Discover the power of Flow Wise, a versatile chatbot building platform, through two compelling examples. This article highlights the key features and functionalities of Flow Wise, showcasing its ability to create chatbots that interact with OpenAI, third-party APIs, and local PDF data. Let's delve into the details of these examples below.
Example 1: Building a Basic Chatbot with OpenAI
The first example demonstrates how to construct a basic chatbot using Flow Wise and OpenAI. By leveraging a prompt template, developers can easily create a chatbox and enable interaction with OpenAI's powerful language models. This functionality opens up a world of possibilities for building intelligent and responsive chatbots that cater to various use cases.
Example 2: Interacting with Local PDF Data
In the second example, Flow Wise showcases its ability to interact with local data, specifically PDF files. By utilizing components such as a PDF data loader, a text separator, an embedding model, and a vector database (such as Pinecone or Chroma), developers can effectively process and store embeddings of the PDF data. The embeddings can then be used to interact with the local PDF data using OpenAI's language models, employing a retrieval QA chain to seamlessly combine all the components.
By following these examples, developers can harness the full potential of Flow Wise and build chatbots that cater to specific needs, whether it involves interacting with OpenAI or leveraging local PDF data.
Q&A
Q: What is Flow Wise?
A: Flow Wise is a chatbot building platform that enables developers to create interactive and intelligent chatbots.
Q: What is the first example discussed in the article?
A: The first example showcases the process of building a basic chatbot using Flow Wise and OpenAI, utilizing a prompt template.
Q: What is the second example mentioned in the article?
A: The second example demonstrates how Flow Wise can interact with local PDF data. It involves using various components such as a PDF data loader, a text separator, an embedding model, and a vector database.
Q: How can Flow Wise interact with local PDF data?
A: Flow Wise utilizes a PDF data loader, a text separator, an embedding model, and a vector database to process and store embeddings of local PDF data. These embeddings are then used to interact with the PDF data using OpenAI's language models.
Q: How does Flow Wise combine different components to interact with PDF data?
A: Flow Wise employs a retrieval QA chain to seamlessly combine the various components involved in interacting with local PDF data, ensuring a cohesive and efficient chatbot experience.
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