codefuse-chatbot/sources/readme_docs/coagent/quick-start-en.md

title	slug	url	aliases
Quick Start	Quick Start	coagent/quick-start	/coagent/quick-start

Quick Start

Attention： Testing has only been conducted on GPT-3.5-turbo and higher models. The models need to possess strong command-following capabilities. It is recommended to test with more powerful models like qwen-72b, openai, etc.

First, set up the LLM configuration

import os, sys
import openai

# llm config
os.environ["API_BASE_URL"] = OPENAI_API_BASE
os.environ["OPENAI_API_KEY"] = "sk-xxx"
openai.api_key = "sk-xxx"
# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"

Next, configure the LLM settings and vector model

from coagent.llm_models.llm_config import EmbedConfig, LLMConfig

llm_config = LLMConfig(
    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
    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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
    )

Finally, choose a pre-existing scenario to execute

from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
from coagent.connector.phase import BasePhase
from coagent.connector.schema import Message

# Copy the data to a working directory; specify the directory if needed (default can also be used)
import shutil
source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
shutil.copy(source_file, JUPYTER_WORK_PATH)

# Choose a scenario to execute
phase_name = "baseGroupPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config, 
)

# round-1: Use a code interpreter to complete tasks
query_content = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
query = Message(
    role_name="human", role_type="user", tools=[],
    role_content=query_content, input_query=query_content, origin_query=query_content,
    )

# phase.pre_print(query)  # This function is used to preview the Prompt of the Agents' execution chain
output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

# round-2: Execute tools
tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])

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"
query = Message(
    role_name="human", role_type="user", tools=tools,
    role_content=query_content, input_query=query_content, origin_query=query_content,
    )

# phase.pre_print(query)  # This function is used to preview the Prompt of the Agents' execution chain
output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

Phase Introduction and Usage

Below are some specific Phase introduced and how to use them.

Feel free to brainstorm and create some interesting cases.

baseGroupPhase

The group usage Phase in autogen

# Copy the data to a working directory; specify the directory if needed (default can also be used)
import shutil
source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
shutil.copy(source_file, JUPYTER_WORK_PATH)

# Set the log level to control the printing of the prompt, LLM output, or other information
os.environ["log_verbose"] = "0"

phase_name = "baseGroupPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config, 
)

# round-1
query_content = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"

query = Message(
    role_name="human", role_type="user", tools=[],
    role_content=query_content, input_query=query_content, origin_query=query_content,
    )

# phase.pre_print(query) # This function is used to preview the Prompt of the Agents' execution chain
output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

baseTaskPhase

The task splitting and multi-step execution scenario in xAgents

# if you want to analyze a data.csv, please put the csv file into a jupyter_work_path (or your defined path)
import shutil
source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
shutil.copy(source_file, JUPYTER_WORK_PATH)

# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

phase_name = "baseTaskPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config,
)
# round-1
query_content = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
query = Message(
    role_name="human", role_type="user",
    role_content=query_content, input_query=query_content, origin_query=query_content,
    )

output_message, output_memory = phase.step(query)

print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

codeReactPhase

The code interpreter scenario based on React

# if you want to analyze a data.csv, please put the csv file into a jupyter_work_path (or your defined path)
import shutil
source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/book_data.csv'
shutil.copy(source_file, JUPYTER_WORK_PATH)

# then, create a data analyze phase
phase_name = "codeReactPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config, 
    jupyter_work_path=JUPYTER_WORK_PATH,
)

# round-1
query_content = "Check if 'employee_data.csv' exists locally, view its columns and data types; then draw a bar chart"
query = Message(
    role_name="human", role_type="user",
    role_content=query_content, input_query=query_content, origin_query=query_content,
    )

output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

codeToolReactPhase

The tool invocation and code interpreter scenario based on the React template

TOOL_SETS = [
     "StockName", "StockInfo", 
    ]
tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])

# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

phase_name = "codeToolReactPhase"

phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config, 
)

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"

query = Message(
  role_name="human", role_type="user", 
  input_query=query_content, role_content=query_content, 
  origin_query=query_content, tools=tools
  )

output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

docChatPhase

The knowledge base retrieval Q&A Phase

# create your knowledge base
from io import BytesIO
from pathlib import Path

from coagent.service.kb_api import create_kb, upload_doc
from coagent.service.service_factory import get_kb_details
from coagent.utils.server_utils import run_async
kb_list = {x["kb_name"]: x for x in get_kb_details(KB_ROOT_PATH)}


# create a knowledge base
kb_name = "example_test"
data = {
    "knowledge_base_name": kb_name,
    "vector_store_type": "faiss", # default
    "kb_root_path": KB_ROOT_PATH, 
    "embed_model": embed_config.embed_model,
    "embed_engine": embed_config.embed_engine, 
    "embed_model_path": embed_config.embed_model_path,
    "model_device": embed_config.model_device,
}
run_async(create_kb(**data))

# add doc to knowledge base
file = os.path.join("D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/sources/docs/langchain_text_10.jsonl")
files = [file]
# if embedding init failed, you can use override = True
data = [{"override": True, "file": f, 
         "knowledge_base_name": kb_name, "not_refresh_vs_cache": False,
         "kb_root_path": KB_ROOT_PATH, "embed_model": embed_config.embed_model,
         "embed_engine": embed_config.embed_engine, "embed_model_path": embed_config.embed_model_path,
         "model_device": embed_config.model_device,
         } 
         for f in files]

for k in data:
    file = Path(file).absolute().open("rb")
    filename = file.name

    from fastapi import UploadFile
    from tempfile import SpooledTemporaryFile

    temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
    temp_file.write(file.read())
    temp_file.seek(0)
    
    k.update({"file": UploadFile(file=temp_file, filename=filename),})
    run_async(upload_doc(**k))


# start to chat with knowledge base
# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

# set chat phase
phase_name = "docChatPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config,
)
# round-1
query_content = "what modules does langchain have?"
query = Message(
    role_name="human", role_type="user", 
    origin_query=query_content,
    doc_engine_name=kb_name, score_threshold=1.0, top_k=3
    )

output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

# round-2
query_content = "What is the purpose of prompts?"
query = Message(
    role_name="human", role_type="user",
    origin_query=query_content,
    doc_engine_name=kb_name, score_threshold=1.0, top_k=3
    )
output_message, output_memory = phase.step(query)

print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

metagpt_code_devlop

The code construction Phase in metagpt

# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

phase_name = "metagpt_code_devlop"
llm_config = LLMConfig(
    model_name="gpt-4", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
    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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
    )

phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config,
)

query_content = "create a snake game by pygame"
query = Message(role_name="human", role_type="user", input_query=query_content, role_content=query_content, origin_query=query_content)

output_message, output_memory = phase.step(query)

print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

searchChatPhase

The fixed Phase: search first, then answer directly with LLM

# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

phase_name = "searchChatPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config, 
)

# round-1
query_content1 = "who is the president of the United States?"
query = Message(
    role_name="human", role_type="user", 
    role_content=query_content1, input_query=query_content1, origin_query=query_content1,
    search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
    )

output_message, output_memory = phase.step(query)

print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

# round-2
query_content2 = "Who was the previous president of the United States, and is there any relationship between the two individuals?"
query = Message(
    role_name="human", role_type="user", 
    role_content=query_content2, input_query=query_content2, origin_query=query_content2,
    search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
    )
output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))

toolReactPhase

The tool invocation scene based on the React template

# log-level，print prompt和llm predict
os.environ["log_verbose"] = "2"

phase_name = "toolReactPhase"
phase = BasePhase(
    phase_name, embed_config=embed_config, llm_config=llm_config,
)

# round-1
tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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"
query = Message(
    role_name="human", role_type="user", tools=tools,
    role_content=query_content, input_query=query_content, origin_query=query_content
    )

# phase.pre_print(query)  # This function is used to preview the Prompt of the Agents' execution chain
output_message, output_memory = phase.step(query)
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))