Feat/assistant app (#2086)

Co-authored-by: chenhe <guchenhe@gmail.com>
Co-authored-by: Pascal M <11357019+perzeuss@users.noreply.github.com>

api/core/tools/tool/builtin_tool.py

@@ -0,0 +1,140 @@
+from core.tools.tool.tool import Tool
+from core.tools.model.tool_model_manager import ToolModelManager
+from core.model_runtime.entities.message_entities import PromptMessage
+from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
+from core.tools.utils.web_reader_tool import get_url
+
+from typing import List
+from enum import Enum
+
+_SUMMARY_PROMPT = """You are a professional language researcher, you are interested in the language
+and you can quickly aimed at the main point of an webpage and reproduce it in your own words but 
+retain the original meaning and keep the key points. 
+however, the text you got is too long, what you got is possible a part of the text.
+Please summarize the text you got.
+"""
+
+
+class BuiltinTool(Tool):
+    """
+        Builtin tool
+
+        :param meta: the meta data of a tool call processing
+    """
+
+    def invoke_model(
+        self, user_id: str, prompt_messages: List[PromptMessage], stop: List[str]
+    ) -> LLMResult:
+        """
+            invoke model
+
+            :param model_config: the model config
+            :param prompt_messages: the prompt messages
+            :param stop: the stop words
+            :return: the model result
+        """
+        # invoke model
+        return ToolModelManager.invoke(
+            user_id=user_id,
+            tenant_id=self.runtime.tenant_id,
+            tool_type='builtin',
+            tool_name=self.identity.name,
+            prompt_messages=prompt_messages,
+        )
+    
+    def get_max_tokens(self) -> int:
+        """
+            get max tokens
+
+            :param model_config: the model config
+            :return: the max tokens
+        """
+        return ToolModelManager.get_max_llm_context_tokens(
+            tenant_id=self.runtime.tenant_id,
+        )
+
+    def get_prompt_tokens(self, prompt_messages: List[PromptMessage]) -> int:
+        """
+            get prompt tokens
+
+            :param prompt_messages: the prompt messages
+            :return: the tokens
+        """
+        return ToolModelManager.calculate_tokens(
+            tenant_id=self.runtime.tenant_id,
+            prompt_messages=prompt_messages
+        )
+    
+    def summary(self, user_id: str, content: str) -> str:
+        max_tokens = self.get_max_tokens()
+
+        if self.get_prompt_tokens(prompt_messages=[
+            UserPromptMessage(content=content)
+        ]) < max_tokens * 0.6:
+            return content
+        
+        def get_prompt_tokens(content: str) -> int:
+            return self.get_prompt_tokens(prompt_messages=[
+                SystemPromptMessage(content=_SUMMARY_PROMPT),
+                UserPromptMessage(content=content)
+            ])
+        
+        def summarize(content: str) -> str:
+            summary = self.invoke_model(user_id=user_id, prompt_messages=[
+                SystemPromptMessage(content=_SUMMARY_PROMPT),
+                UserPromptMessage(content=content)
+            ], stop=[])
+
+            return summary.message.content
+
+        lines = content.split('\n')
+        new_lines = []
+        # split long line into multiple lines
+        for i in range(len(lines)):
+            line = lines[i]
+            if not line.strip():
+                continue
+            if len(line) < max_tokens * 0.5:
+                new_lines.append(line)
+            elif get_prompt_tokens(line) > max_tokens * 0.7:
+                while get_prompt_tokens(line) > max_tokens * 0.7:
+                    new_lines.append(line[:int(max_tokens * 0.5)])
+                    line = line[int(max_tokens * 0.5):]
+                new_lines.append(line)
+            else:
+                new_lines.append(line)
+
+        # merge lines into messages with max tokens
+        messages: List[str] = []
+        for i in new_lines:
+            if len(messages) == 0:
+                messages.append(i)
+            else:
+                if len(messages[-1]) + len(i) < max_tokens * 0.5:
+                    messages[-1] += i
+                if get_prompt_tokens(messages[-1] + i) > max_tokens * 0.7:
+                    messages.append(i)
+                else:
+                    messages[-1] += i
+
+        summaries = []
+        for i in range(len(messages)):
+            message = messages[i]
+            summary = summarize(message)
+            summaries.append(summary)
+
+        result = '\n'.join(summaries)
+
+        if self.get_prompt_tokens(prompt_messages=[
+            UserPromptMessage(content=result)
+        ]) > max_tokens * 0.7:
+            return self.summary(user_id=user_id, content=result)
+        
+        return result
+    
+    def get_url(self, url: str, user_agent: str = None) -> str:
+        """
+            get url
+        """
+        return get_url(url, user_agent=user_agent)