> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nebuly.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Interaction API
The Interaction API lets you send user interactions directly to the Nebuly platform.
The conversation id of the interaction. This parameter can be used to enforce interactions to be grouped by a specific conversation.
The user input in the interaction.
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: `2023-12-07T15:00:00.000Z`
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.
Boolean flag to hide the content of the interaction (input and output) in the Nebuly platform. If set to true, only metadata and traces will be visible.
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.
The end user's country.
The end user's department.
The end user's age group.
A list of feedback actions provided by the end-user during or after the interaction.
The type of action. Accepted values include:
* `thumbs_up`
* `thumbs_down`
* `copy_input`
* `copy_output`
* `paste`
* `comment`
* `regenerate`
* `edit`
* `rating`
As optional extra parameters is possible to include:
* text (string, optional): textual feedback associated with the action (if applicable).
* value (integer, optional): only used for the `rating` action to capture a numerical score.
**Example:**
```json theme={null}
"user_feedback": [
{
"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"
}
]
```
The full trace of your LLM agent or chain.
The LLM used.
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?"
}
]
```
The LLM output message.
The number of input tokens.
The number of output tokens.
The type of data source used, e.g. db\_reader\_tool or vector\_db.
The query to the RAG source.
The RAG source output. It is presented in list format to accommodate multiple outputs for a single query.
Embedding trace details for an LLM.
The model used for embedding.
The input text to be embedded.
Example:
```
"input": "Text to be embedded"
```
The number of input tokens.
Example:
```
"input_tokens": 10
```
Boolean flag to anonymize your data