semantic-search-setup
Setup vector embeddings and semantic search for document collections. Use for AI-powered similarity search, finding related documents, and preparing knowledge bases for RAG systems.
Best use case
semantic-search-setup is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Setup vector embeddings and semantic search for document collections. Use for AI-powered similarity search, finding related documents, and preparing knowledge bases for RAG systems.
Teams using semantic-search-setup should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/semantic-search-setup/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How semantic-search-setup Compares
| Feature / Agent | semantic-search-setup | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Setup vector embeddings and semantic search for document collections. Use for AI-powered similarity search, finding related documents, and preparing knowledge bases for RAG systems.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Semantic Search Setup
## Overview
This skill sets up vector embedding infrastructure for semantic search. Unlike keyword search (FTS5), semantic search finds conceptually similar content even without exact word matches.
## Quick Start
```python
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
# Generate embeddings
texts = ["How to fix a bug", "Debugging software issues"]
embeddings = model.encode(texts, normalize_embeddings=True)
# Compute similarity
similarity = np.dot(embeddings[0], embeddings[1])
print(f"Similarity: {similarity:.3f}") # ~0.85
```
## When to Use
- Adding AI-powered search to document collections
- Finding conceptually related documents
- Preparing knowledge bases for RAG Q&A systems
- Building recommendation systems
- Enabling "more like this" functionality
## Related Skills
- `knowledge-base-builder` - Build the document database first
- `rag-system-builder` - Add AI Q&A on top of semantic search
- `pdf/text-extractor` - Extract text from PDFs
## Version History
- **1.1.0** (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills
- **1.0.0** (2024-10-15): Initial release with sentence-transformers, cosine similarity search, batch processing
## Sub-Skills
- [Best Practices](best-practices/SKILL.md)
## Sub-Skills
- [Execution Checklist](execution-checklist/SKILL.md)
- [Error Handling](error-handling/SKILL.md)
- [Metrics](metrics/SKILL.md)
- [Dependencies](dependencies/SKILL.md)
## Sub-Skills
- [How Semantic Search Works](how-semantic-search-works/SKILL.md)
- [Model Selection](model-selection/SKILL.md)
- [Step 1: Install Dependencies (+5)](step-1-install-dependencies/SKILL.md)
- [1. CPU vs GPU (+3)](1-cpu-vs-gpu/SKILL.md)
- [Status Monitoring](status-monitoring/SKILL.md)
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