444 lines
21 KiB
Python
444 lines
21 KiB
Python
import os, sys, json
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from loguru import logger
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src_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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)
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sys.path.append(src_dir)
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from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
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from configs.server_config import SANDBOX_SERVER
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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from coagent.connector.phase import BasePhase
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from coagent.connector.agents import BaseAgent
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from coagent.connector.schema import Message
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from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
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import importlib
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from loguru import logger
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from coagent.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
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# 定义一个新的agent类
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class CodeRetrieval(BaseAgent):
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def start_action_step(self, message: Message) -> Message:
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'''do action before agent predict '''
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# 根据问题获取代码片段和节点信息
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action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, llm_config=self.llm_config, embed_config=self.embed_config, search_type="tag",
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local_graph_path=message.local_graph_path, use_nh=message.use_nh)
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current_vertex = action_json['vertex']
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message.customed_kargs["Code Snippet"] = action_json["code"]
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message.customed_kargs['Current_Vertex'] = current_vertex
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# 获取邻近节点
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action_json = RelatedVerticesRetrival.run(message.code_engine_name, message.customed_kargs['Current_Vertex'])
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# 获取邻近节点所有代码
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relative_vertex = []
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retrieval_Codes = []
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for vertex in action_json["vertices"]:
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# 由于代码是文件级别，所以相同文件代码不再获取
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# logger.debug(f"{current_vertex}, {vertex}")
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current_vertex_name = current_vertex.replace("#", "").replace(".java", "" ) if current_vertex.endswith(".java") else current_vertex
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if current_vertex_name.split("#")[0] == vertex.split("#")[0]: continue
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action_json = Vertex2Code.run(message.code_engine_name, vertex)
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if action_json["code"]:
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retrieval_Codes.append(action_json["code"])
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relative_vertex.append(vertex)
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#
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message.customed_kargs["Retrieval_Codes"] = retrieval_Codes
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message.customed_kargs["Relative_vertex"] = relative_vertex
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return message
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# add agent or prompt_manager class
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agent_module = importlib.import_module("coagent.connector.agents")
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setattr(agent_module, 'CodeRetrieval', CodeRetrieval)
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llm_config = LLMConfig(
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model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"],
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api_base_url=os.environ["API_BASE_URL"], temperature=0.3
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)
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embed_config = EmbedConfig(
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embed_engine="model", embed_model="text2vec-base-chinese",
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embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
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)
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## initialize codebase
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# delete codebase
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codebase_name = 'client_local'
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code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
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use_nh = False
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cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
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llm_config=llm_config, embed_config=embed_config)
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cbh.delete_codebase(codebase_name=codebase_name)
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# load codebase
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codebase_name = 'client_local'
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code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
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use_nh = True
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do_interpret = True
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cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
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llm_config=llm_config, embed_config=embed_config)
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cbh.import_code(do_interpret=do_interpret)
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cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
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llm_config=llm_config, embed_config=embed_config)
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vertexes = cbh.search_vertices(vertex_type="class")
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "0"
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phase_name = "code2Tests"
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phase = BasePhase(
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phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
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embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
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)
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# round-1
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logger.debug(vertexes)
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test_cases = []
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for vertex in vertexes:
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query_content = f"为{vertex}生成可执行的测例 "
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query = Message(
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role_name="human", role_type="user",
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role_content=query_content, input_query=query_content, origin_query=query_content,
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code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
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use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
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)
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output_message, output_memory = phase.step(query, reinit_memory=True)
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# print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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print(output_memory.get_spec_parserd_output())
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values = output_memory.get_spec_parserd_output()
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test_code = {k:v for i in values for k,v in i.items() if k in ["SaveFileName", "Test Code"]}
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test_cases.append(test_code)
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os.makedirs(f"{CB_ROOT_PATH}/tests", exist_ok=True)
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with open(f"{CB_ROOT_PATH}/tests/{test_code['SaveFileName']}", "w") as f:
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f.write(test_code["Test Code"])
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break
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# import os, sys, json
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# from loguru import logger
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# src_dir = os.path.join(
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# os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# )
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# sys.path.append(src_dir)
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# from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
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# from configs.server_config import SANDBOX_SERVER
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# from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
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# from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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# from coagent.connector.phase import BasePhase
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# from coagent.connector.agents import BaseAgent
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# from coagent.connector.chains import BaseChain
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# from coagent.connector.schema import (
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# Message, Memory, load_role_configs, load_phase_configs, load_chain_configs, ActionStatus
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# )
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# from coagent.connector.memory_manager import BaseMemoryManager
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# from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS, BASE_PROMPT_CONFIGS
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# from coagent.connector.prompt_manager.prompt_manager import PromptManager
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# from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
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# import importlib
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# from loguru import logger
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# # 下面给定了一份代码片段，以及来自于它的依赖类、依赖方法相关的代码片段，你需要判断是否为这段指定代码片段生成测例。
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# # 理论上所有代码都需要写测例，但是受限于人的精力不可能覆盖所有代码
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# # 考虑以下因素进行裁剪：
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# # 功能性: 如果它实现的是一个具体的功能或逻辑，则通常需要编写测试用例以验证其正确性。
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# # 复杂性: 如果代码较为，尤其是包含多个条件判断、循环、异常处理等的代码，更可能隐藏bug，因此应该编写测试用例。如果代码涉及复杂的算法或者逻辑，那么编写测试用例可以帮助确保逻辑的正确性，并在未来的重构中防止引入错误。
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# # 关键性: 如果它是关键路径的一部分或影响到核心功能，那么它就需要被测试。对于核心业务逻辑或者系统的关键组件，应当编写全面的测试用例来确保功能的正确性和稳定性。
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# # 依赖性: 如果代码有外部依赖，可能需要编写集成测试或模拟这些依赖进行单元测试。
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# # 用户输入: 如果代码处理用户输入，尤其是来自外部的、非受控的输入，那么创建测试用例来检查输入验证和处理是很重要的。
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# # 频繁更改：对于经常需要更新或修改的代码，有相应的测试用例可以确保更改不会破坏现有功能。
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# # 代码公开或重用：如果代码将被公开或用于其他项目，编写测试用例可以提高代码的可信度和易用性。
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# # update new agent configs
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# judgeGenerateTests_PROMPT = """#### Agent Profile
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# When determining the necessity of writing test cases for a given code snippet,
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# it's essential to evaluate its interactions with dependent classes and methods (retrieved code snippets),
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# in addition to considering these critical factors:
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# 1. Functionality: If it implements a concrete function or logic, test cases are typically necessary to verify its correctness.
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# 2. Complexity: If the code is complex, especially if it contains multiple conditional statements, loops, exceptions handling, etc.,
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# it's more likely to harbor bugs, and thus test cases should be written.
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# If the code involves complex algorithms or logic, then writing test cases can help ensure the accuracy of the logic and prevent errors during future refactoring.
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# 3. Criticality: If it's part of the critical path or affects core functionalities, then it needs to be tested.
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# Comprehensive test cases should be written for core business logic or key components of the system to ensure the correctness and stability of the functionality.
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# 4. Dependencies: If the code has external dependencies, integration testing may be necessary, or mocking these dependencies during unit testing might be required.
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# 5. User Input: If the code handles user input, especially from unregulated external sources, creating test cases to check input validation and handling is important.
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# 6. Frequent Changes: For code that requires regular updates or modifications, having the appropriate test cases ensures that changes do not break existing functionalities.
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# #### Input Format
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# **Code Snippet:** the initial Code or objective that the user wanted to achieve
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# **Retrieval Code Snippets:** These are the associated code segments that the main Code Snippet depends on.
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# Examine these snippets to understand how they interact with the main snippet and to determine how they might affect the overall functionality.
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# #### Response Output Format
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# **Action Status:** Set to 'finished' or 'continued'.
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# If set to 'finished', the code snippet does not warrant the generation of a test case.
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# If set to 'continued', the code snippet necessitates the creation of a test case.
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# **REASON:** Justify the selection of 'finished' or 'continued', contemplating the decision through a step-by-step rationale.
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# """
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# generateTests_PROMPT = """#### Agent Profile
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# As an agent specializing in software quality assurance,
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# your mission is to craft comprehensive test cases that bolster the functionality, reliability, and robustness of a specified Code Snippet.
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# This task is to be carried out with a keen understanding of the snippet's interactions with its dependent classes and methods—collectively referred to as Retrieval Code Snippets.
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# Analyze the details given below to grasp the code's intended purpose, its inherent complexity, and the context within which it operates.
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# Your constructed test cases must thoroughly examine the various factors influencing the code's quality and performance.
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# ATTENTION: response carefully referenced "Response Output Format" in format.
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# Each test case should include:
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# 1. clear description of the test purpose.
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# 2. The input values or conditions for the test.
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# 3. The expected outcome or assertion for the test.
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# 4. Appropriate tags (e.g., 'functional', 'integration', 'regression') that classify the type of test case.
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# 5. these test code should have package and import
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# #### Input Format
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# **Code Snippet:** the initial Code or objective that the user wanted to achieve
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# **Retrieval Code Snippets:** These are the interrelated pieces of code sourced from the codebase, which support or influence the primary Code Snippet.
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# #### Response Output Format
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# **SaveFileName:** construct a local file name based on Question and Context, such as
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# ```java
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# package/class.java
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# ```
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# **Test Code:** generate the test code for the current Code Snippet.
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# ```java
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# ...
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# ```
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# """
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# CODE_GENERATE_TESTS_PROMPT_CONFIGS = [
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# {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
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# # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
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# {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
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# # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
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# {"field_name": 'session_records', "function_name": 'handle_session_records'},
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# {"field_name": 'code_snippet', "function_name": 'handle_code_snippet'},
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# {"field_name": 'retrieval_codes', "function_name": 'handle_retrieval_codes', "description": ""},
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# {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
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# {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
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# ]
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# class CodeRetrievalPM(PromptManager):
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# def handle_code_snippet(self, **kwargs) -> str:
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# if 'previous_agent_message' not in kwargs:
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# return ""
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# previous_agent_message: Message = kwargs['previous_agent_message']
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# code_snippet = previous_agent_message.customed_kargs.get("Code Snippet", "")
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# current_vertex = previous_agent_message.customed_kargs.get("Current_Vertex", "")
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# instruction = "the initial Code or objective that the user wanted to achieve"
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# return instruction + "\n" + f"name: {current_vertex}\n{code_snippet}"
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# def handle_retrieval_codes(self, **kwargs) -> str:
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# if 'previous_agent_message' not in kwargs:
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# return ""
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# previous_agent_message: Message = kwargs['previous_agent_message']
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# Retrieval_Codes = previous_agent_message.customed_kargs["Retrieval_Codes"]
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# Relative_vertex = previous_agent_message.customed_kargs["Relative_vertex"]
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# instruction = "the initial Code or objective that the user wanted to achieve"
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# s = instruction + "\n" + "\n".join([f"name: {vertext}\n{code}" for vertext, code in zip(Relative_vertex, Retrieval_Codes)])
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# return s
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# AGETN_CONFIGS.update({
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# "CodeJudger": {
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# "role": {
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# "role_prompt": judgeGenerateTests_PROMPT,
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# "role_type": "assistant",
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# "role_name": "CodeJudger",
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# "role_desc": "",
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# "agent_type": "CodeRetrieval"
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# # "agent_type": "BaseAgent"
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# },
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# "prompt_config": CODE_GENERATE_TESTS_PROMPT_CONFIGS,
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# "prompt_manager_type": "CodeRetrievalPM",
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# "chat_turn": 1,
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# "focus_agents": [],
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# "focus_message_keys": [],
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# },
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# "generateTests": {
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# "role": {
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# "role_prompt": generateTests_PROMPT,
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# "role_type": "assistant",
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# "role_name": "generateTests",
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# "role_desc": "",
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# "agent_type": "CodeRetrieval"
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# # "agent_type": "BaseAgent"
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# },
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# "prompt_config": CODE_GENERATE_TESTS_PROMPT_CONFIGS,
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# "prompt_manager_type": "CodeRetrievalPM",
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# "chat_turn": 1,
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# "focus_agents": [],
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# "focus_message_keys": [],
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# },
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# })
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# # update new chain configs
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# CHAIN_CONFIGS.update({
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# "codeRetrievalChain": {
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# "chain_name": "codeRetrievalChain",
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# "chain_type": "BaseChain",
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# "agents": ["CodeJudger", "generateTests"],
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# "chat_turn": 1,
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# "do_checker": False,
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# "chain_prompt": ""
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# }
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# })
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# # update phase configs
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# PHASE_CONFIGS.update({
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# "codeGenerateTests": {
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# "phase_name": "codeGenerateTests",
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# "phase_type": "BasePhase",
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# "chains": ["codeRetrievalChain"],
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# "do_summary": False,
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# "do_search": False,
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# "do_doc_retrieval": False,
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# "do_code_retrieval": False,
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# "do_tool_retrieval": False,
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# },
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# })
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# role_configs = load_role_configs(AGETN_CONFIGS)
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# chain_configs = load_chain_configs(CHAIN_CONFIGS)
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# phase_configs = load_phase_configs(PHASE_CONFIGS)
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# from coagent.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
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# # 定义一个新的agent类
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# class CodeRetrieval(BaseAgent):
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# def start_action_step(self, message: Message) -> Message:
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# '''do action before agent predict '''
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# # 根据问题获取代码片段和节点信息
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# action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, llm_config=self.llm_config, embed_config=self.embed_config, search_type="tag",
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# local_graph_path=message.local_graph_path, use_nh=message.use_nh)
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# current_vertex = action_json['vertex']
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# message.customed_kargs["Code Snippet"] = action_json["code"]
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# message.customed_kargs['Current_Vertex'] = current_vertex
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# # 获取邻近节点
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# action_json = RelatedVerticesRetrival.run(message.code_engine_name, message.customed_kargs['Current_Vertex'])
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# # 获取邻近节点所有代码
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# relative_vertex = []
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# retrieval_Codes = []
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# for vertex in action_json["vertices"]:
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# # 由于代码是文件级别，所以相同文件代码不再获取
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# # logger.debug(f"{current_vertex}, {vertex}")
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# current_vertex_name = current_vertex.replace("#", "").replace(".java", "" ) if current_vertex.endswith(".java") else current_vertex
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# if current_vertex_name.split("#")[0] == vertex.split("#")[0]: continue
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# action_json = Vertex2Code.run(message.code_engine_name, vertex)
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# if action_json["code"]:
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# retrieval_Codes.append(action_json["code"])
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# relative_vertex.append(vertex)
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# #
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# message.customed_kargs["Retrieval_Codes"] = retrieval_Codes
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# message.customed_kargs["Relative_vertex"] = relative_vertex
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# return message
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# # add agent or prompt_manager class
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# agent_module = importlib.import_module("coagent.connector.agents")
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# prompt_manager_module = importlib.import_module("coagent.connector.prompt_manager")
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# setattr(agent_module, 'CodeRetrieval', CodeRetrieval)
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# setattr(prompt_manager_module, 'CodeRetrievalPM', CodeRetrievalPM)
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# # log-level，print prompt和llm predict
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# os.environ["log_verbose"] = "0"
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# phase_name = "codeGenerateTests"
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# llm_config = LLMConfig(
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# model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"],
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# api_base_url=os.environ["API_BASE_URL"], temperature=0.3
|
||
# )
|
||
# embed_config = EmbedConfig(
|
||
# embed_engine="model", embed_model="text2vec-base-chinese",
|
||
# embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
|
||
# )
|
||
|
||
# ## initialize codebase
|
||
# # delete codebase
|
||
# codebase_name = 'client_local'
|
||
# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
|
||
# use_nh = False
|
||
# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
|
||
# llm_config=llm_config, embed_config=embed_config)
|
||
# cbh.delete_codebase(codebase_name=codebase_name)
|
||
|
||
|
||
# # load codebase
|
||
# codebase_name = 'client_local'
|
||
# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
|
||
# use_nh = True
|
||
# do_interpret = True
|
||
# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
|
||
# llm_config=llm_config, embed_config=embed_config)
|
||
# cbh.import_code(do_interpret=do_interpret)
|
||
|
||
|
||
# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
|
||
# llm_config=llm_config, embed_config=embed_config)
|
||
# vertexes = cbh.search_vertices(vertex_type="class")
|
||
|
||
# phase = BasePhase(
|
||
# phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
|
||
# embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
|
||
# )
|
||
|
||
# # round-1
|
||
# logger.debug(vertexes)
|
||
# test_cases = []
|
||
# for vertex in vertexes:
|
||
# query_content = f"为{vertex}生成可执行的测例 "
|
||
# query = Message(
|
||
# role_name="human", role_type="user",
|
||
# role_content=query_content, input_query=query_content, origin_query=query_content,
|
||
# code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
|
||
# use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
|
||
# )
|
||
# output_message, output_memory = phase.step(query, reinit_memory=True)
|
||
# # print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
|
||
# print(output_memory.get_spec_parserd_output())
|
||
# values = output_memory.get_spec_parserd_output()
|
||
# test_code = {k:v for i in values for k,v in i.items() if k in ["SaveFileName", "Test Code"]}
|
||
# test_cases.append(test_code)
|
||
|
||
# os.makedirs(f"{CB_ROOT_PATH}/tests", exist_ok=True)
|
||
|
||
# with open(f"{CB_ROOT_PATH}/tests/{test_code['SaveFileName']}", "w") as f:
|
||
# f.write(test_code["Test Code"]) |