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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
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] | p | So it sounds like a way to meet regulatory accessibility requirements if you have. | [
"blog",
"blog/vector-image-search-rag"
] | 11,403 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48) | [
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] | p | I think it was probably around where you were talking about the multimodal and text to guide the embeddings and potentially would having that allow you to include an image with alternate text? | [
"blog",
"blog/vector-image-search-rag"
] | 11,404 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(49) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,405 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(49) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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] | 11,407 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(49) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50) | [
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] | p | Okay. Yeah. | [
"blog",
"blog/vector-image-search-rag"
] | 11,411 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50) | [
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] | p | All right, Mike, let us know if that answers the question or if you have more. Yes. | [
"blog",
"blog/vector-image-search-rag"
] | 11,412 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50) | [
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] | p | He’s saying, yeah, inserting text with image for people who can’t see. | [
"blog",
"blog/vector-image-search-rag"
] | 11,413 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(51) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,417 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(52) | [
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] | p | So I’ll let you keep cruising and I’ll try not to derail it again. | [
"blog",
"blog/vector-image-search-rag"
] | 11,418 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(52) | [
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] | p | But that was great. | [
"blog",
"blog/vector-image-search-rag"
] | 11,419 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(52) | [
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] | p | It was just so pertinent. | [
"blog",
"blog/vector-image-search-rag"
] | 11,420 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(52) | [
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] | p | I wanted to stop you and ask some questions. | [
"blog",
"blog/vector-image-search-rag"
] | 11,421 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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] | 11,423 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,424 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So as I was saying, you have two agencies coming to take different pictures of the same property. | [
"blog",
"blog/vector-image-search-rag"
] | 11,425 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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] | p | And the thing is that they may not put exactly the same price or the same surface or the same location. | [
"blog",
"blog/vector-image-search-rag"
] | 11,426 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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] | p | So you cannot just match them with metadata. | [
"blog",
"blog/vector-image-search-rag"
] | 11,427 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,428 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(53) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,429 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,430 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,433 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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] | p | So it was basically the task to match images of the same properties. | [
"blog",
"blog/vector-image-search-rag"
] | 11,434 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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] | p | Again on the positive outcomes is that the dino really managed to understand the patterns of the properties without any fine tuning. | [
"blog",
"blog/vector-image-search-rag"
] | 11,435 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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] | p | And it was resilient to read different angles of the same room. | [
"blog",
"blog/vector-image-search-rag"
] | 11,436 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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] | p | So like on the pictures I shown, I just showed, the model was quite good at identifying. | [
"blog",
"blog/vector-image-search-rag"
] | 11,437 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(54) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,441 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(55) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,442 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,444 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(55) | [
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] | p | So we had 15 million vectors and without this indexing, the search could take up to 20, 30 seconds. | [
"blog",
"blog/vector-image-search-rag"
] | 11,445 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(55) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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] | 11,447 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(55) | [
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] | p | And we use quantization as well to save costs because the task was not too hard. | [
"blog",
"blog/vector-image-search-rag"
] | 11,448 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So it wasn’t too annoying to quantize the vectors. | [
"blog",
"blog/vector-image-search-rag"
] | 11,451 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,452 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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] | p | The challenges we faced was really about bathrooms and empty rooms because all bathrooms kind of look similar. | [
"blog",
"blog/vector-image-search-rag"
] | 11,453 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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] | p | They have very similar features and same for empty rooms since there is kind of nothing in them, just windows. | [
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] | 11,454 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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] | p | The model would often put high similarity scores between two bathroom of different properties and same for the empty rooms. | [
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"blog/vector-image-search-rag"
] | 11,455 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,456 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(56) | [
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"blog/vector-image-search-rag"
] | 11,457 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | And again, same thing on the future of image embedding. | [
"blog",
"blog/vector-image-search-rag"
] | 11,459 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | I really think it’s something the technology is ready for and would really help this kind of embedding model jumping onto the text search projects. | [
"blog",
"blog/vector-image-search-rag"
] | 11,460 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | So the principle of retribution generation for those of you who are not familiar with it is just you take some documents, you have an embedding model here, an embedding model trained on text and not on images, which will output representations from these documents, put it in a vector database, and then when a user will ask a question over the documentation, it will create an embedding of the request and retrieve the most similar documents. | [
"blog",
"blog/vector-image-search-rag"
] | 11,461 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | And afterwards we usually pass it to an LLM, which will generate an answer. | [
"blog",
"blog/vector-image-search-rag"
] | 11,462 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | But here in this talk, we won’t focus on the overall product, but really on the vector search part. | [
"blog",
"blog/vector-image-search-rag"
] | 11,463 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | So the two projects was one, as I told you, a rack for my nutrition company, so endosion with around a few hundred thousand of pages, and the second one was for medical companies, so for the doctors. | [
"blog",
"blog/vector-image-search-rag"
] | 11,464 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | So it was really about the documentation search rather than the LLM, because you cannot output any mistake. | [
"blog",
"blog/vector-image-search-rag"
] | 11,465 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(57) | [
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] | p | The model we used was OpenAI Ada two. | [
"blog",
"blog/vector-image-search-rag"
] | 11,466 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,468 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,469 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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] | p | So using an API model made it just much faster. | [
"blog",
"blog/vector-image-search-rag"
] | 11,470 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,471 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,472 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,473 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(58) | [
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] | p | So the massive text embedding benchmark made by hugging face, which basically gathers a lot of embeddings benchmark such as retrieval for instance, and so classified the different model for these benchmarks. | [
"blog",
"blog/vector-image-search-rag"
] | 11,474 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,476 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(59) | [
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] | p | And also it’s probably biased for models trained on close source data sets. | [
"blog",
"blog/vector-image-search-rag"
] | 11,478 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(59) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,479 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(59) | [
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] | p | So they probably trained themselves on these data sets. | [
"blog",
"blog/vector-image-search-rag"
] | 11,480 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(59) | [
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] | p | At least if I were them, it’s what I would do. | [
"blog",
"blog/vector-image-search-rag"
] | 11,481 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(59) | [
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] | p | So I assume they did it to gain some points in these data sets. | [
"blog",
"blog/vector-image-search-rag"
] | 11,482 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(60) | [
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"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,484 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(61) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,485 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(61) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/vector-image-search-rag"
] | 11,487 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(62) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,489 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(62) | [
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] | p | Yeah, that’s why the multilingual is super important for these use cases. | [
"blog",
"blog/vector-image-search-rag"
] | 11,490 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,492 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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] | p | Again, for this one there are models for French working much better than other two, so we may change it afterwards, but right now the performance we have is decent. | [
"blog",
"blog/vector-image-search-rag"
] | 11,493 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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] | p | Since both projects are very similar, I’ll jump into the conclusion for both of them together. | [
"blog",
"blog/vector-image-search-rag"
] | 11,494 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,495 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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"blog",
"blog/vector-image-search-rag"
] | 11,496 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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] | p | And also, quick note, because I did not do it from the start, is that when using an API model, it’s much faster to use it in asynchronous mode like the embedding equation went something like ten times or 100 times faster. | [
"blog",
"blog/vector-image-search-rag"
] | 11,497 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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] | p | So it was definitely, it changed a lot of things. | [
"blog",
"blog/vector-image-search-rag"
] | 11,498 |
[
"Vector Search Complexities: Insights from Projects in Image Search and RAG - Noé Achache | Vector Space Talks - Qdrant",
"Transcript:"
] | /blog/vector-image-search-rag/ | html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(63) | [
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] | p | Again, here we use cudrant mostly to leverage the free tier so they have a free version. | [
"blog",
"blog/vector-image-search-rag"
] | 11,499 |
Subsets and Splits