Anthropic
LiteLLM supports
claude-3(claude-3-haiku-20240307,claude-3-opus-20240229,claude-3-sonnet-20240229)claude-2claude-2.1claude-instant-1.2
API Keys​
import os
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
Usage​
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-3-opus-20240229", messages=messages)
print(response)
Usage - Streaming​
Just set stream=True when calling completion.
import os
from litellm import completion
# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
response = completion(model="claude-3-opus-20240229", messages=messages, stream=True)
for chunk in response:
print(chunk["choices"][0]["delta"]["content"]) # same as openai format
OpenAI Proxy Usage​
Here's how to call Anthropic with the LiteLLM Proxy Server
1. Save key in your environment​
export ANTHROPIC_API_KEY="your-api-key"
2. Start the proxy​
$ litellm --model claude-3-opus-20240229
# Server running on http://0.0.0.0:4000
3. Test it​
- Curl Request
- OpenAI v1.0.0+
- Langchain
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
model = "gpt-3.5-turbo",
temperature=0.1
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
Supported Models​
| Model Name | Function Call |
|---|---|
| claude-3-haiku | completion('claude-3-haiku-20240307', messages) |
| claude-3-opus | completion('claude-3-opus-20240229', messages) |
| claude-3-sonnet | completion('claude-3-sonnet-20240229', messages) |
| claude-2.1 | completion('claude-2.1', messages) |
| claude-2 | completion('claude-2', messages) |
| claude-instant-1.2 | completion('claude-instant-1.2', messages) |
| claude-instant-1 | completion('claude-instant-1', messages) |
Advanced​
Usage - Function Calling​
from litellm import completion
# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
},
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
response = completion(
model="anthropic/claude-3-opus-20240229",
messages=messages,
tools=tools,
tool_choice="auto",
)
# Add any assertions, here to check response args
print(response)
assert isinstance(response.choices[0].message.tool_calls[0].function.name, str)
assert isinstance(
response.choices[0].message.tool_calls[0].function.arguments, str
)
Usage - Vision​
from litellm import completion
# set env
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
def encode_image(image_path):
import base64
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
image_path = "../proxy/cached_logo.jpg"
# Getting the base64 string
base64_image = encode_image(image_path)
resp = litellm.completion(
model="anthropic/claude-3-opus-20240229",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Whats in this image?"},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64," + base64_image
},
},
],
}
],
)
print(f"\nResponse: {resp}")
Usage - "Assistant Pre-fill"​
You can "put words in Claude's mouth" by including an assistant role message as the last item in the messages array.
[!IMPORTANT] The returned completion will not include your "pre-fill" text, since it is part of the prompt itself. Make sure to prefix Claude's completion with your pre-fill.
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [
{"role": "user", "content": "How do you say 'Hello' in German? Return your answer as a JSON object, like this:\n\n{ \"Hello\": \"Hallo\" }"},
{"role": "assistant", "content": "{"},
]
response = completion(model="claude-2.1", messages=messages)
print(response)
Example prompt sent to Claude​
Human: How do you say 'Hello' in German? Return your answer as a JSON object, like this:
{ "Hello": "Hallo" }
Assistant: {
Usage - "System" messages​
If you're using Anthropic's Claude 2.1 with Bedrock, system role messages are properly formatted for you.
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [
{"role": "system", "content": "You are a snarky assistant."},
{"role": "user", "content": "How do I boil water?"},
]
response = completion(model="claude-2.1", messages=messages)
Example prompt sent to Claude​
You are a snarky assistant.
Human: How do I boil water?
Assistant: