What you'll Learn
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Gain a thorough understanding of building document retrieval systems using LangChain.
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Develop practical skills in processing Wikipedia data, chunking documents, and generating embeddings.
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Learn to index data into vector databases and optimize retrieval workflows for efficiency.
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Explore advanced retriever methods and their real-world applications in QA systems.
About the Instructor
Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya
Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence.
Who Should Enroll?
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Individuals looking to enhance their expertise in AI-driven QA systems and explore the capabilities of LangChain.
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For those on their journey to mastering AI and NLP, ready to explore advanced frameworks and make a mark in the tech world.
FAQ
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What is LangChain, and why should I use it for building a search engine?
LangChain is a powerful framework for building applications with language models. It simplifies the process of working with unstructured data, enabling you to create efficient search engines, handle embeddings, and integrate with various retrieval and indexing tools.
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How does a document retriever search engine work?
A document retriever search engine processes unstructured data by chunking it into smaller segments, creating embeddings for those chunks, and indexing them in a vector database. It retrieves relevant chunks based on user queries, making it efficient for tasks like searching large datasets.
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What are document embeddings, and why are they important?
Document embeddings are numerical representations of text data that capture its semantic meaning. They are crucial for enabling search engines to understand and compare the relevance of different text chunks to user queries.
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What is a vector database, and how is it used in this course?
A vector database stores and indexes document embeddings, allowing for efficient similarity searches. In this course, you will learn to use vector databases to power the retrieval functionality of your search engine.
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Why is chunking necessary for document retrieval?
Chunking splits large documents into smaller, manageable pieces. This improves the accuracy of embeddings and ensures that the retriever can focus on the most relevant sections of a document when answering queries.