Nabanita Roy- Building Contextual ChatBot using LLMs, Vector Databases & Python | PyData Global 2023

Large Language Models (LLMs) combine machine learning, deep learning, and generative AI to process text and generate human-like responses based on training data

Core components of building contextual chatbots:

• OpenAI API/models (GPT-3.5, GPT-4)
• Vector databases (ChromaDB) for storing embeddings
• LangChain framework for handling document processing and interactions
• Embeddings to convert text into numerical representations
• RAG (Retrieval Augmented Generation) for context-aware responses

Temperature parameter controls response creativity:

• 0: More factual, precise responses
• 0.5: Balanced creativity and accuracy
• 1+: More creative but potentially less accurate

Key challenges when working with LLMs:

• Context window limitations
• Token limits
• Potential hallucinations/inaccurate information
• Bias in training data
• Cost management

Best practices for implementation:

• Clear prompt engineering with specific instructions
• Proper chunking of large documents
• Setting appropriate context
• Using memory for conversation history
• Implementing proper error handling and validation

Applications include:

• Customer support
• Document analysis
• Content generation
• Code assistance
• Template creation
• Question answering