389 lines
13 KiB
Markdown
389 lines
13 KiB
Markdown
---
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title: Quick Start
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slug: Quick Start
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url: "coagent/quick-start"
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aliases:
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- "/coagent/quick-start"
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---
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## Quick Start
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Attention：
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Testing has only been conducted on GPT-3.5-turbo and higher models.
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The models need to possess strong command-following capabilities.
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It is recommended to test with more powerful models like qwen-72b, openai, etc.
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### First, set up the LLM configuration
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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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### Next, configure the LLM settings and vector model
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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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### Finally, choose a pre-existing scenario to execute
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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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# Copy the data to a working directory; specify the directory if needed (default can also be used)
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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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# Choose a scenario to execute
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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: Use a code interpreter to complete tasks
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query_content = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
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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) # This function is used to preview the Prompt of the Agents' execution chain
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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: Execute tools
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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 = "Please check if there were any issues with the server at 127.0.0.1 at 10 o'clock; help me make a judgment"
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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) # This function is used to preview the Prompt of the Agents' execution chain
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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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## Phase Introduction and Usage
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Below are some specific Phase introduced and how to use them.
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Feel free to brainstorm and create some interesting cases.
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### baseGroupPhase
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The group usage Phase in autogen
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```
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# Copy the data to a working directory; specify the directory if needed (default can also be used)
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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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# Set the log level to control the printing of the prompt, LLM output, or other information
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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 = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
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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) # This function is used to preview the Prompt of the Agents' execution chain
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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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The task splitting and multi-step execution scenario in xAgents
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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 = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
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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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The code interpreter scenario based on React
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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 = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
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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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The tool invocation and code interpreter scenario based on the React template
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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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The knowledge base retrieval Q&A Phase
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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 = "what modules does langchain have?"
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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 = "What is the purpose of prompts?"
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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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The code construction Phase in metagpt
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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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The fixed Phase: search first, then answer directly with LLM
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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 = "who is the president of the United States?"
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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 = "Who was the previous president of the United States, and is there any relationship between the two individuals?"
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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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The tool invocation scene based on the React template
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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 = "Please check if there were any issues with the server at 127.0.0.1 at 10 o'clock; help me make a judgment"
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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) # This function is used to preview the Prompt of the Agents' execution chain
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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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``` |