embedding-strategy

Name: embedding-strategy
Availability: InStock
Author: Agentient

Configure asymmetric embedding paths with RETRIEVAL_DOCUMENT and RETRIEVAL_QUERY task types.

2|1|Updated Jan 31, 2026

Search Tags:#retrieval #pipeline #ingestion #nlp #query #embedding #asymmetric

Ready to run

npx skills add https://github.com/Agentient/vibekit --skill embedding-strategy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: embedding-strategy
Source: https://github.com/Agentient/vibekit/tree/main/plugins/rag-tools/skills/embedding-strategy
Command: npx skills add https://github.com/Agentient/vibekit --skill embedding-strategy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Documents and queries often suffer from semantic mismatch when the same embedding path is used for both ingestion and querying. This Skill introduces an asymmetric embedding strategy to separate ingestion embeddings from query embeddings, reducing retrieval errors and improving relevance.

Core Features & Use Cases

Asymmetric embedding paths: Ingestion uses RETRIEVAL_DOCUMENT while queries use RETRIEVAL_QUERY to maintain task-type separation.
Semantic mismatch prevention and improved retrieval quality across document collections.
Model selection and configuration for embedding pipelines, plus validation of task_type usage.

Quick Start

Configure the embedding pipeline to route documents through RETRIEVAL_DOCUMENT and queries through RETRIEVAL_QUERY.

Frequently Asked Questions about embedding-strategy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

Why does my document retrieval pipeline return irrelevant results for search queries?▼

Retrieval pipelines return irrelevant results when the same embedding path processes both document ingestion and user queries, causing a semantic mismatch. Using separate embedding paths for documents and queries resolves this mismatch and improves search relevance.

What is asymmetric embedding for document retrieval and search pipelines?▼

Asymmetric embedding enforces separate task types for document ingestion and query processing. It applies RETRIEVAL_DOCUMENT for ingestion and RETRIEVAL_QUERY for querying, reducing semantic mismatch and improving retrieval quality across document collections.

How do I configure separate embedding paths for document ingestion and user queries?▼

Configure the embedding pipeline to route documents through the RETRIEVAL_DOCUMENT task type and route user queries through RETRIEVAL_QUERY. This strategy separates ingestion embeddings from query embeddings to maintain task-type separation and improve search relevance.

Can I use a different embedding model for ingestion and querying in my search pipeline?▼

Yes, the pipeline allows embedding model selection and configuration for both ingestion and querying. It includes safeguards to validate correct task_type usage, ensuring that RETRIEVAL_DOCUMENT and RETRIEVAL_QUERY are applied properly across the pipeline.

When should I use separate task types for embedding documents versus search queries?▼

You should use separate task types when documents and queries suffer from semantic mismatch in your retrieval pipeline. Enforcing asymmetric task types prevents retrieval errors by ensuring ingestion and query processing use distinct embedding paths.

What are the limitations of using a single embedding path for both document ingestion and search queries?▼

Using a single embedding path for both ingestion and querying causes semantic mismatch between the document text and query intent. This leads to retrieval errors and reduced relevance, which asymmetric task_type separation is designed to prevent.