forked from phoenix/litellm-mirror
478 lines
12 KiB
Markdown
478 lines
12 KiB
Markdown
import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# LiteLLM - Getting Started
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https://github.com/BerriAI/litellm
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## **Call 100+ LLMs using the OpenAI Input/Output Format**
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- Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints
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- [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
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- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
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- Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
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## How to use LiteLLM
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You can use litellm through either:
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1. [LiteLLM Proxy Server](#litellm-proxy-server-llm-gateway) - Server (LLM Gateway) to call 100+ LLMs, load balance, cost tracking across projects
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2. [LiteLLM python SDK](#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking
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### **When to use LiteLLM Proxy Server (LLM Gateway)**
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:::tip
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Use LiteLLM Proxy Server if you want a **central service (LLM Gateway) to access multiple LLMs**
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Typically used by Gen AI Enablement / ML PLatform Teams
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:::
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- LiteLLM Proxy gives you a unified interface to access multiple LLMs (100+ LLMs)
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- Track LLM Usage and setup guardrails
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- Customize Logging, Guardrails, Caching per project
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### **When to use LiteLLM Python SDK**
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:::tip
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Use LiteLLM Python SDK if you want to use LiteLLM in your **python code**
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Typically used by developers building llm projects
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:::
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- LiteLLM SDK gives you a unified interface to access multiple LLMs (100+ LLMs)
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- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
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## **LiteLLM Python SDK**
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### Basic usage
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<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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</a>
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```shell
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pip install litellm
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```
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<Tabs>
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<TabItem value="openai" label="OpenAI">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{ "content": "Hello, how are you?","role": "user"}]
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)
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```
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</TabItem>
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<TabItem value="anthropic" label="Anthropic">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
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response = completion(
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model="claude-2",
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messages=[{ "content": "Hello, how are you?","role": "user"}]
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)
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```
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</TabItem>
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<TabItem value="vertex" label="VertexAI">
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```python
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from litellm import completion
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import os
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# auth: run 'gcloud auth application-default'
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os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
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os.environ["VERTEX_LOCATION"] = "us-central1"
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response = completion(
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model="chat-bison",
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messages=[{ "content": "Hello, how are you?","role": "user"}]
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)
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```
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</TabItem>
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<TabItem value="hugging" label="HuggingFace">
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```python
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from litellm import completion
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import os
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os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
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# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
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messages=[{ "content": "Hello, how are you?","role": "user"}],
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api_base="https://my-endpoint.huggingface.cloud"
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)
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print(response)
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```
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</TabItem>
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<TabItem value="azure" label="Azure OpenAI">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["AZURE_API_KEY"] = ""
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os.environ["AZURE_API_BASE"] = ""
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os.environ["AZURE_API_VERSION"] = ""
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# azure call
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response = completion(
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"azure/<your_deployment_name>",
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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)
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```
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</TabItem>
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<TabItem value="ollama" label="Ollama">
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```python
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from litellm import completion
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response = completion(
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model="ollama/llama2",
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messages = [{ "content": "Hello, how are you?","role": "user"}],
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api_base="http://localhost:11434"
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)
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```
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</TabItem>
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<TabItem value="or" label="Openrouter">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
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response = completion(
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model="openrouter/google/palm-2-chat-bison",
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messages = [{ "content": "Hello, how are you?","role": "user"}],
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)
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```
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</TabItem>
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</Tabs>
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### Streaming
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Set `stream=True` in the `completion` args.
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<Tabs>
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<TabItem value="openai" label="OpenAI">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{ "content": "Hello, how are you?","role": "user"}],
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stream=True,
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)
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```
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</TabItem>
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<TabItem value="anthropic" label="Anthropic">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
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response = completion(
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model="claude-2",
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messages=[{ "content": "Hello, how are you?","role": "user"}],
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stream=True,
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)
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```
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</TabItem>
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<TabItem value="vertex" label="VertexAI">
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```python
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from litellm import completion
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import os
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# auth: run 'gcloud auth application-default'
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os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
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os.environ["VERTEX_LOCATION"] = "us-central1"
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response = completion(
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model="chat-bison",
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messages=[{ "content": "Hello, how are you?","role": "user"}],
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stream=True,
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)
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```
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</TabItem>
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<TabItem value="hugging" label="HuggingFace">
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```python
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from litellm import completion
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import os
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os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
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# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
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response = completion(
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model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
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messages=[{ "content": "Hello, how are you?","role": "user"}],
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api_base="https://my-endpoint.huggingface.cloud",
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stream=True,
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)
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print(response)
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```
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</TabItem>
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<TabItem value="azure" label="Azure OpenAI">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["AZURE_API_KEY"] = ""
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os.environ["AZURE_API_BASE"] = ""
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os.environ["AZURE_API_VERSION"] = ""
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# azure call
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response = completion(
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"azure/<your_deployment_name>",
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messages = [{ "content": "Hello, how are you?","role": "user"}],
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stream=True,
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)
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```
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</TabItem>
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<TabItem value="ollama" label="Ollama">
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```python
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from litellm import completion
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response = completion(
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model="ollama/llama2",
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messages = [{ "content": "Hello, how are you?","role": "user"}],
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api_base="http://localhost:11434",
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stream=True,
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)
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```
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</TabItem>
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<TabItem value="or" label="Openrouter">
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```python
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from litellm import completion
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import os
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## set ENV variables
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os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
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response = completion(
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model="openrouter/google/palm-2-chat-bison",
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messages = [{ "content": "Hello, how are you?","role": "user"}],
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stream=True,
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)
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```
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</TabItem>
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</Tabs>
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### Exception handling
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LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.
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```python
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from openai.error import OpenAIError
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from litellm import completion
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os.environ["ANTHROPIC_API_KEY"] = "bad-key"
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try:
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# some code
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completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
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except OpenAIError as e:
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print(e)
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```
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### Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
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LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
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```python
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from litellm import completion
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## set env variables for logging tools
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os.environ["HELICONE_API_KEY"] = "your-helicone-key"
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os.environ["LANGFUSE_PUBLIC_KEY"] = ""
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os.environ["LANGFUSE_SECRET_KEY"] = ""
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os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
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os.environ["OPENAI_API_KEY"]
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# set callbacks
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litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to lunary, langfuse, supabase, helicone
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#openai call
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response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
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```
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### Track Costs, Usage, Latency for streaming
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Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
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```python
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import litellm
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# track_cost_callback
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def track_cost_callback(
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kwargs, # kwargs to completion
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completion_response, # response from completion
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start_time, end_time # start/end time
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):
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try:
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response_cost = kwargs.get("response_cost", 0)
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print("streaming response_cost", response_cost)
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except:
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pass
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# set callback
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litellm.success_callback = [track_cost_callback] # set custom callback function
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# litellm.completion() call
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response = completion(
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model="gpt-3.5-turbo",
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messages=[
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{
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"role": "user",
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"content": "Hi 👋 - i'm openai"
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}
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],
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stream=True
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)
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```
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## **LiteLLM Proxy Server (LLM Gateway)**
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Track spend across multiple projects/people
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The proxy provides:
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1. [Hooks for auth](https://docs.litellm.ai/docs/proxy/virtual_keys#custom-auth)
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2. [Hooks for logging](https://docs.litellm.ai/docs/proxy/logging#step-1---create-your-custom-litellm-callback-class)
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3. [Cost tracking](https://docs.litellm.ai/docs/proxy/virtual_keys#tracking-spend)
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4. [Rate Limiting](https://docs.litellm.ai/docs/proxy/users#set-rate-limits)
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### 📖 Proxy Endpoints - [Swagger Docs](https://litellm-api.up.railway.app/)
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Go here for a complete tutorial with keys + rate limits - [**here**](./proxy/docker_quick_start.md)
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### Quick Start Proxy - CLI
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```shell
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pip install 'litellm[proxy]'
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```
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#### Step 1: Start litellm proxy
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<Tabs>
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<TabItem label="pip package" value="pip">
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```shell
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$ litellm --model huggingface/bigcode/starcoder
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#INFO: Proxy running on http://0.0.0.0:4000
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```
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</TabItem>
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<TabItem label="Docker container" value="docker">
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Step 1. CREATE config.yaml
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Example `litellm_config.yaml`
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```yaml
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model_list:
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: azure/<your-azure-model-deployment>
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api_base: os.environ/AZURE_API_BASE # runs os.getenv("AZURE_API_BASE")
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api_key: os.environ/AZURE_API_KEY # runs os.getenv("AZURE_API_KEY")
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api_version: "2023-07-01-preview"
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```
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Step 2. RUN Docker Image
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```shell
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docker run \
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-v $(pwd)/litellm_config.yaml:/app/config.yaml \
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-e AZURE_API_KEY=d6*********** \
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-e AZURE_API_BASE=https://openai-***********/ \
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-p 4000:4000 \
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ghcr.io/berriai/litellm:main-latest \
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--config /app/config.yaml --detailed_debug
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```
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</TabItem>
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</Tabs>
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#### Step 2: Make ChatCompletions Request to Proxy
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```python
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import openai # openai v1.0.0+
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client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
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# request sent to model set on litellm proxy, `litellm --model`
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response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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])
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print(response)
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```
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## More details
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- [exception mapping](./exception_mapping.md)
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- [retries + model fallbacks for completion()](./completion/reliable_completions.md)
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- [proxy virtual keys & spend management](./proxy/virtual_keys.md)
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- [E2E Tutorial for LiteLLM Proxy Server](./proxy/docker_quick_start.md)
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