In this tutorial video, I will guide you through the process of creating your own PDF Chatbot using open-source technologies. With the power of conversational AI, you'll be able to enhance your interaction with PDF documents using just your CPU.
To get started, we'll be using the Langchain framework for text preprocessing and handling chains. This framework provides robust capabilities for processing text, ensuring that your chatbot can understand and respond effectively to your queries.
Next, we'll leverage Sentence Transformers, a powerful embedding model, to enable intelligent understanding and retrieval of information. By incorporating this model, our chatbot will be able to grasp the context of your queries and provide accurate and relevant responses.
For efficient storage and retrieval of vectorized information, we'll utilize the Chroma DB, a high-performance vector database. This will ensure quick access to the relevant information and enhance the overall responsiveness of your chatbot.
To give your chatbot its language capabilities, we'll make use of the Lamini LM 738M, a large language model known for its natural language processing capabilities. With this model as the backbone, your chatbot will be able to provide insightful and contextually relevant responses to your queries.
To create a user-friendly interface, we'll employ Streamlit, a web framework that allows for easy interaction with your PDF Chatbot. With its intuitive design and interactive features, you'll be able to seamlessly engage with your chatbot and explore its capabilities.
By following this tutorial, you'll gain the knowledge and skills to build your own PDF Chatbot using open-source technologies. So let's dive in and unlock the potential of conversational AI for your PDF documents!
AI Anytime's GitHub: [ Ссылка ]
GitHub Repo: [ Ссылка ]
Langchain Docs: [ Ссылка ]
ChromaDB: [ Ссылка ]
Lamini LM 738M: [ Ссылка ]
LLM Playlist: [ Ссылка ]
#langchain #chatbot #python
