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asyncio +import datetime +import json +import os +import re +import subprocess +import sys +import tempfile +import textwrap +from abc import ABC, abstractmethod +from typing import List + +# from dspy import InputField, Module, OutputField, Signature +from pydantic import Field + +from polymind.core.logger import Logger +from polymind.core.message import Message +from polymind.core.tool import BaseTool, Param + +# class CodeGenSignature(Signature): +# """Signature for generating and executing code based on requirements.""" + +# task_context = InputField(description="The context of the task.") +# requirement = InputField(description="The user requirement.") +# previous_errors = InputField(description="Previous errors encountered.") + + +# class OutputParseSignature(Signature): +# """Signature for parsing the output based on the requirement.""" + +# task_context = InputField(description="The context of the task.") +# requirement = InputField(description="The user requirement.") +# output = OutputField(description="The output from the code execution.") + + +# class CodeGenModule(Module): +# """Module for generating code based on requirements.""" + +# signature = CodeGenSignature + +# async def forward(self, requirement: str, previous_errors: List[str]) -> str: +# previous_error = "\n".join(previous_errors) +# current_date = datetime.datetime.now().strftime("%Y-%m-%d") +# prompt = self.codegen_prompt_template.format( +# user_requirement=requirement, current_date=current_date, previous_error=previous_error +# ) +# input_message = Message(content={"input": prompt}) +# response_message = await self._llm_tool(input=input_message) +# generated_text = textwrap.dedent(response_message.content.get("output", "")) +# code = "" +# code_block = re.search(r"```(?:python)?(.*?)```", generated_text, re.DOTALL) +# if code_block: +# code = code_block.group(1).strip() +# return code +# raise ValueError(f"Failed to generate code: {generated_text}") + + +# class OutputParseModule(Module): +# """Module for parsing the output based on requirements.""" + +# signature = OutputParseSignature + +# async def forward(self, requirement: str, output: str) -> str: +# prompt = self.output_extract_template.format(requirement=requirement, output=output) +# input_message = Message(content={"input": prompt}) +# response_message = await self._llm_tool(input=input_message) +# response_blob = response_message.content.get("output", "") +# matches = re.search(r"```json(.*?)```", response_blob, re.DOTALL) +# if not matches: +# raise ValueError(f"Cannot find the parsed output in the response: {response_blob}.") +# parsed_output_json_str = textwrap.dedent(matches.group(1)).strip() +# parsed_output_json = json.loads(parsed_output_json_str) +# if parsed_output_json["status"] != "success": +# raise ValueError(f"Generated output is incorrect: {parsed_output_json['reason']}") +# return json.dumps(parsed_output_json["output"], indent=4) + + +# class CodeGenerationTool(BaseTool, ABC): +# """A tool that can generate code based on user requirements and execute it.""" + +# tool_name: str = Field(default="code_generation_tool", description="The name of the tool.") +# max_attempts: int = Field(default=3, description="The maximum number of attempts to generate the code.") +# descriptions: List[str] = Field( +# default=[ +# "The tool will generate the code to solve the problem based on the requirement.", +# "This tool can use libraries like matplotlib, pandas, yfinance, and numpy to solve problems.", +# "Help program to get the finance data like the stock price or currency exchange rate.", +# "Generate the code to draw the charts based on the requirement and input data.", +# ], +# description="The descriptions of the tool.", +# ) +# skipped_packages: List[str] = [ +# "json", +# "os", +# "sys", +# "subprocess", +# "tempfile", +# "inspect", +# "importlib", +# "re", +# "textwrap", +# "asyncio", +# ] + +# def __init__(self, **kwargs): +# super().__init__(**kwargs) +# self._logger = Logger(__file__) +# self._set_llm_client() +# if getattr(self, "_llm_tool", None) is None: +# raise ValueError("_llm_tool has to be initialized in _set_llm_client().") +# self.codegen_module = CodeGenModule() +# self.output_parse_module = OutputParseModule() + +# @abstractmethod +# def _set_llm_client(self): +# pass + +# def input_spec(self) -> List[Param]: +# return [ +# Param( +# name="query", +# type="str", +# required=True, +# description="A natural language description of the problem or requirement.", +# example="Write a function that takes two numbers as input and returns their sum.", +# ), +# ] + +# def output_spec(self) -> List[Param]: +# return [ +# Param( +# name="code", +# type="str", +# required=True, +# description="The generated code to solve the problem.", +# ), +# Param( +# name="output", +# type="str", +# required=True, +# description="The output of running the generated code.", +# ), +# ] + +# async def _execute(self, input: Message) -> Message: +# previous_errors = [] +# requirement = input.content["query"] +# attempts = 0 +# while attempts < self.max_attempts: +# try: +# code = await self.codegen_module(requirement=requirement, previous_errors=previous_errors) +# output_dict_str: str = await self._code_run(code) +# output = await self.output_parse_module(requirement=requirement, output=output_dict_str) +# return Message(content={"code": code, "output": output}) +# except Exception as e: +# self._logger.warning(f"Error: {e}. Retrying...") +# previous_errors.append(str(e)) +# attempts += 1 +# raise ValueError(f"Failed to generate code after {self.max_attempts} attempts.") + +# async def _code_run(self, code: str) -> str: +# packages = self._extract_required_packages(code) +# if packages: +# await self._install_packages(packages) +# try: +# with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file: +# temp_file.write(code) +# temp_file.seek(0) +# temp_file_name = temp_file.name +# proc = await asyncio.create_subprocess_exec( +# sys.executable, temp_file_name, stdout=subprocess.PIPE, stderr=subprocess.PIPE +# ) +# stdout, stderr = await proc.communicate() +# if proc.returncode != 0: +# raise Exception(f"Error executing code: {stderr.decode()}") +# return stdout.decode() +# finally: +# os.unlink(temp_file_name) + +# def _extract_required_packages(self, code: str) -> List[str]: +# packages = set() +# for line in code.split("\n"): +# line = line.strip() +# import_match = re.match(r"import\s+([a-zA-Z0-9_,\s]+)(\s+as\s+\w+)?", line) +# if import_match: +# for package_with_alias in import_match.group(1).split(","): +# package = package_with_alias.strip().split(" ")[0] +# if "." in package: +# package = package.split(".")[0] +# packages.add(package) +# from_import_match = re.match(r"from\s+([a-zA-Z0-9_\.]+)\s+import", line) +# if from_import_match: +# package = from_import_match.group(1).split(".")[0] +# packages.add(package) +# return list(packages) + +# async def _install_packages(self, packages: List[str]) -> None: +# for package in packages: +# if package in self.skipped_packages: +# continue +# proc = await asyncio.create_subprocess_exec( +# sys.executable, "-m", "pip", "install", package, stdout=subprocess.PIPE, stderr=subprocess.PIPE +# ) +# _, stderr = await proc.communicate() +# if proc.returncode != 0: +# raise Exception(f"Error installing {package}: {stderr.decode()}") + + +class CodeGenerationTool(BaseTool, ABC): + """A tool that can generate code based on user requirements and execute it. + It will first + """ + + tool_name: str = Field(default="code_generation_tool", description="The name of the tool.") + max_attempts: int = Field(default=3, description="The maximum number of attempts to generate the code.") + descriptions: List[str] = Field( + default=[ + "The tool will generate the code to solve the problem based on the requirement.", + "This tool can use libraries like matplotlib, pandas, yfinance, and numpy to solve problems.", + "Help program to get the finance data like the stock price or currency exchange rate.", + "Generate the code to draw the charts based on the requirement and input data.", + ], + description="The descriptions of the tool.", + ) + # The packages that don't need to install in the code generation tool. + skipped_packages: List[str] = [ + "json", + "os", + "sys", + "subprocess", + "tempfile", + "inspect", + "importlib", + "re", + "textwrap", + "asyncio", + ] + + codegen_prompt_template: str = """ + You are a programmer that can generate code based on the requirement to solve the problem. + Please generate the code in python and put it in the code block below. + Note you would need to save the result in a Dict[str, Any] variable named 'output'. + And then print the jsonified dict to stdout. + + An example: + Requirement: Write a function draw a pie chart based on the input data. + Code: + ```python + # Import the library + import matplotlib.pyplot + import json + + data = [10, 20, 30, 40] # Data in user input + plt.pie(data) + # Save the plot to a file + filepath = "pie_chart.png" + matplotlib.pyplot.savefig(filepath) + output = {{"type": "chart path", "filepath": filepath}} + print(json.dumps(output)) + ``` + + Some tips: + 1. Pay special attention on the date requirement, e.g. use "datetime.datetime" to handle date. + 2. When import the library, please use the full name of the library, e.g. "import matplotlib.pyplot". + 3. If the requirement is about drawing a chart, you can use matplotlib to draw the chart. + 4. If the requirement is about retrieve finance data, you can use yfinance to get the stock price. + 5. If the requirement is about mathematical calculation, you can generate corresponding code or using numpy. + + The below is the actual user requirement: + + {user_requirement} + + + In case you want to know the current date: + + {current_date} + + + The previous error if any: + + {previous_error} + + """ + + output_extract_template: str = """ + Your work is to check and extract to check the output (can be any textual form) that + contains the information to solve the problem according to the requirement. + Please check carefully whether the result in the output fulfilled the user requirement. + If the output fulfilled the user requirement, extract it as str and put it into a json blob + with "status" and "output" fields. + + The below is the actual user requirement: + + {requirement} + + + The actual output from the generated code: + + {output} + + + + An example input: + Requirement: Find the stock price of Google on 2024-04-01. + The output of the generated code: + + {{ + "symbol": "GOOGL", + "price": 1000 + }} + + + ```json + {{ + "status": "success", + "output": "The stock price of Google on 2024-04-01 is $1000." + }} + ``` + + + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._logger = Logger(__file__) + self._set_llm_client() + if getattr(self, "_llm_tool", None) is None: + raise ValueError("_llm_tool has to be initialized in _set_llm_client().") + + @abstractmethod + def _set_llm_client(self): + pass + + def input_spec(self) -> List[Param]: + return [ + Param( + name="query", + type="str", + required=True, + description="A natural language description of the problem or requirement.", + example="Write a function that takes two numbers as input and returns their sum.", + ), + ] + + def output_spec(self) -> List[Param]: + return [ + Param( + name="code", + type="str", + required=True, + description="The generated code to solve the problem.", + ), + Param( + name="output", + type="str", + required=True, + description="The output of running the generated code.", + ), + ] + + async def _execute(self, input: Message) -> Message: + previous_errors = [] + requirement = input.content["query"] + attempts = 0 + while attempts < self.max_attempts: + code = await self._code_gen(requirement=requirement, previous_errors=previous_errors) + try: + output_dict_str: str = await self._code_run(code) + except Exception as e: + self._logger.warning(f"Failed to execute code: {e}. Retrying...") + error_message = { + "previous_error": str(e), + "previous_generated_code": code, + } + previous_errors.append(json.dumps(error_message, indent=4)) + attempts += 1 + continue + try: + self._logger.debug(f"Start to parse the output...\n{output_dict_str}") + output = await self._output_parse(requirement=requirement, output=output_dict_str) + return Message(content={"code": code, "output": output}) + except ValueError as e: + self._logger.warning(f"Failed to parse output, error: {e}. Retrying...") + previous_errors.append(str(e)) + attempts += 1 + + raise ValueError(f"Failed to generate code after {self.max_attempts} attempts.") + + async def _code_gen(self, requirement: str, previous_errors: List[str]) -> str: + previous_error = "\n".join(previous_errors) + current_date = datetime.datetime.now().strftime("%Y-%m-%d") + prompt = self.codegen_prompt_template.format( + user_requirement=requirement, current_date=current_date, previous_error=previous_error + ) + input_message = Message(content={"input": prompt}) + response_message = await self._llm_tool(input=input_message) + generated_text = textwrap.dedent(response_message.content.get("output", "")) + code = "" + code_block = re.search(r"```(?:python)?(.*?)```", generated_text, re.DOTALL) + if code_block: + code = code_block.group(1).strip() + return code + self._logger.error(f"Failed to generate code: {generated_text}") + raise ValueError(f"Failed to generate code: {generated_text}") + + async def _code_run(self, code: str) -> str: + packages = self._extract_required_packages(code) + self._logger.debug(f"Code content:\n{code}") + self._logger.debug(f"Required packages: {packages}") + + if packages: + await self._install_packages(packages) + + try: + with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file: + temp_file.write(code) + temp_file.seek(0) + temp_file_name = temp_file.name + + proc = await asyncio.create_subprocess_exec( + sys.executable, temp_file_name, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + stdout, stderr = await proc.communicate() + + if proc.returncode != 0: + self._logger.error(f"Error executing code: {stderr.decode()}") + raise Exception(f"Error executing code: {stderr.decode()}") + self._logger.debug(f"stdout: [{stdout.decode()}]") + result = stdout.decode() # string format of a Dict[str, Any] + self._logger.debug(f"Code execution result:\n[{result}]") + return result + finally: + try: + # Clean up the temporary file + os.unlink(temp_file_name) + except Exception as e: + self._logger.error(f"Failed to delete temporary file: {e}") + + def _extract_required_packages(self, code: str) -> List[str]: + """ + Extract the required package names from the given Python code string. + + Args: + code (str): The Python code string. + + Returns: + List[str]: A list of package names required by the code. + """ + packages = set() + + for line in code.split("\n"): + line = line.strip() + + # Match import statements + import_match = re.match(r"import\s+([a-zA-Z0-9_,\s]+)(\s+as\s+\w+)?", line) + if import_match: + for package_with_alias in import_match.group(1).split(","): + package = package_with_alias.strip().split(" ")[0] + if "." in package: + package = package.split(".")[0] + packages.add(package) + + # Match from-import statements + from_import_match = re.match(r"from\s+([a-zA-Z0-9_\.]+)\s+import", line) + if from_import_match: + package = from_import_match.group(1).split(".")[0] + packages.add(package) + + return list(packages) + + async def _install_packages(self, packages: List[str]) -> None: + for package in packages: + if package in self.skipped_packages: + continue + try: + proc = await asyncio.create_subprocess_exec( + sys.executable, "-m", "pip", "install", package, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + _, stderr = await proc.communicate() + if proc.returncode != 0: + self._logger.error(f"Error installing {package}: {stderr.decode()}") + raise Exception(f"Error installing {package}: {stderr.decode()}") + except Exception as e: + self._logger.error(f"Failed to install package {package}: {e}") + raise + + async def _output_parse(self, requirement: str, output: str) -> str: + """Use LLM to parse the output based on the requirement. + + Args: + requirement (str): The user requirement. + output (str): The output from the code execution captured from stdout. Should be parsible json text. + + Returns: + str: The parsed output. It should be a string representation of Dict[str, Any]. + """ + prompt = self.output_extract_template.format(requirement=requirement, output=output) + input_message = Message(content={"input": prompt}) + response_message = await self._llm_tool(input=input_message) + self._logger.debug(f"Response message:\n{response_message}") + response_blob = response_message.content.get("output", "") + self._logger.debug(f"Response blob:\n{response_blob}") + matches = re.search(r"```json(.*?)```", response_blob, re.DOTALL) + if not matches: + raise ValueError(f"Cannot find the parsed output in the response: {response_blob}.") + parsed_output_json_str = textwrap.dedent(matches.group(1)).strip() + parsed_output_json = json.loads(parsed_output_json_str) + if parsed_output_json["status"] != "success": + raise ValueError(f"Generated output is incorrect: {parsed_output_json['reason']}") + json_str = json.dumps(parsed_output_json["output"], indent=4) + return json_str diff --git a/polymind/core/tool.py b/polymind/core/tool.py index 0948103..1fd1e3d 100644 --- a/polymind/core/tool.py +++ b/polymind/core/tool.py @@ -1,19 +1,16 @@ -import asyncio import datetime import importlib.util import inspect import json import os import re -import subprocess import sys -import tempfile -import textwrap from abc import ABC, abstractmethod from collections.abc import Mapping, Sequence from typing import Any, Dict, List, Union, get_origin from dotenv import load_dotenv +from dspy import Module, Predict, Retrieve from pydantic import BaseModel, Field, field_validator from polymind.core.logger import Logger @@ -387,6 +384,7 @@ class LLMTool(BaseTool, ABC): This tool will get the prompt from "input" and return the response to "output". """ + llm_name: str = Field(..., description="The name of the model.") max_tokens: int = Field(..., description="The maximum number of tokens for the chat.") temperature: float = Field(default=1.0, description="The temperature for the chat.") top_p: float = Field( @@ -501,6 +499,68 @@ async def _execute(self, input: Message) -> Message: return response_message +class CombinedMeta(type(Module), type(BaseTool)): + pass + + +class DspyPipelineTool(BaseTool, Module, metaclass=CombinedMeta): + """The base class that wraps a DSPy module.""" + + tool_name: str = Field(..., description="The name of the tool.") + descriptions: List[str] = Field(..., description="The descriptions of the tool.") + + def __init__(self, **kwargs): + BaseTool.__init__(self, **kwargs) + Module.__init__(self) + self._logger = Logger(__file__) + for name, component in self._define_modules().items(): + object.__setattr__(self, name, component) + + @abstractmethod + def _define_modules(self) -> Dict[str, Union[Predict, Retrieve]]: + """Similar to the constructor of the PyTorch module, this method should define the modules of the tool.""" + pass + + @abstractmethod + def input_spec(self) -> List[Param]: + """Defines the input specification for the tool.""" + pass + + @abstractmethod + def output_spec(self) -> List[Param]: + """Defines the output specification for the tool.""" + pass + + @abstractmethod + def forward(self, **kwargs): + """The forward method of the tool. + Similar to the forward method in PyTorch, this method should define the behavior of the tool. + """ + pass + + async def _execute(self, input: Message) -> Message: + """Execute the tool and return the result. + The input message should contain a "prompt" and optionally a "system_prompt". + """ + kwargs = {} + # Current date time + current_datetime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + kwargs.update({"datetime": current_datetime}) + # Convert the input fields from input message to kwargs + for param in self.input_spec(): + kwargs[param.name] = input.get(param.name, None) + # Invoke the forward method of the tool + output_obj = self.forward(**kwargs) + # Extract the result according to the defined output spec + output = {} + for param in self.output_spec(): + output_field = output_obj.get(param.name, None) + if output_field is None and param.required: + raise ValueError(f"The output object must contain the field '{param.name}'.") + output[param.name] = output_field + return Message(content=output) + + class Embedder(BaseTool, ABC): """The embedder is a tool to generate the embedding for the input.""" @@ -664,308 +724,3 @@ async def _execute(self, input: Message) -> Message: self._logger.debug(f"Start to refine the results...\n{response_message}") response_message = await self._refine(input=input, response=response_message) return response_message - - -class CodeGenerationTool(BaseTool, ABC): - """A tool that can generate code based on user requirements and execute it. - It will first - """ - - tool_name: str = Field(default="code_generation_tool", description="The name of the tool.") - max_attempts: int = Field(default=3, description="The maximum number of attempts to generate the code.") - descriptions: List[str] = Field( - default=[ - "The tool will generate the code to solve the problem based on the requirement.", - "This tool can use libraries like matplotlib, pandas, yfinance, and numpy to solve problems.", - "Help program to get the finance data like the stock price or currency exchange rate.", - "Generate the code to draw the charts based on the requirement and input data.", - ], - description="The descriptions of the tool.", - ) - # The packages that don't need to install in the code generation tool. - skipped_packages: List[str] = [ - "json", - "os", - "sys", - "subprocess", - "tempfile", - "inspect", - "importlib", - "re", - "textwrap", - "asyncio", - ] - - codegen_prompt_template: str = """ - You are a programmer that can generate code based on the requirement to solve the problem. - Please generate the code in python and put it in the code block below. - Note you would need to save the result in a Dict[str, Any] variable named 'output'. - And then print the jsonified dict to stdout. - - An example: - Requirement: Write a function draw a pie chart based on the input data. - Code: - ```python - # Import the library - import matplotlib.pyplot - import json - - data = [10, 20, 30, 40] # Data in user input - plt.pie(data) - # Save the plot to a file - filepath = "pie_chart.png" - matplotlib.pyplot.savefig(filepath) - output = {{"type": "chart path", "filepath": filepath}} - print(json.dumps(output)) - ``` - - Some tips: - 1. Pay special attention on the date requirement, e.g. use "datetime.datetime" to handle date. - 2. When import the library, please use the full name of the library, e.g. "import matplotlib.pyplot". - 3. If the requirement is about drawing a chart, you can use matplotlib to draw the chart. - 4. If the requirement is about retrieve finance data, you can use yfinance to get the stock price. - 5. If the requirement is about mathematical calculation, you can generate corresponding code or using numpy. - - The below is the actual user requirement: - - {user_requirement} - - - In case you want to know the current date: - - {current_date} - - - The previous error if any: - - {previous_error} - - """ - - output_extract_template: str = """ - Your work is to check and extract to check the output (can be any textual form) that - contains the information to solve the problem according to the requirement. - Please check carefully whether the result in the output fulfilled the user requirement. - If the output fulfilled the user requirement, extract it as str and put it into a json blob - with "status" and "output" fields. - - The below is the actual user requirement: - - {requirement} - - - The actual output from the generated code: - - {output} - - - - An example input: - Requirement: Find the stock price of Google on 2024-04-01. - The output of the generated code: - - {{ - "symbol": "GOOGL", - "price": 1000 - }} - - - ```json - {{ - "status": "success", - "output": "The stock price of Google on 2024-04-01 is $1000." - }} - ``` - - - """ - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self._logger = Logger(__file__) - self._set_llm_client() - if getattr(self, "_llm_tool", None) is None: - raise ValueError("_llm_tool has to be initialized in _set_llm_client().") - - @abstractmethod - def _set_llm_client(self): - pass - - def input_spec(self) -> List[Param]: - return [ - Param( - name="query", - type="str", - required=True, - description="A natural language description of the problem or requirement.", - example="Write a function that takes two numbers as input and returns their sum.", - ), - ] - - def output_spec(self) -> List[Param]: - return [ - Param( - name="code", - type="str", - required=True, - description="The generated code to solve the problem.", - ), - Param( - name="output", - type="str", - required=True, - description="The output of running the generated code.", - ), - ] - - async def _execute(self, input: Message) -> Message: - previous_errors = [] - requirement = input.content["query"] - attempts = 0 - while attempts < self.max_attempts: - code = await self._code_gen(requirement=requirement, previous_errors=previous_errors) - try: - output_dict_str: str = await self._code_run(code) - except Exception as e: - self._logger.warning(f"Failed to execute code: {e}. Retrying...") - error_message = { - "previous_error": str(e), - "previous_generated_code": code, - } - previous_errors.append(json.dumps(error_message, indent=4)) - attempts += 1 - continue - try: - self._logger.debug(f"Start to parse the output...\n{output_dict_str}") - output = await self._output_parse(requirement=requirement, output=output_dict_str) - return Message(content={"code": code, "output": output}) - except ValueError as e: - self._logger.warning(f"Failed to parse output, error: {e}. Retrying...") - previous_errors.append(str(e)) - attempts += 1 - - raise ValueError(f"Failed to generate code after {self.max_attempts} attempts.") - - async def _code_gen(self, requirement: str, previous_errors: List[str]) -> str: - previous_error = "\n".join(previous_errors) - current_date = datetime.datetime.now().strftime("%Y-%m-%d") - prompt = self.codegen_prompt_template.format( - user_requirement=requirement, current_date=current_date, previous_error=previous_error - ) - input_message = Message(content={"input": prompt}) - response_message = await self._llm_tool(input=input_message) - generated_text = textwrap.dedent(response_message.content.get("output", "")) - code = "" - code_block = re.search(r"```(?:python)?(.*?)```", generated_text, re.DOTALL) - if code_block: - code = code_block.group(1).strip() - return code - self._logger.error(f"Failed to generate code: {generated_text}") - raise ValueError(f"Failed to generate code: {generated_text}") - - async def _code_run(self, code: str) -> str: - packages = self._extract_required_packages(code) - self._logger.debug(f"Code content:\n{code}") - self._logger.debug(f"Required packages: {packages}") - - if packages: - await self._install_packages(packages) - - try: - with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file: - temp_file.write(code) - temp_file.seek(0) - temp_file_name = temp_file.name - - proc = await asyncio.create_subprocess_exec( - sys.executable, temp_file_name, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - stdout, stderr = await proc.communicate() - - if proc.returncode != 0: - self._logger.error(f"Error executing code: {stderr.decode()}") - raise Exception(f"Error executing code: {stderr.decode()}") - self._logger.debug(f"stdout: [{stdout.decode()}]") - result = stdout.decode() # string format of a Dict[str, Any] - self._logger.debug(f"Code execution result:\n[{result}]") - return result - finally: - try: - # Clean up the temporary file - os.unlink(temp_file_name) - except Exception as e: - self._logger.error(f"Failed to delete temporary file: {e}") - - def _extract_required_packages(self, code: str) -> List[str]: - """ - Extract the required package names from the given Python code string. - - Args: - code (str): The Python code string. - - Returns: - List[str]: A list of package names required by the code. - """ - packages = set() - - for line in code.split("\n"): - line = line.strip() - - # Match import statements - import_match = re.match(r"import\s+([a-zA-Z0-9_,\s]+)(\s+as\s+\w+)?", line) - if import_match: - for package_with_alias in import_match.group(1).split(","): - package = package_with_alias.strip().split(" ")[0] - if "." in package: - package = package.split(".")[0] - packages.add(package) - - # Match from-import statements - from_import_match = re.match(r"from\s+([a-zA-Z0-9_\.]+)\s+import", line) - if from_import_match: - package = from_import_match.group(1).split(".")[0] - packages.add(package) - - return list(packages) - - async def _install_packages(self, packages: List[str]) -> None: - for package in packages: - if package in self.skipped_packages: - continue - try: - proc = await asyncio.create_subprocess_exec( - sys.executable, "-m", "pip", "install", package, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - _, stderr = await proc.communicate() - if proc.returncode != 0: - self._logger.error(f"Error installing {package}: {stderr.decode()}") - raise Exception(f"Error installing {package}: {stderr.decode()}") - except Exception as e: - self._logger.error(f"Failed to install package {package}: {e}") - raise - - async def _output_parse(self, requirement: str, output: str) -> str: - """Use LLM to parse the output based on the requirement. - - Args: - requirement (str): The user requirement. - output (str): The output from the code execution captured from stdout. Should be parsible json text. - - Returns: - str: The parsed output. It should be a string representation of Dict[str, Any]. - """ - prompt = self.output_extract_template.format(requirement=requirement, output=output) - input_message = Message(content={"input": prompt}) - response_message = await self._llm_tool(input=input_message) - self._logger.debug(f"Response message:\n{response_message}") - response_blob = response_message.content.get("output", "") - self._logger.debug(f"Response blob:\n{response_blob}") - matches = re.search(r"```json(.*?)```", response_blob, re.DOTALL) - if not matches: - raise ValueError(f"Cannot find the parsed output in the response: {response_blob}.") - parsed_output_json_str = textwrap.dedent(matches.group(1)).strip() - parsed_output_json = json.loads(parsed_output_json_str) - if parsed_output_json["status"] != "success": - raise ValueError(f"Generated output is incorrect: {parsed_output_json['reason']}") - json_str = json.dumps(parsed_output_json["output"], indent=4) - return json_str diff --git a/polymind/core_tools/llm_tool.py b/polymind/core_tools/llm_tool.py index 22068bd..54f5440 100644 --- a/polymind/core_tools/llm_tool.py +++ b/polymind/core_tools/llm_tool.py @@ -14,10 +14,10 @@ print("Please install the OpenAI and anthropic package with command:", "poetry install -E openai -E anthropic") from pydantic import Field +from polymind.core.codegen import CodeGenerationTool from polymind.core.logger import Logger from polymind.core.message import Message -from polymind.core.tool import (BaseTool, CodeGenerationTool, Embedder, - LLMTool, Param) +from polymind.core.tool import BaseTool, Embedder, LLMTool, Param from polymind.core_tools.rest_api_tool import RestAPITool diff --git a/pyproject.toml b/pyproject.toml index 78c74d0..6850228 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,6 +21,7 @@ openai = { version = "^1.14.0", optional = true } faiss-cpu = { version = "1.8.0", optional = true } anthropic = { version = "^0.25.6", optional = true } pymilvus = { version = "2.3.7", optional = true } +dspy-ai = "^2.4.9" [tool.poetry.group.dev.dependencies] black = "^24.2.0" diff --git a/tests/polymind/core/test_tool.py b/tests/polymind/core/test_tool.py index 15e8485..4b5a93d 100644 --- a/tests/polymind/core/test_tool.py +++ b/tests/polymind/core/test_tool.py @@ -11,8 +11,9 @@ import pytest from pydantic import ValidationError +from polymind.core.codegen import CodeGenerationTool from polymind.core.message import Message -from polymind.core.tool import BaseTool, CodeGenerationTool, Param, ToolManager +from polymind.core.tool import BaseTool, Param, ToolManager class TestParam: