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107 lines
3.3 KiB
Markdown
107 lines
3.3 KiB
Markdown
# Getting Started
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import QuickStart from '../src/components/QuickStart.js'
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LiteLLM simplifies LLM API calls by mapping them all to the [OpenAI ChatCompletion format](https://platform.openai.com/docs/api-reference/chat).
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## basic usage
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By default we provide a free $10 community-key to try all providers supported on LiteLLM.
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```python
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from litellm import completion
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "your-api-key"
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os.environ["COHERE_API_KEY"] = "your-api-key"
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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# openai call
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response = completion(model="gpt-3.5-turbo", messages=messages)
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# cohere call
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response = completion("command-nightly", messages)
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```
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**Need a dedicated key?**
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Email us @ krrish@berri.ai
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Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models)
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More details 👉
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- [Completion() function details](./completion/)
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- [All supported models / providers on LiteLLM](./providers/)
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- [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main)
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## streaming
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Same example from before. Just pass in `stream=True` in the completion args.
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```python
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from litellm import completion
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "openai key"
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os.environ["COHERE_API_KEY"] = "cohere key"
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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# openai call
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response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
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# cohere call
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response = completion("command-nightly", messages, stream=True)
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print(response)
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```
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More details 👉
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- [streaming + async](./completion/stream.md)
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- [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md)
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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 MLflow, 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 (API key set up is not required when using MLflow)
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os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" # get your public key at https://app.lunary.ai/settings
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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["OPENAI_API_KEY"]
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# set callbacks
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litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"] # log input/output to MLflow, langfuse, lunary, 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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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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- [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md)
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