Learn AI-powered document search, RAG, FastAPI, ChromaDB, embeddings, vector search, and Streamlit UI
What you will learn
Set up and configure Mistral AI & Ollama locally for AI-powered applications.
Extract and process text from PDFs, Word, and TXT files for AI search.
Convert text into vector embeddings for efficient document retrieval.
Implement AI-powered search using LangChain and ChromaDB.
Develop a Retrieval-Augmented Generation (RAG) system for better AI answers.
Build a FastAPI backend to process AI queries and document retrieval.
Design an interactive UI using Streamlit for AI-powered knowledge retrieval.
Integrate Mistral AI with LangChain to generate contextual responses.
Optimize AI search performance for faster and more accurate results.
Deploy and run a local AI-powered assistant for real-world use cases.
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- Master the creation of sophisticated AI applications leveraging the power of Mistral AI models.
- Gain hands-on experience in building intelligent systems that can understand and process a wide array of document formats.
- Unlock the potential of vector embeddings to transform unstructured data into a format optimized for rapid and precise information retrieval.
- Develop robust search capabilities that go beyond keyword matching, enabling semantic understanding of user queries.
- Construct advanced Retrieval-Augmented Generation (RAG) pipelines that enrich AI responses with factual, contextually relevant information from your documents.
- Architect and implement efficient backend services using FastAPI to manage AI workflows and data interactions.
- Craft intuitive and engaging user interfaces with Streamlit, making complex AI functionalities accessible to end-users.
- Seamlessly integrate cutting-edge language models with powerful orchestration frameworks like LangChain to build dynamic AI experiences.
- Focus on performance tuning to ensure your AI applications deliver speedy and accurate results, even with large datasets.
- Learn to deploy and manage your AI solutions in a local environment, preparing you for practical, real-world implementation.
- Explore the synergy between local LLMs (via Ollama) and external data sources for enhanced AI intelligence.
- Understand the principles behind efficient data indexing and querying within a vector database context, specifically ChromaDB.
- Discover how to craft effective prompts that guide AI models to produce desired outputs.
- Develop a practical skillset for building end-to-end AI-driven knowledge management systems.
- PROS:
- Builds practical, job-ready skills in a high-demand AI niche.
- Empowers creation of personalized AI assistants and knowledge bases.
- Provides a solid foundation for further exploration in generative AI and RAG architectures.
- CONS:
- Requires a foundational understanding of Python programming to fully benefit from the course.
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