conversation_with_RAG_agents

AgentScope Consultants: 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.

Notice: This example is a Beta version of the AgentScope RAG agent. A formal version will soon be added to src/agentscope/agents, but it may be subject to changes.

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 DASH_SCOPE_API='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 Consultants

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

  python ./rag_example.py

  Setting log_retrieval to false in agent_config.json can hide the retrieved information and provide only answers of agents.

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

  as_studio ./rag_example.py

Customize AgentScope Consultants to other consultants

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

• AgentScope Tutorial Assistant: responsible for answering questions based on AgentScope tutorials (markdown files).
• AgentScope Framework Code Assistant: responsible for answering questions based on AgentScope code base (python files).
• Summarize Assistant: responsible for summarize the questions from the above two agents.

These agents can be configured to answering questions based on other GitHub repo, by simply modifying the input_dir fields in the agent_config.json.

For more advanced customization, we may need to learn a little bit from the following.

RAG modules: In AgentScope, RAG modules are abstract to provide three basic functions: load_data, store_and_index and retrieve. Refer to src/agentscope/rag for more details.

RAG configs: In the example configuration (the rag_config field), all parameters are optional. But if you want to customize them, you may want to learn the following:

• load_data: contains all parameters for the the rag.load_data function. Since the load_data accepts a dataloader object loader, the loader in the config need to have "create_object": true to let a internal parse create a LlamaIndex data loader object. The loader object is an instance of class in module module, with initialization parameters in init_args.

• store_and_index: contains all parameters for the the rag.store_and_index function. For example, you can pass vector_store and retriever configurations in a similar way as the loader mentioned above. For the transformations parameter, you can pass a list of dicts, each of which corresponds to building a NodeParser-kind of preprocessor in Llamaindex.