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import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# LiteLLM - Getting Started
https://github.com/BerriAI/litellm
## **Call 100+ LLMs using the same Input/Output Format**
## Basic usage
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
```shell
pip install litellm
```
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
from litellm import completion
import os
## set ENV variables
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
response = completion(
model="claude-2",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="vertex" label="VertexAI">
```python
from litellm import completion
import os
# auth: run 'gcloud auth application-default'
os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
os.environ["VERTEX_LOCATION"] = "us-central1"
response = completion(
model="chat-bison",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="hugging" label="HuggingFace">
```python
from litellm import completion
import os
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
response = completion(
model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
messages=[{ "content": "Hello, how are you?","role": "user"}],
api_base="https://my-endpoint.huggingface.cloud"
)
print(response)
```
</TabItem>
<TabItem value="azure" label="Azure OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
# azure call
response = completion(
"azure/<your_deployment_name>",
messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
from litellm import completion
response = completion(
model="ollama/llama2",
messages = [{ "content": "Hello, how are you?","role": "user"}],
api_base="http://localhost:11434"
)
```
</TabItem>
<TabItem value="or" label="Openrouter">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
response = completion(
model="openrouter/google/palm-2-chat-bison",
messages = [{ "content": "Hello, how are you?","role": "user"}],
)
```
</TabItem>
</Tabs>
## Streaming
Set `stream=True` in the `completion` args.
<Tabs>
<TabItem value="openai" label="OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
from litellm import completion
import os
## set ENV variables
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
response = completion(
model="claude-2",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="vertex" label="VertexAI">
```python
from litellm import completion
import os
# auth: run 'gcloud auth application-default'
os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
os.environ["VERTEX_LOCATION"] = "us-central1"
response = completion(
model="chat-bison",
messages=[{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="hugging" label="HuggingFace">
```python
from litellm import completion
import os
os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key"
# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
response = completion(
model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
messages=[{ "content": "Hello, how are you?","role": "user"}],
api_base="https://my-endpoint.huggingface.cloud",
stream=True,
)
print(response)
```
</TabItem>
<TabItem value="azure" label="Azure OpenAI">
```python
from litellm import completion
import os
## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""
# azure call
response = completion(
"azure/<your_deployment_name>",
messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
<TabItem value="ollama" label="Ollama">
```python
from litellm import completion
response = completion(
model="ollama/llama2",
messages = [{ "content": "Hello, how are you?","role": "user"}],
api_base="http://localhost:11434",
stream=True,
)
```
</TabItem>
<TabItem value="or" label="Openrouter">
```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key"
response = completion(
model="openrouter/google/palm-2-chat-bison",
messages = [{ "content": "Hello, how are you?","role": "user"}],
stream=True,
)
```
</TabItem>
</Tabs>
## Exception handling
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.
```python
from openai.error import OpenAIError
from litellm import completion
os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try:
# some code
completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
print(e)
```
## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
```python
from litellm import completion
## set env variables for logging tools
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
os.environ["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
```
## Calculate Costs, Usage, Latency
Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost
```python
from litellm import completion, completion_cost
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
response = completion(
model="gpt-3.5-turbo",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
cost = completion_cost(completion_response=response)
print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
```
**Output**
```shell
Cost for completion call with gpt-3.5-turbo: $0.0000775000
```
### Track Costs, Usage, Latency for streaming
Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
```python
import litellm
# track_cost_callback
def track_cost_callback(
kwargs, # kwargs to completion
completion_response, # response from completion
start_time, end_time # start/end time
):
try:
# check if it has collected an entire stream response
if "complete_streaming_response" in kwargs:
# for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost
completion_response=kwargs["complete_streaming_response"]
input_text = kwargs["messages"]
output_text = completion_response["choices"][0]["message"]["content"]
response_cost = litellm.completion_cost(
model = kwargs["model"],
messages = input_text,
completion=output_text
)
print("streaming response_cost", response_cost)
except:
pass
# set callback
litellm.success_callback = [track_cost_callback] # set custom callback function
# litellm.completion() call
response = completion(
model="gpt-3.5-turbo",
messages=[
{
"role": "user",
"content": "Hi 👋 - i'm openai"
}
],
stream=True
)
```
Need a dedicated key? Email us @ [email protected]
## More details
* [exception mapping](./exception_mapping.md)
* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) |