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POST
/
eval
import requests
import json

# Call the eval endpoint

url = "https://api.berri.ai/eval"

data = {"instance_id": "44b52369-7852-487f-ba16-476f0ebeb372", "user_email": "krrish@berri.ai"}

# Always pass in a .csv. Ensure all your questions are under a column called 'Questions'
files = {'data_source': open('test_set.csv', 'rb')}

response = requests.post(url, files=files, data=data)

print(response.text)

api_endpoint = response.json()

Documentation Index

Fetch the complete documentation index at: https://docs.berri.ai/llms.txt

Use this file to discover all available pages before exploring further.

​
Query

​
user_email
string
The email address you are using on your berri.ai account Example: ishaan@berri.ai
​
instance_id
string
The instance you want to query, create_app returns an instance_id that you can query here
​
data_source
file
This must be a .csv. Ensure all questions are under a column called ‘Questions’.
import requests
import json

# Call the eval endpoint

url = "https://api.berri.ai/eval"

data = {"instance_id": "44b52369-7852-487f-ba16-476f0ebeb372", "user_email": "krrish@berri.ai"}

# Always pass in a .csv. Ensure all your questions are under a column called 'Questions'
files = {'data_source': open('test_set.csv', 'rb')}

response = requests.post(url, files=files, data=data)

print(response.text)

api_endpoint = response.json()