docs: add Pydantic2 documentation

docs/my-website/docs/projects/Pydantic2.md

@@ -0,0 +1,158 @@
+# Pydantic2
+
+![Pydantic2](https://raw.githubusercontent.com/markolofsen/pydantic2/main/assets/cover.png)
+
+A powerful AI framework with structured Pydantic response handling, LLM integration, and advanced agent capabilities.
+
+[![Documentation](https://img.shields.io/badge/docs-pydantic.unrealos.com-blue)](https://pydantic.unrealos.com)
+[![PyPI version](https://badge.fury.io/py/pydantic2.svg)](https://badge.fury.io/py/pydantic2)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+
+## Installation
+
+```bash
+pip install pydantic2
+```
+
+Set up your API key:
+```bash
+export OPENROUTER_API_KEY=your_api_key_here
+```
+
+## Quick Start
+
+```python
+from pydantic import BaseModel, Field
+from typing import List
+from pydantic2 import LiteLLMClient, Request
+
+class MovieReview(BaseModel):
+    title: str = Field(description="The title of the movie")
+    rating: float = Field(description="The rating of the movie")
+    pros: List[str] = Field(description="The pros of the movie")
+    cons: List[str] = Field(description="The cons of the movie")
+
+client = LiteLLMClient(Request(
+    model="openrouter/openai/gpt-4o-mini-2024-07-18",
+    answer_model=MovieReview
+))
+
+response = client.generate_response(
+    prompt="Review the movie 'Inception'"
+)
+
+print(f"Title: {response.title}")
+print(f"Rating: {response.rating}/5")
+print("Pros:", ", ".join(response.pros))
+print("Cons:", ", ".join(response.cons))
+```
+
+## Key Features
+
+### Structured Responses
+- Type-safe responses using Pydantic models
+- Automatic validation and parsing
+- IDE support with autocompletion
+- Custom response models with field descriptions
+- Nested model support
+
+### LLM Integration
+- Support for multiple LLM providers
+- Unified API for all models
+- Easy provider switching
+- Automatic retries and fallbacks
+- Streaming support
+
+### Budget Control
+- Built-in cost tracking
+- Budget limits per request/user
+- Usage statistics and analytics
+- Cost estimation before requests
+- Detailed usage reports
+
+### Message Handling
+- System and user messages
+- Conversation history
+- Structured data support
+- Support for code blocks
+- Support for JSON and DataFrame inputs
+
+### Agent System
+- Custom tools and functions
+- Gradio UI integration
+- Extensible framework
+- Tool decorators
+- Memory management
+
+## Configuration
+
+```python
+from pydantic2 import Request
+
+config = Request(
+    # Model settings
+    model="openrouter/openai/gpt-4o-mini-2024-07-18",
+    answer_model=YourModel,
+    temperature=0.7,
+    max_tokens=500,
+
+    # Performance features
+    online=True,
+    cache_prompt=True,
+    max_budget=0.05,
+
+    # User tracking
+    user_id="user123",
+    client_id="my_app"
+)
+```
+
+## Framework Integration
+
+### Django Example
+
+```python
+from rest_framework.views import APIView
+from rest_framework.response import Response
+from pydantic import BaseModel, Field
+from typing import List
+from pydantic2 import Request, LiteLLMClient
+
+class FeedbackAnalysis(BaseModel):
+    summary: str = Field(..., description="Summary of the feedback")
+    sentiment: str = Field(..., description="Detected sentiment")
+    key_points: List[str] = Field(..., description="Key points from the feedback")
+
+class FeedbackView(APIView):
+    def post(self, request):
+        feedback = request.data.get('feedback', '')
+
+        client = LiteLLMClient(Request(
+            model="openrouter/openai/gpt-4o-mini-2024-07-18",
+            answer_model=FeedbackAnalysis,
+            max_budget=0.01,
+            user_id=request.user.id
+        ))
+
+        response = client.generate_response(prompt=feedback)
+        return Response(response.model_dump())
+```
+
+## Why Pydantic2?
+
+- **Type Safety**: Get structured responses with proper type hints and validation
+- **Efficiency**: Reduce boilerplate code and focus on your application logic
+- **Reliability**: Production-tested with comprehensive error handling
+- **Flexibility**: Support for multiple LLM providers and frameworks
+- **Scalability**: Built for both small projects and enterprise applications
+- **Cost Control**: Built-in budget management and usage tracking
+- **Framework Support**: Seamless integration with Django, FastAPI, and more
+- **Developer Experience**: Great IDE support and documentation
+
+## Documentation & Support
+
+- [Full Documentation](https://pydantic.unrealos.com)
+- [GitHub Repository](https://github.com/markolofsen/pydantic2)
+- [GitHub Issues](https://github.com/markolofsen/pydantic2/issues)
+- [GitHub Discussions](https://github.com/markolofsen/pydantic2/discussions)
+

docs/my-website/sidebars.js

@@ -490,6 +490,7 @@ const sidebars = {
             "projects/LiteLLM Proxy",
             "projects/llm_cord",
             "projects/pgai",
+            "projects/Pydantic2",
           ],
         },
         "proxy/pii_masking",