How to Intermediate · 3 min read

Embedding caching strategies

Q: Embedding caching strategies

Use embedding caching to store and reuse vector representations from models like text-embedding-3-small, reducing redundant API calls and latency. Common strategies include local in-memory caches, persistent key-value stores, and hybrid approaches combining fast retrieval with periodic refreshes.

Quick answer

Use embedding caching to store and reuse vector representations from models like text-embedding-3-small, reducing redundant API calls and latency. Common strategies include local in-memory caches, persistent key-value stores, and hybrid approaches combining fast retrieval with periodic refreshes.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup embedding environment

Install the openai Python package and set your API key as an environment variable to access embedding models.

bash

pip install openai>=1.0

Step by step caching example

This example demonstrates caching embeddings locally in a Python dictionary to avoid repeated API calls for the same input text.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Simple in-memory cache dictionary
embedding_cache = {}

def get_embedding(text: str) -> list[float]:
    if text in embedding_cache:
        print("Cache hit")
        return embedding_cache[text]
    print("Cache miss - calling API")
    response = client.embeddings.create(model="text-embedding-3-small", input=text)
    vector = response.data[0].embedding
    embedding_cache[text] = vector
    return vector

# Usage
texts = ["Hello world", "Hello world", "OpenAI embeddings"]
for t in texts:
    vec = get_embedding(t)
    print(f"Embedding length for '{t}':", len(vec))

output

Cache miss - calling API
Embedding length for 'Hello world': 1536
Cache hit
Embedding length for 'Hello world': 1536
Cache miss - calling API
Embedding length for 'OpenAI embeddings': 1536

Common variations

Persistent caching: Use databases like Redis, SQLite, or disk-based key-value stores to cache embeddings across sessions.
Batch embedding requests: Cache embeddings for batches of texts to reduce API calls and improve throughput.
Async caching: Implement asynchronous calls with caching for scalable, non-blocking embedding retrieval.
Cache invalidation: Periodically refresh cached embeddings if the model or input data changes.

Troubleshooting cache issues

If you see stale embeddings, implement cache expiration or versioning to refresh outdated vectors.
For memory overflow, switch from in-memory cache to persistent storage or limit cache size with eviction policies.
Ensure consistent input normalization (e.g., trimming whitespace) to avoid cache misses due to input variations.

✅

Key Takeaways

Cache embeddings locally or persistently to reduce API calls and latency.
Use batch and async embedding requests combined with caching for scalability.
Implement cache invalidation strategies to keep embeddings up to date.

Verified 2026-04 · text-embedding-3-small

Verify ↗