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[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
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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 > ul:nth-of-type(3) > li:nth-of-type(4)
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li
|
We use the defaults in this example.
|
[
"tags",
"tags/n8n"
] | 17,800 |
[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
|
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 > ul:nth-of-type(3) > li:nth-of-type(4) > p
|
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p
|
Recursive Character Text Splitter: Configure the text splitter options.
|
[
"tags",
"tags/n8n"
] | 17,801 |
[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
|
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 > ul:nth-of-type(3) > li:nth-of-type(4) > p
|
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p
|
We use the defaults in this example.
|
[
"tags",
"tags/n8n"
] | 17,802 |
[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
|
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(6)
|
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p
|
Connect the workflow to a manual trigger.
|
[
"tags",
"tags/n8n"
] | 17,803 |
[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
|
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(6)
|
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p
|
Click “Test Workflow” to run it.
|
[
"tags",
"tags/n8n"
] | 17,804 |
[
"N8n - Qdrant",
"Workflow 1: GitHub Repository Ingestion into Qdrant"
] |
/tags/n8n/
|
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(6)
|
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p
|
You should be able to see the progress in real-time as the data is fetched from GitHub, transformed into vectors and loaded into Qdrant.
|
[
"tags",
"tags/n8n"
] | 17,805 |
[
"N8n - Qdrant"
] |
/tags/n8n/
|
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 > h4:nth-of-type(2)
|
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h4
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Workflow 2: Chat Service with Ingested Documents
|
[
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[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
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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(8)
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p
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The workflow use the following nodes:
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[
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[
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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 > ul:nth-of-type(4) > li:nth-of-type(1)
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li
|
Qdrant Vector Store - Retrieve: Configure with Qdrant credentials and the name of the collection the data was loaded into in workflow 1.
|
[
"tags",
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[
"N8n - Qdrant",
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/tags/n8n/
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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 > ul:nth-of-type(4) > li:nth-of-type(1) > p
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p
|
Qdrant Vector Store - Retrieve: Configure with Qdrant credentials and the name of the collection the data was loaded into in workflow 1.
|
[
"tags",
"tags/n8n"
] | 17,809 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(2)
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li
|
Retrieval Q&A Chain: Configure with default values.
|
[
"tags",
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] | 17,810 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(2) > p
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p
|
Retrieval Q&A Chain: Configure with default values.
|
[
"tags",
"tags/n8n"
] | 17,811 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(3)
|
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li
|
Embeddings OpenAI: Configure with OpenAI credentials and the embedding model options.
|
[
"tags",
"tags/n8n"
] | 17,812 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(3)
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li
|
We use the text-embedding-ada-002 model.
|
[
"tags",
"tags/n8n"
] | 17,813 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(3) > p
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p
|
Embeddings OpenAI: Configure with OpenAI credentials and the embedding model options.
|
[
"tags",
"tags/n8n"
] | 17,814 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(3) > p
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p
|
We use the text-embedding-ada-002 model.
|
[
"tags",
"tags/n8n"
] | 17,815 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(4)
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li
|
OpenAI Chat Model: Configure with OpenAI credentials and the chat model name.
|
[
"tags",
"tags/n8n"
] | 17,816 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(4)
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li
|
We use gpt-3.5-turbo for the demo.
|
[
"tags",
"tags/n8n"
] | 17,817 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(4) > p
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p
|
OpenAI Chat Model: Configure with OpenAI credentials and the chat model name.
|
[
"tags",
"tags/n8n"
] | 17,818 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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 > ul:nth-of-type(4) > li:nth-of-type(4) > p
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p
|
We use gpt-3.5-turbo for the demo.
|
[
"tags",
"tags/n8n"
] | 17,819 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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(9)
|
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p
|
Once configured, hit the “Chat” button to initiate the chat interface and begin a conversation with your codebase.
|
[
"tags",
"tags/n8n"
] | 17,820 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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(11)
|
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p
|
To embed the chat in your applications, consider using the @n8n/chat package.
|
[
"tags",
"tags/n8n"
] | 17,821 |
[
"N8n - Qdrant",
"Workflow 2: Chat Service with Ingested Documents"
] |
/tags/n8n/
|
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(11)
|
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p
|
Additionally, N8N supports scheduled workflows and can be triggered by events across various applications.
|
[
"tags",
"tags/n8n"
] | 17,822 |
[
"N8n - Qdrant"
] |
/tags/n8n/
|
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 > h2:nth-of-type(3)
|
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h2
|
Further reading
|
[
"tags",
"tags/n8n"
] | 17,823 |
[
"N8n - Qdrant",
"Further reading"
] |
/tags/n8n/
|
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 > ul:nth-of-type(5) > li:nth-of-type(1)
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li
|
n8n Documentation
|
[
"tags",
"tags/n8n"
] | 17,824 |
[
"N8n - Qdrant",
"Further reading"
] |
/tags/n8n/
|
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 > ul:nth-of-type(5) > li:nth-of-type(2)
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li
|
n8n Qdrant Node documentation
|
[
"tags",
"tags/n8n"
] | 17,825 |
[
"News - Qdrant"
] |
/tags/news/
|
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(1)
|
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p
|
Andrey Vasnetsov, Co-founder and CTO at Qdrant has shared about vector search and applications with Learn NLP Academy.
|
[
"tags",
"tags/news"
] | 17,826 |
[
"News - Qdrant"
] |
/tags/news/
|
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
|
He covered the following topics:
|
[
"tags",
"tags/news"
] | 17,827 |
[
"News - Qdrant"
] |
/tags/news/
|
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 > ul > li:nth-of-type(1)
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li
|
Qdrant search engine and Quaterion similarity learning framework;
|
[
"tags",
"tags/news"
] | 17,828 |
[
"News - Qdrant"
] |
/tags/news/
|
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 > ul > li:nth-of-type(2)
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li
|
Similarity learning to multimodal settings;
|
[
"tags",
"tags/news"
] | 17,829 |
[
"News - Qdrant"
] |
/tags/news/
|
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 > ul > li:nth-of-type(3)
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li
|
Elastic search embeddings vs vector search engines;
|
[
"tags",
"tags/news"
] | 17,830 |
[
"News - Qdrant"
] |
/tags/news/
|
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 > ul > li:nth-of-type(4)
|
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li
|
Vision for vector search evolution;
|
[
"tags",
"tags/news"
] | 17,833 |
[
"News - Qdrant"
] |
/tags/news/
|
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 > ul > li:nth-of-type(7)
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li
|
Finetuning for out of domain.
|
[
"tags",
"tags/news"
] | 17,834 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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(1)
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p
|
Andrey Vasnetsov, Co-founder and CTO at Qdrant has shared about vector search and applications with Learn NLP Academy.
|
[
"categories",
"categories/vector-search"
] | 17,835 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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
|
He covered the following topics:
|
[
"categories",
"categories/vector-search"
] | 17,836 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(1)
|
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li
|
Qdrant search engine and Quaterion similarity learning framework;
|
[
"categories",
"categories/vector-search"
] | 17,837 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(2)
|
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li
|
Similarity learning to multimodal settings;
|
[
"categories",
"categories/vector-search"
] | 17,838 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(3)
|
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li
|
Elastic search embeddings vs vector search engines;
|
[
"categories",
"categories/vector-search"
] | 17,839 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(4)
|
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li
|
Support for multiple embeddings;
|
[
"categories",
"categories/vector-search"
] | 17,840 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(5)
|
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li
|
Fundraising and VC discussions;
|
[
"categories",
"categories/vector-search"
] | 17,841 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(6)
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li
|
Vision for vector search evolution;
|
[
"categories",
"categories/vector-search"
] | 17,842 |
[
"Vector Search - Qdrant"
] |
/categories/vector-search/
|
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 > ul > li:nth-of-type(7)
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li
|
Finetuning for out of domain.
|
[
"categories",
"categories/vector-search"
] | 17,843 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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(1)
|
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p
|
Andrey Vasnetsov, Co-founder and CTO at Qdrant has shared about vector search and applications with Learn NLP Academy.
|
[
"tags",
"tags/webinar"
] | 17,844 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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
|
He covered the following topics:
|
[
"tags",
"tags/webinar"
] | 17,845 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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 > ul > li:nth-of-type(1)
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li
|
Qdrant search engine and Quaterion similarity learning framework;
|
[
"tags",
"tags/webinar"
] | 17,846 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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 > ul > li:nth-of-type(2)
|
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li
|
Similarity learning to multimodal settings;
|
[
"tags",
"tags/webinar"
] | 17,847 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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 > ul > li:nth-of-type(3)
|
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li
|
Elastic search embeddings vs vector search engines;
|
[
"tags",
"tags/webinar"
] | 17,848 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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 > ul > li:nth-of-type(4)
|
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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 > ul > li:nth-of-type(5)
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[
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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 > ul > li:nth-of-type(6)
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li
|
Vision for vector search evolution;
|
[
"tags",
"tags/webinar"
] | 17,851 |
[
"Webinar - Qdrant"
] |
/tags/webinar/
|
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 > ul > li:nth-of-type(7)
|
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li
|
Finetuning for out of domain.
|
[
"tags",
"tags/webinar"
] | 17,852 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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(1)
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p
|
Andrey Vasnetsov, Co-founder and CTO at Qdrant has shared about vector search and applications with Learn NLP Academy.
|
[
"categories",
"categories/webinar"
] | 17,853 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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
|
He covered the following topics:
|
[
"categories",
"categories/webinar"
] | 17,854 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(1)
|
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li
|
Qdrant search engine and Quaterion similarity learning framework;
|
[
"categories",
"categories/webinar"
] | 17,855 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(2)
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li
|
Similarity learning to multimodal settings;
|
[
"categories",
"categories/webinar"
] | 17,856 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(3)
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li
|
Elastic search embeddings vs vector search engines;
|
[
"categories",
"categories/webinar"
] | 17,857 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(4)
|
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li
|
Support for multiple embeddings;
|
[
"categories",
"categories/webinar"
] | 17,858 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(5)
|
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li
|
Fundraising and VC discussions;
|
[
"categories",
"categories/webinar"
] | 17,859 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(6)
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li
|
Vision for vector search evolution;
|
[
"categories",
"categories/webinar"
] | 17,860 |
[
"Webinar - Qdrant"
] |
/categories/webinar/
|
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 > ul > li:nth-of-type(7)
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li
|
Finetuning for out of domain.
|
[
"categories",
"categories/webinar"
] | 17,861 |
[
"Cohere - Qdrant"
] |
/tags/cohere/
|
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(1)
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p
|
For the second edition of our Vector Space Talks we were joined by none other than Cohere’s Head of Machine Learning Nils Reimers.
|
[
"tags",
"tags/cohere"
] | 17,862 |
[
"Cohere - Qdrant"
] |
/tags/cohere/
|
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 > h2
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h2
|
Key Takeaways
|
[
"tags",
"tags/cohere"
] | 17,863 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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:
|
[
"tags",
"tags/cohere"
] | 17,864 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
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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.
|
[
"tags",
"tags/cohere"
] | 17,865 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,866 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,867 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,868 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,869 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,870 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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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Reinforcement Learning from Human Feedback: Nils revealed how they’ve borrowed a technique from reinforcement learning and applied it to their embedding models.
|
[
"tags",
"tags/cohere"
] | 17,871 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
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[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
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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(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.
|
[
"tags",
"tags/cohere"
] | 17,873 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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
|
This allows the model to learn preferences based on human feedback, resulting in highly informative responses.
|
[
"tags",
"tags/cohere"
] | 17,874 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,875 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,876 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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.
|
[
"tags",
"tags/cohere"
] | 17,877 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,878 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,879 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,880 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,881 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,882 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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/cohere"
] | 17,883 |
[
"Cohere - Qdrant",
"Key Takeaways"
] |
/tags/cohere/
|
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
|
If you or anyone you know would like to come on the Vector Space Talks
|
[
"tags",
"tags/cohere"
] | 17,884 |
[
"Embedding Model - Qdrant"
] |
/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(1)
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p
|
For the second edition of our Vector Space Talks we were joined by none other than Cohere’s Head of Machine Learning Nils Reimers.
|
[
"tags",
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[
"Embedding Model - Qdrant"
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/tags/embedding-model/
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h2
|
Key Takeaways
|
[
"tags",
"tags/embedding-model"
] | 17,886 |
[
"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(2)
|
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p
|
Let’s dive right into the five key takeaways from Nils’ talk:
|
[
"tags",
"tags/embedding-model"
] | 17,887 |
[
"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(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.
|
[
"tags",
"tags/embedding-model"
] | 17,888 |
[
"Embedding Model - Qdrant",
"Key Takeaways"
] |
/tags/embedding-model/
|
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li
|
He demonstrated how their model can differentiate between informative and non-informative documents.
|
[
"tags",
"tags/embedding-model"
] | 17,889 |
[
"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(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.
|
[
"tags",
"tags/embedding-model"
] | 17,890 |
[
"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(1) > p
|
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p
|
He demonstrated how their model can differentiate between informative and non-informative documents.
|
[
"tags",
"tags/embedding-model"
] | 17,891 |
[
"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(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.
|
[
"tags",
"tags/embedding-model"
] | 17,892 |
[
"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(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.
|
[
"tags",
"tags/embedding-model"
] | 17,893 |
[
"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(3)
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li
|
Reinforcement Learning from Human Feedback: Nils revealed how they’ve borrowed a technique from reinforcement learning and applied it to their embedding models.
|
[
"tags",
"tags/embedding-model"
] | 17,894 |
[
"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(3)
|
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li
|
This allows the model to learn preferences based on human feedback, resulting in highly informative responses.
|
[
"tags",
"tags/embedding-model"
] | 17,895 |
[
"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(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.
|
[
"tags",
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] | 17,896 |
[
"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(3) > p
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p
|
This allows the model to learn preferences based on human feedback, resulting in highly informative responses.
|
[
"tags",
"tags/embedding-model"
] | 17,897 |
[
"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)
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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.
|
[
"tags",
"tags/embedding-model"
] | 17,898 |
[
"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)
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li
|
It’s all about understanding the context and how embeddings relate to each other.
|
[
"tags",
"tags/embedding-model"
] | 17,899 |
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