rename astraretriever (#399)

integrations/astra/README.md

@@ -42,7 +42,7 @@ or
 
 ## Usage
 
-This package includes Astra Document Store and Astra Retriever classes that integrate with Haystack, allowing you to easily perform document retrieval or RAG with Astra, and include those functions in Haystack pipelines.
+This package includes Astra Document Store and Astra Embedding Retriever classes that integrate with Haystack, allowing you to easily perform document retrieval or RAG with Astra, and include those functions in Haystack pipelines.
 
 ### In order to use the Document Store directly:
 
@@ -78,7 +78,7 @@ document_store = AstraDocumentStore(
 Then you can use the document store functions like count_document below:
 `document_store.count_documents()`
 
-### Using the Astra Retriever with Haystack Pipelines
+### Using the Astra Embedding Retriever with Haystack Pipelines
 
 Create the Document Store object like above, then import and create the Pipeline:
 
@@ -87,8 +87,8 @@ from haystack.preview import Pipeline
 pipeline = Pipeline()
 ```
 
-Add your AstraRetriever into the pipeline
-`pipeline.add_component(instance=AstraSingleRetriever(document_store=document_store), name="retriever")`
+Add your AstraEmbeddingRetriever into the pipeline
+`pipeline.add_component(instance=AstraEmbeddingRetriever(document_store=document_store), name="retriever")`
 
 Add other components and connect them as desired. Then run your pipeline:
 `pipeline.run(...)`

integrations/astra/examples/example.py

@@ -10,7 +10,7 @@
 from haystack.components.writers import DocumentWriter
 from haystack.document_stores.types import DuplicatePolicy
 
-from haystack_integrations.components.retrievers.astra import AstraRetriever
+from haystack_integrations.components.retrievers.astra import AstraEmbeddingRetriever
 from haystack_integrations.document_stores.astra import AstraDocumentStore
 
 logger = logging.getLogger(__name__)
@@ -66,7 +66,7 @@
     instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
-q.add_component("retriever", AstraRetriever(document_store))
+q.add_component("retriever", AstraEmbeddingRetriever(document_store))
 
 q.connect("embedder", "retriever")
 

integrations/astra/examples/pipeline_example.py

@@ -9,7 +9,7 @@
 from haystack.components.writers import DocumentWriter
 from haystack.document_stores.types import DuplicatePolicy
 
-from haystack_integrations.components.retrievers.astra import AstraRetriever
+from haystack_integrations.components.retrievers.astra import AstraEmbeddingRetriever
 from haystack_integrations.document_stores.astra import AstraDocumentStore
 
 logger = logging.getLogger(__name__)
@@ -77,7 +77,7 @@
     instance=SentenceTransformersTextEmbedder(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2"),
     name="embedder",
 )
-rag_pipeline.add_component(instance=AstraRetriever(document_store=document_store), name="retriever")
+rag_pipeline.add_component(instance=AstraEmbeddingRetriever(document_store=document_store), name="retriever")
 rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
 rag_pipeline.add_component(instance=OpenAIGenerator(api_key=os.environ.get("OPENAI_API_KEY")), name="llm")
 rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")

integrations/astra/src/haystack_integrations/components/retrievers/astra/__init__.py

@@ -1,6 +1,6 @@
 # SPDX-FileCopyrightText: 2023-present Anant Corporation <[email protected]>
 #
 # SPDX-License-Identifier: Apache-2.0
-from .retriever import AstraRetriever
+from .retriever import AstraEmbeddingRetriever
 
-__all__ = ["AstraRetriever"]
+__all__ = ["AstraEmbeddingRetriever"]

integrations/astra/src/haystack_integrations/components/retrievers/astra/retriever.py

@@ -10,14 +10,14 @@
 
 
 @component
-class AstraRetriever:
+class AstraEmbeddingRetriever:
     """
     A component for retrieving documents from an AstraDocumentStore.
     """
 
     def __init__(self, document_store: AstraDocumentStore, filters: Optional[Dict[str, Any]] = None, top_k: int = 10):
         """
-        Create an AstraRetriever component. Usually you pass some basic configuration
+        Create an AstraEmbeddingRetriever component. Usually you pass some basic configuration
         parameters to the constructor.
 
         :param filters: A dictionary with filters to narrow down the search space (default is None).
@@ -59,7 +59,7 @@ def to_dict(self) -> Dict[str, Any]:
         )
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "AstraRetriever":
+    def from_dict(cls, data: Dict[str, Any]) -> "AstraEmbeddingRetriever":
         document_store = AstraDocumentStore.from_dict(data["init_parameters"]["document_store"])
         data["init_parameters"]["document_store"] = document_store
         return default_from_dict(cls, data)

integrations/astra/tests/test_retriever.py

@@ -5,7 +5,7 @@
 
 import pytest
 
-from haystack_integrations.components.retrievers.astra import AstraRetriever
+from haystack_integrations.components.retrievers.astra import AstraEmbeddingRetriever
 
 
 @pytest.mark.skipif(
@@ -14,9 +14,9 @@
 @pytest.mark.skipif(os.environ.get("ASTRA_DB_ID", "") == "", reason="ASTRA_DB_ID is not set")
 @pytest.mark.integration
 def test_retriever_to_json(document_store):
-    retriever = AstraRetriever(document_store, filters={"foo": "bar"}, top_k=99)
+    retriever = AstraEmbeddingRetriever(document_store, filters={"foo": "bar"}, top_k=99)
     assert retriever.to_dict() == {
-        "type": "haystack_integrations.components.retrievers.astra.retriever.AstraRetriever",
+        "type": "haystack_integrations.components.retrievers.astra.retriever.AstraEmbeddingRetriever",
         "init_parameters": {
             "filters": {"foo": "bar"},
             "top_k": 99,
@@ -43,7 +43,7 @@ def test_retriever_to_json(document_store):
 @pytest.mark.integration
 def test_retriever_from_json():
     data = {
-        "type": "haystack_integrations.components.retrievers.astra.retriever.AstraRetriever",
+        "type": "haystack_integrations.components.retrievers.astra.retriever.AstraEmbeddingRetriever",
         "init_parameters": {
             "filters": {"bar": "baz"},
             "top_k": 42,
@@ -62,6 +62,6 @@ def test_retriever_from_json():
             },
         },
     }
-    retriever = AstraRetriever.from_dict(data)
+    retriever = AstraEmbeddingRetriever.from_dict(data)
     assert retriever.top_k == 42
     assert retriever.filters == {"bar": "baz"}