rag-system-builder-1-cache-embeddings

Sub-skill of rag-system-builder: 1. Cache Embeddings (+2).

5 stars

Best use case

rag-system-builder-1-cache-embeddings is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of rag-system-builder: 1. Cache Embeddings (+2).

Teams using rag-system-builder-1-cache-embeddings 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

$curl -o ~/.claude/skills/1-cache-embeddings/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/documents/rag-system-builder/1-cache-embeddings/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/1-cache-embeddings/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How rag-system-builder-1-cache-embeddings Compares

Feature / Agent	rag-system-builder-1-cache-embeddings	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?

Sub-skill of rag-system-builder: 1. Cache Embeddings (+2).

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

# 1. Cache Embeddings (+2)

## 1. Cache Embeddings


```python
# Load all embeddings into memory at startup
self.embedding_cache = self._load_all_embeddings()
```

## 2. Use FAISS for Large Collections


```python
import faiss

# Build FAISS index for fast similarity search
index = faiss.IndexFlatIP(dimension)  # Inner product for cosine sim
index.add(embeddings)
```

## 3. Batch Queries


```python
# Process multiple questions efficiently
questions = ["Q1", "Q2", "Q3"]
query_embeddings = model.embed_batch(questions)
```

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