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# AutoAgents | |
<p align="center"><img src="https://raw.githubusercontent.com/AutoLLM/AutoAgents/assets/images/logo.png?raw=true" width=400/></p> | |
Unlock complex question answering in LLMs with enhanced chain-of-thought reasoning and information-seeking capabilities. | |
## π Overview | |
The purpose of this project is to extend LLMs ability to answer more complex questions through chain-of-thought reasoning and information-seeking actions. | |
We are excited to release the initial version of AutoAgents, a proof-of-concept on what can be achieved with only well-written prompts. This is the initial step towards our first big milestone, releasing and open-sourcing the AutoAgents 7B model! | |
Come try out our [Huggingface Space](https://huggingface.co/spaces/AutoLLM/AutoAgents)! | |
## π€ The AutoAgents Project | |
This project demonstrates LLMs capability to execute a complex user goal: understand a user's goal, generate a plan, use proper tools, and deliver a final result. | |
For simplicity, our first attempt starts with a Web Search Agent. | |
## π« How it works: | |
<p align="left"><img src="https://raw.githubusercontent.com/AutoLLM/AutoAgents/assets/images/agent.png" width=830/></p> | |
## π Examples | |
Ask your AutoAgent to do what a real person would do using the internet: | |
For example: | |
*1. Recommend a kid friendly movie that is playing at a theater near Sunnyvale. Give me the showtimes and a link to purchase the tickets* | |
*2. What is the average age of the past three president when they took office* | |
*3. What is the mortgage rate right now and how does that compare to the past two years* | |
## π Roadmap | |
* ~~HuggingFace Space demo using OpenAI models~~ [LINK](https://huggingface.co/spaces/AutoLLM/AutoAgents) | |
* AutoAgents [7B] Model | |
* Initial Release: | |
* Finetune and release a 7B parameter fine-tuned search model | |
* AutoAgents Dataset | |
* A high-quality dataset for a diverse set of search scenarios (why quality and diversity?<sup>[1](https://arxiv.org/abs/2305.11206)</sup>) | |
* Reduce Model Inference Overhead | |
* Affordance Modeling <sup>[2](https://en.wikipedia.org/wiki/Affordance)</sup> | |
* Extend Support to Additional Tools | |
* Customizable Document Search set (e.g. personal documents) | |
* Support Multi-turn Dialogue | |
* Advanced Flow Control in Plan Execution | |
We are actively developing a few interesting things, check back here or follow us on [Twitter](https://twitter.com/AutoLLM) for any new development. | |
If you are interested in any other problems, feel free to shoot us an issue. | |
## π§ How to use this repo? | |
This repo contains the entire code to run the search agent from your local browser. All you need is an OpenAI API key to begin. | |
To run the search agent locally: | |
1. Clone the repo and change the directory | |
```bash | |
git clone https://github.com/AutoLLM/AutoAgents.git | |
cd AutoAgents | |
``` | |
2. Install the dependencies | |
```bash | |
pip install -r requirements.txt | |
``` | |
3. Install the `autoagents` package | |
```bash | |
pip install -e . | |
``` | |
4. Make sure you have your OpenAI API key set as an environment variable. Alternatively, you can also feed it through the input text-box on the sidebar. | |
```bash | |
export OPENAI_API_KEY=sk-xxxxxx | |
``` | |
5. Run the Streamlit app | |
```bash | |
streamlit run autoagents/spaces/app.py | |
``` | |
This should open a browser window where you can type your search query. | |