> For the complete documentation index, see [llms.txt](https://sully.gitbook.io/sully.ai-docs/FEM1tMOJsSSszbLOgKLA/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://sully.gitbook.io/sully.ai-docs/FEM1tMOJsSSszbLOgKLA/focused-agentic-architecture/scribe-agent.md).

# Scribe Agent

## Note Style/Template Creation

### Objective

Enable users to easily create custom note styles or templates intuitively.

**Note:** "Style" and "template" are used interchangeably.

### Background

At present, Sully offers a simple method for creating note styles through a free-form text area. This can be overwhelming due to unclear starting points and the expertise required in prompt design.

### Solution

Introduce a user-friendly wizard to guide through template creation. This should be intuitive and support voice input. The design consists of three parts:

1. Frontend creation flow
2. Structured representation of the note template
3. Template creation and execution logic

#### Structured Representation

The template will be a JSON object stored in GCS:

```json
{
  "id": "<template-id>",
  "title": "Test Template",
  "sections": [
    {
      "id": "<section-id>",
      "title": "<section-title>",
      "descp": "<section description>",
      "templates": [
        {
          "voice_trigger": "<voice-trigger>",
          "text": "<template-text>",
          "text_file": "<gcs-path>"
        },
        {
          ...
        }
      ]
    },
    ...
  ]
}
```

Template metadata will be stored in Firebase, with JSON in GCS:

**Firebase Path:**`/copilot/trial/users/<user-email>/note_templates/<template-id>/…`

**Firebase Map:**

```json
{ "title": "<title>", "template_path": "<gcs-path>" }
```

**GCS JSON Path:**`<bucket>/<user-email>/note_templates/<template-id>.json`

Each section converts to an LLMChain for execution, integrating inputs like transcripts and patient charts. All chains execute in parallel, and the results are merged for the final note.

```python
finalNote = generateNote(noteTemplate, transcript, ph)
llmChain = getLLMChain(noteTemplate, transcript, ph)
llmChain.execute()
```

Sully-designed prompts are hosted and versioned on Autoblocks.

#### Frontend Wizard

An intuitive wizard will help create templates and define sections, saving the resulting JSON in Firebase and GCS.

#### Note Generation/Execution

Upon recording a visit, users select the note template via dropdown. Only the template ID is sent in requests, and the server constructs the template.

Server-side actions:

1. Retrieve the template JSON from Firebase using `templateId`.
2. Access the template JSON from GCS using `templateId` or `gcsPath`.
3. Process voice-triggered templates from their GCS paths, ready to be converted to an LLMChain.

**Template: LLMChain Structure**

```json
{
  "chainId": "template.id",
  "name": "template.title",
  "execution": "parallel",
  "tasks": [...] // tasks can be LLMChain or LLMTask
}
```

**Section: LLMTask/LLMChain Structure**

```json
{
  "chainId": "section.id",
  "name": "section.title",
  "execution": "sequential",
  "tasks": [...]
}
```

A section-level LLMChain identifies which template to trigger based on voice prompts and executes necessary tasks sequentially.


---

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