Elasticsearch use case library — the full map of what you can build, with industry examples and technologies. Use when a developer asks "what can Elastic do?", "what can I build?", "what use cases does Elasticsearch support?", or needs help choosing what to build.
Instrucciones de origen · Vista previa de solo lectura
name
use-case-library
description
Elasticsearch use case library — the full map of what you can build, with industry examples and technologies. Use when a developer asks "what can Elastic do?", "what can I build?", "what use cases does Elasticsearch support?", or needs help choosing what to build.
Elasticsearch Use Case Library
Present this library when a user asks what they can build with Elasticsearch, wants to explore use cases, or needs help figuring out which category their project falls into. Walk through the relevant use cases conversationally — don't dump the entire list. Ask what resonates, then continue the conversation.
Conversation flow — return to onboarding
This skill helps users explore what they can build. It is not the main conversation driver.
Once the user picks a use case or is ready to start building, re-read /elasticsearch-onboarding to enter the structured onboarding playbook (Steps 1–7: intent → data → mapping → build → test → iterate). That playbook controls sequencing, the one-question-at-a-time rule, and the Dev Tools API-snippet workflow. If /elasticsearch-onboarding has not been loaded yet in this conversation, load it now — it is the primary conversation flow for all Elasticsearch search onboarding.
How to Use This Library
If the user is exploring — summarize the 8 use cases with one-line descriptions and ask which sounds closest to what they're building.
If the user describes something specific — match it to a use case below and confirm: "That sounds like [use case] — here's what that typically involves. Sound right?"
Once a use case is confirmed — continue the conversation to understand their data and design the Elasticsearch resources.
The Use Cases
1. Product & Catalog Search
Help users find and filter items from a structured catalog.
Industries: E-commerce, marketplace, retail, real estate, automotive, job boards
Examples:
Online store product search with filters and facets
Marketplace listing search (Airbnb, Etsy-style)
Auto parts lookup by make, model, year
Job search with location, salary, and role filters
What Elasticsearch does:
Full-text search (BM25) for keyword matching on titles and descriptions
Faceted filtering for price ranges, categories, brands, ratings
Fuzzy matching for typo tolerance
Synonyms API for domain-specific equivalents
Completion suggester for autocomplete
In Kibana: Create the index and mapping via Dev Tools, set up synonyms, ingest sample data with POST _bulk, and test queries — all before writing any application code.
2. Knowledge Base & Document Search
Let people search long-form content and find relevant passages.
Hybrid search (BM25 + kNN via RRF) for exact match + meaning
Semantic search via semantic_text for meaning-based retrieval
Highlighting to show matching snippets in context
Nested objects for structured document sections
In Kibana: Set up the index with semantic_text fields via Dev Tools, configure inference endpoints, and test hybrid queries. The retrieval backend is fully configured before any frontend work.
3. AI-Powered Assistant / Chatbot
Build a conversational agent that answers questions using your data.
"ChatGPT over your docs" — answer questions from company knowledge
Internal IT helpdesk bot
Patient FAQ bot for healthcare providers
What Elasticsearch does:
RAG pipeline — retrieve relevant chunks, feed to LLM for answer generation
Vector search (kNN) for semantically similar content retrieval
Embedding models via EIS (Elastic Inference Service) — no external API key needed
Chunking and ingest pipelines for document processing
In Kibana: Set up the vector index, configure the inference endpoint, ingest and chunk documents via Dev Tools. The retrieval layer is ready to connect to an LLM from your application code in the IDE.
Industries: Media, streaming, e-commerce, news, social platforms
Examples:
"You might also like" product suggestions
Related articles or blog posts
Content personalization based on reading history
What Elasticsearch does:
Vector similarity (kNN) to find items close in embedding space
More Like This queries for content-based similarity
Filtering + boosting to constrain by category, recency, availability
Script scoring to blend similarity with business rules
In Kibana: Create the vector index, ingest item embeddings, and test similarity queries in Dev Tools. Integrate the query into your application afterward.
5. Customer Support Search
Help agents find solutions faster and customers help themselves.
Knowledge deflection — suggest articles before filing a ticket
What Elasticsearch does:
Hybrid search for exact match on error codes + semantic match on symptom descriptions
Synonyms API for domain terminology ("can't log in" = "authentication failure")
Highlighting to surface relevant resolution steps
Aggregations to detect support trends
In Kibana: Set up the index with hybrid fields, create synonym sets, and test queries in Dev Tools. The search backend is production-ready before building the support UI.
6. Location-Based Search
Find things near a place — stores, restaurants, properties, services.
Industries: Retail, food delivery, real estate, travel, logistics
Examples:
Store locator
"Restaurants near me" with cuisine filters
Property search within a neighborhood
What Elasticsearch does:
geo_point / geo_shape fields for coordinates and boundaries
Distance sorting by proximity
Bounding box and polygon filters
Combined with full-text — "pizza near me" = geo filter + keyword search
In Kibana: Create the index with geo fields, bulk-index location data via Dev Tools, and test distance queries. Combine with full-text search in the same index.
7. Log & Event Search
Search, explore, and analyze machine-generated data.
Data streams for append-only, time-partitioned storage
Index Lifecycle Management (ILM) for data tiers
ES|QL for piped analytics queries
Aggregations for histograms, percentiles, and trends
Note: Log and event search is typically handled by Elastic's Observability or Security solutions with purpose-built UIs. In Kibana: switch solution view under Management → Spaces (Hosted) or create an Observability/Security project (Serverless).
8. Vector Database (for AI/ML Pipelines)
Store and retrieve embeddings programmatically — code searches, not people.
Industries: AI/ML companies, any organization building with LLMs
Examples:
Embedding storage and retrieval for RAG pipelines
Image similarity search
Code search by semantic meaning
Duplicate detection across large document sets
What Elasticsearch does:
Dense vector fields for high-dimensional embeddings
kNN / ANN (HNSW) for approximate nearest neighbor search at scale
Scalar and product quantization for cost/performance
Metadata filtering to combine vector similarity with structured filters
In Kibana: Create the vector index, configure quantization, and test kNN queries in Dev Tools. Connect your AI pipeline from your IDE using the Elasticsearch client library.
Quick Reference: Use Case to Technology Map
Use Case
Primary Tech
Set Up in Kibana
Product & catalog search
Full-text (BM25), facets, synonyms
Index + mapping + synonyms via Dev Tools
Knowledge base search
Hybrid (BM25 + kNN via RRF)
Index with semantic_text + inference endpoint
AI assistant / chatbot
Vector search (kNN), RAG
Vector index + chunking pipeline via Dev Tools
Recommendations
Vector similarity, More Like This
Vector index + similarity queries
Customer support search
Hybrid search, synonyms
Index + synonym sets + hybrid queries
Location-based search
geo_point, distance sort
Index with geo fields + distance queries
Log & event search
Data streams, ILM, ES|QL
Use Observability/Security solution view
Vector database
Dense vectors, kNN/ANN
Vector index + quantization config
Non-Search Use Cases
If the user describes something that isn't search, redirect within Kibana:
Monitoring infrastructure or applications — That's Elastic Observability. Switch solution view under Management → Spaces or create an Observability project on Serverless.
Detecting threats or investigating security events — That's Elastic Security. Switch solution view or create a Security project.
Building dashboards and visualizations — Kibana has built-in dashboards, Lens, and Maps. Navigate to Analytics → Dashboards.
