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* add autogenstudio docs * update ags readme to point to docs page * update docs * update docs * update faqs * update, fix typos
87 lines
4.6 KiB
Markdown
87 lines
4.6 KiB
Markdown
# AutoGen Studio FAQs
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## Q: How do I specify the directory where files(e.g. database) are stored?
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A: You can specify the directory where files are stored by setting the `--appdir` argument when running the application. For example, `autogenstudio ui --appdir /path/to/folder`. This will store the database (default) and other files in the specified directory e.g. `/path/to/folder/database.sqlite`.
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## Q: Where can I adjust the default skills, agent and workflow configurations?
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A: You can modify agent configurations directly from the UI or by editing the `init_db_samples` function in the `autogenstudio/database/utils.py` file which is used to initialize the database.
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## Q: If I want to reset the entire conversation with an agent, how do I go about it?
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A: To reset your conversation history, you can delete the `database.sqlite` file in the `--appdir` directory. This will reset the entire conversation history. To delete user files, you can delete the `files` directory in the `--appdir` directory.
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## Q: Is it possible to view the output and messages generated by the agents during interactions?
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A: Yes, you can view the generated messages in the debug console of the web UI, providing insights into the agent interactions. Alternatively, you can inspect the `database.sqlite` file for a comprehensive record of messages.
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## Q: Can I use other models with AutoGen Studio?
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Yes. AutoGen standardizes on the openai model api format, and you can use any api server that offers an openai compliant endpoint. In the AutoGen Studio UI, each agent has an `llm_config` field where you can input your model endpoint details including `model`, `api key`, `base url`, `model type` and `api version`. For Azure OpenAI models, you can find these details in the Azure portal. Note that for Azure OpenAI, the `model name` is the deployment id or engine, and the `model type` is "azure".
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For other OSS models, we recommend using a server such as vllm, LMStudio, Ollama, to instantiate an openai compliant endpoint.
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## Q: The server starts but I can't access the UI
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A: If you are running the server on a remote machine (or a local machine that fails to resolve localhost correctly), you may need to specify the host address. By default, the host address is set to `localhost`. You can specify the host address using the `--host <host>` argument. For example, to start the server on port 8081 and local address such that it is accessible from other machines on the network, you can run the following command:
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```bash
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autogenstudio ui --port 8081 --host 0.0.0.0
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```
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## Q: Can I export my agent workflows for use in a python app?
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Yes. In the Build view, you can click the export button to save your agent workflow as a JSON file. This file can be imported in a python application using the `WorkflowManager` class. For example:
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```python
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from autogenstudio import WorkflowManager
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# load workflow from exported json workflow file.
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workflow_manager = WorkflowManager(workflow="path/to/your/workflow_.json")
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# run the workflow on a task
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task_query = "What is the height of the Eiffel Tower?. Dont write code, just respond to the question."
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workflow_manager.run(message=task_query)
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```
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## Q: Can I deploy my agent workflows as APIs?
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Yes. You can launch the workflow as an API endpoint from the command line using the `autogenstudio` commandline tool. For example:
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```bash
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autogenstudio serve --workflow=workflow.json --port=5000
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```
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Similarly, the workflow launch command above can be wrapped into a Dockerfile that can be deployed on cloud services like Azure Container Apps or Azure Web Apps.
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## Q: Can I run AutoGen Studio in a Docker container?
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A: Yes, you can run AutoGen Studio in a Docker container. You can build the Docker image using the provided [Dockerfile](https://github.com/microsoft/autogen/blob/autogenstudio/samples/apps/autogen-studio/Dockerfile) and run the container using the following commands:
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```bash
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FROM python:3.10
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WORKDIR /code
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RUN pip install -U gunicorn autogenstudio
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH \
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AUTOGENSTUDIO_APPDIR=/home/user/app
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WORKDIR $HOME/app
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COPY --chown=user . $HOME/app
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CMD gunicorn -w $((2 * $(getconf _NPROCESSORS_ONLN) + 1)) --timeout 12600 -k uvicorn.workers.UvicornWorker autogenstudio.web.app:app --bind "0.0.0.0:8081"
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```
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Using Gunicorn as the application server for improved performance is recommended. To run AutoGen Studio with Gunicorn, you can use the following command:
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```bash
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gunicorn -w $((2 * $(getconf _NPROCESSORS_ONLN) + 1)) --timeout 12600 -k uvicorn.workers.UvicornWorker autogenstudio.web.app:app --bind
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```
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