Master Vector Database with Python for AI & LLM Use Cases - Learn Vector Database using Python, Pinecone, LangChain, Open AI, Hugging Face and build out AI, ML , Chat applications
What you'll learn
- Pinecone Vector Database, LangChain, Transformer Models for vector embedding, Generative AI, Open AI API Usage, Hugging Face Models
- Master the essential techniques for vector data embedding, indexing, and retrieval.
- A Practical Code Along with Semantic Search Use Case in Detail with Named Entity Recognition
- Developing an AI Chat Bot for Cognitive Search on Private Data Using LangChain
- Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).
- Explore various vector database technologies, including Pinecone, and learn how to set up and configure a vector database environment.
- Learn how vector databases enhance AI workflows by enabling efficient similarity search and nearest neighbor retrieval.
- Gain practical knowledge on integrating vector databases with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn.
- Implement code along exercises to build and optimize vector indexing systems for real-world applications.
- Explore practical use cases of vector databases in AI, generative AI, and LLM, such as recommendation systems, content generation, and language translation.
- Understand how vector databases can handle large-scale datasets and support real-time inference.
- Gain insights into performance optimization techniques, scalability considerations, and best practices for vector database implementation.
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