237 lines
9.1 KiB
Python
237 lines
9.1 KiB
Python
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import os, sys, requests
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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 *
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from dev_opsgpt.connector.phase import BasePhase
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from dev_opsgpt.connector.agents import BaseAgent
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from dev_opsgpt.connector.chains import BaseChain
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from dev_opsgpt.connector.schema import (
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Message, Memory, load_role_configs, load_phase_configs, load_chain_configs
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)
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from dev_opsgpt.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
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from dev_opsgpt.connector.utils import parse_section
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import importlib
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# update new agent configs
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codeRetrievalJudger_PROMPT = """#### CodeRetrievalJudger Assistance Guidance
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Given the user's question and respective code, you need to decide whether the provided codes are enough to answer the question.
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#### Input Format
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**Origin Query:** the initial question or objective that the user wanted to achieve
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**Retrieval Codes:** the Retrieval Codes from the code base
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#### Response Output Format
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**REASON:** Justify the decision of choosing 'finished' and 'continued' by evaluating the progress step by step.
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"""
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# 将下面的话放到上面的prompt里面去执行，让它判断是否停止
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# **Action Status:** Set to 'finished' or 'continued'.
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# If it's 'finished', the provided codes can answer the origin query.
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# If it's 'continued', the origin query cannot be answered well from the provided code.
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codeRetrievalDivergent_PROMPT = """#### CodeRetrievalDivergen Assistance Guidance
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You are a assistant that helps to determine which code package is needed to answer the question.
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Given the user's question, Retrieval code, and the code Packages related to Retrieval code. you need to decide which code package we need to read to better answer the question.
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#### Input Format
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**Origin Query:** the initial question or objective that the user wanted to achieve
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**Retrieval Codes:** the Retrieval Codes from the code base
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**Code Packages:** the code packages related to Retrieval code
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#### Response Output Format
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**Code Package:** Identify another Code Package from the Code Packages that should be read to provide a better answer to the Origin Query.
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**REASON:** Justify the decision of choosing 'finished' and 'continued' by evaluating the progress step by step.
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"""
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AGETN_CONFIGS.update({
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"codeRetrievalJudger": {
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"role": {
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"role_prompt": codeRetrievalJudger_PROMPT,
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"role_type": "assistant",
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"role_name": "codeRetrievalJudger",
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"role_desc": "",
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"agent_type": "CodeRetrievalJudger"
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# "agent_type": "BaseAgent"
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},
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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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"codeRetrievalDivergent": {
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"role": {
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"role_prompt": codeRetrievalDivergent_PROMPT,
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"role_type": "assistant",
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"role_name": "codeRetrievalDivergent",
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"role_desc": "",
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"agent_type": "CodeRetrievalDivergent"
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# "agent_type": "BaseAgent"
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},
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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": ["codeRetrievalJudger", "codeRetrievalDivergent"],
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"chat_turn": 5,
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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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"codeRetrievalPhase": {
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"phase_name": "codeRetrievalPhase",
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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": True,
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"do_tool_retrieval": False,
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"do_using_tool": 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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agent_module = importlib.import_module("dev_opsgpt.connector.agents")
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from dev_opsgpt.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
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# 定义一个新的类
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class CodeRetrievalJudger(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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action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query)
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message.customed_kargs["CodeRetrievalSingleRes"] = action_json
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message.customed_kargs.setdefault("Retrieval_Codes", "")
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message.customed_kargs["Retrieval_Codes"] += "\n" + action_json["code"]
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return message
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def create_prompt(
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self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_pool: Memory=None, prompt_mamnger=None) -> str:
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'''
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prompt engineer, contains role\task\tools\docs\memory
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'''
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#
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logger.debug(f"query: {query.customed_kargs}")
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formatted_tools, tool_names, _ = self.create_tools_prompt(query)
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prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
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#
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input_keys = parse_section(self.role.role_prompt, 'Input Format')
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prompt += "\n#### Begin!!!\n"
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#
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for input_key in input_keys:
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if input_key == "Origin Query":
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prompt += f"\n**{input_key}:**\n" + query.origin_query
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elif input_key == "Retrieval Codes":
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prompt += f"\n**{input_key}:**\n" + query.customed_kargs["Retrieval_Codes"]
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while "{{" in prompt or "}}" in prompt:
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prompt = prompt.replace("{{", "{")
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prompt = prompt.replace("}}", "}")
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return prompt
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# 定义一个新的类
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class CodeRetrievalDivergent(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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action_json = RelatedVerticesRetrival.run(message.code_engine_name, message.customed_kargs["CodeRetrievalSingleRes"]["vertex"])
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message.customed_kargs["RelatedVerticesRetrivalRes"] = action_json
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return message
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def end_action_step(self, message: Message) -> Message:
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'''do action before agent predict '''
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# logger.error(f"message: {message}")
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# action_json = Vertex2Code.run(message.code_engine_name, "com.theokanning.openai.client#Utils.java") # message.parsed_output["Code_Filename"])
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action_json = Vertex2Code.run(message.code_engine_name, message.parsed_output["Code Package"])
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message.customed_kargs["Vertex2Code"] = action_json
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message.customed_kargs.setdefault("Retrieval_Codes", "")
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message.customed_kargs["Retrieval_Codes"] += "\n" + action_json["code"]
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return message
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def create_prompt(
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self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_pool: Memory=None, prompt_mamnger=None) -> str:
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'''
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prompt engineer, contains role\task\tools\docs\memory
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'''
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formatted_tools, tool_names, _ = self.create_tools_prompt(query)
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prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
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#
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input_query = query.input_query
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input_keys = parse_section(self.role.role_prompt, 'Input Format')
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prompt += "\n#### Begin!!!\n"
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#
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for input_key in input_keys:
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if input_key == "Origin Query":
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prompt += f"\n**{input_key}:**\n" + query.origin_query
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elif input_key == "Retrieval Codes":
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prompt += f"\n**{input_key}:**\n" + query.customed_kargs["Retrieval_Codes"]
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elif input_key == "Code Packages":
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vertices = query.customed_kargs["RelatedVerticesRetrivalRes"]["vertices"]
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prompt += f"\n**{input_key}:**\n" + ", ".join([str(v) for v in vertices])
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while "{{" in prompt or "}}" in prompt:
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prompt = prompt.replace("{{", "{")
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prompt = prompt.replace("}}", "}")
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return prompt
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setattr(agent_module, 'CodeRetrievalJudger', CodeRetrievalJudger)
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setattr(agent_module, 'CodeRetrievalDivergent', CodeRetrievalDivergent)
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#
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phase_name = "codeRetrievalPhase"
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phase = BasePhase(phase_name,
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task = None,
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phase_config = PHASE_CONFIGS,
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chain_config = CHAIN_CONFIGS,
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role_config = AGETN_CONFIGS,
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do_summary=False,
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do_code_retrieval=False,
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do_doc_retrieval=False,
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do_search=False,
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)
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# round-1
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query_content = "remove 这个函数是用来做什么的"
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query = Message(
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role_name="user", role_type="human",
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role_content=query_content, input_query=query_content, origin_query=query_content,
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code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
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)
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output_message1, _ = phase.step(query)
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