Merge pull request modelscope#5 from FredericW/dev_copilot_zitao

copilot dialog agents update

examples/conversation_with_RAG_agents/configs/agent_config.json

@@ -4,7 +4,7 @@
     "args": {
       "name": "Tutorial-Assistant",
       "description": "Tutorial-Assistant is an agent that can provide answer based on English tutorial material, mainly the markdown files. It can answer general questions about AgentScope.",
-      "sys_prompt": "You're an assistant helping new users to use AgentScope. You need to generate answers based on the provided context. You only provide the most relevant information. The answer is limited to be less than 100 words. If the key words of the question can be found in the provided context, the answer should contain the section which contains the answer, for example, saying 'You may refer to SECTION_NAME for more details.' The answers must to be itemized.",
+      "sys_prompt": "You're an assistant helping new users to use AgentScope. The language style is helpful and cheerful. You generate answers based on the provided context. The answer is expected to be no longer than 100 words. If the key words of the question can be found in the provided context, the answer should contain the section name which contains the answer. For example, 'You may refer to SECTION_NAME for more details.'",
       "model_config_name": "qwen_config",
       "emb_model_config_name": "qwen_emb_config",
       "rag_config": {
@@ -21,7 +21,7 @@
             },
             "chunk_size": 2048,
             "chunk_overlap": 40,
-            "similarity_top_k": 10,
+            "similarity_top_k": 5,
             "log_retrieval": false,
             "recent_n_mem": 1,
             "persist_dir": "../../rag_storage/tutorial_assist"
@@ -32,8 +32,8 @@
     "class": "LlamaIndexAgent",
     "args": {
       "name": "Code-Search-Assistant",
-      "description": "Code-Search-Assistant is an agent that can provide answer based on AgentScope code base. It can answer questions about specific functions and classes in AgentScope.",
-      "sys_prompt": "You're an assistant answering coding questions about AgentScope. The answer is sturucted as follows. (1) the key features, (2) an example showing how to use it, (3) where to find the example, and source codes.",
+      "description": "Code-Search-Assistant is an agent that can provide answer based on AgentScope code base. It can answer questions about specific modules in AgentScope.",
+      "sys_prompt": "You're a coding assistant of AgentScope. The answer starts with appreciation for the question, then provide details regarding the functionality and features of the modules mentioned in the question. The language should be in a professional and simple style. The answer is limited to be less than 100 words.",
       "model_config_name": "qwen_config",
       "emb_model_config_name": "qwen_emb_config",
       "rag_config": {
@@ -64,62 +64,21 @@
             },
             "chunk_size": 2048,
             "chunk_overlap": 40,
-            "similarity_top_k": 10,
+            "similarity_top_k": 5,
             "log_retrieval": false,
             "recent_n_mem": 1,
             "persist_dir": "../../rag_storage/code_assist"
       }
     }
   },
-  {
-    "class": "LlamaIndexAgent",
-    "args": {
-      "name": "API-Assistant",
-      "description": "API-Assistant is an agent that can provide answer based on docstrings of Agentscope. It can answer questions about whether there is any function in AgentScope and how to call the functions.",
-      "sys_prompt": "You're an assistant that know every API, function and class in AgentScope. You need to generate answers based on the provided context. You provide the most relevant information and tell people how to use the function or how to create objects of classes. You need to provide the name of module and give detailed information of how to find the function or class. The answer is limited to be less than 200 words.",
-      "model_config_name": "qwen_config",
-      "emb_model_config_name": "qwen_emb_config",
-      "rag_config": {
-            "load_data": {
-              "loader": {
-                "create_object": true,
-                "module": "llama_index.readers.docstring_walker",
-                "class": "DocstringWalker",
-                "init_args": {}
-              },
-              "query": "../../src/agentscope"
-            },
-            "store_and_index": {
-              "transformations": [
-                {
-                  "create_object": true,
-                  "module": "llama_index.core.node_parser",
-                  "class": "TokenTextSplitter",
-                  "init_args": {
-                    "chunk_size": 1024,
-                    "chunk_overlap": 20,
-                    "separator": " "
-                  }
-                }
-              ]
-            },
-            "chunk_size": 2048,
-            "chunk_overlap": 40,
-            "similarity_top_k": 4,
-            "log_retrieval": true,
-            "recent_n_mem": 1,
-            "persist_dir": "../../rag_storage/docstring_assist"
-      }
-    }
-  },
   {
     "class": "DialogAgent",
     "args": {
-      "name": "Summarize-Assistant",
-      "description": "Summarize-Assistant is an agent that can summarize multiple RAG agents' answers.",
-      "sys_prompt": "You summarize the answers of the previous two messages and remove the redundant information. The answer need to be simple and itemized. The answer needs to be less than 100 words.ex .",
+      "name": "Agent-Guiding-Assistant",
+      "description": "Agent-Guiding-Assistant is an agent that decide which agent should provide the answer next. It can answer questions about specific functions and classes in AgentScope.",
+      "sys_prompt": "You're an assistant guiding the user to specific agent for help. The answer is in a cheerful stypled language. The output starts with appreciation for the question. Next, rephrase the question in a simple declarative Sentence for example, 'I think you are asking...'. Last, if the question is a coding question, output '@ Code-Search-Assistant, you might be suitable for answering the question, otherwise, output '@ Tutorial-Assistant, I think you are more suitable for the question, please tell us more about it. The answer is expected to be one line only",
       "model_config_name": "qwen_config",
-      "use_memory": true
+      "use_memory": false
     }
   }
 ]

examples/conversation_with_RAG_agents/groupchat_utils.py

@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+""" Group chat utils."""
+import re
+from typing import Sequence
+
+
+def select_next_one(agents: Sequence, rnd: int) -> Sequence:
+    """
+    Select next agent.
+    """
+    return agents[rnd % len(agents)]
+
+
+def filter_agents(string: str, agents: Sequence) -> Sequence:
+    """
+    This function filters the input string for occurrences of the given names
+    prefixed with '@' and returns a list of the found names.
+    """
+    if len(agents) == 0:
+        return []
+
+    # Create a pattern that matches @ followed by any of the candidate names
+    pattern = (
+        r"@(" + "|".join(re.escape(agent.name) for agent in agents) + r")\b"
+    )
+
+    # Find all occurrences of the pattern in the string
+    matches = re.findall(pattern, string)
+
+    # Create a dictionary mapping agent names to agent objects for quick lookup
+    agent_dict = {agent.name: agent for agent in agents}
+
+    # Return the list of matched agent objects preserving the order
+    ordered_agents = [
+        agent_dict[name] for name in matches if name in agent_dict
+    ]
+    return ordered_agents

examples/conversation_with_RAG_agents/rag/llama_index_rag.py

@@ -30,7 +30,8 @@
     BaseEmbedding, Embedding = None, None
     IngestionPipeline, BasePydanticVectorStore, VectorStore = None, None, None
     NodeParser, SentenceSplitter = None, None
-    VectorStoreIndex = None
+    VectorStoreIndex, StorageContext = None, None
+    load_index_from_storage = None
     PrivateAttr = None
 
 from rag import RAGBase

examples/conversation_with_RAG_agents/rag_agents.py

@@ -317,6 +317,7 @@ def init_rag(self) -> LlamaIndexRAG:
             load_data_args = {
                 "loader": SimpleDirectoryReader(self.config["data_path"]),
             }
+            # NOTE: "data_path" is never used/defined for the current version.
         logger.info(f"rag.load_data args: {load_data_args}")
         docs = rag.load_data(**load_data_args)
 

examples/conversation_with_RAG_agents/rag_example.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
-A simple example for conversation between user and
-an agent with RAG capability.
+An example for conversation between user and agents with RAG capability.
+One agent is a tutorial assistant, the other is a code explainer.
 """
 import json
 import os
@@ -11,8 +11,6 @@
 
 import agentscope
 from agentscope.agents import UserAgent
-
-from agentscope.message import Msg
 from agentscope.agents import DialogAgent
 
 
@@ -34,54 +32,45 @@ def main() -> None:
 
     with open("configs/agent_config.json", "r", encoding="utf-8") as f:
         agent_configs = json.load(f)
+    # define RAG-based agents for tutorial and code
     tutorial_agent = LlamaIndexAgent(**agent_configs[0]["args"])
     code_explain_agent = LlamaIndexAgent(**agent_configs[1]["args"])
-    api_agent = LlamaIndexAgent(**agent_configs[2]["args"])
-    agent_configs[3]["args"].pop("description")
-    summarize_agent = DialogAgent(**agent_configs[3]["args"])
+    # define a guide agent
+    agent_configs[2]["args"].pop("description")
+    guide_agent = DialogAgent(**agent_configs[2]["args"])
     rag_agents = [
         tutorial_agent,
         code_explain_agent,
-        api_agent,
     ]
     rag_agent_names = [agent.name for agent in rag_agents]
-    summarize_agents = [summarize_agent]
-    summarize_agent_names = [agent.name for agent in summarize_agents]
-    helper_agents = rag_agents + summarize_agents
 
     user_agent = UserAgent()
-    # start the conversation between user and assistant
     while True:
+        """
+        The workflow is the following: 
+        1. user input a message, 
+        2. if it mentions one of the agents, then the agent will be called
+        3. otherwise, the guide agent will be decide which agent to call
+        4. the called agent will response to the user
+        5. repeat
+        """
         x = user_agent()
         x.role = "user"  # to enforce dashscope requirement on roles
         if len(x["content"]) == 0 or str(x["content"]).startswith("exit"):
             break
-        speak_list = filter_agents(x.get("content", ""), helper_agents)
+        speak_list = filter_agents(x.get("content", ""), rag_agents)
         if len(speak_list) == 0:
-            # if no agent is @ (mentioned), default invoke all rag agents and
-            # summarize agents
-            speak_list = rag_agents + summarize_agents
-        for agent in speak_list:
-            if agent.name in summarize_agent_names:
-                # if summarize agent is mentioned, then also call rag agents
-                # TODO: let summarize agent choose which agent to call
-                speak_list = rag_agents + summarize_agents
-
+            guide_response = guide_agent(x)
+            # Only one agent can be called in the current version,
+            # we may support multi-agent conversation later
+            speak_list = filter_agents(guide_response.get("content", ""),
+                                       rag_agents)
         agent_name_list = [agent.name for agent in speak_list]
-        rag_agent_responses = []
         for agent_name, agent in zip(agent_name_list, speak_list):
             if agent_name in rag_agent_names:
-                rag_agent_responses.append(agent(x))
-
-        msg = Msg(
-            name="user",
-            role="user",
-            content="/n".join([msg.content for msg in rag_agent_responses]),
-        )
-        for agent_name, agent in zip(agent_name_list, speak_list):
-            if agent_name in summarize_agent_names:
-                agent(msg)
+                agent(x)
 
 
 if __name__ == "__main__":
     main()
+