Claude 3 Opus
Model Overview
A highly capable multimodal model designed to process both text and image data. It excels in tasks requiring complex reasoning, mathematical problem-solving, coding, and multilingual text understanding.
How to Make a Call
API Schema
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.
512If set to True, the model response data will be streamed to the client as it is generated using server-sent events.
falseCustom text sequences that will cause the model to stop generating.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Recommended for advanced use cases only. You usually only need to use temperature.
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.
Amount of randomness injected into the response. Defaults to 1.0. Ranges from 0.0 to 1.0. Use temperature closer to 0.0 for analytical / multiple choice, and closer to 1.0 for creative and generative tasks. Note that even with temperature of 0.0, the results will not be fully deterministic.
A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role.
POST /v1/chat/completions HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 418
{
"model": "anthropic/claude-3-opus",
"messages": [
{
"content": "text",
"role": "user"
}
],
"max_tokens": 512,
"stream": false,
"stop_sequences": [
"text"
],
"frequency_penalty": 1,
"top_k": 1,
"metadata": {
"ANY_ADDITIONAL_PROPERTY": "text"
},
"tools": [
{
"name": "text",
"description": "text",
"input_schema": {
"type": "object",
"properties": null,
"ANY_ADDITIONAL_PROPERTY": null
}
}
],
"tool_choice": {
"type": "auto"
},
"top_p": 1,
"temperature": 1,
"system": "text"
}No content
Code Example
import requests
response = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={
"Content-Type":"application/json",
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
"Authorization":"Bearer <YOUR_AIMLAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"claude-3-opus-latest",
"messages":[
{
"role":"user",
# Insert your question for the model here, instead of Hello:
"content":"Hello"
}
]
}
)
data = response.json()
print(data)Last updated
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