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"Embedding Model - Qdrant",
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p
|
Evaluating Embedding Quality: Nils emphasized the importance of evaluating embedding quality in relative terms rather than looking at individual vectors.
|
[
"tags",
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[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(4) > p
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p
|
It’s all about understanding the context and how embeddings relate to each other.
|
[
"tags",
"tags/embedding-model"
] | 17,901 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(5)
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li
|
New Features in the Pipeline: Lastly, Nils gave us a sneak peek at some exciting features they’re developing, including input type support for Langchain and improved compression techniques.
|
[
"tags",
"tags/embedding-model"
] | 17,902 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(5) > p
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p
|
New Features in the Pipeline: Lastly, Nils gave us a sneak peek at some exciting features they’re developing, including input type support for Langchain and improved compression techniques.
|
[
"tags",
"tags/embedding-model"
] | 17,903 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(3)
|
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p
|
Now, here’s a fun fact from the episode: Did you know that the content quality estimation model can’t differentiate between true and fake statements?
|
[
"tags",
"tags/embedding-model"
] | 17,904 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(3)
|
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p
|
It’s a challenging task, and the model relies on the information present in its pretraining data.
|
[
"tags",
"tags/embedding-model"
] | 17,905 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(4)
|
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p
|
We loved having Nils as our guest, check out the full talk below.
|
[
"tags",
"tags/embedding-model"
] | 17,906 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(4)
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p
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If you or anyone you know would like to come on the Vector Space Talks
|
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[
"Vector Space Talk - Qdrant"
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p
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For the second edition of our Vector Space Talks we were joined by none other than Cohere’s Head of Machine Learning Nils Reimers.
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[
"Vector Space Talk - Qdrant"
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h2
|
Key Takeaways
|
[
"categories",
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] | 17,909 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
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html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(2)
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p
|
Let’s dive right into the five key takeaways from Nils’ talk:
|
[
"categories",
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[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
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html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(1)
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li
|
Content Quality Estimation: Nils explained how embeddings have traditionally focused on measuring topic match, but content quality is just as important.
|
[
"categories",
"categories/vector-space-talk"
] | 17,911 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(1)
|
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li
|
He demonstrated how their model can differentiate between informative and non-informative documents.
|
[
"categories",
"categories/vector-space-talk"
] | 17,912 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(1) > p
|
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p
|
Content Quality Estimation: Nils explained how embeddings have traditionally focused on measuring topic match, but content quality is just as important.
|
[
"categories",
"categories/vector-space-talk"
] | 17,913 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(1) > p
|
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p
|
He demonstrated how their model can differentiate between informative and non-informative documents.
|
[
"categories",
"categories/vector-space-talk"
] | 17,914 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(2)
|
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li
|
Compression-Aware Training: He shared how they’ve tackled the challenge of reducing the memory footprint of embeddings, making it more cost-effective to run vector databases on platforms like Qdrant.
|
[
"categories",
"categories/vector-space-talk"
] | 17,915 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(2) > p
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p
|
Compression-Aware Training: He shared how they’ve tackled the challenge of reducing the memory footprint of embeddings, making it more cost-effective to run vector databases on platforms like Qdrant.
|
[
"categories",
"categories/vector-space-talk"
] | 17,916 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(3)
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Reinforcement Learning from Human Feedback: Nils revealed how they’ve borrowed a technique from reinforcement learning and applied it to their embedding models.
|
[
"categories",
"categories/vector-space-talk"
] | 17,917 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(3)
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li
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This allows the model to learn preferences based on human feedback, resulting in highly informative responses.
|
[
"categories",
"categories/vector-space-talk"
] | 17,918 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(3) > p
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p
|
Reinforcement Learning from Human Feedback: Nils revealed how they’ve borrowed a technique from reinforcement learning and applied it to their embedding models.
|
[
"categories",
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[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(3) > p
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p
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This allows the model to learn preferences based on human feedback, resulting in highly informative responses.
|
[
"categories",
"categories/vector-space-talk"
] | 17,920 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(4)
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li
|
Evaluating Embedding Quality: Nils emphasized the importance of evaluating embedding quality in relative terms rather than looking at individual vectors.
|
[
"categories",
"categories/vector-space-talk"
] | 17,921 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(4)
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li
|
It’s all about understanding the context and how embeddings relate to each other.
|
[
"categories",
"categories/vector-space-talk"
] | 17,922 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(4) > p
|
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p
|
Evaluating Embedding Quality: Nils emphasized the importance of evaluating embedding quality in relative terms rather than looking at individual vectors.
|
[
"categories",
"categories/vector-space-talk"
] | 17,923 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(4) > p
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p
|
It’s all about understanding the context and how embeddings relate to each other.
|
[
"categories",
"categories/vector-space-talk"
] | 17,924 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(5)
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li
|
New Features in the Pipeline: Lastly, Nils gave us a sneak peek at some exciting features they’re developing, including input type support for Langchain and improved compression techniques.
|
[
"categories",
"categories/vector-space-talk"
] | 17,925 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(5) > p
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p
|
New Features in the Pipeline: Lastly, Nils gave us a sneak peek at some exciting features they’re developing, including input type support for Langchain and improved compression techniques.
|
[
"categories",
"categories/vector-space-talk"
] | 17,926 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(3)
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p
|
Now, here’s a fun fact from the episode: Did you know that the content quality estimation model can’t differentiate between true and fake statements?
|
[
"categories",
"categories/vector-space-talk"
] | 17,927 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(3)
|
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p
|
It’s a challenging task, and the model relies on the information present in its pretraining data.
|
[
"categories",
"categories/vector-space-talk"
] | 17,928 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(4)
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p
|
We loved having Nils as our guest, check out the full talk below.
|
[
"categories",
"categories/vector-space-talk"
] | 17,929 |
[
"Vector Space Talk - Qdrant",
"Key Takeaways"
] |
/categories/vector-space-talk/
|
html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(4)
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p
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If you or anyone you know would like to come on the Vector Space Talks
|
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[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(1) > h1
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Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers
|
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The partnership between Pienso and Qdrant is set to revolutionize interactive deep learning, making it practical, efficient, and scalable for global customers.
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[
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p
|
Pienso’s low-code platform provides a streamlined and user-friendly process for deep learning tasks.
|
[
"blog",
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] | 17,933 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
This exceptional level of convenience is augmented by Qdrant’s scalable and cost-efficient high vector computation capabilities, which enable reliable retrieval of similar vectors from high-dimensional spaces.
|
[
"blog",
"blog/case-study-pienso"
] | 17,934 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(2)
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Together, Pienso and Qdrant will empower enterprises to harness the full potential of generative AI on a large scale.
|
[
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[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
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p
|
By combining the technologies of both companies, organizations will be able to train their own large language models and leverage them for downstream tasks that demand data sovereignty and model autonomy.
|
[
"blog",
"blog/case-study-pienso"
] | 17,936 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(2)
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p
|
This collaboration will help customers unlock new possibilities and achieve advanced AI-driven solutions.
|
[
"blog",
"blog/case-study-pienso"
] | 17,937 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(2)
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p
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Strengthening LLM Performance
|
[
"blog",
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] | 17,938 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(3)
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p
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Qdrant enhances the accuracy of large language models (LLMs) by offering an alternative to relying solely on patterns identified during the training phase.
|
[
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[
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] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(3)
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p
|
By integrating with Qdrant, Pienso will empower customer LLMs with dynamic long-term storage, which will ultimately enable them to generate concrete and factual responses.
|
[
"blog",
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] | 17,940 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(3)
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p
|
Qdrant effectively preserves the extensive context windows managed by advanced LLMs, allowing for a broader analysis of the conversation or document at hand.
|
[
"blog",
"blog/case-study-pienso"
] | 17,941 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(3)
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p
|
By leveraging this extended context, LLMs can achieve a more comprehensive understanding and produce contextually relevant outputs.
|
[
"blog",
"blog/case-study-pienso"
] | 17,942 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h2:nth-of-type(1)
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h2
|
Joint Dedication to Scalability, Efficiency and Reliability
|
[
"blog",
"blog/case-study-pienso"
] | 17,943 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > blockquote:nth-of-type(1) > p
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p
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“Every commercial generative AI use case we encounter benefits from faster training and inference, whether mining customer interactions for next best actions or sifting clinical data to speed a therapeutic through trial and patent processes.”
|
[
"blog",
"blog/case-study-pienso"
] | 17,944 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > blockquote:nth-of-type(1) > p
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p
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- Birago Jones, CEO, Pienso
|
[
"blog",
"blog/case-study-pienso"
] | 17,945 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
Pienso chose Qdrant for its exceptional LLM interoperability, recognizing the potential it offers in maximizing the power of large language models and interactive deep learning for large enterprises.
|
[
"blog",
"blog/case-study-pienso"
] | 17,946 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
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Qdrant excels in efficient nearest neighbor search, which is an expensive and computationally demanding task.
|
[
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] | 17,947 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
Our ability to store and search high-dimensional vectors with remarkable performance and precision will offer a significant peace of mind to Pienso’s customers.
|
[
"blog",
"blog/case-study-pienso"
] | 17,948 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Joint Dedication to Scalability, Efficiency and Reliability"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
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Through intelligent indexing and partitioning techniques, Qdrant will significantly boost the speed of these searches, accelerating both training and inference processes for users.
|
[
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[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
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h3
|
Scalability: Preparing for Sustained Growth in Data Volumes
|
[
"blog",
"blog/case-study-pienso"
] | 17,950 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Scalability: Preparing for Sustained Growth in Data Volumes"
] |
/blog/case-study-pienso/
|
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p
|
Qdrant’s distributed deployment mode plays a vital role in empowering large enterprises dealing with massive data volumes.
|
[
"blog",
"blog/case-study-pienso"
] | 17,951 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Scalability: Preparing for Sustained Growth in Data Volumes"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(5)
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p
|
It ensures that increasing data volumes do not hinder performance but rather enrich the model’s capabilities, making scalability a seamless process.
|
[
"blog",
"blog/case-study-pienso"
] | 17,952 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Scalability: Preparing for Sustained Growth in Data Volumes"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(5)
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p
|
Moreover, Qdrant is well-suited for Pienso’s enterprise customers as it operates best on bare metal infrastructure, enabling them to maintain complete control over their data sovereignty and autonomous LLM regimes.
|
[
"blog",
"blog/case-study-pienso"
] | 17,953 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Scalability: Preparing for Sustained Growth in Data Volumes"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(5)
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p
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This ensures that enterprises can maintain their full span of control while leveraging the scalability and performance benefits of Qdrant’s solution.
|
[
"blog",
"blog/case-study-pienso"
] | 17,954 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h3:nth-of-type(2)
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h3
|
Efficiency: Maximizing the Customer Value Proposition
|
[
"blog",
"blog/case-study-pienso"
] | 17,955 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Efficiency: Maximizing the Customer Value Proposition"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
Qdrant’s storage efficiency delivers cost savings on hardware while ensuring a responsive system even with extensive data sets.
|
[
"blog",
"blog/case-study-pienso"
] | 17,956 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Efficiency: Maximizing the Customer Value Proposition"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
In an independent benchmark stress test, Pienso discovered that Qdrant could efficiently store 128 million documents, consuming a mere 20.4GB of storage and only 1.25GB of memory.
|
[
"blog",
"blog/case-study-pienso"
] | 17,957 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Efficiency: Maximizing the Customer Value Proposition"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
This storage efficiency not only minimizes hardware expenses for Pienso’s customers, but also ensures optimal performance, making Qdrant an ideal solution for managing large-scale data with ease and efficiency.
|
[
"blog",
"blog/case-study-pienso"
] | 17,958 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h3:nth-of-type(3)
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h3
|
Reliability: Fast Performance in a Secure Environment
|
[
"blog",
"blog/case-study-pienso"
] | 17,959 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(7)
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p
|
Qdrant’s utilization of Rust, coupled with its memmap storage and write-ahead logging, offers users a powerful combination of high-performance operations, robust data protection, and enhanced data safety measures.
|
[
"blog",
"blog/case-study-pienso"
] | 17,960 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(7)
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p
|
Our memmap storage feature offers Pienso fast performance comparable to in-memory storage.
|
[
"blog",
"blog/case-study-pienso"
] | 17,961 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(7)
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p
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In the context of machine learning, where rapid data access and retrieval are crucial for training and inference tasks, this capability proves invaluable.
|
[
"blog",
"blog/case-study-pienso"
] | 17,962 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(7)
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p
|
Furthermore, our write-ahead logging (WAL), is critical to ensuring changes are logged before being applied to the database.
|
[
"blog",
"blog/case-study-pienso"
] | 17,963 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(7)
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p
|
This approach adds additional layers of data safety, further safeguarding the integrity of the stored information.
|
[
"blog",
"blog/case-study-pienso"
] | 17,964 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > blockquote:nth-of-type(2) > p
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p
|
“We chose Qdrant because it’s fast to query, has a small memory footprint and allows for instantaneous setup of a new vector collection that is going to be queried.
|
[
"blog",
"blog/case-study-pienso"
] | 17,965 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > blockquote:nth-of-type(2) > p
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p
|
Other solutions we evaluated had long bootstrap times and also long collection initialization times {..}
|
[
"blog",
"blog/case-study-pienso"
] | 17,966 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > blockquote:nth-of-type(2) > p
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p
|
This partnership comes at a great time, because it allows Pienso to use Qdrant to its maximum potential, giving our customers a seamless experience while they explore and get meaningful insights about their data.”
|
[
"blog",
"blog/case-study-pienso"
] | 17,967 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"Reliability: Fast Performance in a Secure Environment"
] |
/blog/case-study-pienso/
|
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p
|
- Felipe Balduino Cassar, Senior Software Engineer, Pienso
|
[
"blog",
"blog/case-study-pienso"
] | 17,968 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h2:nth-of-type(2)
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h2
|
What’s Next?
|
[
"blog",
"blog/case-study-pienso"
] | 17,969 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"What’s Next?"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
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p
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Pienso and Qdrant are dedicated to jointly develop the most reliable customer offering for the long term.
|
[
"blog",
"blog/case-study-pienso"
] | 17,970 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"What’s Next?"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
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p
|
Our partnership will deliver a combination of no-code/low-code interactive deep learning with efficient vector computation engineered for open source models and libraries.
|
[
"blog",
"blog/case-study-pienso"
] | 17,971 |
[
"Pienso & Qdrant: Future Proofing Generative AI for Enterprise-Level Customers - Qdrant",
"What’s Next?"
] |
/blog/case-study-pienso/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(9)
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p
|
To learn more about how we plan on achieving this, join the founders for a technical fireside chat at 09:30 PST Thursday, 20th July on Discord.
|
[
"blog",
"blog/case-study-pienso"
] | 17,972 |
[
"Powering Bloop semantic code search - Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(1) > h1
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h1
|
Powering Bloop semantic code search
|
[
"blog",
"blog/case-study-bloop"
] | 17,973 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
Founded in early 2021, bloop was one of the first companies to tackle semantic
|
[
"blog",
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] | 17,974 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
search for codebases.
|
[
"blog",
"blog/case-study-bloop"
] | 17,975 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
A fast, reliable Vector Search Database is a core component of a semantic
|
[
"blog",
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[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
search engine, and bloop surveyed the field of available solutions and even considered building
|
[
"blog",
"blog/case-study-bloop"
] | 17,977 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
their own.
|
[
"blog",
"blog/case-study-bloop"
] | 17,978 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(1)
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p
|
They found Qdrant to be the top contender and now use it in production.
|
[
"blog",
"blog/case-study-bloop"
] | 17,979 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(2)
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p
|
This document is intended as a guide for people who intend to introduce semantic search to a novel
|
[
"blog",
"blog/case-study-bloop"
] | 17,980 |
[
"Powering Bloop semantic code search - Qdrant",
"Powering Bloop semantic code search"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(2)
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p
|
field and want to find out if Qdrant is a good solution for their use case.
|
[
"blog",
"blog/case-study-bloop"
] | 17,981 |
[
"Powering Bloop semantic code search - Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h2:nth-of-type(1)
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h2
|
About bloop
|
[
"blog",
"blog/case-study-bloop"
] | 17,982 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
bloop is a fast code-search engine that combines semantic search, regex search
|
[
"blog",
"blog/case-study-bloop"
] | 17,983 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
|
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p
|
and precise code navigation into a single lightweight desktop application that can be run locally.
|
[
"blog",
"blog/case-study-bloop"
] | 17,984 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
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It
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[
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[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
helps developers understand and navigate large codebases, enabling them to discover internal libraries,
|
[
"blog",
"blog/case-study-bloop"
] | 17,986 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
reuse code and avoid dependency bloat. bloop’s chat interface explains complex concepts in simple
|
[
"blog",
"blog/case-study-bloop"
] | 17,987 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
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p
|
language so that engineers can spend less time crawling through code to understand what it does, and
|
[
"blog",
"blog/case-study-bloop"
] | 17,988 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(4)
|
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p
|
more time shipping features and fixing bugs.
|
[
"blog",
"blog/case-study-bloop"
] | 17,989 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
bloop’s mission is to make software engineers autonomous and semantic code search is the cornerstone
|
[
"blog",
"blog/case-study-bloop"
] | 17,990 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
of that vision.
|
[
"blog",
"blog/case-study-bloop"
] | 17,991 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
The project is maintained by a group of Rust and Typescript engineers and ML researchers.
|
[
"blog",
"blog/case-study-bloop"
] | 17,992 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
|
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p
|
It leverages many prominent nascent technologies, such as Tauri, tantivy,
|
[
"blog",
"blog/case-study-bloop"
] | 17,993 |
[
"Powering Bloop semantic code search - Qdrant",
"About bloop"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(6)
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p
|
Qdrant and Anthropic.
|
[
"blog",
"blog/case-study-bloop"
] | 17,994 |
[
"Powering Bloop semantic code search - Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > h2:nth-of-type(2)
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h2
|
About Qdrant
|
[
"blog",
"blog/case-study-bloop"
] | 17,995 |
[
"Powering Bloop semantic code search - Qdrant",
"About Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
|
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p
|
Qdrant is an open-source Vector Search Database written in Rust .
|
[
"blog",
"blog/case-study-bloop"
] | 17,996 |
[
"Powering Bloop semantic code search - Qdrant",
"About Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
|
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p
|
It deploys as an API service providing
|
[
"blog",
"blog/case-study-bloop"
] | 17,997 |
[
"Powering Bloop semantic code search - Qdrant",
"About Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
|
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] |
p
|
a search for the nearest high-dimensional vectors.
|
[
"blog",
"blog/case-study-bloop"
] | 17,998 |
[
"Powering Bloop semantic code search - Qdrant",
"About Qdrant"
] |
/blog/case-study-bloop/
|
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(8)
|
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] |
p
|
With Qdrant, embeddings or neural network encoders
|
[
"blog",
"blog/case-study-bloop"
] | 17,999 |
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