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참고/instructor-main/docs/integrations/litellm.md

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+---
+title: "Structured outputs with LiteLLM, a complete guide w/ instructor"
+description: "Complete guide to using Instructor with LiteLLM's unified interface. Learn how to generate structured, type-safe outputs across multiple LLM providers."
+---
+
+# Structured outputs with LiteLLM, a complete guide w/ instructor
+
+LiteLLM provides a unified interface for multiple LLM providers, making it easy to switch between different models and providers. This guide shows you how to use Instructor with LiteLLM for type-safe, validated responses across various LLM providers.
+
+## Quick Start
+
+Install Instructor with LiteLLM support:
+
+```bash
+pip install "instructor[litellm]"
+```
+
+## Simple User Example (Sync)
+
+```python
+from litellm import completion
+import instructor
+from pydantic import BaseModel
+
+# Enable instructor patches
+client = instructor.from_provider("litellm/gpt-3.5-turbo")
+
+class User(BaseModel):
+    name: str
+    age: int
+
+# Create structured output
+user = client.create(
+    messages=[
+        {"role": "user", "content": "Extract: Jason is 25 years old"},
+    ],
+    response_model=User,
+)
+
+print(user)  # User(name='Jason', age=25)
+```
+
+## Simple User Example (Async)
+
+```python
+import instructor
+from pydantic import BaseModel
+import asyncio
+
+client = instructor.from_provider(
+    "litellm/gpt-3.5-turbo",
+    async_client=True,
+)
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+async def extract_user():
+    user = await client.create(
+        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)
+
+```
+
+## Cost Calculation
+
+In order to calculate the cost of the response, LiteLLM provides a simple `response_cost` attribute on the response object's `_hidden_params` attribute. This is recorded in their documentation [here](https://docs.litellm.ai/docs/completion/token_usage#6-completion_cost).
+
+Here is a code snippet using instructor to calculate the cost of the response:
+
+```python
+import instructor
+from litellm import completion
+from pydantic import BaseModel
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+client = instructor.from_provider("litellm/gpt-3.5-turbo")
+instructor_resp, raw_completion = client.create_with_completion(
+    max_tokens=1024,
+    messages=[
+        {
+            "role": "user",
+            "content": "Extract Jason is 25 years old.",
+        }
+    ],
+    response_model=User,
+)
+
+print(raw_completion._hidden_params["response_cost"])
+#> 0.00189
+```
+
+## Related Resources
+
+- [LiteLLM Documentation](https://docs.litellm.ai/)
+- [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 LiteLLM's latest releases. Check the [changelog](https://github.com/jxnl/instructor/blob/main/CHANGELOG.md) for updates.
+
+Note: Always verify provider-specific features and limitations in their respective documentation before implementation.