# How to make a larger amount of data available for ChatGPT?

**URL:** <https://community.openai.com/t/how-to-make-a-larger-amount-of-data-available-for-chatgpt/61399>\
**Category:** ChatGPT\
**Created:** [February 14, 2023, 8:09pm UTC](https://community.openai.com/t/how-to-make-a-larger-amount-of-data-available-for-chatgpt/61399 "2023-02-14T20:09:06Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![konradk](https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/konradk/32/27510_2.png) [@konradk](https://community.openai.com/u/konradk)\
**Post date:** [February 14, 2023, 9:48pm UTC](https://community.openai.com/t/how-to-make-a-larger-amount-of-data-available-for-chatgpt/61399/2 "2023-02-14T21:48:39Z")

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I think that the Embeddings API is the solution here.  
Check this tutorial: [OpenAI API](https://platform.openai.com/docs/tutorials/web-qa-embeddings)

For quick access you should store vectors in database. More information and list of Vector databases: [OpenAI API](https://platform.openai.com/docs/guides/embeddings/how-can-i-retrieve-k-nearest-embedding-vectors-quickly)

This thread may be helpful: [Storing embeddings in SQL Server? Latency between Redis & Pinecone? Vector DB recommendations? - #2 by raymonddavey](https://community.openai.com/t/storing-embeddings-in-sql-server-latency-between-redis-pinecone-vector-db-recommendations/55134/2)

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