Merge remote-tracking branch 'origin/Agent_Hub_Dev' into Agent_Hub_Dev

pilot/base_modules/agent/controller.py

@@ -69,8 +69,10 @@ async def agent_hub_update(update_param: PluginHubParam = Body()):
     logger.info(f"agent_hub_update:{update_param.__dict__}")
     try:
         agent_hub = AgentHub(PLUGINS_DIR)
+        branch = update_param.branch if update_param.branch is not None and len(update_param.branch) > 0 else  "main"
+        authorization = update_param.authorization if update_param.branch is not None and len(update_param.branch) > 0 else None
         agent_hub.refresh_hub_from_git(
-            update_param.url, update_param.branch, update_param.authorization
+            update_param.url, branch, authorization
         )
         return Result.succ(None)
     except Exception as e:

pilot/base_modules/agent/hub/agent_hub.py

@@ -125,7 +125,7 @@ class AgentHub:
         my_plugin_entity.version = hub_plugin.version
         return my_plugin_entity
 
-    def refresh_hub_from_git(self, github_repo: str = None, branch_name: str = None, authorization: str = None):
+    def refresh_hub_from_git(self, github_repo: str = None, branch_name: str = "main", authorization: str = None):
         logger.info("refresh_hub_by_git start!")
         update_from_git(self.temp_hub_file_path, github_repo, branch_name, authorization)
         git_plugins = scan_plugins(self.temp_hub_file_path)

pilot/connections/base.py

@@ -34,6 +34,9 @@ class BaseConnect(ABC):
     def run(self, session, command: str, fetch: str = "all") -> List:
         pass
 
+    def run_to_df(self, command: str, fetch: str = "all"):
+        pass
+
     def get_users(self):
         pass
 

pilot/connections/rdbms/base.py

@@ -3,7 +3,7 @@ from urllib.parse import quote
 import warnings
 import sqlparse
 import regex as re
-
+import pandas as pd
 from typing import Any, Iterable, List, Optional
 from pydantic import BaseModel, Field, root_validator, validator, Extra
 from abc import ABC, abstractmethod
@@ -357,7 +357,13 @@ class RDBMSDatabase(BaseConnect):
             else:
                 return self.__query(f"SHOW COLUMNS FROM {table_name}")
 
-    def run_no_throw(self, session, command: str, fetch: str = "all") -> List:
+    def run_to_df(self, command: str, fetch: str = "all"):
+        result_lst = self.run(command, fetch)
+        colunms = result_lst[0]
+        values = result_lst[1:]
+        return pd.DataFrame(values, columns=colunms)
+
+    def run_no_throw(self, command: str, fetch: str = "all") -> List:
         """Execute a SQL command and return a string representing the results.
 
         If the statement returns rows, a string of the results is returned.
@@ -366,7 +372,7 @@ class RDBMSDatabase(BaseConnect):
         If the statement throws an error, the error message is returned.
         """
         try:
-            return self.run(session, command, fetch)
+            return self.run( command, fetch)
         except SQLAlchemyError as e:
             """Format the error message"""
             return f"Error: {e}"

pilot/scene/chat_agent/prompt.py

@@ -16,11 +16,14 @@ You need to analyze the user goals and, under the given constraints, prioritize
 Tool list:
     {tool_list}
 Constraint:
-	1. After selecting an available tool, please ensure that the output results include the following parts to use the tool: 
-	<api-call><name>Selected Tool name</name><args><name>value</name></args></api-call>
-	2. If you cannot analyze the exact tool for the problem, you can consider using the search engine tool among the tools first.
-	3. Parameter content may need to be inferred based on the user's goals, not just extracted from text
-	4. If you cannot find a suitable tool, please answer Unable to complete the goal.
+    1. After finding the available tools from the tool list given below, please output the following content to use the tool. Please make sure that the following content only appears once in the output result:
+        <api-call><name>Selected Tool name</name><args><arg1>value</arg1><arg2>value</arg2></args></api-call>
+    2. Please generate the above call text according to the definition of the corresponding tool in the tool list. The reference case is as follows:
+        Introduction to tool function: "Tool name", args: "Parameter 1": "<Parameter 1 value description>", "Parameter 2": "<Parameter 2 value description>" Corresponding call text: <api-call>< name>Tool name</name><args><parameter 1>value</parameter 1><parameter 2>value</parameter 2></args></api-call>
+    3. Generate the call of each tool according to the above constraints. The prompt text for tool use needs to be generated before the tool is used.
+    4. If the user goals cannot be understood and the intention is unclear, give priority to using search engine tools
+    5. Parameter content may need to be inferred based on the user's goals, not just extracted from text
+    6. Constraint conditions and tool information are used as auxiliary information for the reasoning process and should not be expressed in the output content to the user. 
     {expand_constraints}
 User goals:
     {user_goal}

pilot/scene/chat_data/chat_excel/excel_analyze/prompt.py

@@ -35,7 +35,7 @@ _DEFAULT_TEMPLATE_ZH = """
 	5.优先使用数据分析的方式回答，如果用户问题不涉及数据分析内容，你可以按你的理解进行回答
 	6.请确保你的输出内容有良好排版，输出内容均为普通markdown文本,不要用```或者```python这种标签来包围<api-call>的输出内容
 请确保你的输出格式如下:
-    输出给用户的分析文本信息.<api-call><name>[数据展示方式]</name><args><sql>[正确的duckdb数据分析sql]</sql></args></api-call>
+    分析思路简介.<api-call><name>[数据展示方式]</name><args><sql>[正确的duckdb数据分析sql]</sql></args></api-call>
 
 用户问题：{user_input}
 """

pilot/scene/chat_data/chat_excel/excel_reader.py

@@ -5,7 +5,6 @@ import os
 import re
 import sqlparse
 
-import pandas as pd
 import chardet
 import pandas as pd
 import numpy as np

pilot/server/static/_next/static/chunks/pages/knowledge/[knowledgeName]/[id]-cf0fc71091010248.js

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pilot/server/static/_next/static/cwGO_pg91KmcKR8qdJEV3/_buildManifest.js

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pilot/server/static/_next/static/gAikAwoh--ytU5o0fxWfL/_buildManifest.js

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