rag-pipeline

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Aktualisiert23. Februar 2026 um 13:32

RAG setup with Elasticsearch as the retrieval backend

Installation

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

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patrykkopycinski

patrykkopycinski/elastic-cursor-plugin

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

DatenwissenschaftlerInformatik- und Mathematikberufe·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.