lagent
v0.5.0rc1
bergabunglah dengan kami di ? (Twitter), Perselisihan dan WeChat
Instal dari sumber:
git clone https://github.com/InternLM/lagent.git
cd lagent
pip install -e . Lagent terinspirasi oleh filosofi desain PyTorch. Kami berharap analogi lapisan jaringan saraf akan membuat alur kerja lebih jelas dan intuitif, sehingga pengguna hanya perlu fokus pada pembuatan lapisan dan mendefinisikan pesan yang lewat di antara lapisan tersebut dengan cara Pythonic. Ini adalah tutorial sederhana untuk membantu Anda memulai dengan cepat dalam membuat aplikasi multi-agen.
Agen menggunakan AgentMessage untuk komunikasi.
from typing import Dict , List
from lagent . agents import Agent
from lagent . schema import AgentMessage
from lagent . llms import VllmModel , INTERNLM2_META
llm = VllmModel (
path = 'Qwen/Qwen2-7B-Instruct' ,
meta_template = INTERNLM2_META ,
tp = 1 ,
top_k = 1 ,
temperature = 1.0 ,
stop_words = [ '<|im_end|>' ],
max_new_tokens = 1024 ,
)
system_prompt = '你的回答只能从“典”、“孝”、“急”三个字中选一个。'
agent = Agent ( llm , system_prompt )
user_msg = AgentMessage ( sender = 'user' , content = '今天天气情况' )
bot_msg = agent ( user_msg )
print ( bot_msg ) content='急' sender='Agent' formatted=None extra_info=None type=None receiver=None stream_state=
Pesan masukan dan keluaran akan ditambahkan ke memori Agent di setiap penerusan. Ini dilakukan di __call__ daripada forward . Lihat kode semu berikut
def __call__ ( self , * message ):
message = pre_hooks ( message )
add_memory ( message )
message = self . forward ( * message )
add_memory ( message )
message = post_hooks ( message )
return messagePeriksa memori dengan dua cara
memory : List [ AgentMessage ] = agent . memory . get_memory ()
print ( memory )
print ( '-' * 120 )
dumped_memory : Dict [ str , List [ dict ]] = agent . state_dict ()
print ( dumped_memory [ 'memory' ]) [AgentMessage(content='今天天气情况', sender='user', formatted=None, extra_info=None, type=None, receiver=None, stream_state=), AgentMessage(content='急', sender='Agent', formatted=None, extra_info=None, type=None, receiver=None, stream_state=)]
------------------------------------------------------------------------------------------------------------------------
[{'content': '今天天气情况', 'sender': 'user', 'formatted': None, 'extra_info': None, 'type': None, 'receiver': None, 'stream_state': }, {'content': '急', 'sender': 'Agent', 'formatted': None, 'extra_info': None, 'type': None, 'receiver': None, 'stream_state': }]
Hapus memori sesi ini ( session_id=0 secara default):
agent . reset () DefaultAggregator dipanggil untuk merakit dan mengonversi AgentMessage ke format pesan OpenAI.
def forward ( self , * message : AgentMessage , session_id = 0 , ** kwargs ) -> Union [ AgentMessage , str ]:
formatted_messages = self . aggregator . aggregate (
self . memory . get ( session_id ),
self . name ,
self . output_format ,
self . template ,
)
llm_response = self . llm . chat ( formatted_messages , ** kwargs )
...Terapkan agregator sederhana yang dapat menerima beberapa gambar
from typing import List , Union
from lagent . memory import Memory
from lagent . prompts import StrParser
from lagent . agents . aggregator import DefaultAggregator
class FewshotAggregator ( DefaultAggregator ):
def __init__ ( self , few_shot : List [ dict ] = None ):
self . few_shot = few_shot or []
def aggregate ( self ,
messages : Memory ,
name : str ,
parser : StrParser = None ,
system_instruction : Union [ str , dict , List [ dict ]] = None ) -> List [ dict ]:
_message = []
if system_instruction :
_message . extend (
self . aggregate_system_intruction ( system_instruction ))
_message . extend ( self . few_shot )
messages = messages . get_memory ()
for message in messages :
if message . sender == name :
_message . append (
dict ( role = 'assistant' , content = str ( message . content )))
else :
user_message = message . content
if len ( _message ) > 0 and _message [ - 1 ][ 'role' ] == 'user' :
_message [ - 1 ][ 'content' ] += user_message
else :
_message . append ( dict ( role = 'user' , content = user_message ))
return _message
agent = Agent (
llm ,
aggregator = FewshotAggregator (
[
{ "role" : "user" , "content" : "今天天气" },
{ "role" : "assistant" , "content" : "【晴】" },
]
)
)
user_msg = AgentMessage ( sender = 'user' , content = '昨天天气' )
bot_msg = agent ( user_msg )
print ( bot_msg ) content='【多云转晴，夜间有轻微降温】' sender='Agent' formatted=None extra_info=None type=None receiver=None stream_state=
Dalam AgentMessage , formatted dicadangkan untuk menyimpan informasi yang diurai oleh output_format dari output model.
def forward ( self , * message : AgentMessage , session_id = 0 , ** kwargs ) -> Union [ AgentMessage , str ]:
...
llm_response = self . llm . chat ( formatted_messages , ** kwargs )
if self . output_format :
formatted_messages = self . output_format . parse_response ( llm_response )
return AgentMessage (
sender = self . name ,
content = llm_response ,
formatted = formatted_messages ,
)
...Gunakan alat parser sebagai berikut
from lagent . prompts . parsers import ToolParser
system_prompt = "逐步分析并编写Python代码解决以下问题。"
parser = ToolParser ( tool_type = 'code interpreter' , begin = '```python n ' , end = ' n ``` n ' )
llm . gen_params [ 'stop_words' ]. append ( ' n ``` n ' )
agent = Agent ( llm , system_prompt , output_format = parser )
user_msg = AgentMessage (
sender = 'user' ,
content = 'Marie is thinking of a multiple of 63, while Jay is thinking of a '
'factor of 63. They happen to be thinking of the same number. There are '
'two possibilities for the number that each of them is thinking of, one '
'positive and one negative. Find the product of these two numbers.' )
bot_msg = agent ( user_msg )
print ( bot_msg . model_dump_json ( indent = 4 )) {
"content": "首先，我们需要找出63的所有正因数和负因数。63的正因数可以通过分解63的质因数来找出，即\(63 = 3^2 \times 7\)。因此，63的正因数包括1, 3, 7, 9, 21, 和 63。对于负因数，我们只需将上述正因数乘以-1。nn接下来，我们需要找出与63的正因数相乘的结果为63的数，以及与63的负因数相乘的结果为63的数。这可以通过将63除以每个正因数和负因数来实现。nn最后，我们将找到的两个数相乘得到最终答案。nn下面是Python代码实现：nn```pythonndef find_numbers():n # 正因数n positive_factors = [1, 3, 7, 9, 21, 63]n # 负因数n negative_factors = [-1, -3, -7, -9, -21, -63]n n # 找到与正因数相乘的结果为63的数n positive_numbers = [63 / factor for factor in positive_factors]n # 找到与负因数相乘的结果为63的数n negative_numbers = [-63 / factor for factor in negative_factors]n n # 计算两个数的乘积n product = positive_numbers[0] * negative_numbers[0]n n return productnnresult = find_numbers()nprint(result)",
"sender": "Agent",
"formatted": {
"tool_type": "code interpreter",
"thought": "首先，我们需要找出63的所有正因数和负因数。63的正因数可以通过分解63的质因数来找出，即\(63 = 3^2 \times 7\)。因此，63的正因数包括1, 3, 7, 9, 21, 和 63。对于负因数，我们只需将上述正因数乘以-1。nn接下来，我们需要找出与63的正因数相乘的结果为63的数，以及与63的负因数相乘的结果为63的数。这可以通过将63除以每个正因数和负因数来实现。nn最后，我们将找到的两个数相乘得到最终答案。nn下面是Python代码实现：nn",
"action": "def find_numbers():n # 正因数n positive_factors = [1, 3, 7, 9, 21, 63]n # 负因数n negative_factors = [-1, -3, -7, -9, -21, -63]n n # 找到与正因数相乘的结果为63的数n positive_numbers = [63 / factor for factor in positive_factors]n # 找到与负因数相乘的结果为63的数n negative_numbers = [-63 / factor for factor in negative_factors]n n # 计算两个数的乘积n product = positive_numbers[0] * negative_numbers[0]n n return productnnresult = find_numbers()nprint(result)",
"status": 1
},
"extra_info": null,
"type": null,
"receiver": null,
"stream_state": 0
}
ActionExecutor menggunakan struktur data komunikasi yang sama dengan Agent , tetapi memerlukan konten input AgentMessage berupa dict yang berisi:
name : nama alat, misalnya 'IPythonInterpreter' , 'WebBrowser.search' .parameters : argumen kata kunci dari alat API, misalnya {'command': 'import math;math.sqrt(2)'} , {'query': ['recent progress in AI']} .Anda dapat mendaftarkan kait khusus untuk konversi pesan.
from lagent . hooks import Hook
from lagent . schema import ActionReturn , ActionStatusCode , AgentMessage
from lagent . actions import ActionExecutor , IPythonInteractive
class CodeProcessor ( Hook ):
def before_action ( self , executor , message , session_id ):
message = message . copy ( deep = True )
message . content = dict (
name = 'IPythonInteractive' , parameters = { 'command' : message . formatted [ 'action' ]}
)
return message
def after_action ( self , executor , message , session_id ):
action_return = message . content
if isinstance ( action_return , ActionReturn ):
if action_return . state == ActionStatusCode . SUCCESS :
response = action_return . format_result ()
else :
response = action_return . errmsg
else :
response = action_return
message . content = response
return message
executor = ActionExecutor ( actions = [ IPythonInteractive ()], hooks = [ CodeProcessor ()])
bot_msg = AgentMessage (
sender = 'Agent' ,
content = '首先，我们需要...' ,
formatted = {
'tool_type' : 'code interpreter' ,
'thought' : '首先，我们需要...' ,
'action' : 'def find_numbers(): n # 正因数n positive_factors = [1, 3, 7, 9, 21, 63] n # 负因数n negative_factors = [-1, -3, -7, -9, -21, -63] n n # 找到与正因数相乘的结果为63的数n positive_numbers = [63 / factor for factor in positive_factors] n # 找到与负因数相乘的结果为63的数n negative_numbers = [-63 / factor for factor in negative_factors] n n # 计算两个数的乘积n product = positive_numbers[0] * negative_numbers[0] n n return product n n result = find_numbers() n print(result)' ,
'status' : 1
})
executor_msg = executor ( bot_msg )
print ( executor_msg ) content='3969.0' sender='ActionExecutor' formatted=None extra_info=None type=None receiver=None stream_state=
Untuk kenyamanan, Lagent menyediakan InternLMActionProcessor yang disesuaikan dengan pesan yang diformat oleh ToolParser seperti disebutkan di atas.
Lagent mengadopsi desain antarmuka ganda, di mana hampir setiap komponen (LLM, tindakan, pelaksana tindakan...) memiliki varian asinkron yang sesuai dengan mengawali pengidentifikasinya dengan 'Async'. Disarankan untuk menggunakan agen sinkron untuk debugging dan agen asinkron untuk inferensi skala besar guna memaksimalkan sumber daya CPU dan GPU yang menganggur.
Namun, pastikan konsistensi internal agen, yaitu agen asinkron harus dilengkapi dengan LLM asinkron dan pelaksana tindakan asinkron yang menggerakkan alat asinkron.
from lagent . llms import VllmModel , AsyncVllmModel , LMDeployPipeline , AsyncLMDeployPipeline
from lagent . actions import ActionExecutor , AsyncActionExecutor , WebBrowser , AsyncWebBrowser
from lagent . agents import Agent , AsyncAgent , AgentForInternLM , AsyncAgentForInternLM forward alih-alih __call__ subkelas kecuali diperlukan.session_id secara eksplisit, yang dirancang untuk isolasi memori, permintaan LLM, dan pemanggilan alat (misalnya memelihara beberapa lingkungan IPython independen) secara bersamaan.Agen matematika yang memecahkan masalah dengan pemrograman
from lagent . agents . aggregator import InternLMToolAggregator
class Coder ( Agent ):
def __init__ ( self , model_path , system_prompt , max_turn = 3 ):
super (). __init__ ()
llm = VllmModel (
path = model_path ,
meta_template = INTERNLM2_META ,
tp = 1 ,
top_k = 1 ,
temperature = 1.0 ,
stop_words = [ ' n ``` n ' , '<|im_end|>' ],
max_new_tokens = 1024 ,
)
self . agent = Agent (
llm ,
system_prompt ,
output_format = ToolParser (
tool_type = 'code interpreter' , begin = '```python n ' , end = ' n ``` n '
),
# `InternLMToolAggregator` is adapted to `ToolParser` for aggregating
# messages with tool invocations and execution results
aggregator = InternLMToolAggregator (),
)
self . executor = ActionExecutor ([ IPythonInteractive ()], hooks = [ CodeProcessor ()])
self . max_turn = max_turn
def forward ( self , message : AgentMessage , session_id = 0 ) -> AgentMessage :
for _ in range ( self . max_turn ):
message = self . agent ( message , session_id = session_id )
if message . formatted [ 'tool_type' ] is None :
return message
message = self . executor ( message , session_id = session_id )
return message
coder = Coder ( 'Qwen/Qwen2-7B-Instruct' , 'Solve the problem step by step with assistance of Python code' )
query = AgentMessage (
sender = 'user' ,
content = 'Find the projection of $ \ mathbf{a}$ onto $ \ mathbf{b} = '
' \ begin{pmatrix} 1 \ \ -3 \ end{pmatrix}$ if $ \ mathbf{a} \ cdot \ mathbf{b} = 2.$'
)
answer = coder ( query )
print ( answer . content )
print ( '-' * 120 )
for msg in coder . state_dict ()[ 'agent.memory' ]:
print ( '*' * 80 )
print ( f' { msg [ "sender" ] } : n n { msg [ "content" ] } ' )Agen blog asinkron yang meningkatkan kualitas penulisan dengan penyempurnaan mandiri (contoh AutoGen asli)
import asyncio
import os
from lagent . llms import AsyncGPTAPI
from lagent . agents import AsyncAgent
os . environ [ 'OPENAI_API_KEY' ] = 'YOUR_API_KEY'
class PrefixedMessageHook ( Hook ):
def __init__ ( self , prefix : str , senders : list = None ):
self . prefix = prefix
self . senders = senders or []
def before_agent ( self , agent , messages , session_id ):
for message in messages :
if message . sender in self . senders :
message . content = self . prefix + message . content
class AsyncBlogger ( AsyncAgent ):
def __init__ ( self , model_path , writer_prompt , critic_prompt , critic_prefix = '' , max_turn = 3 ):
super (). __init__ ()
llm = AsyncGPTAPI ( model_type = model_path , retry = 5 , max_new_tokens = 2048 )
self . writer = AsyncAgent ( llm , writer_prompt , name = 'writer' )
self . critic = AsyncAgent (
llm , critic_prompt , name = 'critic' , hooks = [ PrefixedMessageHook ( critic_prefix , [ 'writer' ])]
)
self . max_turn = max_turn
async def forward ( self , message : AgentMessage , session_id = 0 ) -> AgentMessage :
for _ in range ( self . max_turn ):
message = await self . writer ( message , session_id = session_id )
message = await self . critic ( message , session_id = session_id )
return await self . writer ( message , session_id = session_id )
blogger = AsyncBlogger (
'gpt-4o-2024-05-13' ,
writer_prompt = "You are an writing assistant tasked to write engaging blogpost. You try to generate the best blogpost possible for the user's request. "
"If the user provides critique, then respond with a revised version of your previous attempts" ,
critic_prompt = "Generate critique and recommendations on the writing. Provide detailed recommendations, including requests for length, depth, style, etc.." ,
critic_prefix = 'Reflect and provide critique on the following writing. n n ' ,
)
user_prompt = (
"Write an engaging blogpost on the recent updates in {topic}. "
"The blogpost should be engaging and understandable for general audience. "
"Should have more than 3 paragraphes but no longer than 1000 words." )
bot_msgs = asyncio . get_event_loop (). run_until_complete (
asyncio . gather (
* [
blogger ( AgentMessage ( sender = 'user' , content = user_prompt . format ( topic = topic )), session_id = i )
for i , topic in enumerate ([ 'AI' , 'Biotechnology' , 'New Energy' , 'Video Games' , 'Pop Music' ])
]
)
)
print ( bot_msgs [ 0 ]. content )
print ( '-' * 120 )
for msg in blogger . state_dict ( session_id = 0 )[ 'writer.memory' ]:
print ( '*' * 80 )
print ( f' { msg [ "sender" ] } : n n { msg [ "content" ] } ' )
print ( '-' * 120 )
for msg in blogger . state_dict ( session_id = 0 )[ 'critic.memory' ]:
print ( '*' * 80 )
print ( f' { msg [ "sender" ] } : n n { msg [ "content" ] } ' )Alur kerja multi-agen yang melakukan pengambilan informasi, pengumpulan data, dan pembuatan diagram (contoh LangGraph asli)
