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qdrant-landing/content/articles/dedicated-service.md
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@@ -114,7 +114,7 @@ In the open-source world, you pay for the resources you use, not the number of d
Resources depend more on the optimal solution for each use case.
As a result, running a dedicated vector search engine can be even cheaper, as it allows optimization specifically for vector search use cases.
For instance, Qdrant implements a number of [quantization techniques](documentation/guides/quantization/) that can significantly reduce the memory footprint of embeddings.
For instance, Qdrant implements a number of [quantization techniques](/documentation/guides/quantization/) that can significantly reduce the memory footprint of embeddings.
In terms of data transfer costs, on most cloud providers, network use within a region is usually free. As long as you put the original source data and the vector store in the same region, there are no added data transfer costs.
qdrant-landing/content/articles/sparse-vectors.md
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@@ -199,8 +199,8 @@ Here are some terms that are added: "Berlin", and "founder" - despite having no
If you're interested in using the higher-performance approach, check out the following models:
1. [naver/efficient-splade-VI-BT-large-doc](huggingface.co/naver/efficient-splade-vi-bt-large-doc)
2. [naver/efficient-splade-VI-BT-large-query](huggingface.co/naver/efficient-splade-vi-bt-large-doc)
1. [naver/efficient-splade-VI-BT-large-doc](https://huggingface.co/naver/efficient-splade-vi-bt-large-doc)
2. [naver/efficient-splade-VI-BT-large-query](https://huggingface.co/naver/efficient-splade-vi-bt-large-doc)
## Why SPLADE works? Term Expansion
qdrant-landing/content/blog/case-study-bloop.md
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@@ -38,7 +38,7 @@ more time shipping features and fixing bugs.
bloop’s mission is to make software engineers autonomous and semantic code search is the cornerstone
of that vision. The project is maintained by a group of Rust and Typescript engineers and ML researchers.
It leverages many prominent nascent technologies, such as [Tauri](http://tauri.app), [tantivy](https://docs.rs/tantivy),
[Qdrant](http://qdrant.tech) and [Anthropic](https://www.anthropic.com/).
[Qdrant](https://qdrant.tech) and [Anthropic](https://www.anthropic.com/).
## About Qdrant
qdrant-landing/content/documentation/cloud/aws-marketplace.md
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@@ -39,4 +39,4 @@ Now that you have signed up via AWS Marketplace, please read our instructions to
2. Learn how to [authenticate and access your cluster](../../cloud/authentication/).
3. Additional open source [documentation](../../troubleshooting/).
3. Additional open source [documentation](/documentation/guides/common-errors/).
qdrant-landing/content/documentation/overview/_index.md
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@@ -21,7 +21,7 @@ learn about one of the most popular and fastest growing vector databases in the
## What is Qdrant?
[Qdrant](http://qdrant.tech) "is a vector similarity search engine that provides a production-ready
[Qdrant](https://qdrant.tech) "is a vector similarity search engine that provides a production-ready
service with a convenient API to store, search, and manage points (i.e. vectors) with an additional
payload." You can think of the payloads as additional pieces of information that can help you
hone in on your search and also receive useful information that you can give to your users.
qdrant-landing/content/documentation/overview/qdrant-alternatives.md
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@@ -67,7 +67,7 @@ There are also various community-driven projects aimed to provide the support fo
maintained, thus not mentioned here. However, it is still possible to interact with both engines through the HTTP REST or gRPC API.
That makes it easy to integrate with any technology of your choice.
If you are a Python user, then both tools are well-integrated with the most popular libraries like [LangChain](../integrations/langchain/), [LlamaIndex](../integrations/llama-index/), [Haystack](../integrations/haystack/), and more.
If you are a Python user, then both tools are well-integrated with the most popular libraries like [LangChain](/documentation/frameworks/langchain/), [LlamaIndex](/documentation/frameworks/llama-index/), [Haystack](/documentation/frameworks/haystack/), and more.
Using any of those libraries makes it easier to experiment with different vector databases, as the transition should be seamless.
## Planning to migrate?
@@ -92,6 +92,6 @@ Migrating from Pinecone to Qdrant involves a series of well-planned steps to ens
1. If you aren't ready yet, [try out Qdrant locally](/documentation/quick-start/) or sign up for [Qdrant Cloud](https://cloud.qdrant.io/).
2. For more basic information on Qdrant read our [Overview](overview/) section or learn more about Qdrant Cloud's [Free Tier](documentation/cloud/).
2. For more basic information on Qdrant read our [Overview](/documentation/overview/) section or learn more about Qdrant Cloud's [Free Tier](/documentation/cloud/).
3. If ready to migrate, please consult our [Comprehensive Guide](https://github.com/NirantK/qdrant_tools) for further details on migration steps.