refactor: update env template

.env.template

@@ -28,11 +28,10 @@ EMBEDDING_ENDPOINT=""
 EMBEDDING_API_VERSION=""
 EMBEDDING_DIMENSIONS=3072
 EMBEDDING_MAX_TOKENS=8191
+EMBEDDING_BATCH_SIZE=36
 # If embedding key is not provided same key set for LLM_API_KEY will be used
 #EMBEDDING_API_KEY="your_api_key"
-# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch,
-#       Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models
-#EMBEDDING_BATCH_SIZE=2048
+
 
 # If using BAML structured output these env variables will be used
 BAML_LLM_PROVIDER=openai

cognee/infrastructure/loaders/external/advanced_pdf_loader.py

@@ -14,14 +14,6 @@ from cognee.infrastructure.loaders.external.pypdf_loader import PyPdfLoader
 
 logger = get_logger(__name__)
 
-try:
-    from unstructured.partition.pdf import partition_pdf
-except ImportError as e:
-    logger.info(
-        "unstructured[pdf] not installed, can't use AdvancedPdfLoader, will use PyPdfLoader instead."
-    )
-    raise ImportError from e
-
 
 @dataclass
 class _PageBuffer:
@@ -88,6 +80,8 @@ class AdvancedPdfLoader(LoaderInterface):
                 **kwargs,
             }
             # Use partition to extract elements
+            from unstructured.partition.pdf import partition_pdf
+
             elements = partition_pdf(**partition_kwargs)
 
             # Process elements into text content