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

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---
title: Quick Start
slug: Quick Start
url: "coagent/quick-start"
aliases:
- "/coagent/quick-start"
---
## Quick Start
2024-01-29 11:40:31 +08:00
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-levelprint 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-levelprint 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-levelprint 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-levelprint 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-levelprint 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-levelprint 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"))
```