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docs/reference/index.md

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+# Reference
+
+```python
+from great_ai import *
+```
+
+## Core
+
+::: great_ai.GreatAI
+    options:
+        filters: ['!__init__']
+        show_root_heading: true
+
+::: great_ai.configure
+    options:
+        show_root_heading: true
+
+::: great_ai.save_model
+    options:
+        show_root_heading: true
+
+::: great_ai.use_model
+    options:
+        show_root_heading: true
+    
+::: great_ai.parameter
+    options:
+        show_root_heading: true
+
+::: great_ai.log_metric
+    options:
+        show_root_heading: true
+
+## Remote calls
+
+::: great_ai.call_remote_great_ai
+    options:
+        show_root_heading: true
+
+::: great_ai.call_remote_great_ai_async
+    options:
+        show_root_heading: true
+
+## Ground-truth
+
+::: great_ai.add_ground_truth
+    options:
+        show_root_heading: true
+
+::: great_ai.query_ground_truth
+    options:
+        show_root_heading: true
+
+::: great_ai.delete_ground_truth
+    options:
+        show_root_heading: true

docs/reference/large-file.md

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+# LargeFile
+
+```python
+from great_ai.large_file import *
+```

docs/reference/utilities.md

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+# Utilities
+
+```python
+from great_ai.utilities import *
+```
+
+## NLP tools
+
+Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.
+
+::: great_ai.utilities.clean
+::: great_ai.utilities.get_sentences
+::: great_ai.utilities.language.predict_language
+::: great_ai.utilities.language.is_english
+::: great_ai.utilities.language.english_name_of_language
+::: great_ai.utilities.evaluate_ranking.evaluate_ranking
+
+## Parallel processing
+
+Multiprocessing and multithreading-based parallelism with support for `async` functions. Its main purpose is to implement [great_ai.GreatAI.process_batch][], however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than [joblib](https://joblib.readthedocs.io/en/latest/parallel.html){ target=_blank } or [multiprocess](https://pypi.org/project/multiprocess/){ target=_blank }.
+
+::: great_ai.utilities.parallel_map.simple_parallel_map
+::: great_ai.utilities.parallel_map.parallel_map
+::: great_ai.utilities.parallel_map.threaded_parallel_map
+
+## Composable parallel processing
+
+Because both [threaded_parallel_map][great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map] and [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.
+
+For more info, check-out [the scraping guide](/how-to-guides/scraping).
+
+::: great_ai.utilities.chunk
+::: great_ai.utilities.unchunk
+
+## Operations
+
+::: great_ai.utilities.config_file.config_file
+::: great_ai.utilities.logger.get_logger

docs/reference/views.md

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+# View models
+
+::: great_ai.views.trace.Trace