Merge pull request #223 from qdrant/fix-snippets

properly assign language to snippets

qdrant-landing/content/articles/chatgpt-plugin.md

@@ -205,7 +205,7 @@ The command will build the image using the Dockerfile and deploy the service at
 URL. Once the command is finished, the service should be running on the hostname we got 
 previously:
 
-```
+```text
 https://your-application-name.fly.dev
 ```
 

qdrant-landing/content/articles/memory-consumption.md

@@ -321,20 +321,20 @@ fio --randrepeat=1 \
 
 Initially, we tested on a network-mounted disk, but its performance was too slow, with a read IOPS of 6366 and a bandwidth of 24.9 MiB/s:
 
-```
+```text
 read: IOPS=6366, BW=24.9MiB/s (26.1MB/s)(8192MiB/329424msec)
 ```
 
 To improve performance, we switched to a local disk, which showed much faster results, with a read IOPS of 63.2k and a bandwidth of 247 MiB/s:
 
-```
+```text
 read: IOPS=63.2k, BW=247MiB/s (259MB/s)(8192MiB/33207msec)
 ```
 
 That gave us a significant speed boost, but we wanted to see if we could improve performance even further.
 To do that, we switched to a machine with a local SSD, which showed even better results, with a read IOPS of 183k and a bandwidth of 716 MiB/s:
 
-```
+```text
 read: IOPS=183k, BW=716MiB/s (751MB/s)(8192MiB/11438msec)
 ```
 

qdrant-landing/content/articles/metric-learning-tips.md

@@ -212,7 +212,7 @@ This payload allows additional filtering during the search request.
 
 Qdrant has a pre-built docker image and start working with it is just as simple as running
 
-```
+```bash
 docker run -p 6333:6333 qdrant/qdrant
 ```
 

qdrant-landing/content/articles/neural-search-tutorial.md

@@ -118,20 +118,20 @@ To start Qdrant, use the instructions on its [homepage](https://github.com/qdran
 
 Download image from [DockerHub](https://hub.docker.com/r/qdrant/qdrant):
 
-```
+```bash
 docker pull qdrant/qdrant
 ```
 
 And run the service inside the docker:
 
-```
+```bash
 docker run -p 6333:6333 \
     -v $(pwd)/qdrant_storage:/qdrant/storage \
     qdrant/qdrant
 ```
 You should see output like this
 
-```
+```text
 ...
 [2021-02-05T00:08:51Z INFO  actix_server::builder] Starting 12 workers
 [2021-02-05T00:08:51Z INFO  actix_server::builder] Starting "actix-web-service-0.0.0.0:6333" service on 0.0.0.0:6333
@@ -149,7 +149,7 @@ To interact with Qdrant from python, I recommend using an out-of-the-box client
 
 To install it, use the following command
 
-```
+```bash
 pip install qdrant-client
 ```
 
@@ -229,7 +229,7 @@ The full code for this step could be found [here](https://github.com/qdrant/qdra
 Now that all the preparations are complete, let's start building a neural search class.
 
 First, install all the requirements:
-```
+```bash
 pip install sentence-transformers numpy
 ```
 
@@ -313,7 +313,7 @@ It is super easy to use and requires minimal code writing.
 
 To install it, use the command
 
-```
+```bash
 pip install fastapi uvicorn
 ```
 
@@ -347,7 +347,7 @@ if __name__ == "__main__":
 
 Now, if you run the service with
 
-```
+```bash
 python service.py
 ```
 

qdrant-landing/content/articles/quantum-quantization.md

@@ -23,7 +23,7 @@ Quantum quantization is a novel approach that leverages the power of quantum com
 
 The conversion of float32 vectors to qbit vectors can be represented by the following formula:
 
-```
+```text
 qbit_vector = Q( float32_vector )
 ```
 

qdrant-landing/content/articles/scalar-quantization.md

@@ -29,7 +29,7 @@ So a single number needs 4 bytes of the memory and a 512-dimensional vector occu
 2 kB. That's only the memory used to store the vector. There is also an overhead of the
 HNSW graph, so as a rule of thumb we estimate the memory size with the following formula:
 
-```
+```text
 memory_size = 1.5 * number_of_vectors * vector_dimension * 4 bytes
 ```
 

qdrant-landing/content/benchmarks/single-node-speed-benchmark.md

@@ -49,7 +49,7 @@ And 8 CPUs + 16Gb RAM for client machine. We were trying to make the bottleneck
 
 ### Experiment setup
 
-```
+```text
  ┌────────┐      ┌──────────┐
  │        ├─────►│          │
  │ Client │      │  Engine  │

qdrant-landing/content/documentation/cloud/capacity.md

@@ -14,7 +14,7 @@ It depends on a number of factors and options you can choose for your collection
 
 If you need to keep all vectors in memory for maximum performance, there is a very rough formula for estimating the needed memory size looks like this:
 
-```
+```text
 memory_size = number_of_vectors * vector_dimension * 4 bytes * 1.5
 ```
 
@@ -46,6 +46,6 @@ the fast search for the most active and recent users.
 
 In this case you can estimate required memory size as follows:
 
-```
+```text
 memory_size = number_of_active_vectors * vector_dimension * 4 bytes * 1.5
 ```

qdrant-landing/content/documentation/quick-start.md

@@ -40,13 +40,13 @@ With python client it is possible to try Qdrant without running docker container
 
 Simply install it
 
-```
+```bash
 pip install qdrant-client
 ```
 
 and initialize client like this:
 
-```
+```python
 from qdrant_client import QdrantClient
 
 client = QdrantClient(":memory:")

qdrant-landing/content/documentation/tutorials/common-errors.md

@@ -9,7 +9,7 @@ weight: 100
 
 Each collection segment needs some files to be open. At some point you may encounter the following errors in your server log:
 
-```
+```text
 Error: Too many files open (OS error 24)
 ```
 

qdrant-landing/content/documentation/tutorials/mighty.md

@@ -22,7 +22,7 @@ Much like Qdrant, the [Mighty](https://max.io/) inference server is written in R
 
 For Mighty, start up a [docker container](https://hub.docker.com/layers/maxdotio/mighty-sentence-transformers/0.9.9/images/sha256-0d92a89fbdc2c211d927f193c2d0d34470ecd963e8179798d8d391a4053f6caf?context=explore) with an open port 5050. Just loading the port in a window shows the following:
 
-```
+```json
 {
   "name": "sentence-transformers/all-MiniLM-L6-v2",
   "architectures": [

qdrant-landing/content/documentation/tutorials/neural-search.md

@@ -109,7 +109,7 @@ docker run -p 6333:6333 \
 ```
 You should see output like this
 
-```
+```text
 ...
 [2021-02-05T00:08:51Z INFO  actix_server::builder] Starting 12 workers
 [2021-02-05T00:08:51Z INFO  actix_server::builder] Starting "actix-web-service-0.0.0.0:6333" service on 0.0.0.0:6333
@@ -310,7 +310,7 @@ if __name__ == "__main__":
 
 3. Run the service.
 
-```
+```bash
 python service.py
 ```
 

qdrant-landing/content/documentation/tutorials/search-beginners.md

@@ -19,24 +19,28 @@ Before you begin, you need to have a [recent version of Python](https://www.pyth
 ## 1. Installation
 
 You need to process your data so that the search engine can work with it. The [Sentence Transformers] framework gives you access to common [Large Language Models] that turn raw data into embeddings.
-```python
+
+```bash
 pip install -U sentence-transformers
 ```
 
 Once encoded, this data needs to be kept somewhere. Qdrant lets you store data as embeddings. You can also use Qdrant to run search queries against this data. This means that you can ask the engine to give you relevant answers that go way beyond keyword matching.
-```python
+
+```bash
 pip install qdrant-client
 ```
 
 ### Import the models 
 
 Once the two main frameworks are defined, you need to specify the exact models this engine will use. 
+
 ```python
 from qdrant_client import models, QdrantClient
 from sentence_transformers import SentenceTransformer
 ```
 
 The [Sentence Transformers](https://www.sbert.net/index.html) framework contains many embedding models. However, [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) is the fastest encoder for this tutorial.
+
 ```python
 encoder = SentenceTransformer('all-MiniLM-L6-v2') 
 ```
@@ -127,11 +131,12 @@ for hit in hits:
 
 The search engine shows three of the most likely responses that have to do with the alien invasion. Each of the responses is assigned a score to show how close the response is to the original inquiry.
 
-```python
+```text
 {'name': 'The War of the Worlds', 'description': 'A Martian invasion of Earth throws humanity into chaos.', 'author': 'H.G. Wells', 'year': 1898} score: 0.570093257022374
 {'name': "The Hitchhiker's Guide to the Galaxy", 'description': 'A comedic science fiction series following the misadventures of an unwitting human and his alien friend.', 'author': 'Douglas Adams', 'year': 1979} score: 0.5040468703143637
 {'name': 'The Three-Body Problem', 'description': 'Humans encounter an alien civilization that lives in a dying system.', 'author': 'Liu Cixin', 'year': 2008} score: 0.45902943411768216
 ```
+
 ### Narrow down the query
 
 How about the most recent book from the early 2000s?
@@ -160,7 +165,7 @@ for hit in hits:
 
 The query has been narrowed down to one result from 2008. 
 
-```python
+```text
 {'name': 'The Three-Body Problem', 'description': 'Humans encounter an alien civilization that lives in a dying system.', 'author': 'Liu Cixin', 'year': 2008} score: 0.45902943411768216
 ```