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GenAI – How To Optimize User Query Component ?
GenAI – How To Optimize User Query Component ? Table Of Contents: What Is Query Input Component? Network Optimization Techniques. Use HTTP/2 or gRPC Compress Payloads Avoid Cold Start Problem (1) What Is Query Input Component ? (2) Network Optimization Techniques. (3) Use HTTP/2 or gRPC (4) Compress Payloads Use Compression (gzip or Brotli) import gzip import requests query = { “user_query”:”…” # a very large string } #Compress JSON compressed_data = gzip.compress(bytes(str(query), ‘utf-8’)) headers = { “Content-Encoding”: “gzip”, “Content-Type”: “application/json” } response = request.post(“http://localhost:8000/rag/query”, data=compressed_data, headers=headers) Use Decompression (gzip or Brotli) from fastapi import FastAPI, Request import gzip import
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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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Machine Learning – Approximate Nearest Neighbors(ANNOY)
ML – Approximate Nearest Neighbors. Table Of Contents: What Is ANNOY Algorithm. Use Case Example. How ANNOY Works Step By Step. Trade Off Controls. Advantages Of Annoy. Limitation Of ANNOY. (1) What Is ANNOY Algorithm ? (2) Example Use Cases. (3) How Annoy Works — Step by Step Step-1: Input Data Preparation Step-2: Build Trees Using Random Projections Step-3: Save The Tree To The Desk Step-4: Querying / Searching (4) Trade-Off Controls (5) Advantages of Annoy (6) Limitation
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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
