Building Chatbots with Langchain and Weaviate: Overcoming GPT Model Limitations

Large language models like OpenAI's GPT have limitations when it comes to using private data and can produce hallucinations.

Langchain is an open source framework that allows developers to integrate large language models with their own data.

This tutorial focuses on using Langchain and Weaviate for building custom chatbots that rely on private data.

📚 The first step in building custom chatbots is extracting and embedding textual data from PDFs and Word documents into vectors for computation.

🔍 Vectors allow for comparison and similarity calculations, enabling the extraction of relevant information from the knowledge database based on user queries.

💻 The implementation of the chatbot code will be done using Python and a Jupyter notebook.

🔑 The video demonstrates how to build custom chatbots using Langchain and Weaviate.

💻 To run the code, you need to install dependencies such as Langchain, Weaviate, and the OpenAI library.

🔐 API keys for Open AI and Weaviate are required to access the models and interact with the accounts.

💡 The video explains how to build custom chatbots using Langchain and Weaviate.

💡 The text data is split into smaller chunks to fit into the GPT model's input size.

💡 The metadata of each chunk includes the source of the text.

📚 Using Langchain and Weaviate to build custom chatbots.

💡 Splitting text into smaller chunks and embedding them into vectors using open AI embeddings.

💻 Storing the vectors into a vector database using the bv8 vector database and connecting to the vv8 cluster.

🔍 Using the openai 88a model for vector similarity comparisons between the vector database and query vectors.

📖 Exploring the vv8 documentation for more details and customization options.

💡 Langchain and Weaviate are used to build custom chatbots.

⚙️ The process involves loading pieces of text into a Vector store and performing a similarity search.

🤖 The chatbot uses the similarity search results to find the most relevant answers.

📚 The tutorial demonstrates how to build custom chatbots using Langchain and Weaviate.

🔍 The chatbot relies on a vector database supplied with PDF files related to GPT and open AI, and only answers questions related to this documentation.

🤖 By using the link chain library and a custom vector database stored in vv8, the chatbot can provide restricted results based on the provided documentation.