Send Interaction
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
The interaction to send to the nebuly platform.
The LLM output in the interaction (the text shown to the user as assistant response).
The start time of the call to the LLM. You can approximate this to the time when the user sends the interaction. The accepted format is the Example:
The end time of the call to the LLM. This is when the user receives the full answer from the model. The accepted format is the Example:
An array of messages, each containing a role and content. Example:
An id or username uniquely identifying the end-user. We recommend hashing their username or email address, in order to avoid sending us any identifying information.
The RAG sources used to produce the output. Note that this is an array of strings so only the name of the RAG source should be given. If you are interested in tracking also the input-output of the RAG source please refer to the
The LLM model you are using. Please note that this is needed if you want to visualize the cost of your requests. Now we support costs only for OpenAI models, cost of other providers coming soon.
Tag user interactions by adding key-value pairs using this parameter. Each key represents the tag name, and the corresponding value is the tag value. For example, if you want to tag an interaction with the model version used to reply to user input, provide it as an argument for nebuly_tags, e.g.
This field is used for the AB testing feature. Please refer to its documentation for further details.
Boolean flag to anonymize your data
The interaction to send to the nebuly platform.
The LLM output in the interaction (the text shown to the user as assistant response).
The start time of the call to the LLM. You can approximate this to the time when the user sends the interaction. The accepted format is the ISO 8601.
Example: 2023-12-07T15:00:00.000Z
The end time of the call to the LLM. This is when the user receives the full answer from the model. The accepted format is the ISO 8601.
Example: 2023-12-07T15:00:10.000Z
An array of messages, each containing a role and content. Example:
"messages": [
{
"role": "system",
"content": "This is a system prompt"
},
{
"role": "user",
"content": "What's the weather like in Turin?"
},
{
"role": "assistant",
"content": "The weather is currently rainy in Turin."
},
{
"role": "user",
"content": "What's the weather like in Rome?"
}
]
An id or username uniquely identifying the end-user. We recommend hashing their username or email address, in order to avoid sending us any identifying information.
The RAG sources used to produce the output. Note that this is an array of strings so only the name of the RAG source should be given. If you are interested in tracking also the input-output of the RAG source please refer to the interaction with trace endpoint.
The LLM model you are using. Please note that this is needed if you want to visualize the cost of your requests. Now we support costs only for OpenAI models, cost of other providers coming soon.
Tag user interactions by adding key-value pairs using this parameter. Each key represents the tag name, and the corresponding value is the tag value.
For example, if you want to tag an interaction with the model version used to reply to user input, provide it as an argument for nebuly_tags, e.g. {"version" => "v1.0.0"}. You have the flexibility to define custom tags, making them available as potential filters on the Nebuly platform.
This field is used for the AB testing feature. Please refer to its documentation for further details.
Boolean flag to anonymize your data