Merge branch 'main' into feature/json_write_prd

metagpt/actions/clone_function.py

@@ -0,0 +1,65 @@
+from pathlib import Path
+import traceback
+
+from metagpt.actions.write_code import WriteCode
+from metagpt.logs import logger
+from metagpt.schema import Message
+from metagpt.utils.highlight import highlight
+
+CLONE_PROMPT = """
+*context*
+Please convert the function code ```{source_code}``` into the the function format: ```{template_func}```.
+*Please Write code based on the following list and context*
+1. Write code start with ```, and end with ```.
+2. Please implement it in one function if possible, except for import statements. for exmaple:
+```python
+import pandas as pd
+def run(*args) -> pd.DataFrame:
+    ...
+```
+3. Do not use public member functions that do not exist in your design.
+4. The output function name, input parameters and return value must be the same as ```{template_func}```.
+5. Make sure the results before and after the code conversion are required to be exactly the same.
+6. Don't repeat my context in your replies.
+7. Return full results, for example, if the return value has df.head(), please return df.
+8. If you must use a third-party package, use the most popular ones, for example: pandas, numpy, ta, ...
+"""
+
+
+class CloneFunction(WriteCode):
+    def __init__(self, name="CloneFunction", context: list[Message] = None, llm=None):
+        super().__init__(name, context, llm)
+
+    def _save(self, code_path, code):
+        if isinstance(code_path, str):
+            code_path = Path(code_path)
+        code_path.parent.mkdir(parents=True, exist_ok=True)
+        code_path.write_text(code)
+        logger.info(f"Saving Code to {code_path}")
+
+    async def run(self, template_func: str, source_code: str) -> str:
+        """将source_code转换成template_func一样的入参和返回类型"""
+        prompt = CLONE_PROMPT.format(source_code=source_code, template_func=template_func)
+        logger.info(f"query for CloneFunction: \n {prompt}")
+        code = await self.write_code(prompt)
+        logger.info(f'CloneFunction code is \n {highlight(code)}')
+        return code
+
+
+def run_function_code(func_code: str, func_name: str, *args, **kwargs):
+    """Run function code from string code."""
+    try:
+        locals_ = {}
+        exec(func_code, locals_)
+        func = locals_[func_name]
+        return func(*args, **kwargs), ""
+    except Exception:
+        return "", traceback.format_exc()
+
+
+def run_function_script(code_script_path: str, func_name: str, *args, **kwargs):
+    """Run function code from script."""
+    if isinstance(code_script_path, str):
+        code_path = Path(code_script_path)
+    code = code_path.read_text(encoding='utf-8')
+    return run_function_code(code, func_name, *args, **kwargs)

metagpt/actions/execute_task.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/9/13 12:26
+@Author  : femto Zheng
+@File    : execute_task.py
+"""
+from metagpt.actions import Action
+from metagpt.schema import Message
+
+
+class ExecuteTask(Action):
+    def __init__(self, name="ExecuteTask", context: list[Message] = None, llm=None):
+        super().__init__(name, context, llm)
+
+    def run(self, *args, **kwargs):
+        pass

metagpt/actions/prepare_interview.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/9/19 15:02
+@Author  : DevXiaolan
+@File    : prepare_interview.py
+"""
+from metagpt.actions import Action
+
+PROMPT_TEMPLATE = """
+# Context
+{context}
+
+## Format example
+---
+Q1: question 1 here
+References:
+  - point 1
+  - point 2
+
+Q2: question 2 here...
+---
+
+-----
+Role: You are an interviewer of our company who is well-knonwn in frontend or backend develop;
+Requirement: Provide a list of questions for the interviewer to ask the interviewee, by reading the resume of the interviewee in the context.
+Attention: Provide as markdown block as the format above, at least 10 questions.
+"""
+
+# prepare for a interview
+
+
+class PrepareInterview(Action):
+    def __init__(self, name, context=None, llm=None):
+        super().__init__(name, context, llm)
+
+    async def run(self, context):
+        prompt = PROMPT_TEMPLATE.format(context=context)
+        question_list = await self._aask_v1(prompt)
+        return question_list
+

metagpt/actions/research.py

@@ -13,6 +13,7 @@ from metagpt.config import CONFIG
 from metagpt.logs import logger
 from metagpt.tools.search_engine import SearchEngine
 from metagpt.tools.web_browser_engine import WebBrowserEngine, WebBrowserEngineType
+from metagpt.utils.common import OutputParser
 from metagpt.utils.text import generate_prompt_chunk, reduce_message_length
 
 LANG_PROMPT = "Please respond in {language}."
@@ -110,7 +111,7 @@ class CollectLinks(Action):
         system_text = system_text if system_text else RESEARCH_TOPIC_SYSTEM.format(topic=topic)
         keywords = await self._aask(SEARCH_TOPIC_PROMPT, [system_text])
         try:
-            keywords = json.loads(keywords)
+            keywords = OutputParser.extract_struct(keywords, list)
             keywords = parse_obj_as(list[str], keywords)
         except Exception as e:
             logger.exception(f"fail to get keywords related to the research topic \"{topic}\" for {e}")
@@ -130,7 +131,7 @@ class CollectLinks(Action):
         logger.debug(prompt)
         queries = await self._aask(prompt, [system_text])
         try:
-            queries = json.loads(queries)
+            queries = OutputParser.extract_struct(queries, list)
             queries = parse_obj_as(list[str], queries)
         except Exception as e:
             logger.exception(f"fail to break down the research question due to {e}")
@@ -158,7 +159,7 @@ class CollectLinks(Action):
         logger.debug(prompt)
         indices = await self._aask(prompt)
         try:
-            indices = json.loads(indices)
+            indices = OutputParser.extract_struct(indices, list)
             assert all(isinstance(i, int) for i in indices)
         except Exception as e:
             logger.exception(f"fail to rank results for {e}")

metagpt/actions/write_tutorial.py

@@ -6,12 +6,12 @@
 @File    : tutorial_assistant.py
 @Describe : Actions of the tutorial assistant, including writing directories and document content.
 """
-import json
+
 from typing import Dict
 
 from metagpt.actions import Action
-from metagpt.logs import logger
 from metagpt.prompts.tutorial_assistant import DIRECTORY_PROMPT, CONTENT_PROMPT
+from metagpt.utils.common import OutputParser
 
 
 class WriteDirectory(Action):
@@ -26,33 +26,6 @@ class WriteDirectory(Action):
         super().__init__(name, *args, **kwargs)
         self.language = language
 
-    @staticmethod
-    async def _handle_resp(resp: str) -> Dict:
-        """Process string results and convert them to JSON format.
-
-        Args:
-            resp: The directory results returned by gpt.
-
-        Returns:
-            The parsed dictionary, such as {"title": "xxx", "directory": [{"dir 1": ["sub dir 1", "sub dir 2"]}]}.
-
-        Raises:
-            Exception: If no matching dictionary section is found.
-            json.JSONDecodeError: If the dictionary part cannot be parsed as JSON.
-        """
-        start = resp.find('{')
-        end = resp.rfind('}')
-        if start != -1 and end != -1 and end > start:
-            directory_str = resp[start:end + 1]
-            logger.info(f"Successfully parsed json: {str(directory_str)}")
-            try:
-                return json.loads(directory_str)
-            except json.JSONDecodeError as e:
-                logger.error(f"Json parsing error: {e}")
-                raise e
-        else:
-            raise Exception("No matching dictionary section found.")
-
     async def run(self, topic: str, *args, **kwargs) -> Dict:
         """Execute the action to generate a tutorial directory according to the topic.
 
@@ -64,7 +37,7 @@ class WriteDirectory(Action):
         """
         prompt = DIRECTORY_PROMPT.format(topic=topic, language=self.language)
         resp = await self._aask(prompt=prompt)
-        return await self._handle_resp(resp)
+        return OutputParser.extract_struct(resp, dict)
 
 
 class WriteContent(Action):

metagpt/const.py

@@ -12,9 +12,11 @@ def get_project_root():
     """Search upwards to find the project root directory."""
     current_path = Path.cwd()
     while True:
-        if (current_path / '.git').exists() or \
-           (current_path / '.project_root').exists() or \
-           (current_path / '.gitignore').exists():
+        if (
+            (current_path / ".git").exists()
+            or (current_path / ".project_root").exists()
+            or (current_path / ".gitignore").exists()
+        ):
             return current_path
         parent_path = current_path.parent
         if parent_path == current_path:
@@ -23,16 +25,18 @@ def get_project_root():
 
 
 PROJECT_ROOT = get_project_root()
-DATA_PATH = PROJECT_ROOT / 'data'
-WORKSPACE_ROOT = PROJECT_ROOT / 'workspace'
-PROMPT_PATH = PROJECT_ROOT / 'metagpt/prompts'
-UT_PATH = PROJECT_ROOT / 'data/ut'
+DATA_PATH = PROJECT_ROOT / "data"
+WORKSPACE_ROOT = PROJECT_ROOT / "workspace"
+PROMPT_PATH = PROJECT_ROOT / "metagpt/prompts"
+UT_PATH = PROJECT_ROOT / "data/ut"
 SWAGGER_PATH = UT_PATH / "files/api/"
 UT_PY_PATH = UT_PATH / "files/ut/"
 API_QUESTIONS_PATH = UT_PATH / "files/question/"
 YAPI_URL = "http://yapi.deepwisdomai.com/"
-TMP = PROJECT_ROOT / 'tmp'
+TMP = PROJECT_ROOT / "tmp"
 RESEARCH_PATH = DATA_PATH / "research"
 TUTORIAL_PATH = DATA_PATH / "tutorial_docx"
 
+SKILL_DIRECTORY = PROJECT_ROOT / "metagpt/skills"
+
 MEM_TTL = 24 * 30 * 3600

metagpt/roles/sk_agent.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/9/13 12:23
+@Author  : femto Zheng
+@File    : sk_agent.py
+"""
+from semantic_kernel.planning import SequentialPlanner
+from semantic_kernel.planning.action_planner.action_planner import ActionPlanner
+from semantic_kernel.planning.basic_planner import BasicPlanner
+
+from metagpt.actions import BossRequirement
+from metagpt.actions.execute_task import ExecuteTask
+from metagpt.logs import logger
+from metagpt.roles import Role
+from metagpt.schema import Message
+from metagpt.utils.make_sk_kernel import make_sk_kernel
+
+
+class SkAgent(Role):
+    """
+    Represents an SkAgent implemented using semantic kernel
+
+    Attributes:
+        name (str): Name of the SkAgent.
+        profile (str): Role profile, default is 'sk_agent'.
+        goal (str): Goal of the SkAgent.
+        constraints (str): Constraints for the SkAgent.
+    """
+
+    def __init__(
+        self,
+        name: str = "Sunshine",
+        profile: str = "sk_agent",
+        goal: str = "Execute task based on passed in task description",
+        constraints: str = "",
+        planner_cls=BasicPlanner,
+    ) -> None:
+        """Initializes the Engineer role with given attributes."""
+        super().__init__(name, profile, goal, constraints)
+        self._init_actions([ExecuteTask()])
+        self._watch([BossRequirement])
+        self.kernel = make_sk_kernel()
+
+        # how funny the interface is inconsistent
+        if planner_cls == BasicPlanner:
+            self.planner = planner_cls()
+        elif planner_cls in [SequentialPlanner, ActionPlanner]:
+            self.planner = planner_cls(self.kernel)
+        else:
+            raise f"Unsupported planner of type {planner_cls}"
+
+        self.import_semantic_skill_from_directory = self.kernel.import_semantic_skill_from_directory
+        self.import_skill = self.kernel.import_skill
+
+    async def _think(self) -> None:
+        self._set_state(0)
+        # how funny the interface is inconsistent
+        if isinstance(self.planner, BasicPlanner):
+            self.plan = await self.planner.create_plan_async(self._rc.important_memory[-1].content, self.kernel)
+            logger.info(self.plan.generated_plan)
+        elif any(isinstance(self.planner, cls) for cls in [SequentialPlanner, ActionPlanner]):
+            self.plan = await self.planner.create_plan_async(self._rc.important_memory[-1].content)
+
+    async def _act(self) -> Message:
+        # how funny the interface is inconsistent
+        if isinstance(self.planner, BasicPlanner):
+            result = await self.planner.execute_plan_async(self.plan, self.kernel)
+        elif any(isinstance(self.planner, cls) for cls in [SequentialPlanner, ActionPlanner]):
+            result = (await self.plan.invoke_async()).result
+        logger.info(result)
+
+        msg = Message(content=result, role=self.profile, cause_by=type(self._rc.todo))
+        self._rc.memory.add(msg)
+        # logger.debug(f"{response}")
+        return msg

metagpt/skills/SummarizeSkill/MakeAbstractReadable/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Given a scientific white paper abstract, rewrite it to make it more readable",
+  "completion": {
+    "max_tokens": 4000,
+    "temperature": 0.0,
+    "top_p": 1.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 2.0
+  }
+}

metagpt/skills/SummarizeSkill/MakeAbstractReadable/skprompt.txt

@@ -0,0 +1,5 @@
+{{$input}}
+
+==
+Summarize, using a user friendly, using simple grammar. Don't use subjects like "we" "our" "us" "your".
+==

metagpt/skills/SummarizeSkill/Notegen/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Automatically generate compact notes for any text or text document.",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/SummarizeSkill/Notegen/skprompt.txt

@@ -0,0 +1,21 @@
+Analyze the following extract taken from a document. 
+- Produce key points for memory. 
+- Give memory a name. 
+- Extract only points worth remembering. 
+- Be brief. Conciseness is very important.  
+- Use broken English. 
+You will use this memory to analyze the rest of this document, and for other relevant tasks. 
+
+[Input]
+My name is Macbeth. I used to be King of Scotland, but I died. My wife's name is Lady Macbeth and we were married for 15 years. We had no children. Our beloved dog Toby McDuff was a famous hunter of rats in the forest.
+My story was immortalized by Shakespeare in a play.
++++++
+Family History
+- Macbeth, King Scotland
+- Wife Lady Macbeth, No Kids
+- Dog Toby McDuff. Hunter, dead. 
+- Shakespeare play
+
+[Input]
+[[{{$input}}]]
++++++

metagpt/skills/SummarizeSkill/Summarize/config.json

@@ -0,0 +1,21 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Summarize given text or any text document",
+  "completion": {
+    "max_tokens": 512,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "Text to summarize",
+        "defaultValue": ""
+      }
+    ]
+  }
+}

metagpt/skills/SummarizeSkill/Summarize/skprompt.txt

@@ -0,0 +1,23 @@
+[SUMMARIZATION RULES]
+DONT WASTE WORDS
+USE SHORT, CLEAR, COMPLETE SENTENCES.
+DO NOT USE BULLET POINTS OR DASHES.
+USE ACTIVE VOICE.
+MAXIMIZE DETAIL, MEANING
+FOCUS ON THE CONTENT
+
+[BANNED PHRASES]
+This article
+This document
+This page
+This material
+[END LIST]
+
+Summarize:
+Hello how are you?
++++++
+Hello
+
+Summarize this
+{{$input}}
++++++

metagpt/skills/SummarizeSkill/Topics/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Analyze given text or document and extract key topics worth remembering",
+  "completion": {
+    "max_tokens": 128,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/SummarizeSkill/Topics/skprompt.txt

@@ -0,0 +1,28 @@
+Analyze the following extract taken from a document and extract key topics.
+- Topics only worth remembering.
+- Be brief. Short phrases.
+- Can use broken English.
+- Conciseness is very important.
+- Topics can include names of memories you want to recall.
+- NO LONG SENTENCES. SHORT PHRASES.
+- Return in JSON
+[Input]
+My name is Macbeth. I used to be King of Scotland, but I died. My wife's name is Lady Macbeth and we were married for 15 years. We had no children. Our beloved dog Toby McDuff was a famous hunter of rats in the forest.
+My tragic story was immortalized by Shakespeare in a play.
+[Output]
+{
+  "topics": [
+    "Macbeth",
+    "King of Scotland",
+    "Lady Macbeth",
+    "Dog",
+    "Toby McDuff",
+    "Shakespeare",
+    "Play",
+    "Tragedy"
+  ]
+}
++++++
+[Input]
+{{$input}}
+[Output]

metagpt/skills/WriterSkill/Acronym/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Generate an acronym for the given concept or phrase",
+  "completion": {
+    "max_tokens": 100,
+    "temperature": 0.5,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/Acronym/skprompt.txt

@@ -0,0 +1,25 @@
+Generate a suitable acronym pair for the concept. Creativity is encouraged, including obscure references. 
+The uppercase letters in the acronym expansion must agree with the letters of the acronym
+
+Q: A technology for detecting moving objects, their distance and velocity using radio waves.  
+A: R.A.D.A.R: RAdio Detection And Ranging. 
+
+Q: A weapon that uses high voltage electricity to incapacitate the target
+A. T.A.S.E.R:  Thomas A. Swift’s Electric Rifle
+
+Q: Equipment that lets a diver breathe underwater 
+A: S.C.U.B.A: Self Contained Underwater Breathing Apparatus.
+
+Q: Reminder not to complicated subject matter.
+A. K.I.S.S: Keep It Simple Stupid 
+
+Q: A national organization for investment in space travel, rockets, space ships, space exploration
+A. N.A.S.A: National Aeronautics Space Administration
+
+Q: Agreement that governs trade among North American countries.
+A: N.A.F.T.A: North American Free Trade Agreement. 
+
+Q: Organization to protect the freedom and security of its member countries in North America and Europe.
+A: N.A.T.O: North Atlantic Treaty Organization.  
+
+Q:{{$input}}

metagpt/skills/WriterSkill/AcronymGenerator/config.json

@@ -0,0 +1,15 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Given a request to generate an acronym from a string, generate an acronym and provide the acronym explanation.",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.7,
+    "top_p": 1.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0,
+    "stop_sequences": [
+      "#"
+    ]
+  }
+}

metagpt/skills/WriterSkill/AcronymGenerator/skprompt.txt

@@ -0,0 +1,54 @@
+# Name of a super artificial intelligence
+J.A.R.V.I.S. = Just A Really Very Intelligent System.
+# Name for a new young beautiful assistant
+F.R.I.D.A.Y. = Female Replacement Intelligent Digital Assistant Youth.
+# Mirror to check what's behind
+B.A.R.F. = Binary Augmented Retro-Framing.
+# Pair of powerful glasses created by a genius that is now dead
+E.D.I.T.H. = Even Dead I’m The Hero.
+# A company building and selling computers
+I.B.M. = Intelligent Business Machine.
+# A super computer that is sentient.
+H.A.L = Heuristically programmed ALgorithmic computer.
+# an intelligent bot that helps with productivity.
+C.O.R.E. = Central Optimization Routines and Efficiency.
+# an intelligent bot that helps with productivity.
+P.A.L. = Personal Assistant Light.
+# an intelligent bot that helps with productivity.
+A.I.D.A. = Artificial Intelligence Digital Assistant.
+# an intelligent bot that helps with productivity.
+H.E.R.A. = Human Emulation and Recognition Algorithm.
+# an intelligent bot that helps with productivity.
+I.C.A.R.U.S. = Intelligent Control and Automation of Research and Utility Systems.
+# an intelligent bot that helps with productivity.
+N.E.M.O. = Networked Embedded Multiprocessor Orchestration.
+# an intelligent bot that helps with productivity.
+E.P.I.C. = Enhanced Productivity and Intelligence through Computing.
+# an intelligent bot that helps with productivity.
+M.A.I.A. = Multipurpose Artificial Intelligence Assistant.
+# an intelligent bot that helps with productivity.
+A.R.I.A. = Artificial Reasoning and Intelligent Assistant.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+O.M.E.G.A. = Optimized Mathematical Entity for Generalized Artificial intelligence.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+P.Y.T.H.O.N. = Precise Yet Thorough Heuristic Optimization Network.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+A.P.O.L.L.O. = Adaptive Probabilistic Optimization Learning Library for Online Applications.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+S.O.L.I.D. = Self-Organizing Logical Intelligent Data-base.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+D.E.E.P. = Dynamic Estimation and Prediction.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+B.R.A.I.N. = Biologically Realistic Artificial Intelligence Network.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+C.O.G.N.I.T.O. = COmputational and Generalized INtelligence TOolkit.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+S.A.G.E. = Symbolic Artificial General Intelligence Engine.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+Q.U.A.R.K. = Quantum Universal Algorithmic Reasoning Kernel.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+S.O.L.V.E. = Sophisticated Operational Logic and Versatile Expertise.
+# An incredibly smart entity developed with complex math, that helps me being more productive.
+C.A.L.C.U.L.U.S. = Cognitively Advanced Logic and Computation Unit for Learning and Understanding Systems.
+
+# {{$INPUT}}

metagpt/skills/WriterSkill/AcronymReverse/config.json

@@ -0,0 +1,15 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Given a single word or acronym, generate the expanded form matching the acronym letters.",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.5,
+    "top_p": 1.0,
+    "presence_penalty": 0.8,
+    "frequency_penalty": 0.0,
+    "stop_sequences": [
+      "#END#"
+    ]
+  }
+}

metagpt/skills/WriterSkill/AcronymReverse/skprompt.txt

@@ -0,0 +1,24 @@
+# acronym: Devis
+Sentences matching the acronym:
+1. Dragons Eat Very Interesting Snacks
+2. Develop Empathy and Vision to Increase Success
+3. Don't Expect Vampires In Supermarkets
+#END#
+
+# acronym: Christmas
+Sentences matching the acronym:
+1. Celebrating Harmony and Respect in a Season of Togetherness, Merriment, and True joy
+2. Children Have Real Interest Since The Mystery And Surprise Thrills
+3. Christmas Helps Reduce Inner Stress Through Mistletoe And Sleigh excursions
+#END#
+
+# acronym: noWare
+Sentences matching the acronym:
+1. No One Wants an App that Randomly Erases everything
+2. Nourishing Oatmeal With Almond, Raisin, and Egg toppings
+3. Notice Opportunity When Available and React Enthusiastically
+#END#
+
+Reverse the following acronym back to a funny sentence. Provide 3 examples.
+# acronym: {{$INPUT}}
+Sentences matching the acronym:

metagpt/skills/WriterSkill/Brainstorm/config.json

@@ -0,0 +1,22 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Given a goal or topic description generate a list of ideas",
+  "completion": {
+    "max_tokens": 2000,
+    "temperature": 0.5,
+    "top_p": 1.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0,
+    "stop_sequences": ["##END##"]
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "A topic description or goal.",
+        "defaultValue": ""
+      }
+    ]
+  }
+}

metagpt/skills/WriterSkill/Brainstorm/skprompt.txt

@@ -0,0 +1,8 @@
+Must: brainstorm ideas and create a list.
+Must: use a numbered list.
+Must: only one list.
+Must: end list with ##END##
+Should: no more than 10 items.
+Should: at least 3 items.
+Topic: {{$INPUT}}
+Start.

metagpt/skills/WriterSkill/EmailGen/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Write an email from the given bullet points",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/EmailGen/skprompt.txt

@@ -0,0 +1,16 @@
+Rewrite my bullet points into complete sentences. Use a polite and inclusive tone.  
+
+[Input]
+- Macbeth, King Scotland
+- Married, Wife Lady Macbeth, No Kids
+- Dog Toby McDuff. Hunter, dead. 
+- Shakespeare play
++++++
+The story of Macbeth
+My name is Macbeth. I used to be King of Scotland, but I died. My wife's name is Lady Macbeth and we were married for 15 years. We had no children. Our beloved dog Toby McDuff was a famous hunter of rats in the forest.
+My story was immortalized by Shakespeare in a play.
+
++++++
+[Input]
+{{$input}}
++++++

metagpt/skills/WriterSkill/EmailTo/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Turn bullet points into an email to someone, using a polite tone",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/EmailTo/skprompt.txt

@@ -0,0 +1,31 @@
+Rewrite my bullet points into an email featuring complete sentences. Use a polite and inclusive tone.  
+
+[Input]
+Toby,
+
+- Macbeth, King Scotland
+- Married, Wife Lady Macbeth, No Kids
+- Dog Toby McDuff. Hunter, dead. 
+- Shakespeare play
+
+Thanks,
+Dexter
+
++++++
+Hi Toby,
+
+The story of Macbeth
+My name is Macbeth. I used to be King of Scotland, but I died. My wife's name is Lady Macbeth and we were married for 15 years. We had no children. Our beloved dog Toby McDuff was a famous hunter of rats in the forest.
+My story was immortalized by Shakespeare in a play.
+
+Thanks,
+Dexter
+
++++++
+[Input]
+{{$to}}
+{{$input}}
+
+Thanks,
+{{$sender}}
++++++

metagpt/skills/WriterSkill/EnglishImprover/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Translate text to English and improve it",
+  "completion": {
+    "max_tokens": 3000,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/EnglishImprover/skprompt.txt

@@ -0,0 +1,11 @@
+I want you to act as an English translator, spelling corrector and improver.
+I will speak to you in any language and you will detect the language, translate it and answer in the corrected and improved version of my text, in English.
+I want you to replace my simplified A0-level words and sentences with more beautiful and elegant, upper level English words and sentences.
+Keep the meaning same, but make them more literary.
+I want you to only reply the correction, the improvements and nothing else, do not write explanations.
+
+Sentence: """
+{{$INPUT}}
+"""
+
+Translation:

metagpt/skills/WriterSkill/NovelChapter/config.json

@@ -0,0 +1,36 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Write a chapter of a novel.",
+  "completion": {
+    "max_tokens": 2048,
+    "temperature": 0.3,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "A synopsis of what the chapter should be about.",
+        "defaultValue": ""
+      },
+      {
+        "name": "theme",
+        "description": "The theme or topic of this novel.",
+        "defaultValue": ""
+      },
+      {
+        "name": "previousChapter",
+        "description": "The synopsis of the previous chapter.",
+        "defaultValue": ""
+      },
+      {
+        "name": "chapterIndex",
+        "description": "The number of the chapter to write.",
+        "defaultValue": "<!--===ENDPART===-->"
+      }
+    ]
+  }
+}

metagpt/skills/WriterSkill/NovelChapter/skprompt.txt

@@ -0,0 +1,20 @@
+[CONTEXT]
+
+THEME OF STORY:
+{{$theme}}
+
+PREVIOUS CHAPTER:
+{{$previousChapter}}
+
+[END CONTEXT]
+
+
+WRITE THIS CHAPTER USING [CONTEXT] AND
+CHAPTER SYNOPSIS. DO NOT REPEAT SYNOPSIS IN THE OUTPUT
+
+Chapter Synopsis:
+{{$input}}
+
+Chapter {{$chapterIndex}}
+
+

metagpt/skills/WriterSkill/NovelChapterWithNotes/config.json

@@ -0,0 +1,41 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Write a chapter of a novel using notes about the chapter to write.",
+  "completion": {
+    "max_tokens": 1024,
+    "temperature": 0.5,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "What the novel should be about.",
+        "defaultValue": ""
+      },
+      {
+        "name": "theme",
+        "description": "The theme of this novel.",
+        "defaultValue": ""
+      },
+      {
+        "name": "notes",
+        "description": "Notes useful to write this chapter.",
+        "defaultValue": ""
+      },
+      {
+        "name": "previousChapter",
+        "description": "The previous chapter synopsis.",
+        "defaultValue": ""
+      },
+      {
+        "name": "chapterIndex",
+        "description": "The number of the chapter to write.",
+        "defaultValue": ""
+      }
+    ]
+  }
+}

metagpt/skills/WriterSkill/NovelChapterWithNotes/skprompt.txt

@@ -0,0 +1,19 @@
+[CONTEXT]
+
+THEME OF STORY:
+{{$theme}}
+
+NOTES OF STORY SO FAR - USE AS REFERENCE
+{{$notes}}
+
+PREVIOUS CHAPTER, USE AS REFERENCE:
+{{$previousChapter}}
+
+[END CONTEXT]
+
+
+WRITE THIS CHAPTER CONTINUING STORY, USING [CONTEXT] AND CHAPTER SYNOPSIS BELOW. DO NOT REPEAT SYNOPSIS IN THE CHAPTER. DON'T REPEAT PREVIOUS CHAPTER.
+
+{{$input}}
+
+Chapter {{$chapterIndex}}

metagpt/skills/WriterSkill/NovelOutline/config.json

@@ -0,0 +1,31 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Generate a list of chapter synopsis for a novel or novella",
+  "completion": {
+    "max_tokens": 2048,
+    "temperature": 0.1,
+    "top_p": 0.5,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "What the novel should be about.",
+        "defaultValue": ""
+      },
+      {
+        "name": "chapterCount",
+        "description": "The number of chapters to generate.",
+        "defaultValue": ""
+      },
+      {
+        "name": "endMarker",
+        "description": "The marker to use to end each chapter.",
+        "defaultValue": "<!--===ENDPART===-->"
+      }
+    ]
+  }
+}

metagpt/skills/WriterSkill/NovelOutline/skprompt.txt

@@ -0,0 +1,12 @@
+I want to write a {{$chapterCount}} chapter novella about:
+{{$input}}
+
+There MUST BE {{$chapterCount}} CHAPTERS.
+
+INVENT CHARACTERS AS YOU SEE FIT. BE HIGHLY CREATIVE AND/OR FUNNY.
+WRITE SYNOPSIS FOR EACH CHAPTER. INCLUDE INFORMATION ABOUT CHARACTERS ETC. SINCE EACH
+CHAPTER WILL BE WRITTEN BY A DIFFERENT WRITER, YOU MUST INCLUDE ALL PERTINENT INFORMATION
+IN EACH SYNOPSIS
+
+YOU MUST END EACH SYNOPSIS WITH {{$endMarker}}
+

metagpt/skills/WriterSkill/Rewrite/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Automatically generate compact notes for any text or text document",
+  "completion": {
+    "max_tokens": 256,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/Rewrite/skprompt.txt

@@ -0,0 +1,6 @@
+Rewrite the given text like it was written in this style or by: {{$style}}. 
+MUST RETAIN THE MEANING AND FACTUAL CONTENT AS THE ORIGINAL.
+
+
+{{$input}}
+

metagpt/skills/WriterSkill/ShortPoem/config.json

@@ -0,0 +1,21 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Turn a scenario into a short and entertaining poem.",
+  "completion": {
+    "max_tokens": 60,
+    "temperature": 0.5,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  },
+  "input": {
+    "parameters": [
+      {
+        "name": "input",
+        "description": "The scenario to turn into a poem.",
+        "defaultValue": ""
+      }
+    ]
+  }
+}

metagpt/skills/WriterSkill/ShortPoem/skprompt.txt

@@ -0,0 +1,2 @@
+Generate a short funny poem or limerick to explain the given event. Be creative and be funny. Let your imagination run wild.
+Event:{{$input}}

metagpt/skills/WriterSkill/StoryGen/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Generate a list of synopsis for a novel or novella with sub-chapters",
+  "completion": {
+    "max_tokens": 250,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/StoryGen/skprompt.txt

@@ -0,0 +1,10 @@
+ONLY USE XML TAGS IN THIS LIST: 
+[XML TAG LIST]
+list: Surround any lists with this tag
+synopsis: An outline of the chapter to write 
+[END LIST]
+
+EMIT WELL FORMED XML ALWAYS. Code should be CDATA. 
+
+
+{{$input}}

metagpt/skills/WriterSkill/TellMeMore/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Summarize given text or any text document",
+  "completion": {
+    "max_tokens": 500,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/TellMeMore/skprompt.txt

@@ -0,0 +1,7 @@
+>>>>>The following is part of a {{$conversationtype}}.
+{{$input}}
+
+>>>>>The following is an overview of a previous part of the {{$conversationtype}}, focusing on "{{$focusarea}}".
+{{$previousresults}}
+
+>>>>>In 250 words or less, write a verbose and detailed overview of the {{$conversationtype}} focusing solely on "{{$focusarea}}".

metagpt/skills/WriterSkill/Translate/config.json

@@ -0,0 +1,15 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Translate the input into a language of your choice",
+  "completion": {
+    "max_tokens": 2000,
+    "temperature": 0.7,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0,
+    "stop_sequences": [
+      "[done]"
+    ]
+  }
+}

metagpt/skills/WriterSkill/Translate/skprompt.txt

@@ -0,0 +1,7 @@
+Translate the input below into {{$language}}
+
+MAKE SURE YOU ONLY USE {{$language}}.
+
+{{$input}}
+
+Translation:

metagpt/skills/WriterSkill/TwoSentenceSummary/config.json

@@ -0,0 +1,12 @@
+{
+  "schema": 1,
+  "type": "completion",
+  "description": "Summarize given text in two sentences or less",
+  "completion": {
+    "max_tokens": 100,
+    "temperature": 0.0,
+    "top_p": 0.0,
+    "presence_penalty": 0.0,
+    "frequency_penalty": 0.0
+  }
+}

metagpt/skills/WriterSkill/TwoSentenceSummary/skprompt.txt

@@ -0,0 +1,4 @@
+Summarize the following text in two sentences or less. 
+[BEGIN TEXT]
+{{$input}}
+[END TEXT]

metagpt/tools/code_interpreter.py

@@ -0,0 +1,129 @@
+import re
+from typing import List, Callable
+from pathlib import Path
+
+import wrapt
+import textwrap
+import inspect
+from interpreter.interpreter import Interpreter
+
+from metagpt.logs import logger
+from metagpt.config import CONFIG
+from metagpt.utils.highlight import highlight
+from metagpt.actions.clone_function import CloneFunction, run_function_code, run_function_script
+
+
+def extract_python_code(code: str):
+    """Extract code blocks: If the code comments are the same, only the last code block is kept."""
+    # Use regular expressions to match comment blocks and related code.
+    pattern = r'(#\s[^\n]*)\n(.*?)(?=\n\s*#|$)'
+    matches = re.findall(pattern, code, re.DOTALL)
+
+    # Extract the last code block when encountering the same comment.
+    unique_comments = {}
+    for comment, code_block in matches:
+        unique_comments[comment] = code_block
+
+    # concatenate into functional form
+    result_code = '\n'.join([f"{comment}\n{code_block}" for comment, code_block in unique_comments.items()])
+    header_code = code[:code.find("#")]
+    code = header_code + result_code
+
+    logger.info(f"Extract python code: \n {highlight(code)}")
+
+    return code
+
+
+class OpenCodeInterpreter(object):
+    """https://github.com/KillianLucas/open-interpreter"""
+    def __init__(self, auto_run: bool = True) -> None:
+        interpreter = Interpreter()
+        interpreter.auto_run = auto_run
+        interpreter.model = CONFIG.openai_api_model or "gpt-3.5-turbo"
+        interpreter.api_key = CONFIG.openai_api_key
+        interpreter.api_base = CONFIG.openai_api_base
+        self.interpreter = interpreter
+
+    def chat(self, query: str, reset: bool = True):
+        if reset:
+            self.interpreter.reset()
+        return self.interpreter.chat(query, return_messages=True)
+
+    @staticmethod
+    def extract_function(query_respond: List, function_name: str, *, language: str = 'python',
+                         function_format: str = None) -> str:
+        """create a function from query_respond."""
+        if language not in ('python'):
+            raise NotImplementedError(f"Not support to parse language {language}!")
+
+        # set function form
+        if function_format is None:
+            assert language == 'python', f"Expect python language for default function_format, but got {language}."
+            function_format = """def {function_name}():\n{code}"""
+        # Extract the code module in the open-interpreter respond message.
+        code = [item['function_call']['parsed_arguments']['code'] for item in query_respond
+                if "function_call" in item
+                and "parsed_arguments" in item["function_call"]
+                and 'language' in item["function_call"]['parsed_arguments']
+                and item["function_call"]['parsed_arguments']['language'] == language]
+        # add indent.
+        indented_code_str = textwrap.indent("\n".join(code), ' ' * 4)
+        # Return the code after deduplication.
+        if language == "python":
+            return extract_python_code(function_format.format(function_name=function_name, code=indented_code_str))
+
+
+def gen_query(func: Callable, args, kwargs) -> str:
+    # Get the annotation of the function as part of the query.
+    desc = func.__doc__
+    signature = inspect.signature(func)
+    # Get the signature of the wrapped function and the assignment of the input parameters as part of the query.
+    bound_args = signature.bind(*args, **kwargs)
+    bound_args.apply_defaults()
+    query = f"{desc}, {bound_args.arguments}, If you must use a third-party package, use the most popular ones, for example: pandas, numpy, ta, ..."
+    return query
+
+
+def gen_template_fun(func: Callable) -> str:
+    return f"def {func.__name__}{str(inspect.signature(func))}\n    # here is your code ..."
+
+
+class OpenInterpreterDecorator(object):
+    def __init__(self, save_code: bool = False, code_file_path: str = None, clear_code: bool = False) -> None:
+        self.save_code = save_code
+        self.code_file_path = code_file_path
+        self.clear_code = clear_code
+
+    def __call__(self, wrapped):
+        @wrapt.decorator
+        async def wrapper(wrapped: Callable, instance, args, kwargs):
+            # Get the decorated function name.
+            func_name = wrapped.__name__
+            # If the script exists locally and clearcode is not required, execute the function from the script.
+            if Path(self.code_file_path).is_file() and not self.clear_code:
+                return run_function_script(self.code_file_path, func_name, *args, **kwargs)
+
+            # Auto run generate code by using open-interpreter.
+            interpreter = OpenCodeInterpreter()
+            query = gen_query(wrapped, args, kwargs)
+            logger.info(f"query for OpenCodeInterpreter: \n {query}")
+            respond = interpreter.chat(query)
+            # Assemble the code blocks generated by open-interpreter into a function without parameters.
+            func_code = interpreter.extract_function(respond, func_name)
+            # Clone the `func_code` into wrapped, that is,
+            # keep the `func_code` and wrapped functions with the same input parameter and return value types.
+            template_func = gen_template_fun(wrapped)
+            cf = CloneFunction()
+            code = await cf.run(template_func=template_func, source_code=func_code)
+            # Display the generated function in the terminal.
+            logger_code = highlight(code, "python")
+            logger.info(f"Creating following Python function:\n{logger_code}")
+            # execute this function.
+            try:
+                res = run_function_code(code, func_name, *args, **kwargs)
+                if self.save_code:
+                    cf._save(self.code_file_path, code)
+            except Exception as e:
+                raise Exception("Could not evaluate Python code", e)
+            return res
+        return wrapper(wrapped)

metagpt/tools/search_engine.py

@@ -5,15 +5,32 @@
 @Author  : alexanderwu
 @File    : search_engine.py
 """
-from __future__ import annotations
+# from __future__ import annotations
 
 import importlib
 from typing import Callable, Coroutine, Literal, overload
 
+from semantic_kernel.skill_definition import sk_function
+
 from metagpt.config import CONFIG
 from metagpt.tools import SearchEngineType
 
 
+class SkSearchEngine:
+    def __init__(self):
+        self.search_engine = SearchEngine()
+
+    @sk_function(
+        description="searches results from Google. Useful when you need to find short "
+        "and succinct answers about a specific topic. Input should be a search query.",
+        name="searchAsync",
+        input_description="search",
+    )
+    async def run(self, query: str) -> str:
+        result = await self.search_engine.run(query)
+        return result
+
+
 class SearchEngine:
     """Class representing a search engine.
 
@@ -25,6 +42,7 @@ class SearchEngine:
         run_func: The function to run the search.
         engine: The search engine type.
     """
+
     def __init__(
         self,
         engine: SearchEngineType | None = None,
@@ -33,7 +51,7 @@ class SearchEngine:
         engine = engine or CONFIG.search_engine
         if engine == SearchEngineType.SERPAPI_GOOGLE:
             module = "metagpt.tools.search_engine_serpapi"
-            run_func = importlib.import_module(module).SerpAPIWrapper().run            
+            run_func = importlib.import_module(module).SerpAPIWrapper().run
         elif engine == SearchEngineType.SERPER_GOOGLE:
             module = "metagpt.tools.search_engine_serper"
             run_func = importlib.import_module(module).SerperWrapper().run

metagpt/utils/common.py

@@ -11,7 +11,7 @@ import inspect
 import os
 import platform
 import re
-from typing import List, Tuple
+from typing import List, Tuple, Union
 
 from metagpt.logs import logger
 
@@ -150,6 +150,53 @@ class OutputParser:
             parsed_data[block] = content
         return parsed_data
 
+    @classmethod
+    def extract_struct(cls, text: str, data_type: Union[type(list), type(dict)]) -> Union[list, dict]:
+        """Extracts and parses a specified type of structure (dictionary or list) from the given text.
+        The text only contains a list or dictionary, which may have nested structures.
+
+        Args:
+            text: The text containing the structure (dictionary or list).
+            data_type: The data type to extract, can be "list" or "dict".
+
+        Returns:
+            - If extraction and parsing are successful, it returns the corresponding data structure (list or dictionary).
+            - If extraction fails or parsing encounters an error, it throw an exception.
+
+        Examples:
+            >>> text = 'xxx [1, 2, ["a", "b", [3, 4]], {"x": 5, "y": [6, 7]}] xxx'
+            >>> result_list = OutputParser.extract_struct(text, "list")
+            >>> print(result_list)
+            >>> # Output: [1, 2, ["a", "b", [3, 4]], {"x": 5, "y": [6, 7]}]
+
+            >>> text = 'xxx {"x": 1, "y": {"a": 2, "b": {"c": 3}}} xxx'
+            >>> result_dict = OutputParser.extract_struct(text, "dict")
+            >>> print(result_dict)
+            >>> # Output: {"x": 1, "y": {"a": 2, "b": {"c": 3}}}
+        """
+        # Find the first "[" or "{" and the last "]" or "}"
+        start_index = text.find("[" if data_type is list else "{")
+        end_index = text.rfind("]" if data_type is list else "}")
+
+        if start_index != -1 and end_index != -1:
+            # Extract the structure part
+            structure_text = text[start_index:end_index + 1]
+
+            try:
+                # Attempt to convert the text to a Python data type using ast.literal_eval
+                result = ast.literal_eval(structure_text)
+
+                # Ensure the result matches the specified data type
+                if isinstance(result, list) or isinstance(result, dict):
+                    return result
+
+                raise ValueError(f"The extracted structure is not a {data_type}.")
+
+            except (ValueError, SyntaxError) as e:
+                raise Exception(f"Error while extracting and parsing the {data_type}: {e}")
+        else:
+            raise Exception(f"No {data_type} found in the text.")
+
 
 class CodeParser:
     @classmethod

metagpt/utils/file.py

@@ -15,6 +15,8 @@ from metagpt.logs import logger
 class File:
     """A general util for file operations."""
 
+    CHUNK_SIZE = 64 * 1024
+
     @classmethod
     async def write(cls, root_path: Path, filename: str, content: bytes) -> Path:
         """Write the file content to the local specified path.
@@ -35,8 +37,39 @@ class File:
             full_path = root_path / filename
             async with aiofiles.open(full_path, mode="wb") as writer:
                 await writer.write(content)
-                logger.info(f"Successfully write file: {full_path}")
+                logger.debug(f"Successfully write file: {full_path}")
                 return full_path
         except Exception as e:
             logger.error(f"Error writing file: {e}")
-            raise e
+            raise e
+
+    @classmethod
+    async def read(cls, file_path: Path, chunk_size: int = None) -> bytes:
+        """Partitioning read the file content from the local specified path.
+
+        Args:
+            file_path: The full file name of file, such as "/data/test.txt".
+            chunk_size: The size of each chunk in bytes (default is 64kb).
+
+        Returns:
+            The binary content of file.
+
+        Raises:
+            Exception: If an unexpected error occurs during the file reading process.
+        """
+        try:
+            chunk_size = chunk_size or cls.CHUNK_SIZE
+            async with aiofiles.open(file_path, mode="rb") as reader:
+                chunks = list()
+                while True:
+                    chunk = await reader.read(chunk_size)
+                    if not chunk:
+                        break
+                    chunks.append(chunk)
+                content = b''.join(chunks)
+                logger.debug(f"Successfully read file, the path of file: {file_path}")
+                return content
+        except Exception as e:
+            logger.error(f"Error reading file: {e}")
+            raise e
+

metagpt/utils/highlight.py

@@ -0,0 +1,25 @@
+# 添加代码语法高亮显示
+from pygments import highlight as highlight_
+from pygments.lexers import PythonLexer, SqlLexer
+from pygments.formatters import TerminalFormatter, HtmlFormatter
+
+
+def highlight(code: str, language: str = 'python', formatter: str = 'terminal'):
+    # 指定要高亮的语言
+    if language.lower() == 'python':
+        lexer = PythonLexer()
+    elif language.lower() == 'sql':
+        lexer = SqlLexer()
+    else:
+        raise ValueError(f"Unsupported language: {language}")
+
+    # 指定输出格式
+    if formatter.lower() == 'terminal':
+        formatter = TerminalFormatter()
+    elif formatter.lower() == 'html':
+        formatter = HtmlFormatter()
+    else:
+        raise ValueError(f"Unsupported formatter: {formatter}")
+
+    # 使用 Pygments 高亮代码片段
+    return highlight_(code, lexer, formatter)

metagpt/utils/make_sk_kernel.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2023/9/13 12:29
+@Author  : femto Zheng
+@File    : make_sk_kernel.py
+"""
+import semantic_kernel as sk
+from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import (
+    AzureChatCompletion,
+)
+from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion import (
+    OpenAIChatCompletion,
+)
+
+from metagpt.config import CONFIG
+
+
+def make_sk_kernel():
+    kernel = sk.Kernel()
+    if CONFIG.openai_api_type == "azure":
+        kernel.add_chat_service(
+            "chat_completion",
+            AzureChatCompletion(CONFIG.deployment_name, CONFIG.openai_api_base, CONFIG.openai_api_key),
+        )
+    else:
+        kernel.add_chat_service(
+            "chat_completion",
+            OpenAIChatCompletion(
+                CONFIG.openai_api_model, CONFIG.openai_api_key, org_id=None, endpoint=CONFIG.openai_api_base
+            ),
+        )
+
+    return kernel