Add article about qdrant 1.2.x (#142)

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Co-authored-by: Tim Visée <tim+github@visee.me>

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---------

Co-authored-by: qdrant <qdrant@users.noreply.github.com>
Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
Co-authored-by: Tim Visée <tim+github@visee.me>

qdrant-landing/content/articles/qdrant-1.2.x.md

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+---
+title: "Introducing Qdrant 1.2.x"
+short_description: "Check out what Qdrant 1.2 brings to vector search"
+description: "Check out what Qdrant 1.2 brings to vector search"
+social_preview_image: /articles_data/qdrant-1.2.x/preview/social_preview.jpg
+small_preview_image: /articles_data/qdrant-1.2.x/icon.svg
+preview_dir: /articles_data/qdrant-1.2.x/preview
+weight: 1
+author: Kacper Łukawski
+author_link: https://medium.com/@lukawskikacper
+date: 2023-05-24T10:45:00+02:00
+draft: false
+keywords:
+  - vector search
+  - new features
+  - product quantization
+  - optional vectors
+  - nested filters
+  - appendable mmap
+  - group requests
+---
+
+A brand-new Qdrant 1.2 release comes packed with a plethora of new features, some of which
+were highly requested by our users. If you want to shape the development of the Qdrant vector
+database, please [join our Discord community](https://qdrant.to/discord) and let us know
+how you use it!
+
+## New features
+
+As usual, a minor version update of Qdrant brings some interesting new features. We love to see your 
+feedback, and we tried to include the features most requested by our community.
+
+### Product Quantization
+
+The primary focus of Qdrant was always performance. That's why we built it in Rust, but we were 
+always concerned about making vector search affordable. From the very beginning, Qdrant offered 
+support for disk-stored collections, as storage space is way cheaper than memory. That's also 
+why we have introduced the [Scalar Quantization](/articles/scalar-quantization) mechanism recently, 
+which makes it possible to reduce the memory requirements by up to four times.
+
+Today, we are bringing a new quantization mechanism to life. A separate article on [Product 
+Quantization](/documentation/quantization/#product-quantization) will describe that feature in more 
+detail. In a nutshell, you can **reduce the memory requirements by up to 64 times**!
+
+### Optional named vectors
+
+Qdrant has been supporting multiple named vectors per point for quite a long time. Those may have 
+utterly different dimensionality and distance functions used to calculate similarity. Having multiple 
+embeddings per item is an essential real-world scenario. For example, you might be encoding textual 
+and visual data using different models. Or you might be experimenting with different models but 
+don't want to make your payloads redundant by keeping them in separate collections.
+
+![Optional vectors](/articles_data/qdrant-1.2.x/optional-vectors.png)
+
+However, up to the previous version, we requested that you provide all the vectors for each point. There 
+have been many requests to allow nullable vectors, as sometimes you cannot generate an embedding or 
+simply don't want to for reasons we don't need to know.
+
+### Grouping requests
+
+Embeddings are great for capturing the semantics of the documents, but we rarely encode larger pieces 
+of data into a single vector. Having a summary of a book may sound attractive, but in reality, we 
+divide it into paragraphs or some different parts to have higher granularity. That pays off when we 
+perform the semantic search, as we can return the relevant pieces only. That's also how modern tools 
+like Langchain process the data. The typical way is to encode some smaller parts of the document and 
+keep the document id as a payload attribute.
+
+![Query without grouping request](/articles_data/qdrant-1.2.x/without-grouping-request.png)
+
+There are cases where we want to find relevant parts, but only up to a specific number of results
+per document (for example, only a single one). Up till now, we had to implement such a mechanism 
+on the client side and send several calls to the Qdrant engine. But that's no longer the case. 
+Qdrant 1.2 provides a mechanism for [grouping requests](/documentation/search/#grouping-api), which 
+can handle that server-side, within a single call to the database. This mechanism is similar to the 
+SQL `GROUP BY` clause.
+
+![Query with grouping request](/articles_data/qdrant-1.2.x/with-grouping-request.png)
+
+You are not limited to a single result per document, and you can select how many entries will be
+returned.
+
+### Nested filters
+
+Unlike some other vector databases, Qdrant accepts any arbitrary JSON payload, including
+arrays, objects, and arrays of objects. You can also [filter the search results using nested 
+keys](/documentation/filtering/#nested-key), even though arrays (using the `[]` syntax). 
+
+Before Qdrant 1.2 it was impossible to express some more complex conditions for the
+nested structures. For example, let's assume we have the following payload:
+
+```json
+{
+  "country": "Japan",
+  "cities": [
+    {
+      "name": "Tokyo",
+      "population": 9.3,
+      "area": 2194
+    },
+    {
+      "name": "Osaka",
+      "population": 2.7,
+      "area": 223
+    },
+    {
+      "name": "Kyoto",
+      "population": 1.5,
+      "area": 827.8
+    }
+  ]
+}
+```
+
+We want to filter out the results to include the countries with a city with over 2 million citizens 
+and an area bigger than 500 square kilometers but no more than 1000. There is no such a city in 
+Japan, looking at our data, but if we wrote the following filter, it would be returned:
+
+```json
+{
+  "filter": {
+    "must": [
+      {
+        "key": "country.cities[].population",
+        "range": {
+          "gte": 2
+        }
+      },
+      {
+        "key": "country.cities[].area",
+        "range": {
+          "gt": 500,
+          "lte": 1000
+        }
+      }
+    ]
+  },
+  "limit": 3
+}
+```
+
+Japan would be returned because Tokyo and Osaka match the first criteria, while Kyoto fulfills 
+the second. But that's not what we wanted to achieve. That's the motivation behind introducing 
+a new type of nested filter.
+
+```json
+{
+  "filter": {
+    "must": [
+      {
+        "nested": {
+          "key": "country.cities",
+          "filter": {
+            "must": [
+              {
+                "key": "population",
+                "range": {
+                  "gte": 2
+                }
+              },
+              {
+                "key": "area",
+                "range": {
+                  "gt": 500,
+                  "lte": 1000
+                }
+              }
+            ]
+          }
+        }
+      }
+    ]
+  },
+  "limit": 3
+}
+```
+
+The syntax is consistent with all the other supported filters and enables new possibilities. In 
+our case, it allows us to express the joined condition on a nested structure and make the results 
+list empty but correct.
+
+## Important changes
+
+The latest release focuses not only on the new features but also introduces some changes making 
+Qdrant even more reliable.
+
+### Recovery mode
+
+There has been an issue in memory-constrained environments, such as cloud, happening when users were 
+pushing massive amounts of data into the service using `wait=false`. This data influx resulted in an 
+overreaching of disk or RAM limits before the Write-Ahead Logging (WAL) was fully applied. This 
+situation was causing Qdrant to attempt a restart and reapplication of WAL, failing recurrently due 
+to the same memory constraints and pushing the service into a frustrating crash loop with many 
+Out-of-Memory errors.
+
+Qdrant 1.2 enters recovery mode, if enabled, when it detects a failure on startup. 
+That makes the service halt the loading of collection data and commence operations in a partial state. 
+This state allows for removing collections but doesn't support search or update functions. 
+**Recovery mode [has to be enabled by user](/documentation/administration/#recovery-mode).**
+
+### Appendable mmap
+
+For a long time, segments using mmap storage were `non-appendable` and could only be constructed by 
+the optimizer. Dynamically adding vectors to the mmap file is fairly complicated and thus not 
+implemented in Qdrant, but we did our best to implement it in the recent release. If you want 
+to read more about segments, check out our docs on [vector storage](/documentation/storage/#vector-storage).
+
+## Security
+
+There are two major changes in terms of [security](/documentation/security/):
+
+1. **API-key support** - basic authentication with a static API key to prevent unwanted access. Previously 
+   API keys were only supported in [Qdrant Cloud](https://cloud.qdrant.io/).
+2. **TLS support** -  to use encrypted connections and prevent sniffing/MitM attacks.
+
+## Release notes
+
+As usual, [our release notes](https://github.com/qdrant/qdrant/releases/tag/v1.2.0) describe all the changes 
+introduced in the latest version.

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