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Python
IntelliNode edited this page Mar 9, 2025
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import requests
import json
url = "/semanticsearch/search"
payload = json.dumps({
"api_key": "",
"provider": "openai",
"input": {
"pivot_item": "Hello from OpenAI!",
"search_array": [
"Greetings from OpenAI!",
"Bonjour de OpenAI!",
"Hola desde OpenAI!"
],
"number_of_matches": 2
}
})
headers = {
'X-API-KEY': '<microservice-key>',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)import requests
import json
url = "/chatbot/chat"
payload = json.dumps({
"api_key": "",
"model": "gpt4",
"provider": "openai",
"input": {
"system": "You are a helpful assistant.",
"messages": [
{
"role": "user",
"content": "recommend a vegetarian dish for dinner"
}
]
}
})
headers = {
'X-API-KEY': '<microservice-key>',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)You can also use a self-hosted vLLM model.
import requests
url = "http://localhost/chatbot/chat"
payload = {
"provider": "vllm",
"serverLink": "<vllm-server-url>",
"model": "mistralai/Mistral-7B-Instruct-v0.2",
"input": {
"system": "You are a helpful assistant.",
"messages": [
{
"role": "user",
"content": "Explain quantum computing briefly."
}
]
}
}
headers = {
'X-API-KEY': 'root',
'Content-Type': 'application/json'
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())You can run evaluations across multiple models and select the suitable model for your use case based on quantitive methods.
Below is an example to compare llama 13b-chat, openai gpt-3.5, and cohere command models.
import requests
import json
url = "http://localhost/evaluate/llm"
payload = json.dumps({
"userInput": "User input or question.",
"targetAnswers": [
"optimal answer example1.",
"optimal answer example2.",
"optimal answer example3." ],
"semantic": {
"api_key": "",
"provider": "openai"
},
"evaluate": [
{
"apiKey": "",
"provider": "replicate",
"type": "chat",
"model": "13b-chat",
"maxTokens": 50
},
{
"apiKey": "",
"provider": "cohere",
"type": "completion",
"model": "command",
"maxTokens": 50
},
{
"apiKey": "",
"provider": "openai",
"type": "chat",
"model": "gpt-3.5-turbo",
"maxTokens": 50,
"temperature": 0.7
}
]
})
headers = {
'X-API-KEY': '<microservice-key>',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)Snapshot of the expected output format:
{
"openai/gpt-3.5-turbo": [{
"prediction": "Photosynthesis is how plants make food for themselves....",
"score_cosine_similarity": 0.9566836802012463,
"score_euclidean_distance": 0.29175853870023755
}],
"cohere/command": [{
"prediction": "Photosynthesis is the process by which plants use the energy .....",
"score_cosine_similarity": 0.9378139154300577,
"score_euclidean_distance": 0.3512465738424273
}],
"replicate/13b-chat": [{
"prediction": "Here's an explanation of photosynthesis in simple terms .....",
"score_cosine_similarity": 0.9096764395396765,
"score_euclidean_distance": 0.4248874961328429
}],
"lookup": {
"cosine_similarity": "a value closer to 1 indicates a higher degree of similarity between two vectors",
"euclidean_distance": "the lower the value, the closer the two points"
}
}Replace the <microservice-key> with the provided microservice key when running the docker image.