rag-pipeline

Estrelas6

Forks0

Atualizado23 de fevereiro de 2026 às 13:32

RAG setup with Elasticsearch as the retrieval backend

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

patrykkopycinski

patrykkopycinski/elastic-cursor-plugin

Abrir repositório GitHub Ver repositórios do creator

Download

Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Cientistas de dadosInformática e Matemática·SOC 15-2051

SKILL.md

readonly

name	rag-pipeline
description	RAG setup with Elasticsearch as the retrieval backend

RAG Pipeline with Elasticsearch

Use when the user wants to build a RAG (retrieval-augmented generation) system using Elasticsearch.

1. Prompt

Clarify: chunking strategy, embedding model, and whether they use Elasticsearch only or with an LLM.

2. Provision

Create an index with a dense_vector field and a text field for chunks.
Create an ingest pipeline with an inference processor to generate embeddings from the text field.
Or use application-side embeddings and index precomputed vectors.

3. Integrate

Bulk index document chunks (with or without pipeline). Use bulk_index for large corpora.
At query time: embed the user question (same model), then run search with a kNN query against the vector field.
Return top-k hits to the LLM as context.

4. Validate

Run a test query and verify retrieved chunks are relevant.
Suggest ELSER/sparse for keyword-style semantics if appropriate.

Mais deste repositório

mesmo repositório

llm-eval-observatory

patrykkopycinski/elastic-cursor-plugin

Debug and analyze LLM eval runs — view traces, compare runs, investigate failures, track costs. Use when debugging @kbn/evals failures, comparing eval runs, or analyzing LLM performance.

2026-03-276

security-inbox

patrykkopycinski/elastic-cursor-plugin

Start your security session with a personalized briefing — attacks, alerts, cases, rules, threat intel. Use as the first thing when starting security work.

2026-03-276

cluster-onboarding

patrykkopycinski/elastic-cursor-plugin

Guide users from zero to a working Elastic cluster — Cloud or on-prem, connection config, first queries, and next steps.

2026-03-276

o11y-service-dashboard

patrykkopycinski/elastic-cursor-plugin

Interactive guide for creating an APM service overview dashboard — discovers service data, presents metrics, and creates a tailored dashboard.

2026-03-276

o11y-slo-setup

patrykkopycinski/elastic-cursor-plugin

Interactive guide for creating SLOs from discovered APM and metric data — identifies candidates, lets user configure targets, and creates SLOs.

2026-03-276

search-index-management

patrykkopycinski/elastic-cursor-plugin

Create, configure, and manage Elasticsearch indices — mappings, settings, templates, data streams, and lifecycle policies.

2026-03-276

name	rag-pipeline
description	RAG setup with Elasticsearch as the retrieval backend

RAG Pipeline with Elasticsearch

Use when the user wants to build a RAG (retrieval-augmented generation) system using Elasticsearch.

1. Prompt

Clarify: chunking strategy, embedding model, and whether they use Elasticsearch only or with an LLM.

2. Provision

Create an index with a dense_vector field and a text field for chunks.
Create an ingest pipeline with an inference processor to generate embeddings from the text field.
Or use application-side embeddings and index precomputed vectors.

3. Integrate

Bulk index document chunks (with or without pipeline). Use bulk_index for large corpora.
At query time: embed the user question (same model), then run search with a kNN query against the vector field.
Return top-k hits to the LLM as context.

4. Validate

Run a test query and verify retrieved chunks are relevant.
Suggest ELSER/sparse for keyword-style semantics if appropriate.