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FAISS - Vector Similarity Search & Embedding Retrieval

Master FAISS for efficient vector similarity search and large-scale embedding-based retrieval to power AI, NLP, and recommendation systems.
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Course Duration: 10 Hours
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FAISS: Vector Similarity Search and Embedding Retrieval is a comprehensive, hands-on course designed for AI engineers, data scientists, machine learning practitioners, and developers who want to build high-performance similarity search systems. As modern AI applications increasingly rely on embeddings and vector representations for tasks like semantic search, recommendation engines, and retrieval-augmented generation (RAG), FAISS has emerged as a critical open-source library for large-scale vector indexing and similarity search.
 
At its core, FAISS (Facebook AI Similarity Search) is a library developed by Meta AI to efficiently search through dense vector datasets. It enables fast nearest-neighbor search on billions of vectors by leveraging optimized indexing structures and GPU acceleration. Unlike traditional databases or search engines, FAISS is built specifically for high-dimensional embeddings generated by AI models like BERT, OpenAI embeddings, and sentence transformers.
 
This course introduces FAISS from the ground up—covering vector similarity fundamentals, index types, search algorithms, and practical integration into modern AI workflows. You’ll learn to create vector indexes, perform ANN (Approximate Nearest Neighbor) search, handle large-scale datasets, and integrate FAISS with tools like LangChain, Hugging Face, and OpenAI APIs. By the end of the course, you’ll be able to design and deploy high-performance semantic search and retrieval pipelines that power cutting-edge AI systems.
 
What is FAISS?
 
FAISS is an open-source library for efficient similarity search and clustering of dense vectors. It provides optimized algorithms for finding nearest neighbors in large vector spaces, enabling fast semantic search and retrieval across massive embedding datasets. FAISS is widely used in AI-powered search engines, recommendation systems, chatbots with retrieval-augmented generation (RAG), and other NLP applications that require embedding comparisons at scale.
 
How to Use This Course Effectively
 
To maximize your learning outcomes:
  1. Start with Fundamentals: Understand embeddings, similarity metrics (cosine, Euclidean), and ANN search basics.
  2. Hands-On Practice: Work with FAISS in Python to create indexes, insert vectors, and query results.
  3. Experiment with Index Types: Learn the differences between Flat, IVF, PQ, and HNSW indexes.
  4. Leverage GPU Acceleration: Practice deploying FAISS with GPU for large-scale search.
  5. Integrate with AI Tools: Combine FAISS with LangChain, Hugging Face models, and OpenAI embeddings.
  6. Apply to RAG Pipelines: Use FAISS to store and retrieve knowledge base vectors for LLM applications.
  7. Optimize for Scale: Learn strategies for memory efficiency, sharding, and distributed FAISS.
  8. Work on Real Projects: Build semantic search engines, recommendation systems, and vector-based chatbots.
  9. Benchmark and Tune: Measure query speed and accuracy trade-offs in different FAISS configurations.
  10. Document and Reuse: Keep reusable code templates and deploy FAISS pipelines in production systems.
This course equips you with the skills to build robust, high-performance vector search and retrieval solutions using FAISS.

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Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand vector similarity search and its role in modern AI workflows.
  2. Install, configure, and use FAISS for vector indexing and retrieval.
  3. Work with different FAISS index types (Flat, IVF, PQ, HNSW).
  4. Implement Approximate Nearest Neighbor (ANN) search for scalability.
  5. Integrate FAISS with embeddings from OpenAI, Hugging Face, and sentence transformers.
  6. Build semantic search engines and recommendation pipelines.
  7. Optimize FAISS for performance using GPU acceleration.
  8. Deploy FAISS indexes in production AI pipelines and RAG workflows.
  9. Handle billion-scale vector datasets efficiently.
  10. Apply FAISS in enterprise-grade AI systems for NLP and search applications.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to Vector Search and FAISS
  • Understanding Embeddings and Similarity Metrics
  • Why FAISS for Large-Scale Search?
  • FAISS Architecture Overview
Module 2: Setting Up FAISS
  • Installation (CPU and GPU Versions)
  • Python API Basics
  • Building a Simple Flat Index
Module 3: Similarity Search Fundamentals
  • Exact Search vs Approximate Nearest Neighbor (ANN)
  • Cosine Similarity vs Euclidean Distance
  • Indexing and Query Basics
Module 4: FAISS Index Types
  • Flat (Brute Force Search)
  • Inverted File (IVF) Indexes
  • Product Quantization (PQ)
  • Hierarchical Navigable Small World (HNSW) Graphs
Module 5: GPU Acceleration and Scaling
  • Using FAISS with GPU for Speedup
  • Hybrid CPU-GPU Workflows
  • Handling Billion-Scale Datasets
Module 6: FAISS Integrations with AI Tools
  • OpenAI Embeddings + FAISS
  • LangChain VectorStore Integration
  • Hugging Face Sentence Transformers
Module 7: Building Semantic Search Applications
  • Creating a Document Embedding Pipeline
  • Querying for Semantic Similarity
  • Ranking and Relevance Scoring
Module 8: FAISS in RAG Pipelines
  • Retrieval-Augmented Generation with FAISS
  • Using FAISS as a Knowledge Base for LLMs
  • Combining FAISS with LangChain Agents
Module 9: Optimization and Tuning
  • Reducing Memory Footprint
  • Index Sharding and Distributed FAISS
  • Balancing Accuracy vs Speed in ANN Search
Module 10: Real-World Projects
  • News Article Semantic Search Engine
  • Product Recommendation System with FAISS
  • Chatbot Knowledge Retrieval Pipeline
Module 11: FAISS for Enterprise Workloads
  • Deploying FAISS in Production (APIs and Microservices)
  • Monitoring and Scaling Strategies
  • Security and Compliance Considerations
Module 12: Interview Questions and Career Path Guidance
Certification Back to Top

Upon completion, learners will receive a Certificate of Completion from Uplatz, demonstrating expertise in FAISS-based vector similarity search and retrieval. This certification validates your ability to implement embedding-based search engines, optimize retrieval pipelines, and integrate FAISS into AI-powered systems. It is highly valuable for professionals working on NLP, search, recommendation engines, or LLMOps (Retrieval-Augmented Generation pipelines).

Career & Jobs Back to Top
FAISS skills are increasingly sought after as AI-driven applications like semantic search, personalized recommendations, and LLM-based assistants gain traction. Mastering FAISS opens doors to roles such as:
  • AI Engineer / LLM Developer
  • Machine Learning Engineer (Search & NLP)
  • Data Scientist (Information Retrieval Focus)
  • LLMOps Engineer (RAG Pipelines)
  • Recommendation Systems Engineer
These roles are prominent in SaaS, e-commerce, AI platforms, research, and enterprise AI. Professionals skilled in FAISS can:
  • Build high-performance embedding search pipelines.
  • Integrate retrieval capabilities into LLM applications.
  • Handle billion-scale vector datasets efficiently for production.
With the surge in RAG-based AI systems, FAISS expertise places you at the forefront of scalable AI infrastructure and retrieval engineering.
Interview Questions Back to Top
  1. What is FAISS, and what problem does it solve?
    FAISS is an open-source library for efficient similarity search on high-dimensional vectors, used for semantic search, recommendation systems, and AI retrieval.
  2. What are embeddings, and why are they important for FAISS?
    Embeddings are vector representations of data (text, images) that capture semantic meaning, allowing FAISS to perform similarity searches effectively.
  3. Explain Flat vs IVF Index in FAISS.
    Flat index performs exact brute-force search, while IVF (Inverted File Index) partitions vectors for faster approximate search.
  4. What is Product Quantization (PQ) in FAISS?
    PQ reduces memory usage by compressing vectors into smaller representations, enabling efficient large-scale ANN search.
  5. How does FAISS integrate with LangChain or LLM workflows?
    FAISS can be used as a LangChain VectorStore to store embeddings and retrieve context for LLM-powered RAG pipelines.
  6. What is ANN (Approximate Nearest Neighbor) search?
    ANN speeds up similarity search by trading off slight accuracy for significant performance gains in large datasets.
  7. How does FAISS leverage GPUs?
    FAISS uses GPU acceleration for parallelized vector operations, drastically improving search speed for massive datasets.
  8. What are common similarity metrics supported in FAISS?
    FAISS supports L2 (Euclidean distance), Inner Product, and Cosine similarity for nearest-neighbor computations.
  9. What is HNSW indexing in FAISS?
    HNSW (Hierarchical Navigable Small World) is a graph-based index providing fast ANN search with high recall.
  10. How would you deploy FAISS in a production pipeline?
    By serving FAISS as an API/microservice, integrating it with embeddings pipelines (e.g., OpenAI), and monitoring performance with scaling strategies.
Course Quiz Back to Top
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