Build a Document Retriever Search Engine with LangChain

  • AdvancedLevel

  • 4314+Students Enrolled

  • 30 MinsDuration

  • 5Average Rating

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About this Course

  • Understand the fundamentals of document retrieval systems, focusing on indexing, querying, and optimizing search algorithms using LangChain.
  • Engage in practical exercises to build and deploy a functional document retrieval search engine, enhancing your problem-solving and coding skills.
  • Learn from industry professionals, gaining insights into best practices and advanced techniques in search engine development with LangChain.​

Learning Outcomes

Building with LangChain

Develop a search engine using LangChain’s AI-powered tools.

Fundamentals of Retrieval

Understand document retrieval, indexing, and search optimization.

Deploy & Optimize Search

Implement and enhance retrieval systems for real-world use.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Build a Document Retriever Search Engine with LangChain Video

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Dipanjan Sarkar

Principal AI Scientist

Dipanjan Sarkar, Lead Data Scientist, Author & Consultant, has 10+ years of expertise in ML, DL, GenAI, CV & NLP. He has led AI initiatives across Fortune 100 firms & startups, building data products & upskilling professionals at all levels

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Dipanjan Sarkar

    Instructor

  • Advanced

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

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

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.

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.

Yes, you will receive a certificate of completion after successfully finishing the course and assessments.

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