Agenta: a tool for evaluating the effectiveness of cue words and models integrated into AI applications

General Introduction

Agenta is an open source AI model management tool that specializes in helping users easily experiment with cue words, test model effects and monitor runs. It is suitable for people who want to develop AI applications quickly, providing a platform that is simple to operate. You can use it to try out the effects of different cue words, compare the answers of multiple AI models, and view the application's runtime data, such as speed and cost, in real time.Agenta supports many common AI frameworks, such as LangChain, and is both powerful and flexible. Because it's open source, it's free for anyone to use, and you can find the code on GitHub to make your own changes. It now has over 2.1k likes on GitHub, which means it's very popular.

Function List

• Cue word experimental area: Type in the cue words on the webpage to try out different AI models and also compare the results.
• Customizing the task flow: You can build your own AI task flow, such as having a model answer questions based on information.
• Model effect test: Check how well the model answered with a tool that supports automatic scoring or asking someone to help look at it.
• Manual inspection support: Can work with the team to compare model answers and pick the best.
• Cue Save: Save the tried and true cue words and call them up whenever you want to use them.
• Real-time operation monitoring: See how much the AI costs to use, how fast it runs, and if there are any problems.

Using Help

Installation process

Agenta can be installed on your own computer and used, or you can use the cloud service. Here are the steps to install it on your computer:

1. Preparing the environment
   • Make sure your computer has Docker and Docker Compose, which are essential tools for running Agenta.
   • Linux or macOS is preferred, Windows users will have to open WSL2 first.
   • Check that Python (3.10 or higher is recommended) and Git are installed, you'll need them later.
2. Download and launch
   • Open a terminal and enter the command to download Agenta:

     mkdir agenta && cd agenta
     curl -L https://raw.githubusercontent.com/agenta-ai/agenta/main/docker-compose.gh.yml -o docker-compose.gh.yml
     

   • Then start the service:

     docker compose -f docker-compose.gh.yml up -d
     

   • Wait a few minutes, open your browser and type http://localhost:3000, and you'll be able to see the page.
3. Setting options (optional)
   • If you don't want Agenta to collect anonymized data, you can change the agenta-web/.env file, put the TELEMETRY_TRACKING_ENABLED set up as falseThe
   • Users who use the command line (CLI) can change the ~/.agenta/config.tomlSet up as telemetry_tracking_enabled = falseThe
4. Check to see if it's loaded.
   • Seeing Agenta's welcome page in your browser is a sign of success.
   • If you want to use the cloud, you can see how to connect to AWS or other cloud services on the official website.

How to use the main functions

1. Cue word experimental area

• How do I get in?: Log in to Agenta and tap "Playground" on the left menu.
• cue: Type the words you want to try in the box, e.g. "Write a short essay".
• model: Select the AI model (e.g. GPT-4) from the list, and you can select more than one for comparison.
• run results (in an election): Tap "Run" to see the answers from the different models.
• modify and replace: Change the prompts if you think any of the answers are bad, and click "Save" when you're satisfied.
• Where is it used?: It's good to try which model answers the question better, or tune the cue word to make the answer more accurate.

2. Build your own mission flow

• New construction process: Click on "Workflows", select "New Workflow", and pick a type (e.g. Q&A flow).
• stuff: Enter the information needed, such as the knowledge base address or task requirements.
• Try it.: Tap "Test" to see if the result is correct.
• Teamwork: Ask your coworkers to change the parameters together and see the results.
• storage and utilization: Save it when it's tuned and be able to take it straight away.
• Where is it used?: Suitable for complex tasks, such as having the AI read information and answer questions.

3. Testing the effectiveness of the model

• start testingClick on "Evaluation" and select "New Evaluation".
• subtesting: Pick an off-the-shelf scoring tool, or write your own test code.
• data running: Put some test questions in, click "Run", and a report card will appear.
• hire someone to look after itIf you want to check manually, click on "Human Eval" and ask someone to pick the answer.
• see how things turn out: There are charts after the test that tell you if the model is good or not.
• Where is it used?: Good for checking that the model works, or looking for problems.

4. Monitoring operations

• Where to lookTap "Monitoring" to see how well the AI is working.
• Look at the data.: Being able to see how much was spent, how fast it ran, and if there were any errors.
• audit trail: Pick an application and look at the details of each request.
• repair issues: If something goes wrong, click "Trace" to find out why.
• How do I change it?: Tune cue words or parameters based on data to make it run better.
• Where is it used?: It's good to keep an eye on it after it goes live to make sure it's okay.

tip

• reticulation: You have to have a stable network when installing, or the Docker download will get stuck.
• scope of one's jurisdiction: Set up permissions for multiple users and don't let anyone change it.
• appeal (for help): See the GitHub documentation for questions, or ask at Slack.

With these steps above, you can quickly get started with Agenta, and it's easy to tune cue words, manage models, and look at data. Whether you're playing by yourself or using it for a team, you'll save a lot of effort.