doc: support groq in hub

docs/hub/groq.md

@@ -0,0 +1,84 @@
+# Structured Outputs with Groq AI
+
+If you want to try this example using `instructor hub`, you can pull it by running
+
+```bash
+instructor hub pull --slug groq --py > groq_example.py
+```
+
+you'll need to sign up for an account and get an API key. You can do that [here](https://console.groq.com/docs/quickstart).
+
+```bash
+export GROQ_API_KEY=<your-api-key-here>
+pip install groq
+```
+
+!!! note "Other Languages"
+
+    This blog post is written in Python, but the concepts are applicable to other languages as well, as we currently have support for [Javascript](https://instructor-ai.github.io/instructor-js), [Elixir](https://hexdocs.pm/instructor/Instructor.html) and [PHP](https://github.com/cognesy/instructor-php/).
+
+<!-- more -->
+
+## Patching
+
+Instructor's patch enhances the openai api it with the following features:
+
+- `response_model` in `create` calls that returns a pydantic model
+- `max_retries` in `create` calls that retries the call if it fails by using a backoff strategy
+
+!!! note "Learn More"
+
+    To learn more, please refer to the [docs](../index.md). To understand the benefits of using Pydantic with Instructor, visit the tips and tricks section of the [why use Pydantic](../why.md) page.
+
+## Groq AI
+
+While Groq AI does not support function calling directly, you can still leverage the MD_JSON mode for structured outputs.
+
+!!! note "Getting access"
+
+    If you want to try this out for yourself check out the [docs](https://console.groq.com/docs/quickstart)
+
+
+```python
+import os
+import instructor
+
+from groq import Groq
+from pydantic import BaseModel
+
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
+
+# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.create methods to support the response_model parameter
+client = instructor.patch(
+    client, mode=instructor.Mode.MD_JSON
+)
+
+# Now, we can use the response_model parameter using only a base model
+# rather than having to use the OpenAISchema class
+class UserExtract(BaseModel):
+    name: str
+    age: int
+
+
+user: UserExtract = client.chat.completions.create(
+    model="mixtral-8x7b-32768",
+    response_model=UserExtract,
+    messages=[
+        {"role": "user", "content": "Extract jason is 25 years old"},
+    ],
+)
+
+assert isinstance(user, UserExtract), "Should be instance of UserExtract"
+assert user.name.lower() == "jason"
+assert user.age == 25
+
+print(user.model_dump_json(indent=2))
+"""
+{
+  "name": "jason",
+  "age": 25
+}
+"""
+```

mkdocs.yml

@@ -175,6 +175,7 @@ nav:
     - Using Llama CPP: 'hub/llama-cpp-python.md'
     - Using Together Compute: 'hub/together.md'
     - Using Anyscale: 'hub/anyscale.md'
+    - Using Groq: 'hub/groq.md'
     - Batch Async Classification w/ Langsmith: 'hub/batch_classification_langsmith.md'
     - Action Items: 'hub/action_items.md'
     - Partial Streaming: 'hub/partial_streaming.md'