Fix/links/docs

CHANGELOG.md

@@ -48,6 +48,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fix vector_index function job.
 - Fix verbosity in component info
 - Change default encoding to sqlvector
+- Fix some links in documentation 
 
 ## [0.3.0](https://github.com/superduper-io/superduper/compare/0.3.0...0.2.0])    (2024-Jun-21)
 

docs/content/apply_api/component.md

@@ -69,12 +69,12 @@ my_component
   |_982378978978978798
 ```
 
-You can read about the serialization mechanism [here](../production/yaml_formalism.md).
+You can read about the serialization mechanism [here](../production/superduper_protocol.md).
 
 ## Read more
 
 ```mdx-code-block
 import DocCardList from '@theme/DocCardList';
 
 <DocCardList />
-```
+```

docs/content/fundamentals/datalayer_overview.md

@@ -46,7 +46,7 @@ specific to `superduper`.
 
 Read more [here](../data_integrations/supported_query_APIs.md).
 
-The databackend is configured by setting the URI `CFG.databackend` in the [configuration system](../setup/configuration.md).
+The databackend is configured by setting the URI `CFG.databackend` in the [configuration system](../get_started/configuration.md).
 
 We support the same databackends as supported by the [`ibis` project](https://ibis-project.org/):
 
@@ -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](../walkthrough/apply_models.md).
+Infer predictions from models hosted by `superduper`. Read more about this and about models [here](../apply_api/model.md).

docs/content/fundamentals/vector_search_algorithm.md

@@ -50,4 +50,4 @@ The most similar `ids` are retrieved. The `select` part of the query is then tra
 a similar query which searches within the retrieved `ids`. The full set of results are returned
 to the client.
 
-Read [here](../walkthrough/vector_search.md) about setting up and detailed usage of vector-search.
+Read [here](../tutorials/vector_search.md) about setting up and detailed usage of vector-search.

docs/content/get_started/configuration.md

@@ -13,7 +13,7 @@ Configurations can either be injected:
 
 - in a YAML file specified by the `SUPERDUPER_CONFIG_FILE` environment variable or
 - through environment variables starting with `SUPERDUPER_`:
-- as `**kwargs` when calling the [`superduper.superduper`](./connecting.md) function (note this is only for development purposes).
+- as `**kwargs` when calling the [`superduper.superduper`](../core_api/connect.md) function (note this is only for development purposes).
 
 Here are the configurable settings and their project defaults 
 (remaining configurations can be viewed in [`superduper.base.config`](https://github.com/superduper/superduper/blob/main/superduper/base/config.py)). Note that as much or as little of this configuration can be specified. The remaining 
@@ -115,4 +115,4 @@ You may view the configuration used by the system with:
 
 ```bash
 python -m superduper config
-```
+```

docs/content/production/development_vs_cluster_mode.md

@@ -1,6 +1,6 @@
 # Developer vs. production mode
 
-Please refer to the [architecture](../fundamentals/architecture.md) page for a detailed description of the `superduper` architecture.
+Please refer to the [architecture](../fundamentals/design.md) page for a detailed description of the `superduper` architecture.
 
 There are several important services in a `superduper` setup which may be run in-process, or in their 
 own micro-services and containers:
@@ -35,4 +35,4 @@ In the most distributed case we have:
 - A [**vector-search** service](vector_comparison_service), which finds similar vectors, given an input vector
 - A [**REST** server](./rest_api)
 
-In the remainder of this section we describe the use of each of these services
+In the remainder of this section we describe the use of each of these services

docs/content/tutorials/rag.md

@@ -112,7 +112,7 @@ This gives users great flexibility with regard to how they fetch the context
 for their downstream models.
 
 We're using OpenAI, but you can use any type of LLm with `superduper`. We have several 
-native integrations (see [here](../ai_integraitons/)) but you can also [bring your own model](../models/bring_your_own_model.md).
+native integrations (see [here](../ai_integraitons/)) but you can also [bring your own model](../models/bring_your_own_models.md).
 
 ```python
 from superduper.ext.llm.prompter import *

docs/content/tutorials/vector_search.md

@@ -1246,7 +1246,7 @@ for r in cursor:
     a similar query which searches within the retrieved `ids`. The full set of results are returned
     to the client.
 
-    Read [here](../walkthrough/vector_search.md) about setting up and detailed usage of vector-search.
+    Read [here](../execute_api/vector_search.md) about setting up and detailed usage of vector-search.
 
     ====================================================================================================
     ====================================================================================================