conversation_with_RAG_agents

AgentScope Copilot: a Multi-Agent RAG Application

• What is this example about? With the provided implementation and configuration, you will obtain three different agents who can help you answer different questions about AgentScope.

• What is this example for? By this example, we want to show how the agent with retrieval augmented generation (RAG) capability can be used to build easily.

Prerequisites

• Cloning repo: This example requires cloning the whole AgentScope repo to local.
• Packages: This example is built on the LlamaIndex package. Thus, some packages need to be installed before running the example.

  pip install llama-index==0.10.30 llama-index-readers-docstring-walker==0.1.3 tree-sitter==0.21.3 tree-sitter-languages==1.10.2

• Model APIs: This example uses Dashscope APIs. Thus, we also need an API key for DashScope.

  export DASHSCOPE_API_KEY='YOUR_API_KEY'

Note: This example has been tested with dashscope_chat and dashscope_text_embedding model wrapper, with qwen-max and text-embedding-v2 models. However, you are welcome to replace the Dashscope language and embedding model wrappers or models with other models you like to test.

Start AgentScope Copilot

• Terminal: The most simple way to execute the AgentScope Copilot is running in terminal.

  python ./rag_example.py

• AS gradio: If you want to have more organized, clean UI, you can also run with our as_gradio.

  as_gradio ./rag_example.py

Agents in the example

After you run the example, you may notice that this example consists of three RAG agents:

• Tutorial-Assistant: responsible for answering questions based on AgentScope tutorials (markdown files).
• Code-Search-Assistant: responsible for answering questions based on AgentScope code base (python files).
• API-Assistant: responsible for answering questions based on AgentScope API documents (html files, generated by sphinx)
• Searching-Assistant: responsible for general search in tutorial and code base (markdown files and code files)
• Agent-Guiding-Assistant: responsible for referring the correct agent(s) among the above ones.

Besides the last Agent-Guiding-Assistant, all other agents can be configured to answering questions based on other GitHub repo by replacing the knowledge.

For more details about how to use the RAG module in AgentScope, please refer to the tutorial.