Introducing RAG module into package

README.md

@@ -107,6 +107,7 @@ the following libraries.
   - [Self-Organizing Conversation](./examples/conversation_self_organizing)
   - [Basic Conversation with LangChain library](./examples/conversation_with_langchain)
   - [Conversation with ReAct Agent](./examples/conversation_with_react_agent)
+  - [Conversation with RAG Agent](./examples/conversation_with_RAG_agents)
 
 - Game
   - [Gomoku](./examples/game_gomoku)

README_ZH.md

@@ -95,6 +95,7 @@ AgentScope支持使用以下库快速部署本地模型服务。
   - [智能体自组织的对话](./examples/conversation_self_organizing)
   - [兼容LangChain的基础对话](./examples/conversation_with_langchain)
   - [与ReAct智能体对话](./examples/conversation_with_react_agent)
+  - [与RAG智能体对话](./examples/conversation_with_RAG_agents)
 
 - 游戏
   - [五子棋](./examples/game_gomoku)

docs/sphinx_doc/en/source/agentscope.agents.rst

@@ -66,3 +66,12 @@ react_agent module
    :members:
    :undoc-members:
    :show-inheritance:
+
+
+rag_agent module
+-------------------------------
+
+.. automodule:: agentscope.agents.rag_agents
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/sphinx_doc/zh_CN/source/agentscope.agents.rst

@@ -66,3 +66,12 @@ react_agent module
    :members:
    :undoc-members:
    :show-inheritance:
+
+
+rag_agent module
+-------------------------------
+
+.. automodule:: agentscope.agents.rag_agents
+   :members:
+   :undoc-members:
+   :show-inheritance:

examples/conversation_with_RAG_agents/README.md

@@ -0,0 +1,40 @@
+# 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.
+
+## 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.
+    ```bash
+    pip install llama-index tree_sitter tree-sitter-languages
+    ```
+* **Model APIs:** This example uses Dashscope APIs. Thus, we also need an API key for DashScope.
+  ```bash
+  export DASH_SCOPE_API='YOUR_API_KEY'
+  ```
+  However, you are welcome to replace the Dashscope language and embedding models with other models you like.
+
+## Start AgentScope Consultants
+* **Terminal:** The most simple way to execute the AgentScope Consultants is running in terminal.
+  ```bash
+  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`.
+  ```bash
+  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`.

examples/conversation_with_RAG_agents/agent_config.json

@@ -0,0 +1,79 @@
+[
+  {
+    "class": "LlamaIndexAgent",
+    "args": {
+      "name": "AgentScope Tutorial Assistant",
+      "sys_prompt": "You're a helpful assistant. You need to generate answers based on the provided context.",
+      "model_config_name": "qwen_config",
+      "emb_model_config_name": "qwen_emb_config",
+      "rag_config": {
+            "load_data": {
+              "loader": {
+                "create_object": true,
+                "module": "llama_index.core",
+                "class": "SimpleDirectoryReader",
+                "init_args": {
+                  "input_dir": "../../docs/sphinx_doc/en/source/tutorial/",
+                  "required_exts": [".md"]
+                }
+              }
+            },
+            "chunk_size": 2048,
+            "chunk_overlap": 40,
+            "similarity_top_k": 10,
+            "log_retrieval": false,
+            "recent_n_mem": 1
+      }
+    }
+  },
+  {
+    "class": "LlamaIndexAgent",
+    "args": {
+      "name": "AgentScope Framework Code Assistant",
+      "sys_prompt": "You're a helpful assistant about coding. You can very familiar with the framework code of AgentScope.",
+      "model_config_name": "qwen_config",
+      "emb_model_config_name": "qwen_emb_config",
+      "rag_config": {
+            "load_data": {
+              "loader": {
+                "create_object": true,
+                "module": "llama_index.core",
+                "class": "SimpleDirectoryReader",
+                "init_args": {
+                  "input_dir": "../../src/agentscope",
+                  "recursive": true,
+                  "required_exts": [".py"]
+                }
+              }
+            },
+            "store_and_index": {
+              "transformations": [
+                {
+                  "create_object": true,
+                  "module": "llama_index.core.node_parser",
+                  "class": "CodeSplitter",
+                  "init_args": {
+                    "language": "python",
+                    "chunk_lines": 100
+                  }
+                }
+              ]
+            },
+            "chunk_size": 2048,
+            "chunk_overlap": 40,
+            "similarity_top_k": 10,
+            "log_retrieval": false,
+            "recent_n_mem": 1
+      }
+    }
+  },
+  {
+    "class": "DialogAgent",
+    "args": {
+      "name": "Summarize Assistant",
+      "sys_prompt": "You are a helpful assistant that can summarize the answers of the previous two messages.",
+      "model_config_name": "qwen_config",
+      "use_memory": true
+    }
+  }
+]

examples/conversation_with_RAG_agents/rag_example.py

@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+"""
+A simple example for conversation between user and
+an agent with RAG capability.
+"""
+import os
+
+import agentscope
+from agentscope.agents import UserAgent
+from agentscope.message import Msg
+
+
+def main() -> None:
+    """A RAG multi-agent demo"""
+    agents = agentscope.init(
+        model_configs=[
+            {
+                "model_type": "dashscope_chat",
+                "config_name": "qwen_config",
+                "model_name": "qwen-max",
+                "api_key": f"{os.environ.get('DASHSCOPE_API_KEY')}",
+            },
+            {
+                "model_type": "dashscope_text_embedding",
+                "config_name": "qwen_emb_config",
+                "model_name": "text-embedding-v2",
+                "api_key": f"{os.environ.get('DASHSCOPE_API_KEY')}",
+            },
+        ],
+        agent_configs="./agent_config.json",
+    )
+
+    tutorial_agent, code_explain_agent, summarize_agent = agents
+
+    user_agent = UserAgent()
+    # start the conversation between user and assistant
+    while True:
+        x = user_agent()
+        x.role = "user"  # to enforce dashscope requirement on roles
+        if len(x["content"]) == 0 or str(x["content"]).startswith("exit"):
+            break
+        tutorial_response = tutorial_agent(x)
+        code_explain = code_explain_agent(x)
+        msg = Msg(
+            name="user",
+            role="user",
+            content=tutorial_response["content"]
+            + "\n"
+            + code_explain["content"]
+            + "\n"
+            + x["content"],
+        )
+        summarize_agent(msg)
+
+
+if __name__ == "__main__":
+    main()

src/agentscope/agents/__init__.py

@@ -9,6 +9,11 @@
 from .rpc_agent import RpcAgentServerLauncher
 from .react_agent import ReActAgent
 
+try:
+    from .rag_agents import RAGAgentBase, LlamaIndexAgent
+except Exception:
+    RAGAgentBase, LlamaIndexAgent = None, None  # type: ignore # NOQA
+
 
 __all__ = [
     "AgentBase",
@@ -19,4 +24,6 @@
     "UserAgent",
     "RpcAgentServerLauncher",
     "ReActAgent",
+    "RAGAgentBase",
+    "LlamaIndexAgent",
 ]