feat: added cortex documentation

docs/integrations/cortex.md

@@ -0,0 +1,178 @@
+---
+title: "Structured outputs with Cortex, a complete guide w/ instructor"
+description: "Learn how to use Cortex with Instructor for structured outputs. Complete guide with examples and best practices."
+---
+
+# Structured outputs with Cortex
+
+Cortex.cpp is a runtime that helps you run open source LLMs out of the box. It supports a wide variety of models and powers their [Jan](https://jan.ai) platform. This guide provides a quickstart on how to use Cortex with instructor for structured outputs.
+
+## Quick Start
+
+Instructor comes with support for the OpenAI client out of the box, so you don't need to install anything extra.
+
+```bash
+pip install "instructor"
+```
+
+Once you've done so, make sure to pull the model that you'd like to use. In this example, we'll be using a quantized llama3.2 model.
+
+```bash
+cortex run llama3.2:3b-gguf-q4-km
+```
+
+Let's start by initializing the client below - note that we need to provide a base URL and an API key here. The API key isn't important, it's just so the OpenAI client doesn't throw an error.
+
+```python
+import os
+from openai import OpenAI
+
+client = from_openai(
+    openai.OpenAI(
+        base_url="http://localhost:39281/v1",
+        api_key="this is a fake api key that doesn't matter",
+    )
+)
+```
+
+## Simple User Example (Sync)
+
+```python
+from instructor import from_openai
+from pydantic import BaseModel
+import openai
+
+client = from_openai(
+    openai.OpenAI(
+        base_url="http://localhost:39281/v1",
+        api_key="this is a fake api key that doesn't matter",
+    )
+)
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+resp = client.chat.completions.create(
+    model="llama3.2:3b-gguf-q4-km",
+    messages=[{"role": "user", "content": "Ivan is 27 and lives in Singapore"}],
+    response_model=User,
+)
+
+print(resp)
+# > name='Ivan', age=27
+```
+
+## Simple User Example (Async)
+
+```python
+import os
+from openai import AsyncOpenAI
+import instructor
+from pydantic import BaseModel
+import asyncio
+
+# Initialize with API key
+client = AsyncOpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+
+# Enable instructor patches for async OpenAI client
+client = instructor.from_openai(client)
+
+class User(BaseModel):
+    name: str
+    age: int
+
+async def extract_user():
+    user = await client.chat.completions.create(
+        model="llama3.2:3b-gguf-q4-km",
+        messages=[
+            {"role": "user", "content": "Extract: Jason is 25 years old"},
+        ],
+        response_model=User,
+    )
+    return user
+
+# Run async function
+user = asyncio.run(extract_user())
+print(user)
+#> User(name='Jason', age=25)
+```
+
+## Nested Example
+
+```python
+from instructor import from_openai
+from pydantic import BaseModel
+import openai
+
+client = from_openai(
+    openai.OpenAI(
+        base_url="http://localhost:39281/v1",
+        api_key="this is a fake api key that doesn't matter",
+    )
+)
+
+
+class Address(BaseModel):
+    street: str
+    city: str
+    country: str
+
+
+class User(BaseModel):
+    name: str
+    age: int
+    addresses: list[Address]
+
+
+user = client.chat.completions.create(
+    model="llama3.2:3b-gguf-q4-km",
+    messages=[
+        {
+            "role": "user",
+            "content": """
+            Extract: Jason is 25 years old.
+            He lives at 123 Main St, New York, USA
+            and has a summer house at 456 Beach Rd, Miami, USA
+        """,
+        },
+    ],
+    response_model=User,
+)
+
+print(user)
+
+#> {
+#>     'name': 'Jason',
+#>     'age': 25,
+#>     'addresses': [
+#>         {
+#>             'street': '123 Main St',
+#>             'city': 'New York',
+#>             'country': 'USA'
+#>         },
+#>         {
+#>             'street': '456 Beach Rd',
+#>             'city': 'Miami',
+#>             'country': 'USA'
+#>         }
+#>     ]
+#> }
+```
+
+In this tutorial we've seen how we can run local models with Cortex while simplifying a lot of the logic around managing retries and function calling with our simple interface.
+
+We'll be publishing a lot more content on Cortex and how to work with local models moving forward so do keep an eye out for that.
+
+## Related Resources
+
+- [Cortex Documentation](https://cortex.so/docs/)
+- [Instructor Core Concepts](../concepts/index.md)
+- [Type Validation Guide](../concepts/validation.md)
+- [Advanced Usage Examples](../examples/index.md)
+
+## Updates and Compatibility
+
+Instructor maintains compatibility with the latest OpenAI API versions and models. Check the [changelog](https://github.com/jxnl/instructor/blob/main/CHANGELOG.md) for updates.

docs/integrations/index.md

@@ -16,6 +16,7 @@ Instructor supports a wide range of AI model providers, each with their own capa
 - [Ollama](./ollama.md) - Run open-source models locally
 - [llama-cpp-python](./llama-cpp-python.md) - Python bindings for llama.cpp
 - [Together AI](./together.md) - Host and run open source models
+- [Cortex](./cortex.md) - Run open source models with Cortex
 
 ### Cloud AI Providers
 

mkdocs.yml

@@ -213,6 +213,7 @@ nav:
     - Azure OpenAI: 'integrations/azure.md'
     - Cerebras: 'integrations/cerebras.md'
     - Cohere: 'integrations/cohere.md'
+    - Cortex: 'integrations/cortex.md'
     - Fireworks: 'integrations/fireworks.md'
     - Gemini: 'integrations/google.md'
     - Groq: 'integrations/groq.md'