document binary quantization (#281)

* document binary quantization

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

* Update qdrant-landing/content/documentation/guides/quantization.md

Co-authored-by: David Myriel <davidmyriel@gmail.com>

---------

Co-authored-by: David Myriel <davidmyriel@gmail.com>

qdrant-landing/content/documentation/guides/quantization.md

@@ -44,6 +44,62 @@ However, this value depends on the data and the quantization parameters.
 Please refer to the [Quantization Tips](#quantization-tips) section for more information on how to optimize the quantization parameters for your use case.
 
 
+## Binary Quantization
+
+*Available as of v1.5.0*
+
+Binary quantization is an extreme case of scalar quantization.
+This feature lets you represent each vector component as a single bit, effectively reducing the memory footprint by a **factor of 32**.
+
+This is the fastest quantization method, since it lets you perform a vector comparison with a few CPU instructions.
+
+Binary quantization can achieve up to a **40x** speedup compared to the original vectors.
+
+However, binary quantization is only efficient for high-dimensional vectors and require a centered distribution of vector components. 
+
+At the moment, binary quantization shows good accuracy results with the following models:
+
+- OpenAI `text-embedding-ada-002` - 1536d tested with [dbpedia dataset](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) achieving 0.98 recall@100 with 4x oversampling
+- Cohere AI `embed-english-v2.0` - 4096d tested on [wikipedia embeddings](https://huggingface.co/datasets/nreimers/wikipedia-22-12-large/tree/main) - 0.98 recall@50 with 2x oversampling
+
+Models with a lower dimensionality or a different distribution of vector components may require additional experiments to find the optimal quantization parameters.
+
+We recommend using binary quantization only with rescoring enabled, as it can significantly improve the search quality
+with just a minor performance impact.
+Additionally, oversampling can be used to tune the tradeoff between search speed and search quality in the query time.
+
+### Binary Quantization as Hamming Distance
+
+The additional benefit of this method is that you can efficiently emulate Hamming distance with dot product.
+
+Specifically, if original vectors contain `{-1, 1}` as possible values, then the dot product of two vectors is equal to the Hamming distance by simply replacing `-1` with `0` and `1` with `1`.
+
+
+<!-- hidden section -->
+
+<details>
+  <summary><b>Sample truth table</b></summary>
+
+| Vector 1 | Vector 2 | Dot product |
+|----------|----------|-------------|
+| 1        | 1        | 1           |
+| 1        | -1       | -1          |
+| -1       | 1        | -1          |
+| -1       | -1       | 1           |
+
+| Vector 1 | Vector 2 | Hamming distance |
+|----------|----------|------------------|
+| 1        | 1        | 0                |
+| 1        | 0        | 1                |
+| 0        | 1        | 1                |
+| 0        | 0        | 0                |
+
+</details>
+
+As you can see, both functions are equal up to a constant factor, which makes similarity search equivalent.
+Binary quantization makes it efficient to compare vectors using this representation.
+
+
 ## Product Quantization
 
 *Available as of v1.2.0*
@@ -60,6 +116,22 @@ Also, product quantization has a loss of accuracy, so it is recommended to use i
 
 Please refer to the [Quantization Tips](#quantization-tips) section for more information on how to optimize the quantization parameters for your use case.
 
+## How to choose the right quantization method
+
+Here is a brief table of the pros and cons of each quantization method:
+
+| Quantization method | Accuracy | Speed        | Compression |
+|---------------------|----------|--------------|-------------|
+| Scalar              | 0.99     | up to x2     | 4           |
+| Product             | 0.7      | 0.5          | up to 64    |
+| Binary              | 0.95*    | up to x40    | 32          |
+
+`*` - for compatible models
+
+* **Binary Quantization** is the fastest method and the most memory-efficient, but it requires a centered distribution of vector components. It is recommended to use with tested models only.
+* **Scalar Quantization** is the most universal method, as it provides a good balance between accuracy, speed, and compression. It is recommended as default quantization if binary quantization is not applicable.
+* **Product Quantization** may provide a better compression ratio, but it has a significant loss of accuracy and is slower than scalar quantization. It is recommended if the memory footprint is the top priority and the search speed is not critical.
+
 ## Setting up Quantization in Qdrant
 
 You can configure quantization for a collection by specifying the quantization parameters in the `quantization_config` section of the collection configuration.
@@ -129,6 +201,48 @@ However, in some setups you might want to keep quantized vectors in RAM to speed
 
 In this case, you can set `always_ram` to `true` to store quantized vectors in RAM.
 
+### Setting up Binary Quantization
+
+To enable binary quantization, you need to specify the quantization parameters in the `quantization_config` section of the collection configuration.
+
+```http
+PUT /collections/{collection_name}
+
+{
+    "vectors": {
+      "size": 1536,
+      "distance": "Cosine"
+    },
+    "quantization_config": {
+        "binary": {
+            "always_ram": true
+        }
+    }
+}
+```
+
+```python
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+
+client = QdrantClient("localhost", port=6333)
+
+client.recreate_collection(
+    collection_name="{collection_name}",
+    vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
+    quantization_config=models.BinaryQuantization(
+        binary=models.BinaryQuantizationConfig(
+            always_ram=True,
+        ),
+    ),
+)
+```
+
+`always_ram` - whether to keep quantized vectors always cached in RAM or not. By default, quantized vectors are loaded in the same way as the original vectors.
+However, in some setups you might want to keep quantized vectors in RAM to speed up the search process.
+
+In this case, you can set `always_ram` to `true` to store quantized vectors in RAM.
+
 ### Setting up Product Quantization
 
 To enable product quantization, you need to specify the quantization parameters in the `quantization_config` section of the collection configuration.