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Based on the provided knowledge sources, it appears that Portkey's Feedback API is indeed a method for gathering user feedback in LlamaIndex. The API allows for the collection of weighted feedback from users, which can be used to refine and improve models and applications over time. Here's an example of how to use the Feedback API with Portkey:
import requests
import json
# Endpoint URL
url = "<https://api.portkey.ai/v1/feedback">
# Headers
headers = {
"x-portkey-api-key": os.environ.get("PORTKEY_API_KEY"),
"Content-Type": "application/json",
}
# Data
data = {"trace_id": "llamaindex_portkey", "value": 1}
# Making the request
response = requests.post(url, headers=headers, data=json.dumps(data))
# Print the response
print(response.text)(Source:
GitHub ())
However, the knowledge sources do not mention any other methods for collecting user feedback with LlamaIndex, nor do they mention integration with Arize or Traceloop. It's possible that other methods or integrations exist, but they are not covered in the provided sources. For a more comprehensive understanding, you may want to refer to the official LlamaIndex documentation or reach out to the developers directly.
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