Laravel

🎤 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: ...