> For the complete documentation index, see [llms.txt](https://docs.amigo.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amigo.ai/developer-guide/classic-api/core-api/users/user-models.md).

# User Models

Amigo maintains an evolving, structured model of each user. You can augment it with additional context from your systems.

## Structure

* Content entries contain natural-language facts about the user.
* Each entry may include supporting `insight_ids` and descriptive `dimensions`.

An example shape:

```json
{
  "user_models": [
    {
      "content": "Tony has hypertension and Type 2 diabetes managed with medication.",
      "insight_ids": ["67fe9cca0e3bf2dbf54e4ef5"],
      "dimensions": [
        {
          "description": "Medical History & Conditions",
          "tags": ["medical history", "conditions", "chronic disease"]
        }
      ]
    }
  ]
}
```

## Update Additional Context (Quick Start)

This is the same endpoint as [Update User](/developer-guide/classic-api/core-api/users.md#update-user); this page covers its `additional_context` usage.

{% openapi src="<https://api.amigo.ai/v1/openapi.json>" path="/v1/{organization}/user/{requested\_user\_id}" method="post" %}
<https://api.amigo.ai/v1/openapi.json>
{% endopenapi %}

Send new facts as concise sentences in `additional_context`.

```python
import os, requests

ORG_ID = os.environ["AMIGO_ORG_ID"]
TOKEN = os.environ["AMIGO_TOKEN"]  # user token, e.g. from Sign in with API key
USER_ID = "user_12345"

facts = [
    "Tony's average fasting glucose over the past week is 105 mg/dL.",
    "He reports mild dizziness after his evening dose of Lisinopril 20 mg.",
    "Tony exercises three times a week (mostly swimming).",
]

resp = requests.post(
    f"https://api.amigo.ai/v1/{ORG_ID}/user/{USER_ID}",
    headers={
        "Authorization": f"Bearer {TOKEN}",
        "Content-Type": "application/json",
    },
    json={"additional_context": facts},
    timeout=15,
)
resp.raise_for_status()
print("✓ user model updated")
```

### Handling 429 (Rate Limits)

```python
import requests, time

def post_with_backoff(url: str, headers: dict, json: dict, max_retries: int = 5):
    delay = 1
    for attempt in range(1, max_retries + 1):
        resp = requests.post(url, headers=headers, json=json)
        if resp.status_code != 429:
            resp.raise_for_status()
            return resp
        retry_after = int(resp.headers.get("Retry-After", delay))
        time.sleep(retry_after)
        delay = min(delay * 2, 60)
    raise RuntimeError("Too many consecutive 429 responses - aborting")
```

## Formatting Guidance

* Provide an array of human-readable strings. Amigo parses prose, not your schema.
* Keep each sentence self-contained and avoid ambiguous pronouns.
* Include units and dates where helpful.

## Common Dimensions

Typical dimensions include:

1. Demographics & Background
2. Medical History
3. Medication Management
4. Vital Signs
5. Emotional Well-being
6. Behavioral Patterns
7. Treatment Goals
8. Social Determinants
9. Communication Preferences
10. Health Monitoring

## Retrieve User Model

{% openapi src="<https://api.amigo.ai/v1/openapi.json>" path="/v1/{organization}/user/{user\_id}/user\_model" method="get" %}
<https://api.amigo.ai/v1/openapi.json>
{% endopenapi %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.amigo.ai/developer-guide/classic-api/core-api/users/user-models.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.