improvements from PR review

integrations/pinecone/pyproject.toml

@@ -11,7 +11,7 @@ requires-python = ">=3.8"
 license = "Apache-2.0"
 keywords = []
 authors = [
-  { name = "John Doe", email = "[email protected]" },
+  { name = "deepset GmbH", email = "[email protected]" },
 ]
 classifiers = [
   "Development Status :: 4 - Beta",

integrations/pinecone/src/pinecone_haystack/document_store.py

@@ -8,7 +8,7 @@
 
 import pandas as pd
 import pinecone
-from haystack import default_from_dict, default_to_dict
+from haystack import default_to_dict
 from haystack.dataclasses import Document
 from haystack.document_stores import DuplicatePolicy
 
@@ -53,7 +53,7 @@ def __init__(
             [API reference](https://docs.pinecone.io/reference/create_index-1).
 
         """
-        api_key = api_key or os.environ.get("PINECONE_API_KEY", None)
+        api_key = api_key or os.environ.get("PINECONE_API_KEY")
         if not api_key:
             msg = (
                 "PineconeDocumentStore expects a Pinecone API key. "
@@ -64,10 +64,22 @@ def __init__(
         pinecone.init(api_key=api_key, environment=environment)
 
         if index not in pinecone.list_indexes():
+            logger.info(f"Index {index} does not exist. Creating a new index.")
             pinecone.create_index(name=index, dimension=dimension, **index_creation_kwargs)
+        else:
+            logger.info(f"Index {index} already exists. Connecting to it.")
 
         self._index = pinecone.Index(index_name=index)
-        self.dimension = self._index.describe_index_stats()["dimension"]
+
+        actual_dimension = self._index.describe_index_stats().get("dimension")
+        if actual_dimension and actual_dimension != dimension:
+            logger.warning(
+                f"Dimension of index {index} is {actual_dimension}, but {dimension} was specified. "
+                "The specified dimension will be ignored."
+                "If you need an index with a different dimension, please create a new one."
+            )
+        self.dimension = actual_dimension or dimension
+
         self._dummy_vector = [0.0] * self.dimension
         self.environment = environment
         self.index = index
@@ -86,10 +98,6 @@ def to_dict(self) -> Dict[str, Any]:
             **self.index_creation_kwargs,
         )
 
-    @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "PineconeDocumentStore":
-        return default_from_dict(cls, data)
-
     def count_documents(self) -> int:
         """
         Returns how many documents are present in the document store.
@@ -110,10 +118,12 @@ def write_documents(self, documents: List[Document], policy: DuplicatePolicy = D
 
         :return: The number of documents written to the document store.
         """
-        if len(documents) > 0:
+        try:
             if not isinstance(documents[0], Document):
-                msg = "param 'documents' must contain a list of objects of type Document"
-                raise ValueError(msg)
+                raise TypeError()
+        except (TypeError, KeyError) as e:
+            msg = "param 'documents' must contain a list of objects of type Document"
+            raise TypeError(msg) from e
 
         if policy not in [DuplicatePolicy.NONE, DuplicatePolicy.OVERWRITE]:
             logger.warning(
@@ -139,8 +149,9 @@ def write_documents(self, documents: List[Document], policy: DuplicatePolicy = D
                 doc_for_pinecone["metadata"]["dataframe"] = document.dataframe.to_json()
             if document.blob is not None:
                 logger.warning(
-                    f"Document {document.id} has a blob. Currently, storing blob in Pinecone is not supported. "
-                    "The blob will be ignored."
+                    f"Document {document.id} has the `blob` field set, but storing `ByteStream` "
+                    "objects in Pinecone is not supported. "
+                    "The content of the `blob` field will be ignored."
                 )
 
             documents_for_pinecone.append(doc_for_pinecone)

integrations/pinecone/tests/conftest.py

@@ -1,5 +1,4 @@
 import time
-from random import randint
 
 import pytest
 
@@ -23,7 +22,7 @@ def document_store(request):
     environment = "gcp-starter"
     index = "default"
     # Use a different namespace for each test so we can run them in parallel
-    namespace = f"{request.node.name}-{randint(0, 1000)}"  # noqa: S311  Ruff complains about using random numbers for cryptographic purposes
+    namespace = f"{request.node.name}-{int(time.time())}"
     dimension = 10
 
     store = PineconeDocumentStore(

integrations/pinecone/tests/test_document_store.py

@@ -1,6 +1,7 @@
 import time
 from unittest.mock import patch
 
+import pytest
 from haystack import Document
 
 from pinecone_haystack.document_store import PineconeDocumentStore
@@ -28,6 +29,31 @@ def test_init(self, mock_pinecone):
         assert document_store.dimension == 30
         assert document_store.index_creation_kwargs == {"metric": "euclidean"}
 
+    @patch("pinecone_haystack.document_store.pinecone")
+    def test_init_api_key_in_environment_variable(self, monkeypatch):
+        monkeypatch.setenv("PINECONE_API_KEY", "fake-api-key")
+
+        PineconeDocumentStore(
+            environment="gcp-starter",
+            index="my_index",
+            namespace="test",
+            batch_size=50,
+            dimension=30,
+            metric="euclidean",
+        )
+
+        assert True
+
+    def test_init_fails_wo_api_key(self, monkeypatch):
+        api_key = None
+        monkeypatch.delenv("PINECONE_API_KEY", raising=False)
+        with pytest.raises(ValueError):
+            PineconeDocumentStore(
+                api_key=api_key,
+                environment="gcp-starter",
+                index="my_index",
+            )
+
     @patch("pinecone_haystack.document_store.pinecone")
     def test_to_dict(self, mock_pinecone):
         mock_pinecone.Index.return_value.describe_index_stats.return_value = {"dimension": 30}
@@ -52,30 +78,6 @@ def test_to_dict(self, mock_pinecone):
             },
         }
 
-    @patch("pinecone_haystack.document_store.pinecone")
-    def test_from_dict(self, mock_pinecone):
-        mock_pinecone.Index.return_value.describe_index_stats.return_value = {"dimension": 30}
-
-        data = {
-            "type": "pinecone_haystack.document_store.PineconeDocumentStore",
-            "init_parameters": {
-                "environment": "gcp-starter",
-                "index": "my_index",
-                "dimension": 30,
-                "namespace": "test",
-                "batch_size": 50,
-                "metric": "euclidean",
-            },
-        }
-
-        document_store = PineconeDocumentStore.from_dict(data)
-        assert document_store.environment == "gcp-starter"
-        assert document_store.index == "my_index"
-        assert document_store.namespace == "test"
-        assert document_store.batch_size == 50
-        assert document_store.dimension == 30
-        assert document_store.index_creation_kwargs == {"metric": "euclidean"}
-
     def test_embedding_retrieval(self, document_store: PineconeDocumentStore, sleep_time):
         docs = [
             Document(content="Most similar document", embedding=[1.0] * 10),

integrations/pinecone/tests/test_write.py

@@ -31,10 +31,10 @@ def test_write_documents_duplicate_overwrite(self, document_store: PineconeDocum
         time.sleep(sleep_time)
         self.assert_documents_are_equal(document_store.filter_documents(), [doc1])
 
-    @pytest.mark.skip(reason="Qdrant only supports UPSERT operations")
+    @pytest.mark.skip(reason="Pinecone only supports UPSERT operations")
     def test_write_documents_duplicate_fail(self, document_store: PineconeDocumentStore):
         ...
 
-    @pytest.mark.skip(reason="Qdrant only supports UPSERT operations")
+    @pytest.mark.skip(reason="Pinecone only supports UPSERT operations")
     def test_write_documents_duplicate_skip(self, document_store: PineconeDocumentStore):
         ...