Fix docs

docs/how-to-guides/create-service.md

@@ -66,7 +66,7 @@ from great_ai import GreatAI, use_model
 def type_safe_greeter(your_name: str, model) -> str:
     return f'Hi {your_name}!'
 
-assert type_safe_greeter('Andras').output == 'Hi Andras'
+assert type_safe_greeter('Andras').output == 'Hi Andras!'
 ```
 
 1. By default, the parameter named `model` will be replaced by the loaded model. This behaviour can be customised by setting the `model_kwarg_name`. This way, even multiple models can be injected into a single function.
@@ -82,14 +82,14 @@ If you wish to turn off logging or specify custom validation for your parameters
     By default, all parameters that are not affected by an explicit [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] are automatically decorated with [@parameter][great_ai.parameter] when [@GreatAI.create][great_ai.GreatAI.create] is called.
 
 ```python title="greeter_with_validation.py"
-from great_ai import GreatAI, use_model
+from great_ai import GreatAI, parameter
 
 @GreatAI.create
 @parameter('your_name', disable_logging=True)
-def type_safe_greeter(your_name: str, model) -> str:
+def type_safe_greeter(your_name: str) -> str:
     return f'Hi {your_name}!'
 
-assert type_safe_greeter('Andras').output == 'Hi Andras'
+assert type_safe_greeter('Andras').output == 'Hi Andras!'
 ```
 
 !!! important

docs/how-to-guides/large-file.md

@@ -1,6 +1,6 @@
 # How to use LargeFiles
 
-The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](https://great-ai.schmelczer.dev/reference/large-file), users have few reasons to use LargeFiles directly.
+The functions [save_model][great_ai.save_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](https://great-ai.schmelczer.dev/reference/large-file), users have few reasons to use LargeFiles directly.
 
 ## Motivation
 
@@ -16,12 +16,12 @@ Often, especially when working with data-heavy applications, large files can pro
     ```python
     from great_ai.large_file import LargeFileS3
 
-    LargeFileS3.configure_credentials({
-        "aws_region_name": "your_region_like_eu-west-2",
-        "aws_access_key_id": "YOUR_ACCESS_KEY_ID",
-        "aws_secret_access_key": "YOUR_VERY_SECRET_ACCESS_KEY",
-        "large_files_bucket_name": "create_a_bucket_and_put_its_name_here",
-    })
+    LargeFileS3.configure_credentials(
+        aws_region_name="your_region_like_eu-west-2",
+        aws_access_key_id="YOUR_ACCESS_KEY_ID",
+        aws_secret_access_key="YOUR_VERY_SECRET_ACCESS_KEY",
+        large_files_bucket_name="create_a_bucket_and_put_its_name_here",
+    )
 
     # Creates a new version and deletes the older version 
     # leaving the three most recently used intact
@@ -44,6 +44,8 @@ Often, especially when working with data-heavy applications, large files can pro
     The local cache can be configured with these properties:
 
     ```python
+    from pathlib import Path
+
     LargeFileS3.cache_path = Path('.cache')
     LargeFileS3.max_cache_size = "30 GB"
     ```

docs/notebook_support.py

@@ -4,16 +4,21 @@ import json
 import os
 from collections import Counter
 from pathlib import Path
-from typing import List, Optional
+from typing import Dict, List, Optional
+
+ROOT = Path(__file__).resolve().parents[1]
+DATA_DIR = ROOT / "docs/tutorial/data"
+SURVEY_DIR = ROOT / "docs/surveys"
+FULL_RUN = os.environ.get("GREAT_AI_NOTEBOOK_FULL") == "1"
 
 
-def setup_notebook(root: Path, workflow: str) -> Path:
+def setup_notebook(workflow: str) -> Path:
     """Keep related notebooks' models and traces together, outside source files."""
     from great_ai import ParallelTinyDbDriver, configure
     from great_ai.large_file import LargeFileLocal
 
-    base = Path(os.environ.get("GREAT_AI_NOTEBOOK_WORKDIR", root / ".cache/notebooks"))
-    suffix = "-full" if os.environ.get("GREAT_AI_NOTEBOOK_FULL") == "1" else ""
+    base = Path(os.environ.get("GREAT_AI_NOTEBOOK_WORKDIR", ROOT / ".cache/notebooks"))
+    suffix = "-full" if FULL_RUN else ""
     work_dir = (base / (workflow + suffix)).resolve()
     work_dir.mkdir(parents=True, exist_ok=True)
     LargeFileLocal.cache_path = work_dir / "models-cache"
@@ -43,7 +48,7 @@ def mag_lines(path: Path, per_label: Optional[int] = None) -> List[str]:
 
 def demo_annotations() -> List[dict]:
     """Synthetic annotations for exercising the pipeline, not measuring quality."""
-    annotations = [{}, {}]
+    annotations: List[Dict[str, int]] = [{}, {}]
     for index, topic in enumerate(
         ["protein", "cancer", "solar", "quantum", "neural", "genome"]
     ):

docs/tutorial/index.md

@@ -40,7 +40,7 @@ After training and evaluating a model, it is exported using [great_ai.save_model
     ```python
     from great_ai.large_file import LargeFileS3
 
-    LargeFileS3.configure(
+    LargeFileS3.configure_credentials(
         aws_region_name='eu-west-2',
         aws_access_key_id='MY_AWS_ACCESS_KEY',
         aws_secret_access_key='MY_AWS_SECRET_KEY',