AI聊天机器人轻松读论文的神奇方法
Table of Contents
- Introduction
- Limitations of ChatGPT
- Introducing langflow
- Installing langflow
- Creating a Chatbot using langflow
- Setting up PDF-Based Chatbot
- Testing the PDF-based Chatbot
- Creating a Chatbot using a research paper
- Testing the research paper-based Chatbot
- Conclusion
Introduction
안녕하세요? Endplan의 은긔입니다. 여러분도 ChatGPT 많이 사용하고 계신가요? 저도 일하거나 코딩할 때 자주 사용하는데요. 사용하다 보면 아쉬운 점이 몇 개 있습니다. ChatGPT가 2021년 9월까지의 데이터로만 학습이 되어 있어서 최신정보에 대한 답변이 불가능 하다는 점과, 학습된 데이터 내에서만 답변을 만들기 때문에 특정 분야의 내용에 대해서 답변이 불가능하다는 점이 있습니다. 최근에 나온 ChatGPT Plus의 플러그인을 사용하면, 앞서 말한 내용들을 해결할 수 있지만 사용량에 관계없이 월 2만원씩 내야 합니다. 하지만 오늘 알려드릴 ‘langflow’를 사용하면 최신정보에 대해서도 답변할 수 있고, 특정 문서를 기준으로도 답변 가능한 Chatbot을 만들 수 있습니다.
Limitations of ChatGPT
ChatGPT has certain limitations that may prove to be a drawback for some users. Firstly, it is trained on data only until September 2021, which means it cannot provide answers based on the latest information. Secondly, it can only generate answers within the scope of its trained data, making it unable to answer questions on specific topics outside its training data. Although the ChatGPT Plus plugin addresses these limitations, it requires a monthly fee of 20,000 won regardless of usage. In the following sections, we will introduce an alternative solution called 'langflow' that overcomes these limitations and allows for up-to-date and document-specific answers.
Introducing langflow
'langflow' is a tool that enables the creation of Chatbots capable of providing answers based on the latest information and specific documents. By using 'langflow', users can develop Chatbots that pay only for the queries they make, rather than a fixed monthly fee. In this article, we will guide You through the process of installing, configuring, and utilizing 'langflow' to Create your own Chatbot that can answer questions based on PDF files and research papers.
Installing langflow
Before we can start creating our Chatbot, we need to install 'langflow'. To do this, make sure you have Python 3.10 or Anaconda installed on your system. If you don't have Python 3.10 installed, you can refer to the link in the upper right corner of the video for instructions on how to install it. As for Anaconda, you can download the installer for Python 3.11 from the Anaconda Website. The specific version Mentioned here is not critical as Anaconda allows for easy version switching. Once you have completed the installation, we can proceed with installing 'langflow'. The installation process is simple and can be found in the 'installation' section on the GitHub project page. If you are running the language model on your local PC, you can use the provided command in the Anaconda Prompt or CMD to install 'langflow'. After the installation is complete, you can execute 'langflow' by typing it in the command line.
Creating a Chatbot using langflow
With 'langflow' successfully installed, we can now proceed to create our own Chatbot. In this tutorial, we will focus on two scenarios: creating a PDF-based Chatbot and building a Chatbot based on a research paper.
To create a PDF-based Chatbot, we will utilize the 'PDF Leader' example provided by the 'langflow' community. By forking the example, we can customize it to our needs. The PDF-based Chatbot allows us to provide answers based on specific PDF documents, making it suitable for situations where certain information needs to be kept private or specific documents serve as valuable sources of information. We will walk through the steps of setting up the API keys, uploading the PDF, and configuring the PDF-based Chatbot.
Setting up PDF-based Chatbot
To set up the PDF-based Chatbot, we need to register payment and create API keys. The payment registration requires providing the necessary card information. Once the payment is confirmed, we can proceed to create the API keys. These keys are essential for using the 'ChatOpenAI' API and the 'OpenAI Embeddings' API. With the API keys in HAND, we can start configuring the PDF-based Chatbot. This involves providing a description of the PDF and specifying a name for it. The description should include Hints or Context for the Chatbot to refer to when answering queries. Once the configuration is complete, we need to compile the Chatbot to enable its functionality.
Testing the PDF-based Chatbot
After compiling the Chatbot, we can test its functionality by asking questions related to the content of the PDF. Example questions could include inquiring about the definition of certain terms or specific details mentioned in the PDF. The Chatbot uses the PDF as a reference to generate the answers, ensuring accurate and contextually Relevant responses. By testing various queries, we can verify the Chatbot's ability to provide answers based solely on the content of the PDF.
Creating a Chatbot using a research paper
In addition to PDF-based Chatbots, 'langflow' also allows us to create Chatbots based on research papers. This can be particularly useful when dealing with cutting-edge topics or areas where up-to-date information is crucial. By specifying a research paper as the basis for the Chatbot, we can generate answers that reflect the Contents of the paper. In this section, we will guide you through the process of creating a research paper-based Chatbot using the 'Stable Diffusion' research paper as an example. Similar to the PDF-based Chatbot, we will need to configure the API keys, provide the necessary information about the research paper, and compile the Chatbot.
Testing the research paper-based Chatbot
Once the research paper-based Chatbot is compiled, we can test its functionality by asking questions related to the content of the paper. By asking specific questions or seeking detailed explanations from the paper, we can assess the Chatbot's ability to generate accurate and informative answers. The research paper-based Chatbot demonstrates its capability to leverage the content of the research paper to provide contextually relevant responses that would otherwise be difficult to obtain using a general ChatGPT model.
Conclusion
In this article, we introduced 'langflow' as a solution to the limitations of ChatGPT and demonstrated how to create Chatbots that can answer questions based on PDF files and research papers. 'langflow' offers the AdVantage of providing up-to-date information and document-specific answers, making it a valuable tool for various applications. By following the steps outlined in this article, users can develop their own Chatbots tailored to their specific needs, whether it's providing answers based on private documents or leveraging the content of research papers. 'langflow' offers a flexible and cost-effective alternative to the fixed monthly fee of ChatGPT Plus, allowing users to pay only for the queries they make. We hope this article has been informative and useful in expanding your knowledge and capabilities with Chatbots.