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Poe API Wrapper

A simple, lightweight and efficient API wrapper for Poe.com

📚 Table of Contents

📚 Table of Contents
✨ Highlights
🔧 Installation
🦄 Documentation
🙌 Contributing
🤝 Copyright
- Copyright Notice

✨ Highlights

Support both Sync and Async

Authentication

Log in with your Poe tokens
Auto Proxy requests
Specify Proxy context

Message Automation

Create new chat thread
Send messages
Stream bot responses
Send concurrent messages
Retry the last message
Support file attachments
Retrieve suggested replies
Stop message generation
Delete chat threads
Clear conversation context
Purge messages of 1 bot
Purge all messages of user
Fetch previous messages
Share and import messages
Get citations

Chat Management

Get Chat Ids & Chat Codes of bot(s)
Get subscription info and remaining points

Bot Management

Get bot info
Get available creation models
Create custom bot
Edit custom bot
Delete a custom bot

Knowledge Base Customization

Get available knowledge bases
Upload knowledge bases for custom bots
Edit knowledge bases for custom bots

Discovery

Get available bots
Get a user's bots
Get available categories
Explore 3rd party bots and users

Bots Group Chat (Beta)

Create a group chat
Delete a group chat
Get created groups
Get group data
Save group chat history
Load group chat history

🔧 Installation

First, install this library with the following command:

pip install -U poe-api-wrapper

Or you can install auto-proxy version of this library for Python 3.9+

pip install -U 'poe-api-wrapper[proxy]'

Quick setup for 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())

You can run an example of this library:

from poe_api_wrapper import PoeExample
tokens = {
    'p-b': ..., 
    'p-lat': ...,
}
PoeExample(tokens=tokens).chat_with_bot()

This library also supports command-line interface:

poe -b P-B_HERE -lat P-LAT_HERE -f FORMKEY_HERE

Tip

Type poe -h for more info

🦄 Documentation

Available Default Bots

Display Name	Model	Token Limit	Words
Assistant	capybara	4K	3K
Claude-3.5-Sonnet	claude_3_igloo	4K	3K
Claude-3-Opus	claude_2_1_cedar	4K	3K
Claude-3-Sonnet	claude_2_1_bamboo	4K	3K
Claude-3-Haiku	claude_3_haiku	4K	3K
Claude-3.5-Sonnet-200k	claude_3_igloo_200k	200K	150K
Claude-3-Opus-200k	claude_3_opus_200k	200K	150K
Claude-3-Sonnet-200k	claude_3_sonnet_200k	200K	150K
Claude-3-Haiku-200k	claude_3_haiku_200k	200K	150K
Claude-2	claude_2_short	4K	3K
Claude-2-100k	a2_2	100K	75K
Claude-instant	a2	9K	7K
Claude-instant-100k	a2_100k	100K	75K
GPT-3.5-Turbo	chinchilla	4K	3K
GPT-3.5-Turbo-Raw	gpt3_5	2k	1.5K
GPT-3.5-Turbo-Instruct	chinchilla_instruct	2K	1.5K
ChatGPT-16k	agouti	16K	12K
GPT-4-Classic	gpt4_classic	2K	1.5K
GPT-4-Turbo	beaver	4K	3K
GPT-4-Turbo-128k	vizcacha	128K	96K
GPT-4o	gpt4_o	4k	3K
GPT-4o-128k	gpt4_o_128k	128K	96K
GPT-4o-Mini	gpt4_o_mini	4K	3K
GPT-4o-Mini-128k	gpt4_o_mini_128k	128K	96K
Google-PaLM	acouchy	8K	6K
Code-Llama-13b	code_llama_13b_instruct	4K	3K
Code-Llama-34b	code_llama_34b_instruct	4K	3K
Solar-Mini	upstage_solar_0_70b_16bit	2K	1.5K
Gemini-1.5-Flash-Search	gemini_pro_search	4K	3K
Gemini-1.5-Pro-2M	gemini_1_5_pro_1m	2M	1.5M

Important

The data on token limits and word counts listed above are approximate and may not be entirely accurate, as the pre-prompt engineering process of poe.com is private and not publicly disclosed.

The table above only shows bots with different display names from their models. Other bots on poe.com have the same display name as model.

How to get your Token

Getting p-b and p-lat cookies (required)

F12 for Devtools (Right-click + Inspect)

Chromium: Devtools > Application > Cookies > poe.com
Firefox: Devtools > Storage > Cookies
Safari: Devtools > Storage > Cookies

Copy the values of p-b and p-lat cookies

Getting formkey (optional)

Important

By default, poe-api-wrapper will automatically retrieve formkey for you. If it doesn't work, please pass this token manually by following these steps:

There are two ways to get formkey:

F12 for Devtools (Right-click + Inspect)

1st Method: Devtools > Network > gql_POST > Headers > Poe-Formkey

Copy the value of Poe-Formkey
2nd Method: Devtools > Console > Type: allow pasting > Paste this script: window.ereNdsRqhp2Rd3LEW()

Copy the result

OpenAI

Read Docs

Available Routes

/models
/chat/completions
/images/generations
/images/edits
/v1/models
/v1/chat/completions
/v1/images/generations
/v1/images/edits

Quick Setup

First, install the additional packages:

pip install -U 'poe-api-wrapper[llm]'

Clone the repo or use the same setup in openai folder:

git clone https://github.com/snowby666/poe-api-wrapper.git
cd poe-api-wrapper\poe_api_wrapper\openai

Modify secrets.json with your own tokens
Run the FastAPI server:

python api.py

Run the examples:

python example.py

Built-in completion (WIP)

OpenAI Proxy Server

Start the server

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")

Chat

Non-streamed example:

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)

Streaming example:

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)

Image input example:

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)

Function calling example:

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())

Images

Create image example:

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)

Edit image example:

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)

Models

List models example:

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)

Retrieve model example:

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)

Basic Usage

Read Docs

Connecting to the API

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'
}

Getting Chat Ids & Chat Codes

# 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})

Getting subscription info and remaining points

data = client.get_settings()
print(data)

Sending messages & Streaming responses

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] Display names are the same as the codenames for custom bots, you can simply pass the bot's display name into client.send_message(bot, message)

Sending concurrent messages

# 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)

Retrying the last message

for chunk in client.retry_message(chatCode):
    print(chunk['response'], end='', flush=True)

Adding file attachments

# 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] The files size limit is different for each model.

Retrieving suggested replies

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)

Stopping message generation

# 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("\nMessage is now cancelled")
        break

Deleting chat threads

# 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)

Clearing conversation context

# 1. Using chatCode
client.chat_break(bot, chatCode="2i58ciex72dom7im83r")
# 2. Using chatId
client.chat_break(bot, chatId=59726162)

Purging messages of 1 bot

# 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)

Purging all messages of user

client.purge_all_conversations()

Fetching previous messsages

# 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 messages with extended metadata (state and creationTime)
# Using chatCode
previous_messages = client.get_previous_messages('code_llama_34b_instruct', chatCode='2itg2a7muygs42v1u0k', include_extended=True)
# Using chatId
previous_messages = client.get_previous_messages('code_llama_34b_instruct', chatId=74411139, include_extended=True)
>> Output:
{'author': 'human', 'text': 'hi there', 'messageId': 2861363514, 'state': 'complete', 'creationTime': 1732029401216595}

# 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] It will fetch messages from the latest to the oldest, but the order to be displayed is reversed.

Getting available knowledge bases

# 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))

Uploading knowledge bases

# 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]}

Editing knowledge bases (Only for plain texts)

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.')

Getting bot info

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='}

Getting available creation models

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']}

Creating a new Bot

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)

Editing a Bot

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] You can also use both knowledgeSourceIdsToAdd and knowledgeSourceIdsToRemove at the same time.

Deleting a Bot

client.delete_bot(handle="BOT_NAME")

Getting available bots (your bots section)

# 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))

Getting a user's bots

handle = 'poe'
print(client.get_user_bots(user=handle))

Getting available categories

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']

Exploring 3rd party bots and users

# 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))

Sharing & Importing messages

# 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'}

Getting citations

print(client.get_citations(messageId=141597902621))

Bots Group Chat

Read Docs

Creating a group chat

bots = [
    {'bot': 'yayayayaeclaude', 'name': 'Yae'}, 
    {'bot': 'gepardL', 'name': 'gepard'}, 
    {'bot': 'SayukiTokihara', 'name': 'Sayuki'}
]

client.create_group(group_name='Hangout', bots=bots)

[!NOTE] bot arg is the model/displayName. name arg is the one you'd mention them in group chat.

Sending messages and Streaming responses in group chat

# User engagement example:
while True: 
    message = str(input('\n\033[38;5;121mYou : \033[0m'))
    prev_bot = ""
    for chunk in client.send_message_to_group(group_name='Hangout', message=message):
        if chunk['bot'] != prev_bot:
            print(f"\n\033[38;5;121m{chunk['bot']} : \033[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\033[38;5;121m{chunk['bot']} : \033[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\033[38;5;121m{chunk['bot']} : \033[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\033[38;5;121m{chunk['bot']} : \033[0m", end='', flush=True)
            prev_bot = chunk['bot']
        print(chunk['response'], end='', flush=True)
    print('\n')

[!NOTE] You can also change your name in group chat by passing a new one to the above function: client.send_message_to_group('Hangout', message=message, user='Danny') If you want to auto save the conversation_log, just simply set this to true: client.send_message_to_group('Hangout', message=message, autosave=True)

Deleting a group chat

client.delete_group(group_name='Hangout')

Getting created groups

print(client.get_available_groups())

Getting group data

print(client.get_group(group_name='Hangout'))

Saving group chat history

# 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)

Loading group chat history

print(client.load_group_history(file_path=local_path))

Misc

Read Docs

How to find chatCode manually?

Here is an example, the chatCode is 23o1gxjhb9cfnlacdcd

What are the file types that poe-api-wrapper support?

Currently, this API only supports these file types for adding attachments

Text files

.pdf	.docx	.txt	.md	.py	.js	.ts	.html	.css	.csv	.c	.cs	.cpp	.lua	.rs	.rb	.go	.java

Media files

.png	.jpg	.jpeg	.gif	.mp4	.mov	.mp3	.wav

🙌 Contributing

We would love to develop poe-api-wrapper together with our community! 💕

Run debug

First, clone this repo:

git clone https://github.com/snowby666/poe-api-wrapper.git
cd poe-api-wrapper

Then run the test cases:

python -m pip install -e .[tests]
tox

Ways to contribute

Try poe-api-wrapper and give feedback
Add new integrations with open PR
Help with open issues or create your own
Share your thoughts and suggestions with us
Request a feature by submitting a proposal
Report a bug
Improve documentation: fix incomplete or missing docs, bad wording, examples or explanations.

Contributors

🤝 Copyright

This program is licensed under the GNU GPL v3. Most code has been written by me, snowby666.

Copyright Notice

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/>.

License

snowby666/poe-api-wrapper

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