384 lines
13 KiB
Markdown
384 lines
13 KiB
Markdown
---
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title: 快速开始
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slug: 快速开始
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url: "coagent/快速开始"
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aliases:
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- "/coagent/快速开始"
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- "/coagent/quick-start-zh"
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---
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## 快速使用
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### 首先，填写LLM配置
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```
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import os, sys
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import openai
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# llm config
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os.environ["API_BASE_URL"] = OPENAI_API_BASE
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os.environ["OPENAI_API_KEY"] = "sk-xxx"
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openai.api_key = "sk-xxx"
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# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
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```
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### 然后设置LLM配置和向量模型配置
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```
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
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)
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```
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### 最后选择一个已有场景进行执行
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```
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from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
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from coagent.connector.phase import BasePhase
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from coagent.connector.schema import Message
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# 选择一个已实现得场景进行执行
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# 如果需要做一个数据分析，需要将数据放到某个工作目录，同时指定工作目录（也可使用默认目录）
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import shutil
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source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
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shutil.copy(source_file, JUPYTER_WORK_PATH)
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# 选择一个场景
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phase_name = "baseGroupPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1 需要通过代码解释器来完成
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query_content = "确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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query = Message(
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role_name="human", role_type="user", tools=[],
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role_content=query_content, input_query=query_content, origin_query=query_content,
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)
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# phase.pre_print(query) # 该功能用于预打印 Agents 执行链路的Prompt
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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# round-2 需要执行工具
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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query_content = "帮我确认下127.0.0.1这个服务器的在10点是否存在异常，请帮我判断一下"
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query = Message(
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role_name="human", role_type="user", tools=tools,
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role_content=query_content, input_query=query_content, origin_query=query_content,
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)
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# phase.pre_print(query) # 该功能用于预打印 Agents 执行链路的Prompt
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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## 场景介绍和使用
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下面是一些具体的场景介绍和使用。
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欢迎大家开脑洞构造一些有趣的case。
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### baseGroupPhase
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autogen的group使用场景
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```
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# 如果需要做一个数据分析，需要将数据放到某个工作目录，同时指定工作目录（也可使用默认目录）
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import shutil
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source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
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shutil.copy(source_file, JUPYTER_WORK_PATH)
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# 设置日志级别，控制打印prompt或者llm 输出或其它信息
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os.environ["log_verbose"] = "0"
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phase_name = "baseGroupPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1
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query_content = "确认本地是否存在book_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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query = Message(
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role_name="human", role_type="user", tools=[],
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role_content=query_content, input_query=query_content, origin_query=query_content,
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)
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# phase.pre_print(query) # 该功能用于预打印 Agents 执行链路的Prompt
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### baseTaskPhase
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xAgents的任务拆分及多步骤执行场景
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```
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# if you want to analyze a data.csv, please put the csv file into a jupyter_work_path (or your defined path)
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import shutil
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source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
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shutil.copy(source_file, JUPYTER_WORK_PATH)
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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phase_name = "baseTaskPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1
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query_content = "确认本地是否存在book_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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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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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### codeReactPhase
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基于 React 的代码解释器场景
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```
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# if you want to analyze a data.csv, please put the csv file into a jupyter_work_path (or your defined path)
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import shutil
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source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
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shutil.copy(source_file, JUPYTER_WORK_PATH)
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# then, create a data analyze phase
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phase_name = "codeReactPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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jupyter_work_path=JUPYTER_WORK_PATH,
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)
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# round-1
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query_content = "确认本地是否存在book_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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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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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### codeToolReactPhase
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基于 React 模板的工具调用和代码解释器场景
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```
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TOOL_SETS = [
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"StockName", "StockInfo",
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]
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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phase_name = "codeToolReactPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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query_content = "查询贵州茅台的股票代码，并查询截止到当前日期(2023年12月24日)的最近10天的每日时序数据，然后用代码画出折线图并分析"
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query = Message(
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role_name="human", role_type="user",
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input_query=query_content, role_content=query_content,
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origin_query=query_content, tools=tools
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### docChatPhase
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知识库检索问答链路
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```
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# create your knowledge base
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from io import BytesIO
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from pathlib import Path
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from coagent.service.kb_api import create_kb, upload_doc
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from coagent.service.service_factory import get_kb_details
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from coagent.utils.server_utils import run_async
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kb_list = {x["kb_name"]: x for x in get_kb_details(KB_ROOT_PATH)}
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# create a knowledge base
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kb_name = "example_test"
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data = {
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"knowledge_base_name": kb_name,
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"vector_store_type": "faiss", # default
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"kb_root_path": KB_ROOT_PATH,
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"embed_model": embed_config.embed_model,
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"embed_engine": embed_config.embed_engine,
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"embed_model_path": embed_config.embed_model_path,
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"model_device": embed_config.model_device,
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}
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run_async(create_kb(**data))
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# add doc to knowledge base
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file = os.path.join("D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/sources/docs/langchain_text_10.jsonl")
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files = [file]
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# if embedding init failed, you can use override = True
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data = [{"override": True, "file": f,
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"knowledge_base_name": kb_name, "not_refresh_vs_cache": False,
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"kb_root_path": KB_ROOT_PATH, "embed_model": embed_config.embed_model,
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"embed_engine": embed_config.embed_engine, "embed_model_path": embed_config.embed_model_path,
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"model_device": embed_config.model_device,
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}
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for f in files]
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for k in data:
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file = Path(file).absolute().open("rb")
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filename = file.name
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from fastapi import UploadFile
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from tempfile import SpooledTemporaryFile
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temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
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temp_file.write(file.read())
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temp_file.seek(0)
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k.update({"file": UploadFile(file=temp_file, filename=filename),})
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run_async(upload_doc(**k))
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# start to chat with knowledge base
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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# set chat phase
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phase_name = "docChatPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1
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query_content = "langchain有哪些模块"
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query = Message(
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role_name="human", role_type="user",
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origin_query=query_content,
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doc_engine_name=kb_name, score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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# round-2
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query_content = "提示（prompts）有什么用？"
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query = Message(
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role_name="human", role_type="user",
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origin_query=query_content,
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doc_engine_name=kb_name, score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### metagpt_code_devlop
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metagpt的代码构造链路
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```
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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phase_name = "metagpt_code_devlop"
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llm_config = LLMConfig(
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model_name="gpt-4", 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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
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)
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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query_content = "create a snake game by pygame"
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query = Message(role_name="human", role_type="user", input_query=query_content, role_content=query_content, origin_query=query_content)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### searchChatPhase
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固定场景链路，先搜索后基于LLM直接回答
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```
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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phase_name = "searchChatPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1
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query_content1 = "美国当前总统是谁？"
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query = Message(
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role_name="human", role_type="user",
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role_content=query_content1, input_query=query_content1, origin_query=query_content1,
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search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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# round-2
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query_content2 = "美国上一任总统是谁，两个人有什么关系没？"
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query = Message(
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role_name="human", role_type="user",
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role_content=query_content2, input_query=query_content2, origin_query=query_content2,
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search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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```
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### toolReactPhase
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基于 React 模板的工具调用场景
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```
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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phase_name = "toolReactPhase"
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phase = BasePhase(
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phase_name, embed_config=embed_config, llm_config=llm_config,
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)
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# round-1
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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query_content = "帮我确认下127.0.0.1这个服务器的在10点是否存在异常，请帮我判断一下"
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query = Message(
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role_name="human", role_type="user", tools=tools,
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role_content=query_content, input_query=query_content, origin_query=query_content
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)
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# phase.pre_print(query) # 该功能用于预打印 Agents 执行链路的Prompt
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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``` |