rag-pipeline

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Actualizado23 de febrero de 2026 a las 13:32

RAG setup with Elasticsearch as the retrieval backend

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Fuente

patrykkopycinski

patrykkopycinski/elastic-cursor-plugin

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

SKILL.md

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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.

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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.