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GenAI – You Are Facing High Latency In RAG Pipeline What Are The Steps You Will Follow To Solve This ?
GenAI – How To Solve Latency In RAG Pipeline ? Table Of Contents: Break Down the Pipeline Components Measure and Profile Latency per Component Query Embedding Generation Time Vector Retrieval / Vector Database Time Reranking (if used) Time LLM Inference Time Prompt Construction Time Network / System-Level Issues Time Parallelize Where Possible Tools & Techniques (1) Breakdown The Pipeline Component (2) Measure And Profile Latency Per Component. (3) Query Input Component Solution: (4) Query Preprocessing & Embedding Component (5) Vector Search Component (6) Vector Search Component (7) Prompt Construction Component (8) LLM Inference Component (9) Post Processing Component (10) Caching/Storage
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GenAI – Scenario Based Q & A
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GenAI – Approximate Nearest Neighbors (ANN)
GenAI – Approximate Nearest Neighbors (ANN) Table Of Contents: Foundational Concepts What is Nearest Neighbor Search (NNS)? Exact vs Approximate Nearest Neighbors Trade-offs: Speed vs Accuracy vs Memory Use cases in GenAI: Semantic Search, RAG, Recommendation Systems Distance Metrics Euclidean Distance Cosine Similarity Manhattan (L1) Distance Dot Product Similarity Choosing the right metric based on data and task Core ANN Algorithms & Techniques Locality-Sensitive Hashing (LSH) Concept and hash function families MinHash, SimHash Hierarchical Navigable Small World Graphs (HNSW) Graph-based ANN Navigation and hierarchy Product Quantization (PQ) Vector compression for large-scale retrieval IVF (Inverted File Index) + PQ Clustering +
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GenAI – Creative Co-Pilot Tools
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GenAI – AI for Accessibility
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GenAI – Crisis Management Simulators (Defense)
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GenAI – Synthetic Biology & Chemistry Design
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GenAI – AI Legal Counsels & Advisors
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GenAI – Enterprise Knowledge Management
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GenAI – Digital Humans / AI Companions
