poe api wrapper
v1.6.0
Простая, легкая и эффективная оболочка API для Poe.com.
pip install -U poe-api-wrapperИли вы можете установить версию этой библиотеки с автоматическим прокси для Python 3.9+.
pip install -U 'poe-api-wrapper[proxy]'Быстрая настройка Async Client:
from poe_api_wrapper import AsyncPoeApi
import asyncio
tokens = {
'p-b' : ...,
'p-lat' : ...,
}
async def main ():
client = await AsyncPoeApi ( tokens = tokens ). create ()
message = "Explain quantum computing in simple terms"
async for chunk in client . send_message ( bot = "gpt3_5" , message = message ):
print ( chunk [ "response" ], end = '' , flush = True )
asyncio . run ( main ()) from poe_api_wrapper import PoeExample
tokens = {
'p-b' : ...,
'p-lat' : ...,
}
PoeExample ( tokens = tokens ). chat_with_bot () poe -b P-B_HERE -lat P-LAT_HERE -f FORMKEY_HERE Кончик
Введите poe -h для получения дополнительной информации.
| Отображаемое имя | Модель | Лимит токенов | Слова | Тип доступа |
|---|---|---|---|---|
| Ассистент | капибара | 4К | 3К | |
| Клод-3.5-Сонет | claude_3_igloo | 4К | 3К | |
| Клод-3-Опус | claude_2_1_cedar | 4К | 3К | |
| Клод-3-Сонет | claude_2_1_bamboo | 4К | 3К | |
| Клод-3-Хайку | claude_3_haiku | 4К | 3К | |
| Клод-3.5-Сонет-200к | claude_3_igloo_200k | 200 тыс. | 150 тыс. | |
| Клод-3-Опус-200к | claude_3_opus_200k | 200 тыс. | 150 тыс. | |
| Клод-3-Сонет-200к | claude_3_sonnet_200k | 200 тыс. | 150 тыс. | |
| Клод-3-Хайку-200к | claude_3_haiku_200k | 200 тыс. | 150 тыс. | |
| Клод-2 | claude_2_short | 4К | 3К | |
| Клод-2-100к | а2_2 | 100 тыс. | 75 тыс. | |
| Клод-мгновенный | а2 | 9К | 7К | |
| Клод-мгновенный-100к | a2_100k | 100 тыс. | 75 тыс. | |
| ГПТ-3,5-Турбо | шиншилла | 4К | 3К | |
| GPT-3.5-Турбо-Сырой | gpt3_5 | 2 тыс. | 1,5 тыс. | |
| GPT-3.5-Турбо-Инструкт | шиншилла_instruct | 2К | 1,5 тыс. | |
| ЧатGPT-16k | агути | 16К | 12К | |
| GPT-4-Классик | gpt4_classic | 2К | 1,5 тыс. | |
| ГПТ-4-Турбо | бобер | 4К | 3К | |
| ГПТ-4-Турбо-128к | вискаша | 128 тыс. | 96К | |
| ГПТ-4о | gpt4_o | 4к | 3К | |
| ГПТ-4о-128к | gpt4_o_128k | 128 тыс. | 96К | |
| GPT-4o-Мини | gpt4_o_mini | 4К | 3К | |
| ГПТ-4о-Мини-128к | gpt4_o_mini_128k | 128 тыс. | 96К | |
| Google-PaLM | тупой | 8К | 6К | |
| Код-Лама-13b | code_llama_13b_instruct | 4К | 3К | |
| Код-Лама-34b | code_llama_34b_instruct | 4К | 3К | |
| Солар-Мини | upstage_solar_0_70b_16bit | 2К | 1,5 тыс. | |
| Gemini-1.5-Flash-Поиск | Gemini_pro_search | 4К | 3К | |
| Близнецы-1,5-Про-2М | Gemini_1_5_pro_1m | 2М | 1,5М |
Важный
Приведенные выше данные об ограничениях токенов и количестве слов являются приблизительными и могут быть не совсем точными, поскольку процесс предварительного проектирования poe.com является частным и не разглашается публично.
В таблице выше показаны только боты с отображаемыми именами, отличными от их моделей. Другие боты на poe.com имеют то же отображаемое имя, что и модель.
Войдите на https://poe.com/
F12 для инструментов разработчика (щелкните правой кнопкой мыши + проверьте)
Скопируйте значения файлов cookie pb и p-lat
Важный
По умолчанию poe-api-wrapper автоматически получит для вас ключ формы. Если это не сработает, передайте этот токен вручную, выполнив следующие действия:
Получить formkey можно двумя способами:
F12 для инструментов разработчика (щелкните правой кнопкой мыши + проверьте)
1-й метод: Инструменты разработчика > Сеть > gql_POST > Заголовки > Poe-Formkey
Скопируйте значение Poe-Formkey
2-й метод: Инструменты разработчика > Консоль > Тип: allow pasting > Вставьте этот скрипт: window.ereNdsRqhp2Rd3LEW()
Скопируйте результат
pip install -U 'poe-api-wrapper[llm]'openai : git clone https://github.com/snowby666/poe-api-wrapper.git
cd poe-api-wrapperpoe_api_wrapperopenaiИзмените secrets.json своими собственными токенами.
Запустите сервер FastAPI:
python api.py python example.py from poe_api_wrapper import PoeServer
tokens = [
{ "p-b" : "XXXXXXXX" , "p-lat" : "XXXXXXXX" },
{ "p-b" : "XXXXXXXX" , "p-lat" : "XXXXXXXX" },
{ "p-b" : "XXXXXXXX" , "p-lat" : "XXXXXXXX" }
]
PoeServer ( tokens = tokens )
# You can also specify address and port (default is 127.0.0.1:8000)
PoeServer ( tokens = tokens , address = "0.0.0.0" , port = "8080" ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
response = client . chat . completions . create (
model = "gpt-3.5-turbo" ,
messages = [
{ "role" : "system" , "content" : "You are a helpful assistant." },
{ "role" : "user" , "content" : "Hello!" }
]
)
print ( response . choices [ 0 ]. message . content ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
stream = client . chat . completions . create (
model = "gpt-3.5-turbo" ,
messages = [
{ "role" : "user" , "content" : "this is a test request, write a short poem" }
],
stream = True
)
for chunk in stream :
print ( chunk . choices [ 0 ]. delta . content or "" , end = "" , flush = True )
# Set max_tokens
stream_2 = client . chat . completions . create (
model = "claude-instant" ,
messages = [
{ "role" : "user" , "content" : "Can you tell me about the creation of blackholes?" }
],
stream = True ,
max_tokens = 20 , # if max_tokens reached, finish_reason will be 'length'
)
for chunk in stream_2 :
print ( chunk . choices [ 0 ]. delta . content or "" , end = "" , flush = True )
# Include usage
stream_3 = client . chat . completions . create (
model = "claude-instant" ,
messages = [
{ "role" : "user" , "content" : "Write a 100-character meta description for my blog post about llamas" }
],
stream = True ,
max_tokens = 4096 ,
stream_options = {
"include_usage" : True # last chunk contains prompts_tokens, completion_tokens and total_tokens
}
)
for chunk in stream_3 :
print ( chunk , end = " n n " , flush = True ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
# Legacy style (https://platform.openai.com/docs/api-reference/chat/create)
response = client . chat . completions . create (
model = "claude-3.5-sonnet" ,
messages = [
{
"role" : "user" ,
"content" : [
{ "type" : "text" , "text" : "What's in this image?" },
{
"type" : "image_url" ,
"image_url" : "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" ,
}
],
}
]
)
# New style (https://platform.openai.com/docs/guides/vision)
response = client . chat . completions . create (
model = "claude-3.5-sonnet" ,
messages = [
{
"role" : "user" ,
"content" : [
{ "type" : "text" , "text" : "What's in this image?" },
{
"type" : "image_url" ,
"image_url" : {
"url" : "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
}
],
}
]
)
# Multiple images
response = client . chat . completions . create (
model = "gpt-4o" ,
messages = [
{
"role" : "user" ,
"content" : [
{
"type" : "text" ,
"text" : "What are in these images? Is there any difference between them?" ,
},
{
"type" : "image_url" ,
"image_url" : {
"url" : "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" ,
},
},
{
"type" : "image_url" ,
"image_url" : {
"url" : "https://imgcdn.stablediffusionweb.com/2024/4/29/0b0b8798-1965-4e3d-b0a8-d153728320d4.jpg" ,
}
}
]
}
]
)
# Base64 image
import base64
# Function to encode the image
def encode_image ( image_path ):
with open ( image_path , "rb" ) as image_file :
return base64 . b64encode ( image_file . read ()). decode ( 'utf-8' )
# Path to your image
image_path = "path_to_your_image.jpg"
# Getting the base64 string
base64_image = encode_image ( image_path )
response = client . chat . completions . create (
model = "gpt-4o" ,
messages = [
{
"role" : "user" ,
"content" : [
{
"type" : "text" ,
"text" : "What’s in this image?"
},
{
"type" : "image_url" ,
"image_url" : {
"url" : f"data:image/jpeg;base64, { base64_image } "
}
}
]
}
]
)
print ( response . choices [ 0 ]. message . content ) import openai , json
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
TEST_MODEL = "gpt-4o-mini"
# Example dummy function hard coded to return the same weather
# In production, this could be your backend API or an external API
def get_current_temperature ( location , unit = "fahrenheit" ):
"""Get the current weather in a given location"""
if "tokyo" in location . lower ():
return json . dumps ({ "location" : "Tokyo" , "temperature" : "10" , "unit" : unit })
elif "san francisco" in location . lower ():
return json . dumps ({ "location" : "San Francisco" , "temperature" : "72" , "unit" : unit })
elif "paris" in location . lower ():
return json . dumps ({ "location" : "Paris" , "temperature" : "22" , "unit" : unit })
else :
return json . dumps ({ "location" : location , "temperature" : "unknown" })
def get_rain_probability ( location ):
"""Get the probability of rain in a given location"""
if "tokyo" in location . lower ():
return json . dumps ({ "location" : "Tokyo" , "rain_probability" : "10%" })
elif "san francisco" in location . lower ():
return json . dumps ({ "location" : "San Francisco" , "rain_probability" : "20%" })
elif "paris" in location . lower ():
return json . dumps ({ "location" : "Paris" , "rain_probability" : "30%" })
else :
return json . dumps ({ "location" : location , "rain_probability" : "unknown" })
def run_conversation ():
# Step 1: send the conversation and available functions to the model
messages = [
{ 'role' : 'user' , 'content' : "Hello there. What the weather like in Tokyo?" },
{ 'role' : 'assistant' , 'content' : "Let me check the weather for you." },
{ 'role' : 'user' , 'content' : "What is the chance of raining in paris? Can you also tell me the temperature in Tokyo and LA?" },
]
tools = [
{
"type" : "function" ,
"function" : {
"name" : "get_current_temperature" ,
"description" : "Get the current temperature for a specific location" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"location" : {
"type" : "string" ,
"description" : "The city and state, e.g., San Francisco, CA"
},
"unit" : {
"type" : "string" ,
"enum" : [ "Celsius" , "Fahrenheit" ],
"description" : "The temperature unit to use. Infer this from the user's location."
}
},
"required" : [ "location" , "unit" ]
}
}
},
{
"type" : "function" ,
"function" : {
"name" : "get_rain_probability" ,
"description" : "Get the probability of rain for a specific location" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"location" : {
"type" : "string" ,
"description" : "The city and state, e.g., San Francisco, CA"
}
},
"required" : [ "location" ]
}
}
}
]
response = client . chat . completions . create (
model = TEST_MODEL ,
messages = messages ,
tools = tools ,
tool_choice = { "type" : "function" , "function" : { "name" : "get_current_temperature" }},
)
response_message = response . choices [ 0 ]. message
print ( " n " , response_message , " n " )
tool_calls = response_message . tool_calls
# Step 2: check if the model wanted to call a function
if tool_calls :
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = {
"get_current_temperature" : get_current_temperature ,
"get_rain_probability" : get_rain_probability
} # only two functions in this example, but you can have multiple
messages . append ( response_message ) # extend conversation with assistant's reply
# Step 4: send the info for each function call and function response to the model
for tool_call in tool_calls :
print ( tool_call , " n " )
function_name = tool_call . function . name
function_to_call = available_functions [ function_name ]
function_args = json . loads ( tool_call . function . arguments )
function_response = function_to_call ( ** function_args )
messages . append (
{
"tool_call_id" : tool_call . id ,
"role" : "tool" ,
"name" : function_name ,
"content" : function_response ,
}
) # extend conversation with function response
second_response = client . chat . completions . create (
model = TEST_MODEL ,
messages = messages ,
) # get a new response from the model where it can see the function response
return second_response . choices [ 0 ]. message . content
print ( run_conversation ()) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
images_url = client . images . generate (
model = "playground-v2.5" ,
prompt = "A cute baby sea otter" ,
n = 2 , # The number of images to generate
size = "1792x1024" # The size of image (view models.json for available sizes)
)
print ( images_url ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
images_url = client . images . edit (
image = "https://imgcdn.stablediffusionweb.com/2024/4/29/0b0b8798-1965-4e3d-b0a8-d153728320d4.jpg" ,
model = "sdxl" ,
prompt = "A cute baby sea otter wearing a raincoat" ,
n = 1 , # The number of images to generate
size = "1024x1024" # The size of image (view models.json for available sizes)
)
print ( images_url ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
models = client . models . list ()
print ( models ) import openai
client = openai . OpenAI ( api_key = "anything" , base_url = "http://127.0.0.1:8000/v1/" , default_headers = { "Authorization" : "Bearer anything" })
model = client . models . retrieve ( "gpt-3.5-turbo-instruct" )
print ( model ) tokens = {
'p-b' : 'p-b cookie here' ,
'p-lat' : 'p-lat cookie here' ,
}
# Default setup
from poe_api_wrapper import PoeApi
client = PoeApi ( tokens = tokens )
# Using Client with auto_proxy (default is False)
client = PoeApi ( tokens = tokens , auto_proxy = True )
# Passing proxies manually
proxy_context = [
{ "https://" : X1 , "http://" : X1 },
{ "https://" : X2 , "http://" : X2 },
...
]
client = PoeApi ( tokens = tokens , proxy = proxy_context )
# Add formkey and cloudflare cookies to pass challenges
tokens = {
'p-b' : 'p-b cookie here' ,
'p-lat' : 'p-lat cookie here' ,
'formkey' : 'formkey here' ,
'__cf_bm' : '__cf_bm cookie here' ,
'cf_clearance' : 'cf_clearance cookie here'
} # Get chat data of all bots (this will fetch all available threads)
print ( client . get_chat_history ()[ 'data' ])
>> Output :
{ 'chinchilla' : [{ 'chatId' : 74397929 , 'chatCode' : '2ith0h11zfyvsta1u3z' , 'id' : 'Q2hhdDo3NDM5NzkyOQ==' , 'title' : 'Comparison' }], 'code_llama_7b_instruct' : [{ 'chatId' : 74397392 , 'chatCode' : '2ithbduzsysy3g178hb' , 'id' : 'Q2hhdDo3NDM5NzM5Mg==' , 'title' : 'Decent Programmers' }], 'a2' : [{ 'chatId' : 74396838 , 'chatCode' : '2ith9nikybn4ksn51l8' , 'id' : 'Q2hhdDo3NDM5NjgzOA==' , 'title' : 'Reverse Engineering' }, { 'chatId' : 74396452 , 'chatCode' : '2ith79n4x0p0p8w5yue' , 'id' : 'Q2hhdDo3NDM5NjQ1Mg==' , 'title' : 'Clean Code' }], 'leocooks' : [{ 'chatId' : 74396246 , 'chatCode' : '2ith82wj0tjrggj46no' , 'id' : 'Q2hhdDo3NDM5NjI0Ng==' , 'title' : 'Pizza perfection' }], 'capybara' : [{ 'chatId' : 74396020 , 'chatCode' : '2ith5o3p8c5ajkdwd3k' , 'id' : 'Q2hhdDo3NDM5NjAyMA==' , 'title' : 'Greeting' }]}
# Get chat data of a bot (this will fetch all available threads)
print ( client . get_chat_history ( "a2" )[ 'data' ])
>> Output :
{ 'a2' : [{ 'chatId' : 74396838 , 'chatCode' : '2ith9nikybn4ksn51l8' , 'id' : 'Q2hhdDo3NDM5NjgzOA==' , 'title' : 'Reverse Engineering' }, { 'chatId' : 74396452 , 'chatCode' : '2ith79n4x0p0p8w5yue' , 'id' : 'Q2hhdDo3NDM5NjQ1Mg==' , 'title' : 'Clean Code' }]}
# Get a defined number of most recent chat threads (using count param will ignore interval param)
# Fetching all bots
print ( client . get_chat_history ( count = 20 )[ 'data' ])
# Fetching 1 bot
print ( client . get_chat_history ( bot = "a2" , count = 20 )[ 'data' ])
# You can pass the number of bots fetched for each interval to both functions. (default is 50)
# Fetching 200 chat threads of all bots each interval
print ( client . get_chat_history ( interval = 200 )[ 'data' ])
# Fetching 200 chat threads of a bot each interval
print ( client . get_chat_history ( bot = "a2" , interval = 200 )[ 'data' ])
# Pagination Example:
# Fetch the first 20 chat threads
history = client . get_chat_history ( count = 20 )
pages = [ history [ 'data' ]]
new_cursor = history [ 'cursor' ]
# Set a while loop with a condition of your choice
while new_cursor != None :
# Fetch the next 20 chat threads with new_cursor
new_history = client . get_chat_history ( count = 20 , cursor = new_cursor )
# Append the next 20 chat threads
new_cursor = new_history [ 'cursor' ]
pages . append ( new_history [ 'data' ])
# Print the pages (20 chat threads each page)
for page in range ( len ( pages )):
print ( f'This is page { page + 1 } ' )
for bot , value in pages [ page ]. items ():
for thread in value :
print ({ bot : thread }) data = client . get_settings ()
print ( data ) bot = "a2"
message = "What is reverse engineering?"
# Create new chat thread
# Streamed example:
for chunk in client . send_message ( bot , message ):
print ( chunk [ "response" ], end = "" , flush = True )
print ( " n " )
# Non-streamed example:
for chunk in client . send_message ( bot , message ):
pass
print ( chunk [ "text" ])
# You can get chatCode and chatId of created thread to continue the conversation
chatCode = chunk [ "chatCode" ]
chatId = chunk [ "chatId" ]
# You can also retrieve msgPrice
msgPrice = chunk [ "msgPrice" ]
# Send message to an existing chat thread
# 1. Using chatCode
for chunk in client . send_message ( bot , message , chatCode = "2i58ciex72dom7im83r" ):
print ( chunk [ "response" ], end = "" , flush = True )
# 2. Using chatId
for chunk in client . send_message ( bot , message , chatId = 59726162 ):
print ( chunk [ "response" ], end = "" , flush = True )
# 3. Specify msgPrice manually (the wrapper automatically gets this, but you can also pass the param for less resources consumed)
for chunk in client . send_message ( bot , message , chatId = 59726162 , msgPrice = msgPrice ):
print ( chunk [ "response" ], end = "" , flush = True )[!NOTE] Отображаемые имена такие же, как кодовые имена для пользовательских ботов, вы можете просто передать отображаемое имя бота в
client.send_message(bot, message)
# Use at your own risk, increase timeout to avoid ratelimit (default is 20)
import time , threading
thread_count = 0
def message_thread ( prompt , counter ):
global thread_count
try :
for chunk in client . send_message ( "gpt3_5" , prompt ):
pass
print ( prompt + " n " + chunk [ "text" ] + " n " * 3 )
thread_count -= 1
except Exception as e :
pass
prompts = [
"Write a paragraph about the impact of social media on mental health." ,
"Write a paragraph about the history and significance of the Olympic Games." ,
"Write a paragraph about the effects of climate change on the world's oceans." ,
"Write a paragraph about the benefits and drawbacks of remote work for employees and companies." ,
"Write a paragraph about the role of technology in modern education." ,
"Write a paragraph about the history and impact of the Civil Rights Movement in America." ,
"Write a paragraph about the impact of COVID-19 on global economies." ,
"Write a paragraph about the rise and fall of the Roman Empire." ,
"Write a paragraph about the benefits and drawbacks of genetically modified organisms (GMOs)." ,
"Write a paragraph about the impact of globalization on cultural identity." ,
"Write a paragraph about the history and significance of the Mona Lisa painting." ,
"Write a paragraph about the benefits and drawbacks of renewable energy sources." ,
"Write a paragraph about the impact of social media on political discourse." ,
"Write a paragraph about the history and impact of the Industrial Revolution." ,
"Write a paragraph about the benefits and drawbacks of online shopping for consumers and businesses." ,
"Write a paragraph about the impact of artificial intelligence on the job market." ,
"Write a paragraph about the history and significance of the Great Wall of China." ,
"Write a paragraph about the benefits and drawbacks of standardized testing in schools." ,
"Write a paragraph about the impact of the feminist movement on women's rights." ,
"Write a paragraph about the history and impact of the American Revolution."
]
for i in range ( len ( prompts )):
t = threading . Thread ( target = message_thread , args = ( prompts [ i ], i ), daemon = True )
t . start ()
thread_count += 1
time . sleep ( 1 )
while thread_count :
time . sleep ( 0.01 ) for chunk in client . retry_message ( chatCode ):
print ( chunk [ 'response' ], end = '' , flush = True ) # Web urls example:
file_urls = [ "https://elinux.org/images/c/c5/IntroductionToReverseEngineering_Anderson.pdf" ,
"https://www.kcl.ac.uk/warstudies/assets/automation-and-artificial-intelligence.pdf" ]
for chunk in client . send_message ( bot , "Compare 2 files and describe them in 300 words" , file_path = file_urls ):
print ( chunk [ "response" ], end = "" , flush = True )
# Local paths example:
local_paths = [ "c: \ users \ snowby666 \ hello_world.py" ]
for chunk in client . send_message ( bot , "What is this file about?" , file_path = local_paths ):
print ( chunk [ "response" ], end = "" , flush = True )[!NOTE] Предельный размер файлов различен для каждой модели.
for chunk in client . send_message ( bot , "Introduce 5 books about clean code" , suggest_replies = True ):
print ( chunk [ "response" ], end = "" , flush = True )
print ( " n " )
for reply in chunk [ "suggestedReplies" ]:
print ( reply ) # You can use an event to trigger this function
# Example:
# Note that keyboard library may not be compatible with MacOS, Linux, Ubuntu
import keyboard
for chunk in client . send_message ( bot , message ):
print ( chunk [ "response" ], end = "" , flush = True )
# Press Q key to stop the generation
if keyboard . is_pressed ( 'q' ):
client . cancel_message ( chunk )
print ( " n Message is now cancelled" )
break # Delete 1 chat
# Using chatCode
client . delete_chat ( bot , chatCode = "2i58ciex72dom7im83r" )
# Using chatId
client . delete_chat ( bot , chatId = 59726162 )
# Delete n chats
# Using chatCode
client . delete_chat ( bot , chatCode = [ "LIST_OF_CHAT_CODES" ])
# Using chatId
client . delete_chat ( bot , chatId = [ "LIST_OF_CHAT_IDS" ])
# Delete all chats of a bot
client . delete_chat ( bot , del_all = True ) # 1. Using chatCode
client . chat_break ( bot , chatCode = "2i58ciex72dom7im83r" )
# 2. Using chatId
client . chat_break ( bot , chatId = 59726162 ) # Purge a defined number of messages (default is 50)
# 1. Using chatCode
client . purge_conversation ( bot , chatCode = "2i58ciex72dom7im83r" , count = 10 )
# 2. Using chatId
client . purge_conversation ( bot , chatId = 59726162 , count = 10 )
# Purge all messsages of the thread
# 1. Using chatCode
client . purge_conversation ( bot , chatCode = "2i58ciex72dom7im83r" , del_all = True )
# 2. Using chatId
client . purge_conversation ( bot , chatId = 59726162 , del_all = True ) client . purge_all_conversations () # Get a defined number of messages (default is 50)
# Using chatCode
previous_messages = client . get_previous_messages ( 'code_llama_34b_instruct' , chatCode = '2itg2a7muygs42v1u0k' , count = 2 )
# Using chatId
previous_messages = client . get_previous_messages ( 'code_llama_34b_instruct' , chatId = 74411139 , count = 2 )
for message in previous_messages :
print ( message )
>> Output :
{ 'author' : 'human' , 'text' : 'nice to meet you' , 'messageId' : 2861709279 }
{ 'author' : 'code_llama_34b_instruct' , 'text' : " Nice to meet you too! How are you doing today? Is there anything on your mind that you'd like to talk about? I'm here to listen and help" , 'messageId' : 2861873125 }
# Get all previous messages
# Using chatCode
previous_messages = client . get_previous_messages ( 'code_llama_34b_instruct' , chatCode = '2itg2a7muygs42v1u0k' , get_all = True )
# Using chatId
previous_messages = client . get_previous_messages ( 'code_llama_34b_instruct' , chatId = 74411139 , get_all = True )
for message in previous_messages :
print ( message )
>> Output :
{ 'author' : 'human' , 'text' : 'hi there' , 'messageId' : 2861363514 }
{ 'author' : 'code_llama_34b_instruct' , 'text' : " Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?" , 'messageId' : 2861363530 }
{ 'author' : 'chat_break' , 'text' : "" , 'messageId' : 2872383991 }
{ 'author' : 'human' , 'text' : 'nice to meet you' , 'messageId' : 2861709279 }
{ 'author' : 'code_llama_34b_instruct' , 'text' : " Nice to meet you too! How are you doing today? Is there anything on your mind that you'd like to talk about? I'm here to listen and help" , 'messageId' : 2861873125 }[!NOTE] Он будет выбирать сообщения от самого последнего к самому старому, но порядок отображения будет обратным.
# Get a defined number of sources (default is 10)
print ( client . get_available_knowledge ( botName = "BOT_NAME" , count = 2 ))
>> Output :
{ 'What is Quora?' : [ 86698 ], 'Founders of Quora' : [ 86705 ]}
# Get all available sources
print ( client . get_available_knowledge ( botName = "BOT_NAME" , get_all = True )) # Web urls example:
file_urls = [ "https://elinux.org/images/c/c5/IntroductionToReverseEngineering_Anderson.pdf" ,
"https://www.kcl.ac.uk/warstudies/assets/automation-and-artificial-intelligence.pdf" ]
source_ids = client . upload_knowledge ( file_path = file_urls )
print ( source_ids )
>> Output :
{ 'er-1-intro_to_re.pdf' : [ 86344 ], 'automation-and-artificial-intelligence.pdf' : [ 86345 ]}
# Local paths example:
local_paths = [ "c: \ users \ snowby666 \ hello_world.py" ]
source_ids = client . upload_knowledge ( file_path = local_paths )
print ( source_ids )
>> Output :
{ 'hello_world.py' : [ 86523 ]}
# Plain texts example:
knowledges = [
{
"title" : "What is Quora?" ,
"content" : "Quora is a popular online platform that enables users to ask questions on various topics and receive answers from a diverse community. It covers a wide range of subjects, from academic and professional queries to personal experiences and opinions, fostering knowledge-sharing and meaningful discussions among its users worldwide."
},
{
"title" : "Founders of Quora" ,
"content" : "Quora was founded by two individuals, Adam D'Angelo and Charlie Cheever. Adam D'Angelo, who previously served as the Chief Technology Officer (CTO) at Facebook, and Charlie Cheever, a former Facebook employee as well, launched Quora in June 2009. They aimed to create a platform that would enable users to ask questions and receive high-quality answers from knowledgeable individuals. Since its inception, Quora has grown into a widely used question-and-answer platform with a large user base and a diverse range of topics covered."
},
]
source_ids = client . upload_knowledge ( text_knowledge = knowledges )
print ( source_ids )
>> Output :
{ 'What is Quora?' : [ 86368 ], 'Founders of Quora' : [ 86369 ]}
# Hybrid example:
source_ids = client . upload_knowledge ( file_path = file_urls , text_knowledge = knowledges )
print ( source_ids )
>> Output :
{ 'What is Quora?' : [ 86381 ], 'Founders of Quora' : [ 86383 ], 'er-1-intro_to_re.pdf' : [ 86395 ], 'automation-and-artificial-intelligence.pdf' : [ 86396 ]} client . edit_knowledge ( knowledgeSourceId = 86381 , title = 'What is Quora?' , content = 'Quora is a question-and-answer platform where users can ask questions, provide answers, and engage in discussions on various topics.' ) bot = 'gpt-4'
print ( client . get_botInfo ( handle = bot ))
>> Output :
{ 'handle' : 'GPT-4' , 'model' : 'beaver' , 'supportsFileUpload' : True , 'messageTimeoutSecs' : 15 , 'displayMessagePointPrice' : 350 , 'numRemainingMessages' : 20 , 'viewerIsCreator' : False , 'id' : 'Qm90OjMwMDc=' } print ( client . get_available_creation_models ())
>> Output :
{ 'text' : [ 'claude_3_igloo' , 'gpt4_o_mini' , 'gpt4_o' , 'gemini_1_5_flash' , 'gemini_1_5_pro' , 'claude_2_1_bamboo' , 'claude_3_haiku' , 'claude_2_1_cedar' , 'gemini_1_5_flash_128k' , 'gemini_1_5_pro_128k' , 'gemini_1_5_flash_1m' , 'gemini_1_5_pro_1m' , 'gpt4_o_mini_128k' , 'gpt4_o_128k' , 'beaver' , 'gemini_pro' , 'chinchilla' , 'vizcacha' , 'claude_3_igloo_200k' , 'claude_3_sonnet_200k' , 'claude_3_haiku_200k' , 'claude_3_opus_200k' , 'mixtral8x7bchat' , 'claude_2_short' , 'a2_2' , 'mythomaxl213b' , 'a2' , 'a2_100k' ], 'image' : [ 'playgroundv25' , 'ideogram' , 'dalle3' , 'stablediffusion3' , 'sd3turbo' , 'stablediffusionxl' ], 'video' : [ 'pika' ]} client . create_bot ( handle = "BOT_NAME" , prompt = "PROMPT_HERE" , base_model = "a2" )
# Using knowledge bases (you can use source_ids from uploaded knowledge bases for your custom bot)
client . create_bot ( handle = "BOT_NAME" , prompt = "PROMPT_HERE" , base_model = "a2" , knowledgeSourceIds = source_ids , shouldCiteSources = True ) client . edit_bot ( handle = "BOT_NAME" , prompt = "PROMPT_HERE" , new_handle = "NEW_BOT_NAME" , base_model = 'chinchilla' )
# Adding knowledge bases
client . edit_bot ( handle = "BOT_NAME" , prompt = "PROMPT_HERE" , new_handle = "NEW_BOT_NAME" , base_model = 'chinchilla' , knowledgeSourceIdsToAdd = source_ids , shouldCiteSources = True )
# Removing knowledge bases
client . edit_bot ( handle = "BOT_NAME" , prompt = "PROMPT_HERE" , new_handle = "NEW_BOT_NAME" , base_model = 'chinchilla' , knowledgeSourceIdsToRemove = source_ids , shouldCiteSources = True )[!TIP] Вы также можете использовать одновременно и
knowledgeSourceIdsToAdd, иknowledgeSourceIdsToRemove.
client . delete_bot ( handle = "BOT_NAME" ) # Get a defined number of bots (default is 25)
print ( client . get_available_bots ( count = 10 ))
# Get all available bots
print ( client . get_available_bots ( get_all = True )) handle = 'poe'
print ( client . get_user_bots ( user = handle )) print ( client . get_available_categories ())
>> Output :
[ 'Official' , 'Popular' , 'New' , 'ImageGen' , 'AI' , 'Professional' , 'Funny' , 'History' , 'Cooking' , 'Advice' , 'Mind' , 'Programming' , 'Travel' , 'Writing' , 'Games' , 'Learning' , 'Roleplay' , 'Utilities' , 'Sports' , 'Music' ] # Explore section example:
# Get a defined number of bots (default is 50)
print ( client . explore ( count = 10 ))
# Get all available bots
print ( client . explore ( explore_all = True ))
# Search for bots by query example:
# Get a defined number of bots (default is 50)
print ( client . explore ( search = "Midjourney" , count = 30 ))
# Get all available bots
print ( client . explore ( search = "Midjourney" , explore_all = True ))
# Search for bots by category example (default is defaultCategory):
# Get a defined number of bots (default is 50)
print ( client . explore ( categoryName = "Popular" , count = 30 ))
# Get all available bots
print ( client . explore ( categoryName = "AI" , explore_all = True ))
# Search for people example:
# Get a defined number of people (default is 50)
print ( client . explore ( search = "Poe" , entity_type = 'user' , count = 30 ))
# Get all available people
print ( client . explore ( search = "Poe" , entity_type = 'user' , explore_all = True )) # Share a defined number of messages (from the lastest to the oldest)
# Using chatCode
shareCode = client . share_chat ( "a2" , chatCode = "2roap5g8nd7s28ul836" , count = 10 )
# Using chatId
shareCode = client . share_chat ( "a2" , chatId = 204052028 , count = 10 )
# Share all messages
# Using chatCode
shareCode = client . share_chat ( "a2" , chatCode = "2roap5g8nd7s28ul836" )
# Using chatId
shareCode = client . share_chat ( "a2" , chatId = 204052028 )
# Set up the 2nd Client and import messages from the shareCode
client2 = PoeApi ( "2nd_TOKEN_HERE" )
print ( client2 . import_chat ( bot , shareCode ))
>> Output :
{ 'chatId' : 72929127 , 'chatCode' : '2iw0xcem7a18wy1avd3' } print ( client . get_citations ( messageId = 141597902621 )) bots = [
{ 'bot' : 'yayayayaeclaude' , 'name' : 'Yae' },
{ 'bot' : 'gepardL' , 'name' : 'gepard' },
{ 'bot' : 'SayukiTokihara' , 'name' : 'Sayuki' }
]
client . create_group ( group_name = 'Hangout' , bots = bots ) [!NOTE]
botarg — это модель/имя дисплея.namearg — это тот, который вы бы упомянули в групповом чате.
# User engagement example:
while True :
message = str ( input ( ' n 33 [38;5;121mYou : 33 [0m' ))
prev_bot = ""
for chunk in client . send_message_to_group ( group_name = 'Hangout' , message = message ):
if chunk [ 'bot' ] != prev_bot :
print ( f" n 33 [38;5;121m { chunk [ 'bot' ] } : 33 [0m" , end = '' , flush = True )
prev_bot = chunk [ 'bot' ]
print ( chunk [ 'response' ], end = '' , flush = True )
print ( ' n ' )
# Auto-play example:
while True :
prev_bot = ""
for chunk in client . send_message_to_group ( group_name = 'Hangout' , autoplay = True ):
if chunk [ 'bot' ] != prev_bot :
print ( f" n 33 [38;5;121m { chunk [ 'bot' ] } : 33 [0m" , end = '' , flush = True )
prev_bot = chunk [ 'bot' ]
print ( chunk [ 'response' ], end = '' , flush = True )
print ( ' n ' )
# Preset history example:
preset_path = "c: \ users \ snowby666 \ preset.json"
prev_bot = ""
for chunk in client . send_message_to_group ( group_name = 'Hangout' , autoplay = True , preset_history = preset_path ):
if chunk [ 'bot' ] != prev_bot :
print ( f" n 33 [38;5;121m { chunk [ 'bot' ] } : 33 [0m" , end = '' , flush = True )
prev_bot = chunk [ 'bot' ]
print ( chunk [ 'response' ], end = '' , flush = True )
print ( ' n ' )
while True :
for chunk in client . send_message_to_group ( group_name = 'Hangout' , autoplay = True ):
if chunk [ 'bot' ] != prev_bot :
print ( f" n 33 [38;5;121m { chunk [ 'bot' ] } : 33 [0m" , end = '' , flush = True )
prev_bot = chunk [ 'bot' ]
print ( chunk [ 'response' ], end = '' , flush = True )
print ( ' n ' )[!NOTE] Вы также можете изменить свое имя в групповом чате, передав новое в вышеуказанную функцию:
client.send_message_to_group('Hangout', message=message, user='Danny')Если вы хотите автоматически сохранять разговорный журнал, просто установите для этого значения значение true:client.send_message_to_group('Hangout', message=message, autosave=True)
client . delete_group ( group_name = 'Hangout' ) print ( client . get_available_groups ()) print ( client . get_group ( group_name = 'Hangout' )) # Save as json in the same directory
client . save_group_history ( group_name = 'Hangout' )
# Save with a local path (json only)
local_path = "c: \ users \ snowby666 \ log.json"
client . save_group_history ( group_name = 'Hangout' , file_path = local_path ) print ( client . load_group_history ( file_path = local_path ))Вот пример: код чата: 23o1gxjhb9cfnlacdcd.
В настоящее время этот API поддерживает только эти типы файлов для добавления вложений.
| .docx | .текст | .md | .py | .js | .тс | .html | .css | .csv | .с | .cs | .cpp | .lua | .rs | .rb | .идти | .java | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| .png | .jpg | .jpeg | .gif | .mp4 | .mov | .mp3 | .wav |
|---|---|---|---|---|---|---|---|
Мы хотели бы разработать poe-api-wrapper вместе с нашим сообществом! ?
Сначала клонируйте этот репозиторий:
git clone https://github.com/snowby666/poe-api-wrapper.git
cd poe-api-wrapperЗатем запустите тестовые примеры:
python -m pip install -e .[tests]
toxЭта программа распространяется под лицензией GNU GPL v3. Большую часть кода написал я, Snowby666.
snowby666/poe-api-wrapper: A simple, lightweight and efficient API wrapper for Poe.com
Copyright (C) 2023 snowby666
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
