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209,353 次观看 • 2 年前 •via X (Twitter)

10 条评论

Eugene Yan 的头像
Eugene Yan2 年前

bm25 & np.array is all you need

saint 的头像
saint2 年前

>increase the value of k real

darren 的头像
darren2 年前

hacked together database? you mean np.array

Grant♟️ 的头像
Grant♟️2 年前

based

vish 的头像
vish2 年前

those in arena, they know instead of similarity searching the query with chunks, do a similarity search of fictional answer to chunks

Rani 的头像
Rani2 年前

ty for the laugh (lots of truths in this) I'm still learning, but I think it starts from the design of your vector DB though. Rather than stuffing everything in one db and using scuffed queries, trying to make 2 and 2 equal 5... It's asking more fundamental questions: -What are you querying? -What do you want to get? -How does this VDB fit into the larger application? -How can you re-organize, reformat, re-index your data to minimize the complexity of the retrieval task: aka, making distinct chunks more dissimilar to each other? -How can you optimize your queries? -Do you need to split the db into several smaller db with more targeted data? -Do you need a re-ranking system? Get a first batch of data, re-rank using a different/more precise query. Probably more efficient than having a single query trying to find a needle in a haystack, and flipping a coin each time. Of course, keeping in mind time, number of operations, handling failure cases, etc...

Michael P. Frank 💻🔜♻️ 的头像
Michael P. Frank 💻🔜♻️2 年前

That’s pretty hilarious 😂 My Telegram bots work like this, but they’re just a hobby project, not a mission-critical enterprise application, so it’s not like it matters lol

Hamel Husain 的头像
Hamel Husain2 年前

This is amazing

json 的头像
json2 年前

Bloody banger man

Grant♟️ 的头像
Grant♟️2 年前

Lolll pretty much

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