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functionality/rag/README.md

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+# 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
+```