Hybrid Search Done Right: Stop Calling Metadata Filters "Hybrid"

Hybrid Search Done Right: Stop Calling Metadata Filters “Hybrid” Everyone’s talking about hybrid search right now. But here’s the uncomfortable truth: 👉 Just because you glued vector search onto your database and added metadata filters doesn’t mean you’ve built true hybrid search. That’s like duct-taping a spoiler on a hatchback and calling it a race car. 🚗💨 Hybrid search is more than just “keyword + vector + filter.” It’s about field-level design, reranking, scoring, and scale. ...

August 25, 2025 · 4 min · Ashish Tiwari
Generative AI and LLM in PHP | Ashish Tiwari | Laracon India 2025

Laracon 2024

🎤 Talk Summary: No-Code RAG Chatbot with PHP, LLMs & Elasticsearch Speaker: Ashish Diwali (Senior Developer Advocate, Elastic) 🔑 Introduction Topic: Integrating Generative AI (LLMs) with PHP. Goal: Show how to build chat assistants, semantic search, and vector search without heavy ML expertise. Demo focus: Using Elasticsearch + PHP + LLM (LLaMA 3.1). 🧩 Core Concepts 1. Prompt Engineering LLMs generate responses based on prompts → predicting next words. Techniques: Zero-shot inference → direct classification or tagging. One-shot inference → provide one example in the prompt. Few-shot inference → multiple examples → useful for structured outputs (SQL, JSON, XML). Iteration + context = In-context learning (ICL). 2. LLM Limitations ❌ Hallucinations (wrong answers). ❌ Complex to build/train from scratch. ❌ No real-time / private data access. ❌ Privacy & security concerns (especially in banking, public sector). 3. RAG (Retrieval-Augmented Generation) Solution to limitations. Workflow: User query → hits database/vector DB (e.g., Elasticsearch). Retrieve top 5–10 relevant docs. Pass as context window → LLM generates accurate answer. Benefits: Grounded responses. Works with private data. Avoids retraining large models. 🔍 Semantic & Vector Search Semantic Search: Understands meaning, not just keywords. Example: “best city” ↔ “beautiful city.” Vector Search: Text, images, and audio converted into embeddings (arrays of floats). Enables image search, recommendation systems, music search (via humming). Similarity algorithms: cosine similarity, dot product, nearest neighbors. 🛠️ Tools & Demo Elephant Library (PHP) Open-source PHP library for GenAI apps. Supports: LLMs: OpenAI, Mistral, Anthropic, LLaMA. Vector DBs: Elasticsearch, Pinecone, Chroma, etc. Features: document chunking, embedding generation, semantic retrieval, Q&A (RAG). Demo Flow Ingestion: ...

August 25, 2025 · 2 min · Ashish Tiwari
Privacy-First Conversation: Building No-Code Chat Assistants With ElasticSearch And Amazon Bedrock

AWS Community Day Mumbai 2024

🚀 No-Code Chatbot with Elasticsearch + AWS Bedrock (Talk Summary) Speaker: Ashish (Senior Developer Advocate, Elastic) Event: AWS Community Day Mumbai 2024 🔑 Why Search Still Matters with LLMs LLMs (like ChatGPT) are powerful but face: ❌ Hallucinations 💰 High cost per query 🔒 No access to private / real-time data ✅ Search grounds LLMs in reliable, domain-specific info. ⚡ Elasticsearch Capabilities Traditional keyword search + modern vector search. Real-world use cases: 📍 Geospatial queries (ride-sharing, food delivery) ❤️ Matchmaking 📊 Observability dashboards 📝 Centralized logging (Elastic Stack: Elasticsearch, Kibana, Beats, Logstash) 🤖 Retrieval-Augmented Generation (RAG) Workflow: ...

August 25, 2025 · 1 min · Ashish Tiwari
GIDS 2024 - Smart Search with RAG: Elasticsearch Meets Language Models

GIDS 2024 - Smart Search with RAG: Elasticsearch Meets Language Models

Introduction In today’s data-driven world, just having a search engine is not enough; the key is making it smart. Enter Elasticsearch Relevance Engine (ESRE) augmented with Retrieval Augmented Generation (RAG), a powerful solution that marries Elasticsearch’s superior search capabilities with Large Language Models (LLMs) like ChatGPT for precise, contextual querying over proprietary datasets. This session is a hands-on guide that will show you how to amplify the power of Elasticsearch with advanced LLMs. ...

June 3, 2024 · 1 min · Ashish Tiwari
Vector and hybrid search with Elasticsearch

Elasticsearch: Vector and Hybrid Search

Introduction Search is not just traditional TF/IDF any more but the current trend of machine learning and models has opened another dimension for search. This talk gives an overview of: Classic search and its limitations. What is a model and how can you use it. How to use vector search or hybrid search in Elasticsearch. Where OpenAI’s ChatGPT or similar LLMs come into play to with Elastic. Check how to leverage Leverage ChatGPT with Elasticsearch. ...

August 29, 2023 · 1 min · Ashish Tiwari