Send Interaction with trace

curl --request POST \
  --url https://backend.nebuly.com/event-ingestion/api/v2/events/trace_interaction \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "interaction": {
    "conversation_id": "<string>",
    "input": "<string>",
    "output": "<string>",
    "time_start": "<string>",
    "time_end": "<string>",
    "end_user": "<string>",
    "tags": {},
    "feature_flag": [
      "<string>"
    ]
  },
  "traces": [
    {
      "model": "<string>",
      "messages": [
        {}
      ],
      "output": "<string>",
      "input_tokens": 123,
      "output_tokens": 123,
      "source": "<string>",
      "input": "<string>",
      "outputs": [
        "<string>"
      ]
    }
  ],
  "feedback_actions": [
    {}
  ],
  "anonymize": true
}'

POST

event-ingestion

api

events

trace_interaction

Send Interaction with trace

curl --request POST \
  --url https://backend.nebuly.com/event-ingestion/api/v2/events/trace_interaction \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "interaction": {
    "conversation_id": "<string>",
    "input": "<string>",
    "output": "<string>",
    "time_start": "<string>",
    "time_end": "<string>",
    "end_user": "<string>",
    "tags": {},
    "feature_flag": [
      "<string>"
    ]
  },
  "traces": [
    {
      "model": "<string>",
      "messages": [
        {}
      ],
      "output": "<string>",
      "input_tokens": 123,
      "output_tokens": 123,
      "source": "<string>",
      "input": "<string>",
      "outputs": [
        "<string>"
      ]
    }
  ],
  "feedback_actions": [
    {}
  ],
  "anonymize": true
}'

You can use this endpoint to send a user interaction with its generation trace. A generation trace is a list of chain steps that the AI agentic system performed before generating the response.

For users of the european region, the correct endpoint is https://backend.eu.nebuly.com/event-ingestion/api/v2/events/trace_interaction.

To correctly track the costs of the interaction, it is mandatory to provide the API calls made to the LLM as LLMTraces. Ensure to pass the input and output tokens consumed to have more accurate cost estimation.

interaction

object

required

The interaction to send to the nebuly platform.

Show properties

conversation_id

string

required

The conversation id of the interaction. This parameter can be used to enforce interactions to be grouped by a specific conversation.

input

string

required

The user input in the interaction.

output

string

required

The LLM output in the interaction (the text shown to the user as assistant response).

time_start

string

required

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

time_end

string

required

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: 2023-12-07T15:00:00.000Z

end_user

string

required

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.

tags

dict<string, string>

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.

feature_flag

string[]

This field is used for the AB testing feature. Please refer to its documentation for further details.

traces

object[]

required

The full trace of your LLM agent or chain.

Show properties

model

string

required

The LLM used.

messages

array

required

A list of messages from the conversation so far, following the same format used by OpenAI Chat Completion endpoints.Each message has two required fields:

role: the role of who is sending the message. Possible values are: system, user, assistant, tool.
content: the content of the message.

The messages in the list should be ordered according to the conversation’s sequence, from the oldest message to the most recent.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?"
    }
]

output

string

required

The LLM output message.

input_tokens

integer

The number of input tokens.

output_tokens

integer

The number of output tokens.

feedback_actions

object[]

A list of feedback actions provided by the end-user during or after the interaction.

Show properties

Each object in the array can contain:

slug: (string) – the type of action. Accepted values include:
- thumbs_up
- thumbs_down
- copy_input
- copy_output
- paste
- comment
- regenerate
- edit
- rating
text: (string, optional) – textual feedback associated with the action (if applicable).
value: (number, optional) – only used for the rating action to capture a numerical score.

Example:

"feedback_actions": [
{
    "slug": "thumbs_up",
    "text": "Very helpful response!"
},
{
    "slug": "comment",
    "text": "Can you explain more about this?"
},
{
    "slug": "rating",
    "value": 4,
    "text": "Pretty good!"
},
{
    "slug": "regenerate"
}
]

anonymize

boolean

default:"true"

Boolean flag to anonymize your data

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Send Interaction with trace