docs: Update integration docs to recommend UV package manager

docs/integrations/anthropic.md

@@ -7,69 +7,24 @@ description: Learn how to combine Anthropic and Instructor clients to create use
 
 Now that we have a [Anthropic](https://www.anthropic.com/) client, we can use it with the `instructor` client to make requests.
 
-Let's first install the instructor client with anthropic support
+Let's first install the instructor client with anthropic support:
 
-```
-pip install "instructor[anthropic]"
-```
-
-Once we've done so, getting started is as simple as using our `from_anthropic` method to patch the client up.
-
-```python
-from pydantic import BaseModel
-from typing import List
-import anthropic
-import instructor
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
 
-# Patching the Anthropics client with the instructor for enhanced capabilities
-client = instructor.from_anthropic(
-    anthropic.Anthropic(),
-)
+    # Install instructor
+    uv pip install "instructor[anthropic]"
+    ```
 
+=== "pip"
+    ```bash
+    pip install "instructor[anthropic]"
+    ```
 
-class Properties(BaseModel):
-    name: str
-    value: str
 
 
-class User(BaseModel):
-    name: str
-    age: int
-    properties: List[Properties]
-
-
-# client.messages.create will also work due to the instructor client
-user_response = client.chat.completions.create(
-    model="claude-3-haiku-20240307",
-    max_tokens=1024,
-    max_retries=0,
-    messages=[
-        {
-            "role": "user",
-            "content": "Create a user for a model with a name, age, and properties.",
-        }
-    ],
-    response_model=User,
-)  # type: ignore
-
-print(user_response.model_dump_json(indent=2))
-"""
-{
-  "name": "John Doe",
-  "age": 35,
-  "properties": [
-    {
-      "name": "City",
-      "value": "New York"
-    },
-    {
-      "name": "Occupation",
-      "value": "Software Engineer"
-    }
-  ]
-}
-"""
-```
 
 ## Streaming Support
 

docs/integrations/azure.md

@@ -9,23 +9,22 @@ This guide demonstrates how to use Azure OpenAI with instructor for structured o
 
 ## Installation
 
-We can use the same installation as we do for OpenAI since the default `openai` client ships with an AzureOpenAI client.
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
 
-First, install the required dependencies:
+    # Install instructor
+    uv pip install "instructor[azure]"
+    ```
 
-```bash
-pip install instructor
-```
-
-Next, make sure that you've enabled Azure OpenAI in your Azure account and have a deployment for the model you'd like to use. [Here is a guide to get started](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal)
+=== "pip"
+    ```bash
+    pip install "instructor[azure]"
+    ```
 
-Once you've done so, you'll have an endpoint and a API key to be used to configure the client.
 
-```bash
-instructor.exceptions.InstructorRetryException: Error code: 401 - {'statusCode': 401, 'message': 'Unauthorized. Access token is missing, invalid, audience is incorrect (https://cognitiveservices.azure.com), or have expired.'}
-```
 
-If you see an error like the one above, make sure you've set the correct endpoint and API key in the client.
 
 ## Authentication
 

docs/integrations/cerebras.md

@@ -12,8 +12,31 @@ Cerebras provides hardware-accelerated AI models optimized for high-performance
 Install Instructor with Cerebras support:
 
 ```bash
-pip install "instructor[cerebras_cloud_sdk]"
-```
+=== "UV (Recommended)"
+    ```bash
+=== "UV (Recommended)"
+    ```bash
+Let's first install the instructor client with cerebras support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[cerebras]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[cerebras]"
+    ```
+
+
+
+
+
+
 
 ## Simple User Example (Sync)
 

docs/integrations/cohere.md

@@ -13,10 +13,15 @@ You'll need a cohere API key which can be obtained by signing up [here](https://
 
 ## Setup
 
-```
-pip install "instructor[cohere]"
-
-```
+=== "UV (Recommended)"
+    ```bash
+    uv pip install "instructor[cohere]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[cohere]"
+    ```
 
 Export your key:
 

docs/integrations/deepseek.md

@@ -14,8 +14,28 @@ This guide covers everything you need to know about using DeepSeek with Instruct
 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"
-```
+=== "UV (Recommended)"
+    ```bash
+Let's first install the instructor client with deepseek support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[deepseek]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[deepseek]"
+    ```
+
+
+
+
+
 
 ⚠️ **Important**: You must set your DeepSeek API key before using the client. You can do this in two ways:
 

docs/integrations/fireworks.md

@@ -12,8 +12,31 @@ Fireworks provides efficient and cost-effective AI models with enterprise-grade
 Install Instructor with Fireworks support:
 
 ```bash
-pip install "instructor[fireworks-ai]"
-```
+=== "UV (Recommended)"
+    ```bash
+=== "UV (Recommended)"
+    ```bash
+Let's first install the instructor client with fireworks support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[fireworks]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[fireworks]"
+    ```
+
+
+
+
+
+
 
 ## Simple User Example (Sync)
 

docs/integrations/google.md

@@ -11,9 +11,24 @@ This guide will show you how to use Instructor with the Google.GenerativeAI libr
 
 Google's Gemini models provide powerful AI capabilities with multimodal support. This guide shows you how to use Instructor with Google's Gemini models for type-safe, validated responses.
 
-```bash
-pip install "instructor[google-generativeai]
-```
+Let's first install the instructor client with Google/Gemini support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[google]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[google]"
+    ```
+
+
+
 
 ## Simple User Example (Sync)
 

docs/integrations/groq.md

@@ -11,9 +11,31 @@ you'll need to sign up for an account and get an API key. You can do that [here]
 
 ```bash
 export GROQ_API_KEY=<your-api-key-here>
-pip install "instructor[groq]"
 ```
 
+Let's first install the instructor client with groq support:
+```bash
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[groq]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[groq]"
+    ```
+
+
+
+
+
+
+
 ## Groq AI
 
 Groq supports structured outputs with their new `llama-3-groq-70b-8192-tool-use-preview` model.

docs/integrations/litellm.md

@@ -12,8 +12,31 @@ LiteLLM provides a unified interface for multiple LLM providers, making it easy
 Install Instructor with LiteLLM support:
 
 ```bash
-pip install "instructor[litellm]"
-```
+=== "UV (Recommended)"
+    ```bash
+=== "UV (Recommended)"
+    ```bash
+Let's first install the instructor client with litellm support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[litellm]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[litellm]"
+    ```
+
+
+
+
+
+
 
 ## Simple User Example (Sync)
 

docs/integrations/llama-cpp-python.md

@@ -16,6 +16,22 @@ This guide demonstrates how to use llama-cpp-python with Instructor to generate
 
 Open-source LLMS are gaining popularity, and llama-cpp-python has made the `llama-cpp` model available to obtain structured outputs using JSON schema via a mixture of [constrained sampling](https://llama-cpp-python.readthedocs.io/en/latest/#json-schema-mode) and [speculative decoding](https://llama-cpp-python.readthedocs.io/en/latest/#speculative-decoding).
 
+Let's first install the instructor client with llama-cpp-python support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor with llama-cpp-python support
+    uv pip install "instructor[llama]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[llama]"
+    ```
+
 They also support a [OpenAI compatible client](https://llama-cpp-python.readthedocs.io/en/latest/#openai-compatible-web-server), which can be used to obtain structured output as a in process mechanism to avoid any network dependency.
 
 <!-- more -->

docs/integrations/mistral.md

@@ -18,6 +18,25 @@ Mistral Large is the flagship model from Mistral AI, supporting 32k context wind
 
 By the end of this blog post, you will learn how to effectively utilize Instructor with Mistral Large.
 
+Let's first install the instructor client with Mistral support:
+
+=== "UV (Recommended)"
+    ```bash
+    # Install UV if you haven't already
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+
+    # Install instructor
+    uv pip install "instructor[mistral]"
+    ```
+
+=== "pip"
+    ```bash
+    pip install "instructor[mistral]"
+    ```
+
+
+
+
 ```python
 import os
 from pydantic import BaseModel