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Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engin

Coming at this from a diffeeent angle, does anyone have any links to tutorials for use-cases? I’d love to see what vectorDB hype is about but as a regular engineer I’m unable to even grasp how to use a vectorDB

simonw 3y ago
I wrote one here: https://simonwillison.net/2023/Jan/13/semantic-search-answers/ https://simonwillison.net/2023/Jan/13/semantic-search-answer...
ZephyrBlu 3y ago
Not a tutorial, but TLDR vector DBs are specialized DBs that store embeddings. Embeddings are vector representations of data (E.g. text or images), which means you can compare them in a quantifiable way. This enables use cases like semantic search and Retrieval-Augmented Generation (RAG) as mentioned in the article. Semantic search is: I search for "royal" and I get results that mention "king" or "queen" because they are semantically similar. RAG is: I make a query asking, "tell me about the English royal family", semantically similar information is fetched using semantic search and provided as context to an LLM to generate an answer.
deleted 3y ago
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estreeper 3y ago
I recently wrote a tutorial on making a vector driven semantic search app using all open source tools (pgvector, Instructor, and Flask) that might be helpful: https://revelry.co/insights/open-source-semantic-vector-search-with-instructor-pgvector-and-flask/ https://revelry.co/insights/open-source-semantic-vector-sear...
empath-nirvana 3y ago
I'll give you an example of something i did with a vector database. I was playing around with making my own UI for interfacing with chatgpt. I saved the chat transcripts in a normal postgres DB, along with the open AI embeddings for each message in a vector db, with a pointer to the message id in postgres in the vector DB metadata. Then as you chatted, i had chatgpt continuously creating a summary of the current conversation you were having in the background and doing a search in the vector db for previous messages about whatever we're talking about, and it would inject that into the chat context invisibly. So you can do something like say: "Hey do you remember when we talked about baseball" and it would find a previous conversation where you talked about so and so hitting a home run into the context and the bot would have access to that, even though you never mentioned the word "baseball" in the previous conversation -- home run is semantically similar enough that it finds it. If you're using openai embeddings as your vectors, it's _extremely_ impressive how well it finds similar topics, even when the actual words used are completely different.
gk1 3y ago
We made an entire learning center for interested folks like you: https://www.pinecone.io/learn/ https://www.pinecone.io/learn/ I recommend starting at https://www.pinecone.io/learn/vector-database/ https://www.pinecone.io/learn/vector-database/

simonw 3y ago

I wrote one here: https://simonwillison.net/2023/Jan/13/semantic-search-answers/ https://simonwillison.net/2023/Jan/13/semantic-search-answer...

ZephyrBlu 3y ago

Not a tutorial, but TLDR vector DBs are specialized DBs that store embeddings. Embeddings are vector representations of data (E.g. text or images), which means you can compare them in a quantifiable way. This enables use cases like semantic search and Retrieval-Augmented Generation (RAG) as mentioned in the article. Semantic search is: I search for "royal" and I get results that mention "king" or "queen" because they are semantically similar. RAG is: I make a query asking, "tell me about the English royal family", semantically similar information is fetched using semantic search and provided as context to an LLM to generate an answer.

deleted 3y ago

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estreeper 3y ago

I recently wrote a tutorial on making a vector driven semantic search app using all open source tools (pgvector, Instructor, and Flask) that might be helpful: https://revelry.co/insights/open-source-semantic-vector-search-with-instructor-pgvector-and-flask/ https://revelry.co/insights/open-source-semantic-vector-sear...

empath-nirvana 3y ago

I'll give you an example of something i did with a vector database. I was playing around with making my own UI for interfacing with chatgpt. I saved the chat transcripts in a normal postgres DB, along with the open AI embeddings for each message in a vector db, with a pointer to the message id in postgres in the vector DB metadata. Then as you chatted, i had chatgpt continuously creating a summary of the current conversation you were having in the background and doing a search in the vector db for previous messages about whatever we're talking about, and it would inject that into the chat context invisibly. So you can do something like say: "Hey do you remember when we talked about baseball" and it would find a previous conversation where you talked about so and so hitting a home run into the context and the bot would have access to that, even though you never mentioned the word "baseball" in the previous conversation -- home run is semantically similar enough that it finds it. If you're using openai embeddings as your vectors, it's _extremely_ impressive how well it finds similar topics, even when the actual words used are completely different.

gk1 3y ago

We made an entire learning center for interested folks like you: https://www.pinecone.io/learn/ https://www.pinecone.io/learn/ I recommend starting at https://www.pinecone.io/learn/vector-database/ https://www.pinecone.io/learn/vector-database/