# RAG in AgentScope

This example includes three scripts to demonstrate how to use Retrieval-Augmented Generation (RAG) in AgentScope:

- the basic usage of RAG module in AgentScope in ``basic_usage.py``,
- a simple agentic use case of RAG in ``agentic_usage.py``, and
- integrate RAG into ``ReActAgent`` class by retrieving input message(s) at the beginning of each reply in ``react_agent_integration.py``.
- build multimodal RAG in ``multimodal_rag.py``.

> The agentic usage and static integration has their own advantages and limitations.
>  - The agentic usage requires more powerful LLMs to manage the retrieval process, but it's more flexible and the agent can adjust the retrieval strategy dynamically
>  - The static integration is more straightforward and easier to implement, but it's less flexible and the input message maybe not specific enough, leading to less relevant retrieval results.

> Note: The example is built with DashScope chat model. If you want to change the model in this example, don't forget
> to change the formatter at the same time! The corresponding relationship between built-in models and formatters are
> list in [our tutorial](https://doc.agentscope.io/tutorial/task_prompt.html#id1)

## Quick Start

Install the latest agentscope library from PyPI or source, then run the following command to run the example:

- the basic usage:
```bash
python basic_usage.py
```

- the agentic usage:
```bash
python agentic_usage.py
```

- the static integration:
```bash
python react_agent_integration.py
```

- the multimodal RAG:
```bash
python multimodal_rag.py
```