Platform: Fix lots of broken links (#497)

Author: Paul-Cornell

examplecode/codesamples/api/huggingchat.mdx

@@ -2,7 +2,7 @@
 title: Query processed PDF with HuggingChat
 ---
 
-This example uses the [Unstructured Ingest Python library](/ingestion/overview#unstructured-ingest-python-library) or the 
+This example uses the [Unstructured Ingest Python library](/ingestion/python-ingest) or the 
 [Unstructured JavaScript/TypeScript SDK](/platform-api/partition-api/sdk-jsts) to send a PDF file to 
 the [Unstructured Platform Partition Endpoint](/platform-api/partition-api/overview) for processing. Unstructured processes the PDF and extracts the PDF's content. 
 This example then sends some of the content to [HuggingChat](https://huggingface.co/chat/), Hugging Face's open-source AI chatbot, 
@@ -11,7 +11,7 @@ along with some queries about this content.
 To run this example, you'll need:
 
 - The [hugchat](https://pypi.org/project/hugchat/) package for Python, or the [huggingface-chat](https://www.npmjs.com/package/huggingface-chat) package for JavaScript/TypeScript.
-- Your Unstructured API key and API URL. [Get an API key and API URL](/platform-api/parition-api/overview). 
+- Your Unstructured API key and API URL. [Get an API key and API URL](/platform-api/partition-api/overview). 
 - Your Hugging Face account's email address and account password. [Get an account](https://huggingface.co/join).
 - A PDF file for Unstructured to process. This example uses a sample PDF file containing the text of the United States Constitution, 
 available for download from [https://constitutioncenter.org/media/files/constitution.pdf](https://constitutioncenter.org/media/files/constitution.pdf).
@@ -37,7 +37,7 @@ import HuggingChatTSExampleCode from '/snippets/examples/huggingchat.ts.mdx';
     </Accordion>
     <Accordion title="JavaScript/TypeScript SDK">
         <Warning>
-            Unstructured recommends that you use the [Unstructured Ingest Python library](/ingestion/overview#unstructured-ingest-python-library) instead. 
+            Unstructured recommends that you use the [Unstructured Ingest Python library](/ingestion/python-ingest) instead. 
 
             The Ingest Python library provides faster processing of larger individual files, and faster and easier processing of multiple files at a time in batches.
         </Warning>

examplecode/tools/langflow.mdx

@@ -246,7 +246,7 @@ To do this, you can:
 
 - [Use Unstructured Ingest to create a pipeline](/ingestion/overview) that relies on any available 
   [source connector](/ingestion/source-connectors/overview) to connect to 
-  [Astra DB](/ingestion/destination-connector/astradb). Run this pipeline outside of Langflow anytime you have new documents in that non-local source location that 
+  [Astra DB](/ingestion/destination-connectors/astradb). Run this pipeline outside of Langflow anytime you have new documents in that non-local source location that 
   you want Unstructured to process and then insert the new processed data into Astra DB. Then, back in the Langflow project, 
   use the **Playground** to ask additonal questions, which will now include the new data when generating answers.
 
@@ -274,16 +274,16 @@ Or, [use Unstructured Ingest to create a pipeline](/ingestion/overview) that rel
 [source connector](/ingestion/source-connectors/overview) to connect to 
 one of the following available vector stores that Langflow also supports:
 
-- [Chroma DB](/ingestion/destination-connector/chroma)
-- [Couchbase](/ingestion/destination-connector/couchbase)
-- [Elasticsearch](/ingestion/destination-connector/elasticsearch)
-- [Milvus](/ingestion/destination-connector/milvus)
-- [MongoDB](/ingestion/destination-connector/mongodb)
-- [OpenSearch](/ingestion/destination-connector/opensearch)
-- [Pinecone](/ingestion/destination-connector/pinecone)
-- [Qdrant](/ingestion/destination-connector/qdrant)
-- [Vectara](/ingestion/destination-connector/vectara)
-- [Weaviate](/ingestion/destination-connector/weaviate)
+- [Chroma DB](/ingestion/destination-connectors/chroma)
+- [Couchbase](/ingestion/destination-connectors/couchbase)
+- [Elasticsearch](/ingestion/destination-connectors/elasticsearch)
+- [Milvus](/ingestion/destination-connectors/milvus)
+- [MongoDB](/ingestion/destination-connectors/mongodb)
+- [OpenSearch](/ingestion/destination-connectors/opensearch)
+- [Pinecone](/ingestion/destination-connectors/pinecone)
+- [Qdrant](/ingestion/destination-connectors/qdrant)
+- [Vectara](/ingestion/destination-connectors/vectara)
+- [Weaviate](/ingestion/destination-connectors/weaviate)
 
 Run this pipeline outside of Langflow anytime you have new documents in the source location that 
 you want Unstructured to process and then insert the new processed data into the vector store. Then, back in the Langflow project, 

ingestion/ingest-cli.mdx

@@ -8,7 +8,7 @@ The Unstructured Ingest CLI enables you to use command-line scripts to send file
 <Note>
     The Unstructured Ingest CLI does not work with the Unstructured Platform API.
 
-    For information about the Unstructured Platform API, see the [Unstructured Platform API Overview](/platform/api/overview).
+    For information about the Unstructured Platform API, see the [Unstructured Platform API Overview](/platform-api/api/overview).
 </Note>
 
 ## Installation 
@@ -34,7 +34,7 @@ For additional installation options, see [Unstructured Ingest CLI](/ingestion/ov
 To call the Unstructured Ingest CLI, follow this calling pattern, where:
 
 - `<source>` is the command name for one of the available [source](/ingestion/source-connectors/overview) (input) connectors, such as `local` for a local source location, `azure` for an Azure Storage account source, `s3` for an Amazon S3 bucket source, and so on.
-- `<destination>` is the command name for one of the available [destination](/ingestion/destination-connector/overview) (output) connectors, such as `local` for a local destination, `azure` for an Azure Storage account destination, `s3` for an Amazon S3 bucket destination, and so on.
+- `<destination>` is the command name for one of the available [destination](/ingestion/destination-connectors/overview) (output) connectors, such as `local` for a local destination, `azure` for an Azure Storage account destination, `s3` for an Amazon S3 bucket destination, and so on.
 - `<setting>` is one or more command-line options for specifying how and where Unstructured will ingest the files from the `<source>`, or how and where to deliver the processed data to the `<destination>`. 
 
 ```bash CLI
@@ -51,6 +51,6 @@ unstructured-ingest \
     --<settingN> <valueN>
 ```
 
-To learn how to use the Unstructured Ingest CLI to work with a specific source (input) and destination (output) location, see the CLI script examples for the [source](/ingestion/source-connectors/overview) and [destination](/ingestion/destination-connector/overview) connectors that are available for you to choose from.
+To learn how to use the Unstructured Ingest CLI to work with a specific source (input) and destination (output) location, see the CLI script examples for the [source](/ingestion/source-connectors/overview) and [destination](/ingestion/destination-connectors/overview) connectors that are available for you to choose from.
 
 See also the [ingest configuration](/ingestion/ingest-configuration/overview) settings for command-line options that enable you to further control how batches are sent and processed.

ingestion/ingest-dependencies.mdx

@@ -6,7 +6,7 @@ When you install the [Unstructured Ingest CLI](/ingestion/ingest-cli) and the
 [Unstructured Ingest Python library](/ingestion/python-ingest) by running the command
 `pip install unstructured-ingest` by itself, you get the following by default:
 
-- The [local source connector](/ingestion/source-connectors/local) and the [local destination connector](/ingestion/destination-connector/local).
+- The [local source connector](/ingestion/source-connectors/local) and the [local destination connector](/ingestion/destination-connectors/local).
 - Support for the following file types:
 
 | File type |

ingestion/overview.mdx

@@ -127,7 +127,7 @@ import GeneratePythonCodeExamples from '/snippets/ingestion/code-generator.mdx';
 
 - [Ingest configuration](/ingestion/ingest-configuration/overview) settings enable you to control how batches are sent and processed.
 - [Source connectors](/ingestion/source-connectors/overview) enable you to send batches from local or remote locations to be ingested by Unstructured for processing.
-- [Destination connectors](/ingestion/destination-connector/overview) enable Unstructured to send the processed data to local or remote locations.
+- [Destination connectors](/ingestion/destination-connectors/overview) enable Unstructured to send the processed data to local or remote locations.
 
 ## See also
 

ingestion/python-ingest.mdx

@@ -8,7 +8,7 @@ The Unstructured Ingest Python library enables you to use Python code to send fi
 <Note>
     The Unstructured Ingest Python library does not work with the Unstructured Platform API.
 
-    For information about the Unstructured Platform API, see the [Unstructured Platform API Overview](/platform/api/overview).
+    For information about the Unstructured Platform API, see the [Unstructured Platform API Overview](/platform-api/api/overview).
 </Note>
 
 The following 3-minute video shows how to use the Unstructured Ingest Python library to send multiple PDFs from a local directory in batches to be ingested by Unstructured for processing:
@@ -23,8 +23,6 @@ The following 3-minute video shows how to use the Unstructured Ingest Python lib
   allowfullscreen
 ></iframe>
 
-[Learn more](/overview#unstructured-ingest-python).
-
 ## Installation
 
 One approach to get started quickly with the Unstructured Ingest Python library is to install Python and then run the following command:
@@ -39,7 +37,7 @@ import AdditionalIngestDependencies from '/snippets/general-shared-text/ingest-d
 
 <AdditionalIngestDependencies />
 
-For additional installation options, and information about v2 and v1 implementations in this library, see the [Unstructured Ingest Python library](/ingestion/overview#unstructured-ingest-python-library) in the [Ingest](/ingestion/overview) section.
+For additional installation options, and information about v2 and v1 implementations in this library, see the [Unstructured Ingest Python library](/ingestion/python-ingest) in the [Ingest](/ingestion/overview) section.
 
 <Info>To migrate from older, deprecated versions of the Ingest Python library that used `pip install unstructured`, see the [migration guide](/ingestion/overview#migration-guide).</Info>
 
@@ -58,6 +56,6 @@ import AzureAPIPyV1 from '/snippets/destination_connectors/azure.v1.py.mdx';
 
 </CodeGroup>
 
-To learn how to use the Unstructured Ingest Python library to work with a specific source (input) and destination (output) location, see the Python code examples for the [source](/ingestion/source-connectors/overview) and [destination](/ingestion/destination-connector/overview) connectors that are available for you to choose from.
+To learn how to use the Unstructured Ingest Python library to work with a specific source (input) and destination (output) location, see the Python code examples for the [source](/ingestion/source-connectors/overview) and [destination](/ingestion/destination-connectors/overview) connectors that are available for you to choose from.
 
 See also the [ingest configuration](/ingestion/ingest-configuration/overview) settings that enable you to further control how batches are sent and processed.

meta-prompt/llms.txt

@@ -736,7 +736,7 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Azure Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/azure)
+**Reference:** [Azure Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/azure)
 
 2. DataBricks Volumes
 
@@ -882,7 +882,7 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Databricks Volumes Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/databricks-volumes)
+**Reference:** [Databricks Volumes Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/databricks-volumes)
 
 3. Weaviate
 
@@ -948,7 +948,7 @@ The Weaviate destination connector enables you to batch process and store struct
      }
      ```  
    - [Schema Reference](https://weaviate.io/developers/weaviate/config-refs/schema)  
-   - [Document Elements and Metadata](https://docs.unstructured.io/latform-api/partition-api/document-elements)  
+   - [Document Elements and Metadata](https://docs.unstructured.io/platform-api/partition-api/document-elements)  
 
 ---
 
@@ -1054,7 +1054,7 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Weaviate Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/weaviate)
+**Reference:** [Weaviate Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/weaviate)
 
 4. Pinecone 
 
@@ -1184,7 +1184,7 @@ if __name__ == "__main__":
 - Ensure the Pinecone schema aligns with the data structure produced by Unstructured for smooth ingestion.  
 - This example uses the local source connector; you can replace it with other supported connectors as needed.
 
-**Reference:** [Pinecone Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/pinecone)
+**Reference:** [Pinecone Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/pinecone)
 
 5. S3 
 
@@ -1330,7 +1330,7 @@ if __name__ == "__main__":
 - This example uses the local source connector; other connectors can be substituted.  
 - Use `--anonymous` for anonymous bucket access where applicable.  
 
-**Reference:** [S3 Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/s3)
+**Reference:** [S3 Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/s3)
 
 # Unstructured Ingest Best Practices
 
@@ -1539,7 +1539,7 @@ Partitioning strategies in Unstructured are used to preprocess documents like PD
 
 ---
 
-**Learn More**: [Document Elements and Metadata](https://docs.unstructured.io/latform-api/partition-api/document-elements)
+**Learn More**: [Document Elements and Metadata](https://docs.unstructured.io/platform-api/partition-api/document-elements)
 
 5. Tables as HTML
 

meta-prompt/splits/2.txt

@@ -238,7 +238,7 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Azure Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/azure)
+**Reference:** [Azure Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/azure)
 
 2. DataBricks Volumes
 
@@ -384,7 +384,7 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Databricks Volumes Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/databricks-volumes)
+**Reference:** [Databricks Volumes Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/databricks-volumes)
 
 3. Weaviate
 
@@ -450,7 +450,7 @@ The Weaviate destination connector enables you to batch process and store struct
      }
      ```  
    - [Schema Reference](https://weaviate.io/developers/weaviate/config-refs/schema)  
-   - [Document Elements and Metadata](https://docs.unstructured.io/latform-api/partition-api/document-elements)  
+   - [Document Elements and Metadata](https://docs.unstructured.io/platform-api/partition-api/document-elements)  
 
 ---
 
@@ -556,4 +556,4 @@ if __name__ == "__main__":
     ).run()
 ```
 
-**Reference:** [Weaviate Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/weaviate)
+**Reference:** [Weaviate Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/weaviate)

meta-prompt/splits/3.txt

@@ -126,7 +126,7 @@ if __name__ == "__main__":
 - Ensure the Pinecone schema aligns with the data structure produced by Unstructured for smooth ingestion.  
 - This example uses the local source connector; you can replace it with other supported connectors as needed.
 
-**Reference:** [Pinecone Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/pinecone)
+**Reference:** [Pinecone Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/pinecone)
 
 5. S3 
 
@@ -272,7 +272,7 @@ if __name__ == "__main__":
 - This example uses the local source connector; other connectors can be substituted.  
 - Use `--anonymous` for anonymous bucket access where applicable.  
 
-**Reference:** [S3 Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connector/s3)
+**Reference:** [S3 Destination Connector Documentation](https://docs.unstructured.io/ingestion/destination-connectors/s3)
 
 # Unstructured Ingest Best Practices
 
@@ -481,7 +481,7 @@ Partitioning strategies in Unstructured are used to preprocess documents like PD
 
 ---
 
-**Learn More**: [Document Elements and Metadata](https://docs.unstructured.io/latform-api/partition-api/document-elements)
+**Learn More**: [Document Elements and Metadata](https://docs.unstructured.io/platform-api/partition-api/document-elements)
 
 5. Tables as HTML
 

open-source/core-functionality/embedding.mdx

@@ -4,7 +4,7 @@ title: Embedding
 
 The Unstructured open-source library does not offer built-in support for calling embedding providers to obtain embeddings for pieces of text.
 
-Alternatively, the [Unstructured Ingest CLI](/ingestion/overview#unstructured-ingest-cli) and the [Unstructured Ingest Python library](/ingestion/overview#unstructured-ingest-python-library) 
+Alternatively, the [Unstructured Ingest CLI](/ingestion/overview#unstructured-ingest-cli) and the [Unstructured Ingest Python library](/ingestion/python-ingest) 
 offer built-in support for calling embedding providers as part of an ingest pipeline. [Learn how](/platform-api/partition-api/embedding).
 
 Also, you can use common third-party tools and libraries to get embeddings for document elements' text within JSON files that are