Superduper renaming superduper

docs/content/ai_integrations/transformers.md

@@ -50,7 +50,7 @@ For a fully worked out training/ fine-tuning use-case refer to the [use-cases se
 
 ### LLM fine-tuning
 
-superduper provides a convenient fine-tuning method based on the [trl](https://huggingface.co/docs/trl/index) framework to help you train data in the database.
+`superduper` provides a convenient fine-tuning method based on the [trl](https://huggingface.co/docs/trl/index) framework to help you train data in the database.
 
 ### Supported Features
 

docs/content/api/base/superduper.md

@@ -13,7 +13,7 @@ superduper(item: Optional[Any] = None,
 | item | A database or model |
 | kwargs | Additional keyword arguments to pass to the component |
 
-Superduper API to automatically wrap an object to a db or a component.
+`superduper` API to automatically wrap an object to a db or a component.
 
 Attempts to automatically wrap an item in a superduper.ioponent by
 using duck typing to recognize it.

docs/content/api/components/component.md

@@ -58,8 +58,8 @@ Component(self,
 | uuid | UUID of the leaf. |
 | artifacts | A dictionary of artifacts paths and `DataType` objects |
 
-Base class for all components in superduper.
+Base class for all components in `superduper`.
 
-Class to represent superduper serializable entities
+Class to represent `superduper` serializable entities
 that can be saved into a database.
 

docs/content/apply_api/checkpoint.md

@@ -1,6 +1,6 @@
 # `Checkpoint`
 
-- Save intermediate results of training via superduper
+- Save intermediate results of training via `superduper`
 - Load a different point of the training process by specifying `Checkpoint` explicitly
 - Particularly useful with deep-learning models
 

docs/content/apply_api/cron_job.md

@@ -1,7 +1,7 @@
 # `Cron Job`
 
 - Iterate computations, queries and actions on a crontab
-- Can be deployed on superduper Enterprise
+- Can be deployed on Superduper Enterprise
 
 ***Usage pattern***
 

docs/content/apply_api/model.md

@@ -1,6 +1,6 @@
 # `Model`
 
-- Wrap a standard AI model with functionality necessary for superduper
+- Wrap a standard AI model with functionality necessary for `superduper`
 - Configure validation and training of a model on database data
 
 ***Dependencies***
@@ -28,7 +28,7 @@ by adding the parameters `datatype=...` or `output_schema=...`.
 
 ## Implementations
 
-Here are a few superduper native implementations:
+Here are a few `superduper` native implementations:
 
 **`ObjectModel`**
 
@@ -47,7 +47,7 @@ db.apply(m)
 
 **`QueryModel`**
 
-Use a superduper query to extract data from `db`
+Use a `superduper` query to extract data from `db`
 
 ```python
 from superduper.components.model import QueryModel

docs/content/apply_api/plugin.md

@@ -2,7 +2,7 @@
 
 - Supports a plugin system that dynamically loads Python modules and packages at runtime.
 - Supports functioning as subcomponents, providing dependency support for custom models and other local code.
-- Capable of applying to a database and storing in the artifact_store, exporting as a superduper format package for sharing with others.
+- Capable of applying to a database and storing in the artifact_store, exporting as a `superduper` format package for sharing with others.
 - Supports automatic installation of dependencies listed in the requirements.txt file under the plugin.
 
 ***Usage pattern***
@@ -86,15 +86,15 @@ db.apply(model)
 
 Initialization and installation
 
-- During plugin initialization, superduper loads the component’s Python module or package into `sys.modules`, allowing subsequent use of native import statements for plugin utilization.
+- During plugin initialization, `superduper` loads the component’s Python module or package into `sys.modules`, allowing subsequent use of native import statements for plugin utilization.
 - If the plugin package includes a `requirements.txt`, dependencies are installed prior to loading the Python code.
 - The plugin is installed only once per process; if it detects that the same plugin has already been installed in the current runtime, the installation is skipped.
 
 Usage
 
-- When exported locally, the plugin component saves all necessary dependency files for the plugins into the superduper package, allowing for sharing to different locations.
+- When exported locally, the plugin component saves all necessary dependency files for the plugins into the `superduper` package, allowing for sharing to different locations.
 - When executing `db.apply(plugin)`, the necessary Python dependency environment files for the plugin are saved in the artifact_store. During `db.load("plugin", "plugin_identifier")`, these files are downloaded to the local `~/.superduper/plugin/` directory, followed by the initialization and installation of the plugin.
-- As a sub-component, superduper’s encode and decode logic ensures that the plugin is loaded prior to the parent component to maintain dependency integrity.
+- As a sub-component, Superduper’s encode and decode logic ensures that the plugin is loaded prior to the parent component to maintain dependency integrity.
 
 
 ***See also***

docs/content/apply_api/template.md

@@ -5,7 +5,7 @@
 - `Template` is leveraged by `Application`.
 - Snapshot allows users to know that their validation comparisons are apples-to-apples
 - A `Template` is useful for sharing, migrating and distributing AI components
-- A `Template` may be applied to any superduper deployment
+- A `Template` may be applied to any Superduper deployment
 
 ***Usage pattern***
 

docs/content/apply_api/trigger.md

@@ -2,7 +2,7 @@
 
 - Listen for update, inserts and deletes
 - Take a specific action contigent on these changes
-- Can be deployed on superduper Enterprise
+- Can be deployed on Superduper Enterprise
 
 ***Usage pattern***
 

docs/content/core_api/apply.md

@@ -26,7 +26,7 @@ db.apply(component)
 A `Model` is a wrapper around a standard ML/ AI model. It may contain additional functionality, such as 
 pre- and post-processing, and encoding/ decoding data into/ from the correct type required by the database.
 
-`db.apply(model)` tells superduper to store the model and its metadata in the system.
+`db.apply(model)` tells `superduper` to store the model and its metadata in the system.
 
 If additional configurations, such as training parameters, are added to the `Model` then the `db.apply` command
 will also train the component on data in superduper.
@@ -35,13 +35,13 @@ Read more about `Model` [here](../apply_api/model).
 
 ### `Listener`
 
-A `Listener` wraps a `Model`. The `db.apply(listener)` tells superduper to "listen" for incoming data and to compute outputs on those data, saving them back in superduper.
+A `Listener` wraps a `Model`. The `db.apply(listener)` tells `superduper` to "listen" for incoming data and to compute outputs on those data, saving them back in `superduper`.
 
 Read more about `Listener` [here](../apply_api/listener).
 
 ### `VectorIndex`
 
-A `VectorIndex` wraps one or two `Listener` components, and tells superduper that the outputs computed, should
+A `VectorIndex` wraps one or two `Listener` components, and tells `superduper` that the outputs computed, should
 be made searchable via vector-search queries.
 
 Read more about `VectorIndex` [here](../apply_api/vector_index).

docs/content/core_api/connect.md

@@ -1,6 +1,6 @@
 # Connect
 
-The standard way to connect to superduper is via the `superduper` decorator:
+The standard way to connect to Superduper is via the `superduper` decorator:
 
 ## Development mode
 
@@ -23,7 +23,7 @@ For a semi-exhaustive list of possible connections see [here](../reusable_snippe
 
 ### Fine grained configuration
 
-superduper chooses default `artifact_store` (file blob storage) and `metadata_store` (AI metadata) values for your connection. These defaults may be overridden directly:
+`superduper` chooses default `artifact_store` (file blob storage) and `metadata_store` (AI metadata) values for your connection. These defaults may be overridden directly:
 
 ```python
 db = superduper(
@@ -66,4 +66,4 @@ metadata_store: <metadata-store-uri>
 ## Next steps
 
 `db` is now your connection to your data, models, and model meta-data.
-Now that you have established this connection you are ready to build, deploy and manage AI with superduper.
+Now that you have established this connection you are ready to build, deploy and manage AI with Superduper.

docs/content/data_integrations/intro.md

@@ -1,6 +1,6 @@
 # Data integrations
 
-superduper integrates with 3 types of data-backend:
+Superduper integrates with 3 types of data-backend:
 
 - NoSQL
     - [MongoDB Community Edition](https://www.mongodb.com/try/download/community)
@@ -17,7 +17,7 @@ superduper integrates with 3 types of data-backend:
     - [Pandas](https://pandas.pydata.org/docs/)
 
 Although these data-backends provide very different functionality, 
-with superduper they are accessible via a uniform API.
+with Superduper they are accessible via a uniform API.
 
 However, developers should bear in mind that there are a few 
 differences between MongoDB and SQL data-backends.

docs/content/execute_api/basic_insertion.md

@@ -1,6 +1,6 @@
 # Basic insertion
 
-superduper supports inserting data wrapped as dictionaries in Python.
+Superduper supports inserting data wrapped as dictionaries in Python.
 These dictionaries may contain basic JSON-compatible data, but also 
 other data-types to be handled with `DataType` components. All data inserts are wrapped with the `Document` wrapper:
 

docs/content/execute_api/data_encodings_and_schemas.md

@@ -1,6 +1,6 @@
 # (Optional) Setting up tables and encodings
 
-`superduper` has flexible support for data-types. In both MongoDB and SQL databases,
+Superduper has flexible support for data-types. In both MongoDB and SQL databases,
 one can uses `superduper.DataType` to define one's own data-types.
 
 If no-datatypes are provided, `superduper` [uses fallbacks](./auto_data_types.md) to encode and decode data.

docs/content/execute_api/inserting_data.md

@@ -1,6 +1,6 @@
 # Data inserts
 
-superduper allows developers to insert data from a variety of sources, 
+Superduper allows developers to insert data from a variety of sources, 
 encoding and decoding objects, such as images and videos, not usually handled 
 explicitly by the `db.databackend`.
 

docs/content/execute_api/mongodb_queries.md

@@ -1,6 +1,6 @@
 # MongoDB select queries
 
-superduper supports the `pymongo` query API to build select queries.
+Superduper supports the `pymongo` query API to build select queries.
 There is one slight difference however, since queries built with `pymongo`'s formalism
 are executed lazily:
 

docs/content/execute_api/native_vector_search.md

@@ -19,7 +19,7 @@ export SUPERDUPER_CLUSTER_VECTOR_SEARCH_TYPE=native
 If `superduper` detects this configuration, it uses the inbuilt mechanism 
 of your `db.databackend` to perform the vector-comparison.
 
-Currently `superduper` supports the native implementation of these databases:
+Currently Superduper supports the native implementation of these databases:
 
 - MongoDB Atlas
 

docs/content/execute_api/overview.md

@@ -1,11 +1,11 @@
 # Execute API
 
-superduper implements 2 main classes of `db.databackend`:
+Superduper implements 2 main classes of `db.databackend`:
 
 - [MongoDB](../data_integrations/mongodb)
 - [SQL backends](../data_integrations/sql)
 
-Correspondingly, superduper currently has 2 flavours of query API:
+Correspondingly, Superduper currently has 2 flavours of query API:
 
 - [`pymongo`](https://pymongo.readthedocs.io/en/stable/)
 - [`ibis`](https://ibis-project.org/)

docs/content/execute_api/referring_to_data_from_diverse_sources.md

@@ -4,8 +4,8 @@ sidebar_position: 15
 
 # Working with external data sources
 
-superduper supports data added from external data-sources.
-When doing this, `superduper` supports:
+Superduper supports data added from external data-sources.
+When doing this, Superduper supports:
 
 - web URLs
 - URIs of objects in `s3` buckets

docs/content/execute_api/sidecar_index_vector_search.md

@@ -1,6 +1,6 @@
 # Sidecar vector-comparison integration
 
-For databases which don't have their own vector-search implementation, `superduper` offers 
+For databases which don't have their own vector-search implementation, Superduper offers 
 2 integrations:
 
 - In memory vector-search

docs/content/execute_api/sql_queries.md

@@ -1,8 +1,8 @@
 # SQL select queries
 
-In order to support as many data-backends as possible, superduper supports the `ibis` query API to build SQL queries.
+In order to support as many data-backends as possible, Superduper supports the `ibis` query API to build SQL queries.
 
-With `superduper` one would write:
+With Superduper one would write:
 
 ```python
 t = db['my_table']

docs/content/execute_api/using_hybrid_storage_to_handle_large_data_blobs.md

@@ -10,7 +10,7 @@ Some applications require large data-blobs and objects, which are either larger
 - large audio
 - videos
 
-In order to handle such data, superduper provides a few options when 
+In order to handle such data, Superduper provides a few options when 
 creating a `DataType` via the `encodable` parameter.
 
 ## Artifact store reference with `encodable='artifact'`
@@ -75,7 +75,7 @@ You may verify that with this command:
 iconv -f ISO-8859-1 -t UTF-8 artifacts/866cf8526595d3620d6045172fb16d1efefac4b1
 ```
 
-The superduper query reloads the data and passes it to the query result, 
+The Superduper query reloads the data and passes it to the query result, 
 without any user intervention.
 
 ## Just-in-time loading with `encodable='lazy_artifact'`:

docs/content/execute_api/vector_search.md

@@ -1,6 +1,6 @@
 # Vector-search
 
-superduper aims to provide first-class support for 
+Superduper aims to provide first-class support for 
 vector-search, including embedding computation in preparation
 and run-time, as well as executing the fast vector-comparison 
 and returning results in a way compatible with standard database

docs/content/fundamentals/data_transmision.md

@@ -14,7 +14,7 @@ In AI, typical types of data are:
 - **...bespoke in house data**
 
 Most databases don't support any data other than numbers and text.
-superduper enables the use of these more interesting data-types using the `Document` wrapper.
+Superduper enables the use of these more interesting data-types using the `Document` wrapper.
 
 ### `Document`
 

docs/content/fundamentals/datalayer_overview.md

@@ -4,17 +4,17 @@ sidebar_position: 3
 
 # Datalayer
 
-The `Datalayer` is the principle point of entry in `superduper` for:
+The `Datalayer` is the principle point of entry in Superduper for:
 
 - Communicating with the database
 - Instructing models and other components to work together with the database
-- Accessing and storing meta-data about your `superduper` models and data
+- Accessing and storing meta-data about your Superduper models and data
 
 Technically, the `Datalayer` "wires together" several important backends involved in the AI workflow:
 
 - Querying the database via the **databackend**
 - Storing and retrieving serialized model-weights and other artifacts from the **artifact store**
-- Storing and retrieval important meta-data, from the **meta-data store** and information about models and other components which are to be installed with `superduper`
+- Storing and retrieval important meta-data, from the **meta-data store** and information about models and other components which are to be installed with Superduper
 - Performing computations over the data in the **databackend** using the models saved in the **artifact store**
 
 ```python
@@ -196,4 +196,4 @@ Validate your components (mostly models)
 
 ### `db.predict`
 
-Infer predictions from models hosted by `superduper`. Read more about this and about models [here](../apply_api/model.md).
+Infer predictions from models hosted by Superduper. Read more about this and about models [here](../apply_api/model.md).

docs/content/fundamentals/design.md

@@ -1,12 +1,12 @@
 # Architecture
 
-Here is a schematic of the `superduper` design.
+Here is a schematic of the Superduper design.
 
 ![](/img/light.png)
 
 ### Explanation
 
-1. `superduper` expects data and components to be added/ updated from a range of client-side mechanisms: **scripts**, **apps**, **notebooks** or **third-party database clients** (possibly non-python).
+1. Superduper expects data and components to be added/ updated from a range of client-side mechanisms: **scripts**, **apps**, **notebooks** or **third-party database clients** (possibly non-python).
 
 1. Users and programs can add **components** (**models**, data **encoders**, **vector-indexes** and more) from the client-side. These large items are stored in the **artifact-store** and are tracked via the **meta-data** store.
 

docs/content/fundamentals/overview.md

@@ -1,5 +1,5 @@
 # Fundamentals
 
-In this section, we try and guide developers through the principles and algorithms underlying `superduper`.
+In this section, we try and guide developers through the principles and algorithms underlying Superduper.
 
 For a more "how-to" approach refer first to the [tutorials](../tutorials/intro.md), [use-cases](../../use_cases), [reusable snippets](../reusable_snippets/), and to [core api](../core_api/) usage.

docs/content/fundamentals/vector_search_algorithm.md

@@ -4,12 +4,12 @@ sidebar_position: 7
 
 # Vector-search
 
-superduper allows users to implement vector-search in their database by either 
+Superduper allows users to implement vector-search in their database by either 
 using in-database functionality, or via a sidecar implementation with `lance` and `FastAPI`.
 
 ## Philosophy
 
-In `superduper`, from a user point-of-view vector-search isn't a completely different beast than other ways of 
+In Superduper, from a user point-of-view vector-search isn't a completely different beast than other ways of 
 using the system:
 
 - The vector-preparation is exactly the same as preparing outputs with any model, 

docs/content/get_started/configuration.md

@@ -6,7 +6,7 @@ tags:
 
 # Configure
 
-superduper provides a range of configurable options for setting
+Superduper provides a range of configurable options for setting
 up your environment:
 
 Configurations can either be injected: