In this video, we explore the process of creating a chat with PDF app without writing any code. The app leverages the Bubble platform and the capabilities of the CPT 3.5 and 4 language models. We will be utilizing the retrieval augmented generation (RAG) approach, which involves retrieving data from a database and generating responses based on that data.
Requirements for Building the App
To build the app, you will need a Bubble account, access to open AI models and API keys, a vector database (such as Pinecone), and either the Langchain or Langflow tool. These tools enable seamless integration with the AI language models, allowing you to create powerful applications without writing any code.
Splitting PDF into Chunks for Efficient Processing
The app will split the PDF document into smaller, more manageable chunks. This step is crucial for working effectively with large language models, as it enables more efficient processing and analysis of the document's content.
Converting Text Chunks into Numerical Vectors
Using the open AI Ada model, the text chunks from the PDF are converted into numerical vectors. These vector representations capture the semantic meaning of the text, allowing for more advanced analysis and retrieval.
Storing Vector Representations in a Database
The vector representations of the text chunks are stored in a vector database, such as Pinecone. This database serves as a repository for efficient retrieval and comparison of the document information.
Retrieving Relevant Answers using Similarity Search
When a user asks a question about the PDF, the app converts the question into vectors and performs a similarity search in the vector database. This search retrieves the most relevant answer based on the similarity between the question and the stored vector representations.
This process of building a chat with PDF app can be generalized to other AI applications that involve retrieving and generating responses from data. By leveraging the power of AI language models and the simplicity of the Bubble platform, developers can create sophisticated applications without the need for coding expertise.
Q&A:
Q: What tools are recommended for building the chat with PDF app without code?
A: The recommended tools include Bubble platform, open AI models and API keys, a vector database (such as Pinecone), and either the Langchain or Langflow tool.
Q: How is the PDF document processed in the app?
A: The PDF is split into smaller chunks to facilitate more efficient processing and analysis using the language models.
Q: What is the purpose of converting text chunks into numerical vectors?
A: Converting text chunks into numerical vectors enables advanced analysis and retrieval based on the semantic meaning of the text.
Q: How are relevant answers retrieved for user questions?
A: The app converts the user's question into vectors and performs a similarity search in the vector database to retrieve the most relevant answer.
Q: Can this approach be applied to other AI applications?
A: Yes, this approach can be generalized to other AI applications that involve retrieving and generating responses from data, providing a versatile solution for various use cases.
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