128 lines
5.0 KiB
Python
128 lines
5.0 KiB
Python
# encoding: utf-8
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'''
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@author: 温进
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@file: codeChatPhaseLocal_example.py
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@time: 2024/1/31 下午4:32
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@desc:
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'''
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import os, sys, requests
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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import requests
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from typing import List
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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.schema import Message, Memory
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from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "1"
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llm_config = LLMConfig(
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model_name="gpt-3.5-turbo", model_device="cpu",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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# delete codebase
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codebase_name = 'client_nebula'
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code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
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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 = 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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# initialize codebase
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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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# chat with codebase
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phase_name = "codeChatPhase"
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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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# remove 这个函数是做什么的 => 基于标签
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# 有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码 => 基于描述
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# 有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串 => 基于描述
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## 需要启动容器中的nebula，采用use_nh=True来构建代码库，是可以通过cypher来查询
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# round-1
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query_content = "代码一共有多少类"
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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="client_1", 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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print(output_message1)
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# round-2
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query_content = "代码库里有哪些函数，返回5个就行"
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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="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
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)
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output_message2, _ = phase.step(query)
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print(output_message2)
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# round-3
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query_content = "remove 这个函数是做什么的"
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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=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
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use_nh=False, local_graph_path=CB_ROOT_PATH
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)
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output_message3, output_memory3 = phase.step(query)
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print(output_memory3.to_str_messages(return_all=True, content_key="parsed_output_list"))
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#
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# # round-4
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query_content = "有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码"
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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="description",
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use_nh=False, local_graph_path=CB_ROOT_PATH
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)
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output_message4, output_memory4 = phase.step(query)
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print(output_memory4.to_str_messages(return_all=True, content_key="parsed_output_list"))
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# # round-5
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query_content = "有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串"
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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="description",
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use_nh=False, local_graph_path=CB_ROOT_PATH
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)
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output_message5, output_memory5 = phase.step(query)
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print(output_memory5.to_str_messages(return_all=True, content_key="parsed_output_list"))
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