How to beginner · 3 min read

OpenAI vector store expiration policy

Quick answer

OpenAI vector stores do not have a built-in expiration policy; data retention and expiration depend on the storage solution you use (e.g., Pinecone, Chroma). You must manage expiration or deletion of vectors explicitly in your vector database or storage layer when integrating with OpenAI embeddings.

PREREQUISITES

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

Setup

Install the openai Python SDK and set your API key as an environment variable.

Run pip install openai to install the SDK.
Set your API key in your shell: export OPENAI_API_KEY='your_api_key_here' (Linux/macOS) or setx OPENAI_API_KEY "your_api_key_here" (Windows).

bash

pip install openai

Step by step

OpenAI itself does not provide a vector store or automatic expiration for embeddings. You generate embeddings with OpenAI models and store them in a vector database like Pinecone or Chroma, which handle expiration policies.

Here is an example of generating embeddings with OpenAI and storing them in Pinecone, where you control expiration by deleting vectors manually or via TTL features in Pinecone.

python

import os
from openai import OpenAI
from pinecone import Pinecone

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

# Generate embedding for a sample text
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="OpenAI vector store expiration policy"
)
embedding = response.data[0].embedding

# Initialize Pinecone client
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
index = pc.Index("example-index")

# Upsert vector with an ID
index.upsert(vectors=[{"id": "vec1", "values": embedding}])

# To delete vectors (simulate expiration), call:
# index.delete(ids=["vec1"])

print("Embedding stored. Manage expiration via your vector DB.")

output

Embedding stored. Manage expiration via your vector DB.

Common variations

You can use other vector databases like Chroma or FAISS for storing embeddings. These do not have built-in expiration policies either, so you must implement cleanup logic yourself.

For async usage with OpenAI embeddings:

python

import os
import asyncio
from openai import OpenAI

async def generate_embedding():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    response = await client.embeddings.acreate(
        model="text-embedding-3-small",
        input="Async embedding generation"
    )
    return response.data[0].embedding

embedding = asyncio.run(generate_embedding())
print("Async embedding generated.")

output

Async embedding generated.

Troubleshooting

If vectors persist longer than expected: Check your vector database's retention or TTL settings; OpenAI does not auto-expire embeddings.
If you cannot delete vectors: Verify your vector store permissions and API keys.
If embedding generation fails: Confirm your OpenAI API key is valid and environment variable is set.

✅

Key Takeaways

OpenAI does not provide vector store expiration; manage data lifecycle in your vector database.
Use vector DB features like TTL or manual deletion to expire vectors.
Embedding generation and storage are separate; expiration is your responsibility.
Async embedding generation is supported via OpenAI SDK's async methods.
Check vector DB permissions and API keys if deletion or expiration fails.

Verified 2026-04 · text-embedding-3-small, gpt-4o-mini

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