ``` + + + +## Advanced Usage + +### Customizing provider settings + +You can customize the provider settings by utilizing the `create__provider` function. This allows you to +pass in additional settings like API keys, base URLs, etc. For example, if you want to use the OpenAI provider with a +custom base URL, you can do the following: + +```python +from ai_sdk.openai import create_openai_provider, OpenAIProviderSettings + +provider = create_openai_provider( + settings=OpenAIProviderSettings( + base_url="https://api.openai.com", + api_key="your_api_key" + ) +) + +response = generate_text( + model=provider("gpt-4o"), + prompt="Hello, world!" +) + +print(response.text) +``` + +For a full list of available settings for each provider, please refer to each provider's documentation. \ No newline at end of file diff --git a/docs/content/guides/overview.mdx b/docs/content/guides/overview.mdx new file mode 100644 index 0000000..829c310 --- /dev/null +++ b/docs/content/guides/overview.mdx @@ -0,0 +1,62 @@ +import { Cards } from 'nextra/components' +import { FiMessageSquare, FiGrid, FiTool } from 'react-icons/fi' +import { Callout } from 'nextra/components' + +# Overview + +The AI SDK was heavily inspired by the [AI SDK](https://sdk.vercel.ai/) built by the Vercel team. This SDK is +a partial port of that JavaScript SDK to Python. + +## Installation + +```bash +pip install ai-sdk-python +``` + +## Usage + +The AI SDK is designed to make it easy to work with different AI providers whether that is to generate text +or structured outputs. All you need to do is import the library and call the `generate_text` function to get +started: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4o"), + prompt="Hello, world!" +) + +print(response.text) +``` + +You can learn more about using the AI SDK in our guides: + + + } + title="Prompts" + href="/guides/prompts" + arrow + /> + } + title="Structured Outputs" + href="/guides/structured-outputs" + arrow + /> + } + title="Tools" + href="/guides/tools" + arrow + /> + + +You can also find a full list of all the providers we support +in the Providers section. + + + If you would like us to support a new provider, please reach out to us on [Github](https://github.com/jverre/ai-sdk/issues). + diff --git a/docs/content/guides/structured_outputs.mdx b/docs/content/guides/structured_outputs.mdx new file mode 100644 index 0000000..7a110b6 --- /dev/null +++ b/docs/content/guides/structured_outputs.mdx @@ -0,0 +1,220 @@ +import { Steps, Tabs, Callout } from 'nextra/components' + +# Structured Outputs + +While LLMs are great at generating text, it can be useful to get structured outputs instead. The AI +SDK provides a `generate_object` function that allows you to do just that, you provide a schema and +a prompt and the LLM will generate an object that matches the schema. + +The `generate_object` function will choose the best way to generate the object based on +what is supported by the provider you are using. This includes: + +1. Explicit JSON support: OpenAI supports passing in a JSON schema that constrains the output of +the model. +2. Tool calls: Tool calls rely on specific argument formats, this can be abused to generate structured +outputs. +3. Text mode: If none of the above are supported, we will default back to prompting the LLM to +answer the prompts in a structured way. This is the least reliable method and can often lead to parsing +errors. + +## Basic Usage + +To generate structured output, you need to: +1. Define your output structure using Pydantic models +2. Use the `generate_object` function with your model + +Here's a simple example: + +```python +from pydantic import BaseModel +from ai_sdk import generate_object +from ai_sdk.openai import openai + +class WeatherResponse(BaseModel): + temperature: float + condition: str + humidity: int + wind_speed: float + +response = generate_object( + model=openai("gpt-4"), + schema=WeatherResponse, + prompt="What's the weather like in Paris today?" +) + +print(response.object.temperature) # e.g., 22.5 +print(response.object.condition) # e.g., "sunny" +``` + +## Understanding the Response + +The `generate_object` function returns an `ObjectResult` that contains: + +```python +class ObjectResult: + object: BaseModel # Your structured data + finish_reason: FinishReason # Why generation stopped + usage: Usage # Token usage statistics + request: RequestMetadata # Request information + response: ResponseMetadata # Response information + warnings: List[Warning] # Any warnings generated +``` + + + + ```python + class MovieReview(BaseModel): + title: str + rating: int + review: str + + response = generate_object( + model=openai("gpt-4"), + output_model=MovieReview, + prompt="Review the movie 'Inception'" + ) + print(f"Rating: {response.object.rating}/10") + print(response.usage.total_tokens) # Token usage + ``` + + + ```python + from pydantic import BaseModel, Field + + class Rating(BaseModel): + score: int = Field(ge=1, le=10) # Between 1-10 + review: str = Field(min_length=10) + + response = generate_object( + model=openai("gpt-4"), + schema=Rating, + prompt="Rate this product" + ) + # Will automatically validate score is 1-10 + # and review is at least 10 chars + ``` + + + ```python + from typing import List + + class Character(BaseModel): + name: str + age: int + skills: List[str] + + class Story(BaseModel): + title: str + genre: str + characters: List[Character] + plot_summary: str + + response = generate_object( + model=openai("gpt-4"), + schema=Story, + prompt="Create a sci-fi story" + ) + for char in response.object.characters: + print(f"{char.name}: {', '.join(char.skills)}") + ``` + + + +## Advanced Features + +### Using System Messages + +You can provide system messages to guide the structured output generation: + +```python +class ProductReview(BaseModel): + pros: List[str] + cons: List[str] + verdict: str + +response = generate_object( + model=openai("gpt-4"), + schema=ProductReview, + system="You are a critical product reviewer. Be honest about both positives and negatives.", + prompt="Review the latest iPhone" +) +``` + +### Nested Models + +The AI SDK supports complex nested models with full validation: + +```python +from typing import Optional, Dict + +class Address(BaseModel): + street: str + city: str + country: str + postal_code: str + +class Contact(BaseModel): + email: str + phone: Optional[str] + address: Address + metadata: Dict[str, str] = {} + +response = generate_object( + model=openai("gpt-4"), + schema=Contact, + prompt="Generate contact info for John Doe" +) +``` + + + The LLM will automatically format its response to match your model's structure, including all required fields and proper types. + + +### Error Handling + +When using structured outputs, you might encounter validation errors when the LLM generates data that doesn't match your model's requirements. The SDK provides the `AI_ObjectValidationError` class for handling these cases: + +```python +from ai_sdk.core.errors import AI_ObjectValidationError + +try: + response = generate_object( + model=openai("gpt-4"), + output_model=YourModel, + prompt="Your prompt" + ) +except AI_ObjectValidationError as e: + print("Validation error:", e.message) +except AI_APICallError as e: + print(f"API error (status {e.status_code}):", str(e)) + if e.is_retryable: + print("This error can be retried") +``` + + + The `AI_ObjectValidationError` provides a descriptive message about what fields failed validation and why. + + + + Always implement error handling when using structured outputs in production. The LLM might occasionally generate invalid data that needs to be handled gracefully. + + +You might also encounter API-level errors when making requests. The `AI_APICallError` provides detailed information about the failed request: +- Status code +- Whether the error is retryable +- Response body +- Request details + +```python +try: + response = generate_object( + model=openai("gpt-4"), + output_model=YourModel, + prompt="Your prompt" + ) +except AI_APICallError as e: + print(f"API call failed:") + print(f" URL: {e.url}") + print(f" Status: {e.status_code}") + print(f" Response: {e.response_body}") + print(f" Retryable: {e.is_retryable}") diff --git a/docs/content/guides/text_generation.mdx b/docs/content/guides/text_generation.mdx new file mode 100644 index 0000000..61e10e6 --- /dev/null +++ b/docs/content/guides/text_generation.mdx @@ -0,0 +1,204 @@ +import { Steps, Tabs, Callout } from 'nextra/components' + +# Text Generation + +When using LLMs, the most common task is to generate text. For this, the Python AI SDK provides a simple `generate_text` function. + +## Getting started + +### Using prompts + +The simplest way to generate text is to pass in a prompt and a model. For example: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4"), + prompt="Hello, world!" +) + +print(response.text) +``` + +### Adding a system prompt + +System prompts allow you to guide your LLM to behave in a specific way. You can pass in a system prompt by setting the `system` parameter: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4"), + system="You are a helpful assistant.", + prompt="Hello, world!", +) +``` + +## Understanding the Response + +The `generate_text` function returns a `TextResult` object which contains comprehensive information about the generation: + +```python +class TextResult: + text: str # The generated text response + finish_reason: FinishReason # Why the generation stopped (length, stop token, etc) + usage: Usage # Token usage statistics + tool_calls: List[ToolCall] # Any tool calls made during generation + tool_results: List[Result] # Results from tool calls + request: RequestMetadata # Information about the request + response: ResponseMetadata # Information about the response + warnings: List[Warning] # Any warnings generated + provider_metadata: Dict # Provider-specific metadata +``` + + + + ```python + response = generate_text( + model=openai("gpt-4"), + prompt="What is the capital of France?" + ) + print(response.text) # "The capital of France is Paris." + print(response.finish_reason) # "stop" + ``` + + + ```python + response = generate_text( + model=openai("gpt-4"), + prompt="Explain quantum computing" + ) + print(f"Prompt tokens: {response.usage.prompt_tokens}") + print(f"Completion tokens: {response.usage.completion_tokens}") + print(f"Total tokens: {response.usage.total_tokens}") + ``` + + + + + The `provider_metadata` field contains additional information specific to each provider. For example, OpenAI might include model-specific parameters while Anthropic might include different metadata. + + +## Going beyond simple prompts with messages + +While the `prompt` parameter is useful for simple tasks, it is often better to pass in a list of messages for more complex interactions: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4"), + messages=[ + {"role": "user", "content": "Hello, world!"} + ] +) +``` + +### Message Types + +The Python AI SDK supports four different message types: + +- `system`: A system message that guides the behavior of the LLM +- `user`: A user message that acts as a prompt +- `assistant`: A message from the assistant +- `tool`: A message from a tool + +#### System messages + +System messages (sometimes referred to as `system prompt` or `developer messages`) are used to guide the behavior of the LLM. They +are typically used to set the tone or provide instructions for the LLM: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4"), + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello, world!"} + ] +) +``` + +#### User messages + +User messages support both text and image content: + + + + ```python + response = generate_text( + model=openai("gpt-4"), + messages=[{"role": "user", "content": "Hello, world!"}] + ) + ``` + + + ```python + response = generate_text( + model=openai("gpt-4"), + messages=[{ + "role": "user", + "content": [ + {"type": "text", "text": "Hello, world!"}, + {"type": "text", "text": "This is a test."} + ] + }] + ) + ``` + + + ```python + response = generate_text( + model=openai("gpt-4"), + messages=[{ + "role": "user", + "content": [{ + "type": "image", + "image": "https://example.com/image.jpg" + }] + }] + ) + ``` + + + +#### Assistant messages + +Assistant messages represent responses from the LLM, useful for maintaining conversation context: + +```python +response = generate_text( + model=openai("gpt-4"), + messages=[ + {"role": "user", "content": "Hello, world!"}, + {"role": "assistant", "content": "Hi, how can I help you today?"}, + {"role": "user", "content": "What is your name?"} + ] +) +``` + +#### Tool messages + +Tool messages contain results from tool calls and help provide context about previous tool interactions: + +```python +response = generate_text( + model=openai("gpt-4"), + messages=[ + {"role": "user", "content": "What's the weather like?"}, + {"role": "tool", "content": {"temperature": 22, "condition": "sunny"}}, + {"role": "assistant", "content": "The weather is sunny with a temperature of 22°C."} + ] +) +``` + + + Not all providers support all message types. Check the provider's documentation for supported message types. + + diff --git a/docs/content/index.mdx b/docs/content/index.mdx new file mode 100644 index 0000000..e3d9046 --- /dev/null +++ b/docs/content/index.mdx @@ -0,0 +1,56 @@ +import { Callout } from 'nextra/components' +import { Steps } from 'nextra/components' + +# Home + +The Python AI SDK is heavily inspired (ie. a port) of the awesome +[AI SDK](https://sdk.vercel.ai/) built by [Vercel](https://vercel.com). + +I have been a big fan of the Vercel AI SDK and have always wanted to use +it as part of my Python projects and decided to recreate it in Python. + + + If you have any feedback or feature requests, just reach out on [Github](https://github.com/jverre/ai-sdk). + + +## Getting started + +To get started, simply follow the steps: + + +### Install the package + +You can install the Python AI SDK using pip: + +```bash +pip install ai-sdk-python +``` + +### Making your first LLM call + +Once the package is installed, you can make your first LLM call using the `generate_text` function: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4o"), + prompt="Hello, world!" +) + +print(response.text) +``` + +### Adding tracing + +The Python AI SDK supports tracing which means you can save all your LLM calls for future analysis. + +For this, the AI SDK relies on the [Opik](https://github.com/comet-ml/opik) library. To get started, +the only thing you need to do is configure the Opik library: + +```bash +opik.configure() +``` + + diff --git a/docs/content/providers/_meta.js b/docs/content/providers/_meta.js new file mode 100644 index 0000000..e810f8d --- /dev/null +++ b/docs/content/providers/_meta.js @@ -0,0 +1,5 @@ +export default { + openai: 'OpenAI', + anthropic: 'Anthropic', + openrouter: 'OpenRouter', + } \ No newline at end of file diff --git a/docs/content/providers/anthropic.mdx b/docs/content/providers/anthropic.mdx new file mode 100644 index 0000000..223ee04 --- /dev/null +++ b/docs/content/providers/anthropic.mdx @@ -0,0 +1,160 @@ +import { Callout } from 'nextra/components' + +# Anthropic Provider + +The Anthropic provider gives you access to Claude models within the AI SDK. It supports Claude 3 models with capabilities for chat, image understanding, and tool use. + +## Basic Usage + +The simplest way to use Anthropic models is with the `anthropic` function: + +```python +from ai_sdk import generate_text +from ai_sdk.anthropic import anthropic + +response = generate_text( + model=anthropic("claude-3-opus-20240229"), + prompt="Hello, world!" +) +``` + + +## Supported Models + +The following table shows all supported Claude models and their capabilities: + +| Model | Chat | Images | Tool Calls | +|-------|------|--------|------------| +| claude-3-7-sonnet-20250219 | ✅ | ✅ | ✅ | +| claude-3-5-sonnet-20241022 | ✅ | ✅ | ✅ | +| claude-3-5-sonnet-20240620 | ✅ | ✅ | ✅ | +| claude-3-5-haiku-20241022 | ✅ | ❌ | ✅ | +| claude-3-opus-20240229 | ✅ | ✅ | ✅ | +| claude-3-sonnet-20240229 | ✅ | ✅ | ❌ | +| claude-3-haiku-20240307 | ✅ | ✅ | ✅ | + + + All models support basic chat functionality. Image and tool call support varies by model. + + +## Configuration + +You can configure the Anthropic provider with various settings: + +```python +from ai_sdk.anthropic import create_anthropic_provider, AnthropicProviderSettings + +provider = create_anthropic_provider( + settings=AnthropicProviderSettings( + api_key="your-api-key", # Optional: can also use ANTHROPIC_API_KEY env var + base_url="https://api.anthropic.com", # Optional: defaults to Anthropic's API + headers={"Custom-Header": "value"} # Optional: additional headers + ) +) + +model = provider("claude-3-opus-20240229") +``` + +### Authentication + +The provider will look for your API key in the following order: +1. The `api_key` parameter in settings +2. The `ANTHROPIC_API_KEY` environment variable + + + Never hardcode your API key in your code. Use environment variables instead. + + +## Model Settings + +When creating a model, you can customize its behavior: + +```python +from ai_sdk.anthropic import anthropic, AnthropicChatSettings + +model = anthropic( + "claude-3-opus-20240229", + settings=AnthropicChatSettings( + sendReasoning=True # Enable reasoning in responses + ) +) +``` + +## Working with Images + +For models that support image input, you can pass images via URL or base64: + +```python +response = generate_text( + model=anthropic("claude-3-opus-20240229"), + messages=[{ + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + {"type": "image", "image": "https://example.com/image.jpg"} + ] + }] +) +``` + + + Image support is available on specific Claude 3 models only. Check `SUPPORTED_IMAGE_MODELS` for compatibility. + + +## Using Tool Calls + +Tools can be defined using Pydantic models and optional execution functions: + +```python +from pydantic import BaseModel +from ai_sdk import generate_text +from typing import Dict + +class WeatherTool(BaseModel): + location: str + units: str = "celsius" + +# Define tools with optional execution functions +tools: Dict[str, dict] = { + "weather": { + "description": "Get the weather in a location", + "parameters": WeatherTool, + "execute": lambda args: { + "temperature": 22, + "condition": "sunny" + } + } +} + +response = generate_text( + model=anthropic("claude-3-opus-20240229"), + prompt="What's the weather in Paris?", + tools=tools, + max_steps=2 # Allow multiple steps for tool execution +) + +# Access results +print(response.text) # Final response +print(response.tool_calls) # List of tool calls made +print(response.tool_results) # List of tool results +``` + +The response includes: +- `text`: The final generated text +- `tool_calls`: List of `ToolCallPart` objects containing: + - `tool_call_id`: Unique identifier for the call + - `tool_name`: Name of the tool called + - `args`: Dictionary of arguments passed +- `tool_results`: List of `ToolResultPart` objects containing: + - `tool_call_id`: Matching ID from the call + - `tool_name`: Name of the tool + - `result`: The result returned + - `is_error`: Optional error flag + + + When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response. + + + + Not all Claude models support tool calls. Check the compatibility table above for supported models. + \ No newline at end of file diff --git a/docs/content/providers/openai.mdx b/docs/content/providers/openai.mdx new file mode 100644 index 0000000..471d4a8 --- /dev/null +++ b/docs/content/providers/openai.mdx @@ -0,0 +1,178 @@ +import { Callout } from 'nextra/components' + +# OpenAI Provider + +The OpenAI provider allows you to use OpenAI's models within the AI SDK. It supports chat-based models like GPT-4 and GPT-3.5. + +## Basic Usage + +The simplest way to use OpenAI models is with the `openai` function: + +```python +from ai_sdk import generate_text +from ai_sdk.openai import openai + +response = generate_text( + model=openai("gpt-4"), + prompt="Hello, world!" +) +``` + + +## Supported Models + +The following table shows all supported OpenAI models and their capabilities: + +| Model | Chat | Images | Tool Calls | JSON Mode | System Messages | +|-------|------|--------|------------|-----------|-----------------| +| gpt-4o | ✅ | ✅ | ✅ | ✅ | ✅ | +| gpt-4o-mini | ✅ | ✅ | ✅ | ✅ | ✅ | +| gpt-4-turbo | ✅ | ✅ | ❌ | ❌ | ✅ | +| gpt-4 | ✅ | ❌ | ❌ | ❌ | ✅ | +| gpt-3.5-turbo | ✅ | ❌ | ❌ | ❌ | ✅ | +| o1 | ✅ | ✅ | ✅ | ✅ | ✅ | +| o1-mini | ✅ | ✅ | ✅ | ✅ | ❌ | +| o1-preview | ✅ | ❌ | ❌ | ❌ | ❌ | +| o3-mini | ✅ | ❌ | ✅ | ✅ | ✅ | +| chatgpt-4o-latest | ✅ | ✅ | ❌ | ❌ | ✅ | + +## Configuration + +You can configure the OpenAI provider in several ways: + +```python +from ai_sdk.openai import create_openai_provider, OpenAIProviderSettings + +provider = create_openai_provider( + settings=OpenAIProviderSettings( + api_key="your-api-key", # Optional: can also use OPENAI_API_KEY env var + organization="your-org-id", # Optional: for enterprise customers + project="your-project", # Optional: for project tracking + base_url="https://api.openai.com" # Optional: defaults to OpenAI's API + ) +) + +model = provider("gpt-4") +``` + +### Authentication + +The provider will look for your API key in the following order: +1. The `api_key` parameter in settings +2. The `OPENAI_API_KEY` environment variable + + + Never hardcode your API key in your code. Use environment variables instead. + + +### Custom Headers + +You can add custom headers to all requests: + +```python +provider = create_openai_provider( + settings=OpenAIProviderSettings( + headers={ + "Custom-Header": "value" + } + ) +) +``` + +### Using Alternative Base URLs + +If you're using Azure OpenAI or a proxy, you can change the base URL: + +```python +provider = create_openai_provider( + settings=OpenAIProviderSettings( + base_url="https://your-custom-endpoint.com" + ) +) +``` + +## Model Settings + +When creating a model, you can customize its behavior: + +```python +from ai_sdk.openai import openai, OpenAIChatSettings + +model = openai( + "gpt-4", + settings=OpenAIChatSettings( + # Add model-specific settings here + ) +) +``` + +## Working with Images + +For models that support image input: + +```python +response = generate_text( + model=openai("gpt-4o"), + messages=[{ + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + {"type": "image", "image": "https://example.com/image.jpg"} + ] + }] +) +``` + +## Using Tool Calls + +Tools can be defined using Pydantic models and optional execution functions: + +```python +from pydantic import BaseModel +from ai_sdk import generate_text +from typing import Dict + +class WeatherTool(BaseModel): + location: str + units: str = "celsius" + +# Define tools with optional execution functions +tools: Dict[str, dict] = { + "weather": { + "description": "Get the weather in a location", + "parameters": WeatherTool, + "execute": lambda args: { + "temperature": 22, + "condition": "sunny" + } + } +} + +response = generate_text( + model=openai("gpt-4o"), + prompt="What's the weather in Paris?", + tools=tools, + max_steps=2 # Allow multiple steps for tool execution +) + +# Access results +print(response.text) # Final response +print(response.tool_calls) # List of tool calls made +print(response.tool_results) # List of tool results +``` + +The response includes: +- `text`: The final generated text +- `tool_calls`: List of `ToolCallPart` objects containing: + - `tool_call_id`: Unique identifier for the call + - `tool_name`: Name of the tool called + - `args`: Dictionary of arguments passed +- `tool_results`: List of `ToolResultPart` objects containing: + - `tool_call_id`: Matching ID from the call + - `tool_name`: Name of the tool + - `result`: The result returned + - `is_error`: Optional error flag + + + When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response. + diff --git a/docs/content/providers/openrouter.mdx b/docs/content/providers/openrouter.mdx new file mode 100644 index 0000000..a7ca630 --- /dev/null +++ b/docs/content/providers/openrouter.mdx @@ -0,0 +1,193 @@ +import { Callout } from 'nextra/components' + +# OpenRouter Provider + +The OpenRouter provider allows you to access various AI models through OpenRouter's API. It provides a unified interface for multiple model providers with support for advanced features like image understanding and tool calls. + +## Basic Usage + +The simplest way to use OpenRouter models is with the `openrouter` function: + +```python +from ai_sdk import generate_text +from ai_sdk.openrouter import openrouter + +response = generate_text( + model=openrouter("openai/gpt-4o"), + prompt="Hello, world!" +) +``` + +## Features + +OpenRouter supports all major capabilities across its models: + +- ✅ Chat Conversations +- ✅ Image Understanding +- ✅ Tool Calls +- ✅ JSON Mode +- ✅ System Messages + +## Configuration + +You can configure the OpenRouter provider with various settings: + +```python +from ai_sdk.openrouter import create_openrouter_provider, OpenRouterProviderSettings + +provider = create_openrouter_provider( + settings=OpenRouterProviderSettings( + api_key="your-api-key", # Optional: can also use OPENROUTER_API_KEY env var + base_url="https://openrouter.ai/api", # Optional: defaults to OpenRouter's API + headers={"Custom-Header": "value"} # Optional: additional headers + ) +) + +model = provider("openai/gpt-4o") +``` + +### Authentication + +The provider will look for your API key in the following order: +1. The `api_key` parameter in settings +2. The `OPENROUTER_API_KEY` environment variable + + + Never hardcode your API key in your code. Use environment variables instead. + + +## Model Settings + +When creating a model, you can customize its behavior: + +```python +from ai_sdk.openrouter import openrouter, OpenRouterChatSettings + +model = openrouter( + "your-chosen-model", + settings=OpenRouterChatSettings( + logit_bias=None, # Optional: adjust token probabilities + parallel_tool_calls=True, # Optional: enable parallel tool execution + structured_outputs=True, # Optional: enable structured output format + user="user-id" # Optional: user identifier + ) +) +``` + +## Working with Images + +OpenRouter supports image input for compatible models: + +```python +response = generate_text( + model=openrouter("openai/gpt-4o"), + messages=[{ + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + {"type": "image", "image": "https://example.com/image.jpg"} + ] + }] +) +``` + +## Using Tool Calls + +Tools can be defined using Pydantic models and optional execution functions: + +```python +from pydantic import BaseModel +from ai_sdk import generate_text +from typing import Dict + +class WeatherTool(BaseModel): + location: str + units: str = "celsius" + +# Define tools with optional execution functions +tools: Dict[str, Tool] = { + "weather": { + "description": "Get the weather in a location", + "parameters": WeatherTool, + "execute": lambda location: { + "location": location, + "temperature": 22, + "condition": "sunny" + } + } +} + +response = generate_text( + model=openrouter("openai/gpt-4o"), # or openrouter("your-model") + prompt="What's the weather in Paris?", + tools=tools, + max_steps=2 # Allow multiple steps for tool execution +) + +# Access results +print(response.text) # Final response +print(response.tool_calls) # List of tool calls made +print(response.tool_results) # List of tool results +``` + +The response includes: +- `text`: The final generated text +- `tool_calls`: List of `ToolCallPart` objects containing: + - `tool_call_id`: Unique identifier for the call + - `tool_name`: Name of the tool called + - `args`: Dictionary of arguments passed +- `tool_results`: List of `ToolResultPart` objects containing: + - `tool_call_id`: Matching ID from the call + - `tool_name`: Name of the tool + - `result`: The result returned + - `is_error`: Optional error flag + + + When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response. + + +## Message Types + +OpenRouter supports various message types: +- `system`: System-level instructions +- `developer`: Developer-specific instructions +- `user`: User messages (text or images) +- `assistant`: AI responses +- `tool`: Tool call results + +```python +response = generate_text( + model=openrouter("your-chosen-model"), + messages=[ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hello!"}, + {"role": "assistant", "content": "Hi there!"}, + {"role": "user", "content": "How are you?"} + ] +) +``` + + + OpenRouter automatically handles the conversion between different provider message formats, making it easier to work with various models through a single interface. + + +## Error Handling + +OpenRouter provides detailed error information when requests fail: + +```python +try: + response = generate_text( + model=openrouter("your-chosen-model"), + prompt="Your prompt" + ) +except AI_APICallError as e: + print(f"API call failed:") + print(f" Status: {e.status_code}") + print(f" Response: {e.response_body}") + print(f" Retryable: {e.is_retryable}") +``` + + + Always implement proper error handling in production. OpenRouter will indicate if errors are retryable through the `is_retryable` flag. + diff --git a/docs/mdx-components.js b/docs/mdx-components.js new file mode 100644 index 0000000..9c55d84 --- /dev/null +++ b/docs/mdx-components.js @@ -0,0 +1,12 @@ +import { useMDXComponents as getThemeComponents } from 'nextra-theme-docs' // nextra-theme-blog or your custom theme + +// Get the default MDX components +const themeComponents = getThemeComponents() + +// Merge components +export function useMDXComponents(components) { + return { + ...themeComponents, + ...components + } +} \ No newline at end of file diff --git a/docs/next.config.mjs b/docs/next.config.mjs new file mode 100644 index 0000000..1fbe766 --- /dev/null +++ b/docs/next.config.mjs @@ -0,0 +1,17 @@ +import nextra from 'nextra' + +const withNextra = nextra({ + theme: 'nextra-theme-docs', + themeConfig: './theme.config.jsx' +}) + +// You can include other Next.js configuration options here, in addition to Nextra settings: +export default withNextra({ + output: 'export', + images: { + unoptimized: true, + }, + basePath: process.env.NODE_ENV === 'production' ? 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"dependencies": { + "next": "^15.2.4", + "nextra": "^4.2.17", + "nextra-theme-docs": "^4.2.17", + "react": "^19.1.0", + "react-dom": "^19.1.0", + "react-icons": "^5.5.0", + "typescript": "^5.8.2" + }, + "devDependencies": { + "gh-pages": "^6.3.0" + } +} diff --git a/src/ai_sdk/anthropic/__init__.py b/src/ai_sdk/anthropic/__init__.py index be6c24a..87b4261 100644 --- a/src/ai_sdk/anthropic/__init__.py +++ b/src/ai_sdk/anthropic/__init__.py @@ -1,3 +1,3 @@ -from .provider import anthropic, create_anthropic_provider +from .provider import anthropic, create_anthropic_provider, AnthropicProviderSettings -__all__ = ["anthropic", "create_anthropic_provider"] \ No newline at end of file +__all__ = ["anthropic", "create_anthropic_provider", "AnthropicProviderSettings"] \ No newline at end of file diff --git a/src/ai_sdk/openai/__init__.py b/src/ai_sdk/openai/__init__.py index e59bc7b..ae27ba8 100644 --- a/src/ai_sdk/openai/__init__.py +++ b/src/ai_sdk/openai/__init__.py @@ -1,3 +1,3 @@ -from .provider import openai, create_openai_provider +from .provider import openai, create_openai_provider, OpenAIProviderSettings -__all__ = ["openai", "create_openai_provider"] \ No newline at end of file +__all__ = ["openai", "create_openai_provider", "OpenAIProviderSettings"] \ No newline at end of file diff --git a/src/ai_sdk/openrouter/__init__.py b/src/ai_sdk/openrouter/__init__.py index 4fbc0e7..5ccfee7 100644 --- a/src/ai_sdk/openrouter/__init__.py +++ b/src/ai_sdk/openrouter/__init__.py @@ -1,3 +1,3 @@ -from .provider import openrouter, create_openrouter_provider +from .provider import openrouter, create_openrouter_provider, OpenRouterProviderSettings -__all__ = ["openrouter", "create_openrouter_provider"] \ No newline at end of file +__all__ = ["openrouter", "create_openrouter_provider", "OpenRouterProviderSettings"] \ No newline at end of file