Merge pull request #226 from weaviate/v1-34/jph-comments

Draft comments

Author: g-despot

_includes/code/howto/manage-data.collections.py

@@ -212,7 +212,7 @@
 # Test
 collection = client.collections.use("Article")
 config = collection.config.get()
-assert config.vector_config["default"].vector_index_config.filter_strategy == "sweeping"
+assert config.vector_config["default"].vector_index_config.filter_strategy == "acorn"
 assert isinstance(
     config.vector_config["default"].vector_index_config, _VectorIndexConfigHNSW
 )

_includes/code/howto/manage-data.import.py

@@ -578,7 +578,7 @@ def add_object(obj) -> None:
 
 # highlight-start
 # Use `experimental` for server-side batching. The client will send data
-# in chunks and the server will dynamically manage the import process.
+# in batches at a rate specified by the server.
 with collection.batch.experimental() as batch:
     for data_row in data_rows:
         batch.add_object(

_includes/configuration/bq-compression-parameters.mdx

@@ -2,5 +2,5 @@
 | :---------------------- | :------ | :------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `bq` : `enabled`        | boolean | `false` | Enable BQ. Weaviate uses binary quantization (BQ) compression when `true`. <br/><br/> The Python client does not use the `enabled` parameter. To enable BQ with the v4 client, set a `quantizer` in the collection definition.                                              |
 | `bq` : `rescoreLimit`   | integer | -1      | The minimum number of candidates to fetch before rescoring.                                                                                                                                                                                                                 |
-| `bq` : `cache`          | boolean | `false` | Whether to use the vector cache.<br/> (only when using the `flat` vector index type)                                                                                                                                                                                        |
+| `bq` : `cache`          | boolean | `false` | Whether to cache the vectors in memory.<br/> (only when using the `flat` vector index type)                                                                                                                                                                                        |
 | `vectorCacheMaxObjects` | integer | `1e12`  | Maximum number of objects in the memory cache. By default, this limit is set to one trillion (`1e12`) objects when a new collection is created. For sizing recommendations, see [Vector cache considerations](/weaviate/concepts/vector-index#vector-cache-considerations). |

_includes/configuration/rq-compression-parameters.mdx

@@ -2,5 +2,5 @@
 | :---------------------- | :------ | :------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `rq`: `bits`            | integer | `8`     | The number of bits used to quantize each data point. Value can be `8` or `1`. <br/> <br/>Learn more about [8-bit](/weaviate/concepts/vector-quantization#8-bit-rq) and [1-bit](/weaviate/concepts/vector-quantization#1-bit-rq) RQ.                                         |
 | `rq`: `rescoreLimit`    | integer | `-1`    | The minimum number of candidates to fetch before rescoring.                                                                                                                                                                                                                 |
-| `rq` : `cache`          | boolean | `false` | Whether to use the vector cache.<br/> (only when using the `flat` vector index type)                                                                                                                                                                                        |
+| `rq` : `cache`          | boolean | `false` | Whether to cache the vectors in memory.<br/> (only when using the `flat` vector index type)                                                                                                                                                                                        |
 | `vectorCacheMaxObjects` | integer | `1e12`  | Maximum number of objects in the memory cache. By default, this limit is set to one trillion (`1e12`) objects when a new collection is created. For sizing recommendations, see [Vector cache considerations](/weaviate/concepts/vector-index#vector-cache-considerations). |

docs/weaviate/concepts/vector-quantization.md

@@ -118,7 +118,7 @@ When SQ is enabled, Weaviate boosts recall by over-fetching compressed results.
 
 ## Rotational quantization
 
-**Rotational quantization (RQ)** is a quantization technique that provides significant compression while maintaining high recall in internal testing. Unlike SQ, RQ requires no training phase and can be enabled immediately at index creation. RQ is available in two variants: **8-bit RQ** and **1-bit RQ**.
+**Rotational quantization (RQ)** provides significant compression while maintaining high recall. Unlike SQ, RQ requires no training phase and can be enabled immediately at index creation. RQ is available in: **8-bit** and **1-bit** variants.
 
 ### 8-bit RQ
 
@@ -202,7 +202,7 @@ You might be also interested in our blog post [HNSW+PQ - Exploring ANN algorithm
 
 ### With a flat index
 
-[RQ](#rotational-quantization) and [BQ](#binary-quantization) can use a [flat index](./indexing/inverted-index.md). A flat index search reads from disk, compression reduces the amount of data Weaviate has to read so searches are faster.
+[RQ](#rotational-quantization) and [BQ](#binary-quantization) can be applied to a [flat index](./indexing/inverted-index.md). As a flat index search is a brute-force method, compression reduces the amount of data Weaviate has to read and increases speed.
 
 ## Rescoring
 

pyproject.toml

@@ -17,6 +17,9 @@ dependencies = [
     "python-dotenv>=1.1.1",
     "tqdm>=4.67.1",
     "weaviate-agents>=0.12.0",
-    "weaviate-client>=4.16.6",
+    "weaviate-client",
     "weaviate-demo-datasets>=0.7.0",
 ]
+
+[tool.uv.sources]
+weaviate-client = { git = "https://github.com/weaviate/weaviate-python-client.git", rev = "dev/1.34" }

tests/docker-compose-anon-2.yml

@@ -8,7 +8,7 @@ services:
     - '8080'
     - --scheme
     - http
-    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0
+    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0-rc.0
     ports:
     - 8090:8080
     - 50061:50051

tests/docker-compose-anon-bind.yml

@@ -8,7 +8,7 @@ services:
     - '8080'
     - --scheme
     - http
-    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0
+    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0-rc.0
     ports:
     - 8380:8080
     - 50351:50051

tests/docker-compose-anon-clip.yml

@@ -8,7 +8,7 @@ services:
     - '8080'
     - --scheme
     - http
-    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0
+    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0-rc.0
     ports:
     - 8280:8080
     - 50251:50051

tests/docker-compose-anon-offload.yml

@@ -8,7 +8,7 @@ services:
     - '8080'
     - --scheme
     - http
-    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0
+    image: cr.weaviate.io/semitechnologies/weaviate:1.34.0-rc.0
     ports:
     - 8080:8080
     - 50051:50051