embedding-strategies
GEO / AI searchwshobson/agentsskills.sh ↗
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Our category
GEO / AI search
ours
Last read
Sep 3, 2026
from the directory
Our brief
oursThis skill helps users select and optimize embedding models for semantic search and Retrieval-Augmented Generation (RAG) applications. It provides templates and guidance on choosing appropriate models, optimizing chunking strategies, or reducing embedding dimensions for specific domains like code, finance, or law.
- List of floating-point embeddings (vectors)
- Text list/string to embed
- Model name (e.g., voyage-3-large)
- Optional dimension integer
optional
The evidence shows that while some templates require paid API keys, others support local deployment using Sentence Transformers.
not detected
The evidence does not contain information regarding required registration.
The skill cannot decide which embedding model is best without knowing the specific use case; it also provides templates for embedding but does not perform chunking or preprocessing itself.
Evidencestatic findingsplugins/llm-application-dev/skills/embedding-strategies/references/details.md:1-91plugins/llm-application-dev/skills/embedding-strategies/SKILL.md:1-66
[{"code":"env_var","match":"VOYAGE_API_KEY","path":"references/details.md"}]# embedding-strategies — templates and worked examples
## Templates
### Template 1: Voyage AI Embeddings (Recommended for Claude)
```python
from langchain_voyageai import VoyageAIEmbeddings
from typing import List
import os
# Initialize Voyage AI embeddings (recommended by Anthropic for Claude)
embeddings = VoyageAIEmbeddings(
model="voyage-3-large",
voyage_api_key=os.environ.get("VOYAGE_API_KEY")
)
def get_embeddings(texts: List[str]) -> List[List[float]]:
"""Get embeddings from Voyage AI."""
return embeddings.embed_documents(texts)
def get_query_embedding(query: str) -> List[float]:
"""Get single query embedding."""
return embeddings.embed_query(query)
# Specialized models for domains
code_embeddings = VoyageAIEmbeddings(model="voyage-code-3")
finance_embeddings = VoyageAIEmbeddings(model="voyage-finance-2")
legal_embeddings = VoyageAIEmbeddings(model="voyage-law-2")
```
### Template 2: OpenAI Embeddings
```python
from openai import OpenAI
from typing import List
import numpy as np
client = OpenAI()
def get_embeddings(
texts: List[str],
model: str = "text-embedding-3-small",
dimensions: int = None
) -> List[List[float]]:
"""Get e--- name: embedding-strategies description: Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains. --- # Embedding Strategies Guide to selecting and optimizing embedding models for vector search applications. ## When to Use This Skill - Choosing embedding models for RAG - Optimizing chunking strategies - Fine-tuning embeddings for domains - Comparing embedding model performance - Reducing embedding dimensions - Handling multilingual content ## Core Concepts ### 1. Embedding Model Comparison (2026) | Model | Dimensions | Max Tokens | Best For | | -------------------------- | ---------- | ---------- | ----------------------------------- | | **voyage-3-large** | 1024 | 32000 | Claude apps (Anthropic recommended) | | **voyage-3** | 1024 | 32000 | Claude apps, cost-effective | | **voyage-code-3** | 1024 | 32000 | Code search | | **voyage-finance-2** | 1024 | 32000 | Financial
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Installfrom the directory
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What is inside itfrom the directory
2 files — names only. The directory does not report sizes.
What the auditors foundfrom the directory
A skill is instructions your agent will follow and scripts it may run, so who checked it matters as much as how many people installed it.
Installs, reading by readingours
Axis starts at 9.9k, not zero — the range is 9.9k to 10k.
Asked out loudspoken, not typed
The same skill in the words people use speaking to an assistant rather than typing into a box. Each one carries the situation it came from, and each answer says only what the skill's own files support.
It depends on the embedding model you choose; some templates use paid APIs that require an API key, while others support local models using Sentence Transformers.
The evidence does not specify any registration requirements for this skill.