FilterSearch -> QdrantSearch, Readme fixes

README.md

@@ -50,7 +50,7 @@ cd src/examples && poetry run jupyter lab
 
 ## How to use this in your project
 
-To install this as a dependency in your project, you will need to get a [Github access token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).
+To install this as a dependency in your project, you need a [Github access token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).
 
 Set your access token:
 
@@ -66,7 +66,7 @@ Let's install the package:
 pip install git+https://$GITHUB_TOKEN@github.com/aleph-alpha-intelligence-layer/intelligence-layer.git
 ```
 
-Now, the intelligence layer should be available and ready to use as a Python package.
+Now the Intelligence Layer should be available as a Python package and ready to use.
 
 ```py
 from intelligence_layer.core.task import Task

src/intelligence_layer/use_cases/classify/embedding_based_classify.py

@@ -53,8 +53,7 @@ class EmbeddingBasedClassify(Task[ClassifyInput, ClassifyOutput]):
         scoring: Configure how to calculate the final score.
 
     Attributes:
-        METADATA_LABEL_NAME: The metadata field for label name for the `InMemoryRetriever`
-            instance.
+        METADATA_LABEL_NAME: The metadata field name for 'label' in the retriever.
 
     Example:
         >>> labels_with_examples = [
@@ -101,7 +100,7 @@ def __init__(
             k=top_k_per_label,
             retriever_type=RetrieverType.SYMMETRIC,
         )
-        self._filter_search = QdrantSearch(retriever)
+        self._qdrant_search = QdrantSearch(retriever)
 
     def run(self, input: ClassifyInput, logger: DebugLogger) -> ClassifyOutput:
         self._validate_input_labels(input)
@@ -147,7 +146,7 @@ def _label_search(
                 ]
             ),
         )
-        return self._filter_search.run(search_input, logger)
+        return self._qdrant_search.run(search_input, logger)
 
     def _calculate_scores(
         self, results_per_label: Sequence[SearchOutput]

src/intelligence_layer/use_cases/search/qdrant_search.py

@@ -10,7 +10,7 @@
 
 
 class QdrantSearchInput(BaseModel):
-    """The input for a `FilterSearch` task.
+    """The input for a `QdrantSearch` task.
 
     Attributes:
         query: The text to be searched with.
@@ -39,8 +39,8 @@ class QdrantSearch(Task[QdrantSearchInput, SearchOutput]):
         >>>     )
         >>> ]
         >>> retriever = InMemoryRetriever(client, documents)
-        >>> task = FilterSearch(retriever)
-        >>> input = FilterSearchInput(
+        >>> task = QdrantSearch(retriever)
+        >>> input = QdrantSearchInput(
         >>>     query="When did East and West Germany reunite?"
         >>>     filter=models.Filter(
         >>>         must=[
@@ -51,7 +51,7 @@ class QdrantSearch(Task[QdrantSearchInput, SearchOutput]):
         >>>         ]
         >>>     )
         >>> )
-        >>> logger = InMemoryLogger(name="Filter Search")
+        >>> logger = InMemoryLogger(name="Qdrant Search")
         >>> output = task.run(input, logger)
     """
 

tests/use_cases/search/test_qdrant_search.py

@@ -33,7 +33,9 @@ def in_memory_retriever_documents() -> Sequence[Document]:
 
 
 @fixture
-def filter_search(asymmetric_in_memory_retriever: QdrantInMemoryRetriever) -> QdrantSearch:
+def filter_search(
+    asymmetric_in_memory_retriever: QdrantInMemoryRetriever,
+) -> QdrantSearch:
     return QdrantSearch(asymmetric_in_memory_retriever)