> For the complete documentation index, see [llms.txt](https://www.conserver.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.conserver.io/tools/vcon-apps-and-stores/vcon-app-template.md).

# vCon App Template

Describes vConDiary, the example Streamlit app in vcon-app-template, and how to run it against your own MongoDB.

**Repo:** [vcon-dev/vcon-app-template](https://github.com/vcon-dev/vcon-app-template)

The repo is an example vCon app, not a framework. Its README calls the app vConDiary: a Streamlit application for managing and processing vCons that uses MongoDB for storage, OpenAI for transcription and summarization, and CarrierX as an optional source. The code is `vcondiary.py` and `suggestionbox.py`. Copy it and change it for your own app.

## Run it

You need Python, a MongoDB instance holding vCons, and an OpenAI key. The README recommends Python 3.13.1 and a virtual environment.

```bash
git clone https://github.com/vcon-dev/vcon-app-template
cd vcon-app-template
python3 -m venv ~/venvs/vcondiary && source ~/venvs/vcondiary/bin/activate
pip install -r requirements.txt
```

Create `.streamlit/secrets.toml`:

```toml
[openai]
api_key = "your-openai-key"
organization = "your-org-id"
project = "your-project-id"

[mongo_db]
url = "mongodb://localhost:27017/"
db = "vcons"
collection = "vcons"

[conserver]
api_url = "http://conserver:8000"
auth_token = "your-auth-token"
```

Then start it:

```bash
streamlit run vcondiary.py
```

## See also

* [vCon Apps and Stores](/tools/vcon-apps-and-stores.md)
* [Quick Start From Template](/vcon-adapters/quick-start-from-template.md), which is about adapters, not apps


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://www.conserver.io/tools/vcon-apps-and-stores/vcon-app-template.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.