Update the documentation for the Anthropic integration

docs/reference/models/anthropic.md

@@ -0,0 +1,45 @@
+# Anthropic
+
+!!! Installation
+
+    You need to install the `anthropic` library to be able to use the Anthropic API in Outlines. Or alternatively you can run:
+
+    ```bash
+    pip install "outlines[anthropic]"
+    ```
+
+## Anthropic models
+
+Outlines supports models available via the Anthropic API, e.g. Claude 3.5 Haiku or Claude 3.5 Sonner. You can initialize the model by passing the model name to `outlines.models.Anthropic`:
+
+```python
+from outlines import models
+
+model = models.Anthropic("claude-3-5-haiku-latest")
+model = models.Anthropic("claude-3-5-sonnet-latest")
+```
+
+Check the [Anthropic documentation](https://docs.anthropic.com/en/docs/about-claude/models) for an up-to-date list of available models. You can pass any paramater you would pass to the Anthropic SDK as keyword arguments:
+
+```python
+model = models.Anthropic(
+    "claude-3.5-haiku-latest",
+    api_key="<my api key>"
+)
+```
+
+## Text generation
+
+To generate text using an Anthropic model you need to build a `Generator` object, possibly with the desired output type. You can then call the model by calling the `Generator`. The method accepts every argument that you could pass to the `client.completions.create` function, as keyword arguments:
+
+```python
+from outlines import models, Generator
+
+model = models.Anthropic("claude-3-5-haiku-latest")
+generator = Generator(model)
+result = generator("Prompt", max_tokens=1024)
+```
+
+See the [Anthropic SDK documentation](https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/resources/messages.py) for the list of available arguments.
+
+The Anthropic API currently does not support structured generation.

pyproject.toml

@@ -50,6 +50,7 @@ vllm = ["vllm", "transformers", "numpy2"]
 transformers = ["transformers", "accelerate", "datasets", "numpy<2"]
 mlxlm = ["mlx-lm", "datasets"]
 openai = ["openai"]
+anthropic = ["anthropic"]
 gemini = ["google-generativeai"]
 llamacpp = ["llama-cpp-python", "transformers", "datasets", "numpy<2"]
 exllamav2 = ["exllamav2"]