Endpoints
The most important API endpoints developers will want to use.
/chat
API Endpoint
/chat
API EndpointThe /chat endpoint is designed to handle chat requests, providing both streaming and non-streaming response options. It supports image content and ensures that the chat responses are processed efficiently. Before you can start using the API, you need to generate an API key.
Using the API Key
With your API key in hand, you can now make authenticated requests to the /chat endpoint. Below are examples of how to use the API for different scenarios.
Streaming Response Example
For a streaming response, where messages are sent and received in real-time, use the following Python code snippet:
import requests
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json',
}
data = {
"model": "gpt-4o-mini",
"messages": [
{
"role": "system",
"content": "Your system prompt here"
},
{
"role": "user",
"content": "What is in these documents?"
}
],
"openai_key": "YOUR-OPENAI-KEY-HERE",
"temperature": 0.1,
"course_name": "your-course-name",
"stream": True,
"api_key": "YOUR_API_KEY"
}
response = requests.post(url, headers=headers, json=data)
print(response.text)
Non-Streaming Response Example
The non- streaming response will contain BOTH the LLM response and the relevant contexts
import requests
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json'
}
data = {
"model": "gpt-4o-mini",
"messages": [
{
"role": "system",
"content": "Your system prompt here"
},
{
"role": "user",
"content": "What is in these documents?"
}
],
"openai_key": "YOUR-OPENAI-KEY-HERE",
"temperature": 0.1,
"course_name": "your-course-name",
"stream": False,
"api_key": "YOUR_API_KEY"
}
response = requests.post(url, headers=headers, json=data)
print(response.message)
print(response.contexts)
Retrieval Only
import requests
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json'
}
data = {
"model": "gpt-4o-mini",
"messages": [
{
"role": "system",
"content": "Your system prompt here"
},
{
"role": "user",
"content": "What is in these documents?"
}
],
"openai_key": "YOUR-OPENAI-KEY-HERE",
"temperature": 0.1,
"course_name": "your-course-name",
"retrieval_only": true
}
response = requests.post(url, headers=headers, json=data)
print(response.contexts)
Image Input Example
To send an image as part of the conversation, include the image URL in the messages array:
Note: Image input is only allowed with gpt-4-vision-preview model for now
import requests
import json
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json'
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "system",
"content": "Your system prompt here"
},
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "you image url here"
}
},
{
"type": "text",
"text": "Give me more information on the action depicted in this image."
}
]
}
],
"openai_key": "YOUR-OPENAI-KEY-HERE",
"temperature": 0.1,
"course_name": "your-course-name",
"stream": False,
"api_key": "YOUR_API_KEY"
}
response = requests.post(url, headers=headers, json=data)
print(response.text)
Multiple Messages in a Conversation
import requests
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json',
}
data = {
"model": "gpt-4",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "What can you tell me about the history of artificial intelligence?"
},
{
"role": "assistant",
"content": "Artificial intelligence has a long history dating back to the mid-20th century, with key milestones such as the development of the Turing Test and the creation of early neural networks."
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Here is an image related to AI, can you analyze it?"
},
{
"type": "image_url",
"image_url": {
"url": "https://example.com/path-to-your-image.png"
}
}
]
}
],
"openai_key": "YOUR-OPENAI-KEY-HERE",
"temperature": 0.1,
"course_name": "your-course-name",
"stream": True,
"api_key": "YOUR_API_KEY"
}
response = requests.post(url, headers=headers, json=data)
print(response.text)
NCSA hosted models example
The best free option to use UIUC chat API is with LLAMA 3.1 70b model, hosted at NCSA.
This model is free, but it's not the best performing. We recommend GPT-4o/GPT-4o-mini
for its superior instruction following, response quality and ability to cite its source.
import requests
url = "https://uiuc.chat/api/chat-api/chat"
headers = {
'Content-Type': 'application/json',
}
data = {
"model": "llama3.1:70b",
"messages": [
{
"role": "system",
"content": "Your system prompt here"
},
{
"role": "user",
"content": "What is in these documents?"
}
],
"temperature": 0.1,
"course_name": "your-course-name",
"stream": True,
"api_key": "YOUR_API_KEY"
}
response = requests.post(url, headers=headers, json=data)
print(response.text)
Tool Use
Tools will be automatically invoked based on LLM's response. There's currently no way to force tool invocation, you will have to encourage the LLM to use tools via prompting.
For superior instruction following, GPT-4o model is always used for tool selection.
Coming soon
Document ingest via API. Currently only supported via the website GUI.
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