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[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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p
|
To give you some intuition, let’s consider a practical example:
|
[
"articles",
"articles/dedicated-service"
] | 1,300 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
Assume we have a database with 1 million records.
|
[
"articles",
"articles/dedicated-service"
] | 1,301 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
This is a small database by modern standards of any relational database.
|
[
"articles",
"articles/dedicated-service"
] | 1,302 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
You can probably use the smallest free tier of any cloud provider to host it.
|
[
"articles",
"articles/dedicated-service"
] | 1,303 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
But if we want to use this database for vector search, 1 million OpenAI text-embedding-ada-002 embeddings will take ~6Gb of RAM (sic!).
|
[
"articles",
"articles/dedicated-service"
] | 1,304 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
As you can see, the vector search use case completely overwhelmed the main database resource requirements.
|
[
"articles",
"articles/dedicated-service"
] | 1,305 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
In practice, this means that your main database becomes burdened with high memory requirements and can not scale efficiently, limited by the size of a single machine.
|
[
"articles",
"articles/dedicated-service"
] | 1,306 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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] |
p
|
Fortunately, the data synchronization problem is not new and definitely not unique to vector search.
|
[
"articles",
"articles/dedicated-service"
] | 1,307 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
There are many well-known solutions, starting with message queues and ending with specialized ETL tools.
|
[
"articles",
"articles/dedicated-service"
] | 1,308 |
[
"Vector Search as a dedicated service - Qdrant",
"Having a dedicated vector database requires complex data synchronization."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
For example, we recently released our integration with Airbyte, allowing you to synchronize data from various sources into Qdrant incrementally.
|
[
"articles",
"articles/dedicated-service"
] | 1,309 |
[
"Vector Search as a dedicated service - Qdrant"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h6:nth-of-type(4)
|
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h6
|
You have to pay for a vector service uptime and data transfer of both solutions.
|
[
"articles",
"articles/dedicated-service"
] | 1,310 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
In the open-source world, you pay for the resources you use, not the number of different databases you run.
|
[
"articles",
"articles/dedicated-service"
] | 1,311 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
Resources depend more on the optimal solution for each use case.
|
[
"articles",
"articles/dedicated-service"
] | 1,312 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
As a result, running a dedicated vector search engine can be even cheaper, as it allows optimization specifically for vector search use cases.
|
[
"articles",
"articles/dedicated-service"
] | 1,313 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
|
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p
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For instance, Qdrant implements a number of quantization techniques that can significantly reduce the memory footprint of embeddings.
|
[
"articles",
"articles/dedicated-service"
] | 1,314 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
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In terms of data transfer costs, on most cloud providers, network use within a region is usually free.
|
[
"articles",
"articles/dedicated-service"
] | 1,315 |
[
"Vector Search as a dedicated service - Qdrant",
"You have to pay for a vector service uptime and data transfer of both solutions."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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] |
p
|
As long as you put the original source data and the vector store in the same region, there are no added data transfer costs.
|
[
"articles",
"articles/dedicated-service"
] | 1,316 |
[
"Vector Search as a dedicated service - Qdrant"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h6:nth-of-type(5)
|
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h6
|
What is more seamless than your current database adding vector search capability?
|
[
"articles",
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[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
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p
|
In contrast to the short-term attractiveness of integrated solutions, dedicated search engines propose flexibility and a modular approach.
|
[
"articles",
"articles/dedicated-service"
] | 1,318 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
|
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p
|
You don’t need to update the whole production database each time some of the vector plugins are updated.
|
[
"articles",
"articles/dedicated-service"
] | 1,319 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
|
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p
|
Maintenance of a dedicated search engine is as isolated from the main database as the data itself.
|
[
"articles",
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] | 1,320 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
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p
|
In fact, integration of more complex scenarios, such as read/write segregation, is much easier with a dedicated vector solution.
|
[
"articles",
"articles/dedicated-service"
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[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
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p
|
You can easily build cross-region replication to ensure low latency for your users.
|
[
"articles",
"articles/dedicated-service"
] | 1,322 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > figure:nth-of-type(2) > figcaption > p
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p
|
Read/Write segregation + cross-regional deployment
|
[
"articles",
"articles/dedicated-service"
] | 1,323 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
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p
|
It is especially important in large enterprise organizations, where the responsibility for different parts of the system is distributed among different teams.
|
[
"articles",
"articles/dedicated-service"
] | 1,324 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
|
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p
|
In those situations, it is much easier to maintain a dedicated search engine for the AI team than to convince the core team to update the whole primary database.
|
[
"articles",
"articles/dedicated-service"
] | 1,325 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31)
|
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p
|
Finally, the vector capabilities of the all-in-one database are tied to the development and release cycle of the entire stack.
|
[
"articles",
"articles/dedicated-service"
] | 1,326 |
[
"Vector Search as a dedicated service - Qdrant",
"What is more seamless than your current database adding vector search capability?"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31)
|
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p
|
Their long history of use also means that they need to pay a high price for backward compatibility.
|
[
"articles",
"articles/dedicated-service"
] | 1,327 |
[
"Vector Search as a dedicated service - Qdrant"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h6:nth-of-type(6)
|
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h6
|
Databases can support RAG use-case end-to-end.
|
[
"articles",
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[
"Vector Search as a dedicated service - Qdrant",
"Databases can support RAG use-case end-to-end."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32)
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p
|
Putting aside performance and scalability questions, the whole discussion about implementing RAG in the DBs assumes that the only detail missing in traditional databases is the vector index and the ability to make fast ANN queries.
|
[
"articles",
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[
"Vector Search as a dedicated service - Qdrant",
"Databases can support RAG use-case end-to-end."
] |
/articles/dedicated-service/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
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p
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In fact, the current capabilities of vector search have only scratched the surface of what is possible.
|
[
"articles",
"articles/dedicated-service"
] | 1,330 |
[
"Vector Search as a dedicated service - Qdrant",
"Databases can support RAG use-case end-to-end."
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
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For example, in our recent article, we discuss the possibility of building an exploration API to fuel the discovery process - an alternative to kNN search, where you don’t even know what exactly you are looking for.
|
[
"articles",
"articles/dedicated-service"
] | 1,331 |
[
"Vector Search as a dedicated service - Qdrant"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(2)
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h2
|
Summary
|
[
"articles",
"articles/dedicated-service"
] | 1,332 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
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p
|
Ultimately, you do not need a vector database if you are looking for a simple vector search functionality with a small amount of data.
|
[
"articles",
"articles/dedicated-service"
] | 1,333 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
|
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p
|
We genuinely recommend starting with whatever you already have in your stack to prototype.
|
[
"articles",
"articles/dedicated-service"
] | 1,334 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
|
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p
|
But you need one if you are looking to do more out of it, and it is the central functionality of your application.
|
[
"articles",
"articles/dedicated-service"
] | 1,335 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
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p
|
It is just like using a multi-tool to make something quick or using a dedicated instrument highly optimized for the use case.
|
[
"articles",
"articles/dedicated-service"
] | 1,336 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35)
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p
|
Large-scale production systems usually consist of different specialized services and storage types for good reasons since it is one of the best practices of modern software architecture.
|
[
"articles",
"articles/dedicated-service"
] | 1,337 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35)
|
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p
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Comparable to the orchestration of independent building blocks in a microservice architecture.
|
[
"articles",
"articles/dedicated-service"
] | 1,338 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36)
|
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p
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When you stuff the database with a vector index, you compromise both the performance and scalability of the main database and the vector search capabilities.
|
[
"articles",
"articles/dedicated-service"
] | 1,339 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36)
|
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p
|
There is no one-size-fits-all approach that would not compromise on performance or flexibility.
|
[
"articles",
"articles/dedicated-service"
] | 1,340 |
[
"Vector Search as a dedicated service - Qdrant",
"Summary"
] |
/articles/dedicated-service/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36)
|
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p
|
So if your use case utilizes vector search in any significant way, it is worth investing in a dedicated vector search engine, aka vector database.
|
[
"articles",
"articles/dedicated-service"
] | 1,341 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(1)
|
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li
|
Home
|
[
"articles",
"articles/fastembed"
] | 1,342 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(2)
|
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li
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/
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[
"articles",
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[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(3)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(2)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(5)
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FastEmbed: Fast and Lightweight Embedding Generation for Text
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[
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/articles/fastembed/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1
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h1
|
FastEmbed: Fast and Lightweight Embedding Generation for Text
|
[
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[
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/articles/fastembed/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
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Data Science and Machine Learning practitioners often find themselves navigating through a labyrinth of models, libraries, and frameworks.
|
[
"articles",
"articles/fastembed"
] | 1,348 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"FastEmbed: Fast and Lightweight Embedding Generation for Text"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
|
Which model to choose, what embedding size, how to approach tokenizing, these are just some questions you are faced with when starting your work.
|
[
"articles",
"articles/fastembed"
] | 1,349 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"FastEmbed: Fast and Lightweight Embedding Generation for Text"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
|
We understood how, for many data scientists, they wanted an easier and intuitive means to do their embedding work.
|
[
"articles",
"articles/fastembed"
] | 1,350 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"FastEmbed: Fast and Lightweight Embedding Generation for Text"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
|
This is why we built FastEmbed (docs: https://qdrant.github.io/fastembed/) —a Python library engineered for speed, efficiency, and above all, usability.
|
[
"articles",
"articles/fastembed"
] | 1,351 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"FastEmbed: Fast and Lightweight Embedding Generation for Text"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
|
We have created easy to use default workflows, handling the 80% use cases in NLP embedding.
|
[
"articles",
"articles/fastembed"
] | 1,352 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(1)
|
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h3
|
Current State of Affairs for Generating Embeddings
|
[
"articles",
"articles/fastembed"
] | 1,353 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
Usually you make embedding by utilizing PyTorch or TensorFlow models under the hood.
|
[
"articles",
"articles/fastembed"
] | 1,354 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
But using these libraries comes at a cost in terms of ease of use and computational speed.
|
[
"articles",
"articles/fastembed"
] | 1,355 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
This is at least in part because these are built for both: model inference and improvement e.g. via fine-tuning.
|
[
"articles",
"articles/fastembed"
] | 1,356 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
To tackle these problems we built a small library focused on the task of quickly and efficiently creating text embeddings.
|
[
"articles",
"articles/fastembed"
] | 1,357 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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] |
p
|
We also decided to start with only a small sample of best in class transformer models.
|
[
"articles",
"articles/fastembed"
] | 1,358 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
By keeping it small and focused on a particular use case, we could make our library focused without all the extraneous dependencies.
|
[
"articles",
"articles/fastembed"
] | 1,359 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
We ship with limited models, quantize the model weights and seamlessly integrate them with the ONNX Runtime.
|
[
"articles",
"articles/fastembed"
] | 1,360 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Current State of Affairs for Generating Embeddings"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
FastEmbed strikes a balance between inference time, resource utilization and performance (recall/accuracy).
|
[
"articles",
"articles/fastembed"
] | 1,361 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2)
|
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] |
h3
|
Quick Example
|
[
"articles",
"articles/fastembed"
] | 1,362 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Quick Example"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4)
|
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] |
p
|
Here is an example of how simple we have made embedding text documents:
|
[
"articles",
"articles/fastembed"
] | 1,363 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Quick Example"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
[
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] |
p
|
These 3 lines of code do a lot of heavy lifting for you: They download the quantized model, load it using ONNXRuntime, and then run a batched embedding creation of your documents.
|
[
"articles",
"articles/fastembed"
] | 1,364 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(3)
|
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] |
h3
|
Code Walkthrough
|
[
"articles",
"articles/fastembed"
] | 1,365 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
|
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] |
p
|
Let’s delve into a more advanced example code snippet line-by-line:
|
[
"articles",
"articles/fastembed"
] | 1,366 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
|
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] |
p
|
Here, we import the FlagEmbedding class from FastEmbed and alias it as Embedding.
|
[
"articles",
"articles/fastembed"
] | 1,367 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
|
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] |
p
|
This is the core class responsible for generating embeddings based on your chosen text model.
|
[
"articles",
"articles/fastembed"
] | 1,368 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
|
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] |
p
|
This is also the class which you can import directly as DefaultEmbedding which is BAAI/bge-small-en-v1.5
|
[
"articles",
"articles/fastembed"
] | 1,369 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
|
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] |
p
|
In this list called documents, we define four text strings that we want to convert into embeddings.
|
[
"articles",
"articles/fastembed"
] | 1,370 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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] |
p
|
Note the use of prefixes “passage” and “query” to differentiate the types of embeddings to be generated.
|
[
"articles",
"articles/fastembed"
] | 1,371 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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p
|
This is inherited from the cross-encoder implementation of the BAAI/bge series of models themselves.
|
[
"articles",
"articles/fastembed"
] | 1,372 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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p
|
This is particularly useful for retrieval and we strongly recommend using this as well.
|
[
"articles",
"articles/fastembed"
] | 1,373 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
|
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p
|
The use of text prefixes like “query” and “passage” isn’t merely syntactic sugar; it informs the algorithm on how to treat the text for embedding generation.
|
[
"articles",
"articles/fastembed"
] | 1,374 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
|
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p
|
A “query” prefix often triggers the model to generate embeddings that are optimized for similarity comparisons, while “passage” embeddings are fine-tuned for contextual understanding.
|
[
"articles",
"articles/fastembed"
] | 1,375 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
|
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p
|
If you omit the prefix, the default behavior is applied, although specifying it is recommended for more nuanced results.
|
[
"articles",
"articles/fastembed"
] | 1,376 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
Next, we initialize the Embedding model with the default model: BAAI/bge-small-en-v1.5.
|
[
"articles",
"articles/fastembed"
] | 1,377 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
|
The default model and several other models have a context window of maximum 512 tokens.
|
[
"articles",
"articles/fastembed"
] | 1,378 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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] |
p
|
This maximum limit comes from the embedding model training and design itself.If you’d like to embed sequences larger than that, we’d recommend using some pooling strategy to get a single vector out of the sequence.
|
[
"articles",
"articles/fastembed"
] | 1,379 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
|
For example, you can use the mean of the embeddings of different chunks of a document.
|
[
"articles",
"articles/fastembed"
] | 1,380 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
|
This is also what the SBERT Paper recommends
|
[
"articles",
"articles/fastembed"
] | 1,381 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
|
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] |
p
|
This model strikes a balance between speed and accuracy, ideal for real-world applications.
|
[
"articles",
"articles/fastembed"
] | 1,382 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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] |
p
|
Finally, we call the embed() method on our embedding_model object, passing in the documents list.
|
[
"articles",
"articles/fastembed"
] | 1,383 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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] |
p
|
The method returns a Python generator, so we convert it to a list to get all the embeddings.
|
[
"articles",
"articles/fastembed"
] | 1,384 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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] |
p
|
These embeddings are NumPy arrays, optimized for fast mathematical operations.
|
[
"articles",
"articles/fastembed"
] | 1,385 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
|
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] |
p
|
The embed() method returns a list of NumPy arrays, each corresponding to the embedding of a document in your original documents list.
|
[
"articles",
"articles/fastembed"
] | 1,386 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
|
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] |
p
|
The dimensions of these arrays are determined by the model you chose e.g. for “BAAI/bge-small-en-v1.5” it’s a 384-dimensional vector.
|
[
"articles",
"articles/fastembed"
] | 1,387 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Code Walkthrough"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
|
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] |
p
|
You can easily parse these NumPy arrays for any downstream application—be it clustering, similarity comparison, or feeding them into a machine learning model for further analysis.
|
[
"articles",
"articles/fastembed"
] | 1,388 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(1)
|
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h2
|
Key Features
|
[
"articles",
"articles/fastembed"
] | 1,389 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Key Features"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
|
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p
|
FastEmbed is built for inference speed, without sacrificing (too much) performance:
|
[
"articles",
"articles/fastembed"
] | 1,390 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Key Features"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(1)
|
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li
|
50% faster than PyTorch Transformers
|
[
"articles",
"articles/fastembed"
] | 1,391 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Key Features"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(2)
|
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li
|
Better performance than Sentence Transformers and OpenAI Ada-002
|
[
"articles",
"articles/fastembed"
] | 1,392 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Key Features"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(3)
|
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li
|
Cosine similarity of quantized and original model vectors is 0.92
|
[
"articles",
"articles/fastembed"
] | 1,393 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Key Features"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
We use BAAI/bge-small-en-v1.5 as our DefaultEmbedding, hence we’ve chosen that for comparison:
|
[
"articles",
"articles/fastembed"
] | 1,394 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(2)
|
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] |
h2
|
Under the Hood
|
[
"articles",
"articles/fastembed"
] | 1,395 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Under the Hood"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
|
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p
|
Quantized Models: We quantize the models for CPU (and Mac Metal) – giving you the best buck for your compute model.
|
[
"articles",
"articles/fastembed"
] | 1,396 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Under the Hood"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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p
|
Our default model is so small, you can run this in AWS Lambda if you’d like!
|
[
"articles",
"articles/fastembed"
] | 1,397 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Under the Hood"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
Shout out to Huggingface’s Optimum – which made it easier to quantize models.
|
[
"articles",
"articles/fastembed"
] | 1,398 |
[
"FastEmbed: Fast and Lightweight Embedding Generation for Text - Qdrant",
"Under the Hood"
] |
/articles/fastembed/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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] |
p
|
Reduced Installation Time:
|
[
"articles",
"articles/fastembed"
] | 1,399 |
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