import re from typing import List, Union from langchain.chains import LLMChain from langchain.agents import Tool, LLMSingleActionAgent, AgentExecutor, AgentOutputParser from langchain.schema import AgentAction, AgentFinish from langchain.agents import initialize_agent from langchain.prompts import StringPromptTemplate from agents.promopts import code_generate_agent_template from agents.tools.smart_domain.api_layer_code_tool import apiLayerCodeGenerator from agents.tools.smart_domain.domain_layer_code_tool import domainLayerCodeGenerator from agents.tools.smart_domain.entity import entityCodeGenerator from agents.tools.smart_domain.association import associationCodeGenerator from agents.tools.smart_domain.db_entity_repository import dbEntityRepositoryCodeGenerator from agents.tools.smart_domain.association_impl import asociationImplCodeGenerator from agents.tools.smart_domain.persistent_layer_code_tool import persistentLayerCodeGenerator from models import llm class CustomPromptTemplate(StringPromptTemplate): # The template to use template: str # The list of tools available tools: List[Tool] def format(self, **kwargs) -> str: # Get the intermediate steps (AgentAction, Observation tuples) # Format them in a particular way intermediate_steps = kwargs.pop("intermediate_steps") thoughts = "" for action, observation in intermediate_steps: thoughts += action.log thoughts += f"\nObservation: {observation}\nThought: " # Set the agent_scratchpad variable to that value kwargs["agent_scratchpad"] = thoughts # Create a tools variable from the list of tools provided kwargs["tools"] = "\n".join( [f"{tool.name}: {tool.description}" for tool in self.tools]) # Create a list of tool names for the tools provided kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools]) return self.template.format(**kwargs) class CustomOutputParser(AgentOutputParser): def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]: # Check if agent should finish if "Final Answer:" in llm_output: return AgentFinish( # Return values is generally always a dictionary with a single `output` key # It is not recommended to try anything else at the moment :) return_values={"output": llm_output.split( "Final Answer:")[-1].strip()}, log=llm_output, ) # Parse out the action and action input regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" match = re.search(regex, llm_output, re.DOTALL) if not match: raise ValueError(f"Could not parse LLM output: `{llm_output}`") action = match.group(1).strip() action_input = match.group(2) # Return the action and action input return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output) # chatllm=ChatOpenAI(temperature=0) # code_genenrate_memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) # code_generate_agent = initialize_agent(tools, chatllm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, memory=memory, verbose=True) # agent = initialize_agent( # tools=tools, llm=llm_chain, template=AGENT_PROMPT, stop=["\nObservation:"], agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) code_agent_tools = [domainLayerCodeGenerator, entityCodeGenerator, associationCodeGenerator, persistentLayerCodeGenerator, dbEntityRepositoryCodeGenerator, asociationImplCodeGenerator, apiLayerCodeGenerator] def code_agent_executor() -> AgentExecutor: output_parser = CustomOutputParser() AGENT_PROMPT = CustomPromptTemplate( template=code_generate_agent_template, tools=code_agent_tools, # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically # This includes the `intermediate_steps` variable because that is needed input_variables=["input", "intermediate_steps"] ) code_llm_chain = LLMChain(llm=llm(temperature=0.7), prompt=AGENT_PROMPT) tool_names = [tool.name for tool in code_agent_tools] code_agent = LLMSingleActionAgent( llm_chain=code_llm_chain, output_parser=output_parser, stop=["\nObservation:"], allowed_tools=tool_names, ) code_agent_executor = AgentExecutor.from_agent_and_tools( agent=code_agent, tools=code_agent_tools, verbose=True) return code_agent_executor # if __name__ == "__main__": # response = domainLayerChain.run("""FeatureConfig用于配置某个Feature中控制前端展示效果的配置项 # FeatureConfig主要属性包括：featureKey(feature标识)、data（配置数据）、saData（埋点数据）、status（状态）、标题、描述、创建时间、更新时间 # FeatureConfig中status为枚举值，取值范围为（DRAFT、PUBLISHED、DISABLED） # FeatureConfig新增后status为DRAFT、执行发布操作后变为PUBLISHED、执行撤销操作后变为DISABLED # 状态为DRAFT的FeatureConfig可以执行编辑、发布、撤销操作 # 发布后FeatureConfig变为PUBLISHED状态，可以执行撤销操作 # 撤销后FeatureConfig变为DISABLED状态，不可以执行编辑、发布、撤销操作 # """) # print(response) # response = persistentChain.run(""" # Entity: # ``` # public class FeatureConfig { # private FeatureConfigId id; # private FeatureConfigDescription description; # public enum FeatureConfigStatus { # DRAFT, PUBLISHED, DISABLED; # } # public record FeatureConfigId(String id) {} # public record FeatureKey(String key) {} # public record FeatureConfigData(String data) {} # public record FeatureConfigSaData(String saData) {} # @Builder # public record FeatureConfigDescription(FeatureKey featureKey, FeatureConfigData data, FeatureConfigSaData saData, String title, String description, # FeatureConfigStatus status, LocalDateTime createTime, LocalDateTime updateTime) {} # public void update(FeatureConfigDescription description) { # this.title = description.title(); # this.description = description.description(); # this.updateTime = LocalDateTime.now(); # } # public void publish() { # this.status = FeatureConfigStatus.PUBLISHED; # this.updateTime = LocalDateTime.now(); # } # public void disable() { # this.status = FeatureConfigStatus.DISABLED; # this.updateTime = LocalDateTime.now(); # } # } # ``` # Association: # ``` # public interface FeatureConfigs { # Flux findAllByFeatureKey(String featureKey); # Mono findById(FeatureConfigId id); # Mono save(FeatureConfig featureConfig); # } # ``` # """) # print(response) # response = apiChain.run(""" # Entity: # ``` # public class FeatureConfig { # private FeatureConfigId id; # private FeatureConfigDescription description; # public enum FeatureConfigStatus { # DRAFT, PUBLISHED, DISABLED; # } # public record FeatureConfigId(String id) {} # public record FeatureKey(String key) {} # public record FeatureConfigData(String data) {} # public record FeatureConfigSaData(String saData) {} # @Builder # public record FeatureConfigDescription(FeatureKey featureKey, FeatureConfigData data, FeatureConfigSaData saData, String title, String description, # FeatureConfigStatus status, LocalDateTime createTime, LocalDateTime updateTime) {} # public void update(FeatureConfigDescription description) { # this.title = description.title(); # this.description = description.description(); # this.updateTime = LocalDateTime.now(); # } # public void publish() { # this.status = FeatureConfigStatus.PUBLISHED; # this.updateTime = LocalDateTime.now(); # } # public void disable() { # this.status = FeatureConfigStatus.DISABLED; # this.updateTime = LocalDateTime.now(); # } # } # ``` # Association: # ``` # public interface FeatureConfigs { # Flux findAllByFeatureKey(String featureKey); # Mono findById(FeatureConfigId id); # Mono save(FeatureConfig featureConfig); # Mono update(FeatureConfigId id, FeatureConfigDescription description); # Mono publish(FeatureConfigId id); # Mono disable(FeatureConfigId id); # } # ``` # """) # print(response) # if __name__ == "code_generate": # response = code_agent_executor.run(""" # 根据如下需求generate domain layer code: # --- # FeatureConfig用于配置某个Feature中控制前端展示效果的配置项 # FeatureConfig主要属性包括：featureKey(feature标识)、data（配置数据）、saData（埋点数据）、status（状态）、标题、描述、创建时间、更新时间 # FeatureConfig中status为枚举值，取值范围为（DRAFT、PUBLISHED、DISABLED） # FeatureConfig新增后status为DRAFT、执行发布操作后变为PUBLISHED、执行撤销操作后变为DISABLED # 状态为DRAFT的FeatureConfig可以执行编辑、发布、撤销操作 # 发布后FeatureConfig变为PUBLISHED状态，可以执行撤销操作 # 撤销后FeatureConfig变为DISABLED状态，不可以执行编辑、发布、撤销操作 # --- # """) # print(response)