diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index 21140f29b..6aa4b1979 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -153,6 +153,90 @@ print(response) ## Advanced +## Usage - Function Calling + +```python +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 + +```python +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. diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index d744596c4..c5b12d4c4 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # AWS Bedrock Anthropic, Amazon Titan, A121 LLMs are Supported on Bedrock @@ -29,11 +32,193 @@ os.environ["AWS_SECRET_ACCESS_KEY"] = "" os.environ["AWS_REGION_NAME"] = "" response = completion( - model="bedrock/anthropic.claude-instant-v1", + model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0", messages=[{ "content": "Hello, how are you?","role": "user"}] ) ``` +## OpenAI Proxy Usage + +Here's how to call Anthropic with the LiteLLM Proxy Server + +### 1. Save key in your environment + +```bash +export AWS_ACCESS_KEY_ID="" +export AWS_SECRET_ACCESS_KEY="" +export AWS_REGION_NAME="" +``` + +### 2. Start the proxy + +```bash +$ litellm --model anthropic.claude-3-sonnet-20240229-v1:0 + +# Server running on http://0.0.0.0:8000 +``` + +### 3. Test it + + + + + +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:8000" +) + +# 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) + +``` + + + +```python +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://0.0.0.0:8000", # 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) +``` + + + +## Usage - Function Calling + +```python +from litellm import completion + +# set env +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +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="bedrock/anthropic.claude-3-sonnet-20240229-v1:0", + 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 + +```python +from litellm import completion + +# set env +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + + +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="bedrock/anthropic.claude-3-sonnet-20240229-v1:0", + 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" If you're using Anthropic's Claude with Bedrock, you can "put words in Claude's mouth" by including an `assistant` role message as the last item in the `messages` array. @@ -288,6 +473,7 @@ Here's an example of using a bedrock model with LiteLLM | Model Name | Command | |----------------------------|------------------------------------------------------------------| +| Anthropic Claude-V3 | `completion(model='bedrock/anthropic.claude-3-sonnet-20240229-v1:0', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | | Anthropic Claude-V2.1 | `completion(model='bedrock/anthropic.claude-v2:1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | | Anthropic Claude-V2 | `completion(model='bedrock/anthropic.claude-v2', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | | Anthropic Claude-Instant V1 | `completion(model='bedrock/anthropic.claude-instant-v1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` |