Building Chatbots to Chat with Your Data | Retrieval QA Chain & Streamlit UI | Part 3
Welcome to Part 3 of our Ultimate Guide on Building Chatbots to Chat with Your Data using Python and powerful libraries like LangChain, Streamlit, and ChromaDB. In this exciting installment, we take your chatbot project to the next level by exploring Retrieval Question Answering (QA) techniques, adding memory to your chatbot, and creating a sleek Streamlit user interface to house your chatbot.
📂 Topics Covered:
Retrieval QA Chain:
Dive deep into advanced document retrieval techniques like Maximum Marginal Relevance, Contextual Compression, and Self Query.
Learn how to enhance your chatbot's ability to retrieve relevant information from your data.
Adding Memory to Your Chatbot:
Discover how to equip your chatbot with memory capabilities, making it context-aware and better at handling user queries.
Understand the importance of maintaining conversation history and how to implement it effectively.
Building the Streamlit UI:
Step-by-step guidance on creating a user-friendly and interactive Streamlit user interface to host your chatbot.
Showcase your chatbot's capabilities and provide a seamless user experience.
🔥 Tags to Make This Video Go Viral:
#Chatbots #DataChat #Python #LangChain #Streamlit #ChromaDB #RetrievalQA #ChatbotMemory #StreamlitUI #DataScience #ArtificialIntelligence #ProgrammingTutorial #AIChatbotTutorial #TechExplained #Coding #UserInterface
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