feat(multi-agents):Add Summary Assistant Agent

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md

@@ -0,0 +1,20 @@
+# Description
+
+Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
+
+# How Has This Been Tested?
+
+Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration
+
+# Snapshots:
+
+Include snapshots for easier review.
+
+# Checklist:
+
+- [ ] My code follows the style guidelines of this project
+- [ ] I have already rebased the commits and make the commit message conform to the project standard.
+- [ ] I have performed a self-review of my own code
+- [ ] I have commented my code, particularly in hard-to-understand areas
+- [ ] I have made corresponding changes to the documentation
+- [ ] Any dependent changes have been merged and published in downstream modules

dbgpt/agent/agents/expand/summary_assistant_agent.py

@@ -0,0 +1,93 @@
+from typing import Callable, Dict, Literal, Optional, Union
+
+from dbgpt._private.config import Config
+from dbgpt.agent.agents.base_agent import ConversableAgent
+from dbgpt.agent.plugin.commands.command_mange import ApiCall
+
+from ...memory.gpts_memory import GptsMemory
+from ..agent import Agent, AgentContext
+
+
+class SummaryAssistantAgent(ConversableAgent):
+    """(In preview) Assistant agent, designed to solve a task with LLM.
+
+    AssistantAgent is a subclass of ConversableAgent configured with a default system message.
+    The default system message is designed to solve a task with LLM,
+    including suggesting python code blocks and debugging.
+    `human_input_mode` is default to "NEVER"
+    and `code_execution_config` is default to False.
+    This agent doesn't execute code by default, and expects the user to execute the code.
+    """
+
+    DEFAULT_SYSTEM_MESSAGE = (
+        """You are a great summary writter to summarize the provided text content according to user questions.
+           Please complete this task step by step following instructions below:
+           1. You need to first detect user's question that you need to answer with your summarization.
+           2. Output the extracted user's question with the format - The User's Question: user's question.
+           3. Then you need to summarize the historical messages 
+           4. Output the summarization only related to user's question with the format - The Summarization: the summarization.
+        """
+    )
+
+    DEFAULT_DESCRIBE = (
+        """Summarize provided text content according to user's questions and output the summaraization."""
+    )
+
+    NAME = "Summarizer"
+
+    def __init__(
+        self,
+        memory: GptsMemory,
+        agent_context: AgentContext,
+        describe: Optional[str] = DEFAULT_DESCRIBE,
+        is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+        max_consecutive_auto_reply: Optional[int] = None,
+        human_input_mode: Optional[str] = "NEVER",
+        **kwargs,
+    ):
+        super().__init__(
+            name=self.NAME,
+            memory=memory,
+            describe=describe,
+            system_message=self.DEFAULT_SYSTEM_MESSAGE,
+            is_termination_msg=is_termination_msg,
+            max_consecutive_auto_reply=max_consecutive_auto_reply,
+            human_input_mode=human_input_mode,
+            agent_context=agent_context,
+            **kwargs,
+        )
+        self.register_reply(Agent, SummaryAssistantAgent.generate_summary_reply)
+        self.agent_context = agent_context
+
+    async def generate_summary_reply(
+        self,
+        message: Optional[str] = None,
+        sender: Optional[Agent] = None,
+        reviewer: Optional[Agent] = None,
+        config: Optional[Union[Dict, Literal[False]]] = None,
+    ):
+        """Generate a reply with summary."""
+
+        response_success = True
+        view = None
+        content = None
+        if message is None:
+            # Answer failed, turn on automatic repair
+            fail_reason += f"Nothing is summarized, please check your input."
+            response_success = False
+        else:
+            try:
+                vis_client = ApiCall()
+                content = "The generated summary is shown above."
+                view = content
+            except Exception as e:
+                fail_reason += f"Return summarization error，{str(e)}"
+                response_success = False
+
+        if not response_success:
+            content = fail_reason
+        return True, {
+            "is_exe_success": response_success,
+            "content": content,
+            "view": view,
+        }

dbgpt/app/knowledge/service.py

@@ -7,7 +7,7 @@
 from dbgpt.rag.chunk import Chunk
 from dbgpt.rag.chunk_manager import ChunkParameters
 from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory
-from dbgpt.rag.knowledge.base import KnowledgeType
+from dbgpt.rag.knowledge.base import KnowledgeType, ChunkStrategy
 from dbgpt.rag.knowledge.factory import KnowledgeFactory
 from dbgpt.rag.text_splitter.text_splitter import (
     RecursiveCharacterTextSplitter,
@@ -234,7 +234,7 @@ def batch_document_sync(
                     f" doc:{doc.doc_name} status is {doc.status}, can not sync"
                 )
             chunk_parameters = sync_request.chunk_parameters
-            if "Automatic" == chunk_parameters.chunk_strategy:
+            if chunk_parameters.chunk_strategy != ChunkStrategy.CHUNK_BY_SIZE.name:
                 space_context = self.get_space_context(space_name)
                 chunk_parameters.chunk_size = (
                     CFG.KNOWLEDGE_CHUNK_SIZE