免费的 Auto GPT (不需要付费的 API) 是一个存储库,提供了 Auto GPT 的简单版本,这是一种自主的 AI 代理,能够独立执行任务。
免费的 Auto GPT (不需要付费的 API) 是一个存储库,提供了 Auto GPT 的简单版本,这是一种自主的 AI 代理,能够独立执行任务。
Tired of paying for OPENAI, PINECONE, GOOGLESEARCH APIs to try out the latest developments in the AI field? Perfect, **this is the repository for you! **
For any problem open an ISSUE , the project is very simple so any help is welcome.
Are you bored reading? Do you want to try our project now⏳? Open the notebook on Colab everything is ready!
RUN NOW ON COLAB ⚠️ Abusing this tool is at your own risk
By the way, thank you so much for and all the support!!
Hello everyone :smiling_face_with_three_hearts: ,
I wanted to start by talking about how important it is to democratize AI. Unfortunately, most new applications or discoveries in this field end up enriching some big companies, leaving behind small businesses or simple projects. One striking example of this is Autogpt, an autonomous AI agent capable of performing tasks.
Autogpt and similar projects like BabyAGI only work with paid APIs, which is not fair. That's why I tried to recreate a simpler but very interesting and, above all, open-source version of Autogpt that does not require any API and does not need any particular hardware.
I believe that by providing free and open-source AI tools, we can give small businesses and individuals the opportunity to create new and innovative projects without the need for significant financial investment. This will allow for more equitable and diverse access to AI technology, which is essential for advancing society as a whole.
F12.__Secure-next-auth.session-token cookie in Application > Storage > Cookies > https://chat.openai.com.Cookie Value field in .env file..env file this line : USE_GPT4 = TrueF12.__Secure-1PSID cookie in Application > Storage > Cookies Cookie Value field in .env file.Now add you Cookie and Token in .env file .
Added the possibility to use Docker image using Dev Container in VSCode. How to run it :
RUN NOW ON COLAB ⚠️ Abusing this tool is at your own risk
Or use Locally :
python3 -m pip install -r requirements.txtRUN NOW ON COLAB ⚠️ Abusing this tool is at your own risk
Or use Locally :
python3 -m pip install -r requirements.txtRUN NOW ON COLAB ⚠️ Abusing this tool is at your own risk
Or use Locally :
python3 -m pip install -r requirements.txtRUN NOW ON COLAB ⚠️ Abusing this tool is at your own risk
Or use Locally :
python3 -m pip install -r requirements.txtstreamlit run Camel.pyTo create an open-source version of Autogpt that does not require paid APIs or specific hardware, we performed a reverse engineering process on ChatGPT, a language model developed by OpenAI. By doing so, we were able to use the agents and new technologies of langchain for free.
We then created a custom LLM wrapper with langchain, **which can be used as a plug-and-play solution with any langchain function or tool **.
from FreeLLM import ChatGPTAPI
# Instantiate a ChatGPT object with your token
llm = ChatGPTAPI.ChatGPT((token="YOURTOKEN") #for start new chat
# If you have a Plus subscription , you can use GPT4 model
llm = ChatGPTAPI.ChatGPT((token="YOURTOKEN", model="gpt4") # REQUIRED CHATGPT PLUS subscription
# or if if u would to start from an existing chat
# llm = ChatGPTAPI.ChatGPT(token = "YOUR-TOKEN", conversation = "Add-XXXX-XXXX-Convesation-ID")
# Generate a response based on the given prompt
response = llm("Hello, how are you?")
# Print the response
print(response)
The code snippet provided above shows how to use our custom ChatGPT LLM class to interact with the language model. It requires a token from the ChatGPT API, which can be obtained from https://chat.openai.com/api/auth/session.
Please note that there is a limit of 50 requests per hour for each account on the ChatGPT API . Therefore, we implemented a call counter in our ChatGPT class to prevent exceeding this limit.
from FreeLLM import HuggingChatAPI
# Instantiate a ChatGPT object with your token
llm = HuggingChatAPI.HuggingChat() #for start new chat
# Generate a response based on the given prompt
response = llm("Hello, how are you?")
# Print the response
print(response)
…
from FreeLLM import BardChatAPI
# Instantiate a Bard CHAT object with your cookie path
llm=BardChatAPI.BardChat(cookie="cookie") #for start new chat
# Generate a response based on the given prompt
response = llm("Hello, how are you?")
# Print the response
print(response)
We believe that our open-source version of Autogpt will promote equitable and diverse access to AI technology and empower individuals and small businesses to create innovative AI projects without significant financial investment.
This is an example of CUSTOM agent, in less of 60 line of code and totally for free, with:
…
By the way, thank you so much for and all the support!!
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