📃 docs(Learn): Loading Data (#14762)

* 📃 docs(Learn): Loading Data

1. add understanding\using_llms\using_llms.md missing full stop
2. fix understanding\loading\loading.md DatabaseReader link
3. add module_guides\loading\node_parsers\modules.md node_parsers modules SemanticSplitterNodeParser video link
4. fix docs\docs\examples\ingestion\document_management_pipeline.ipynb redis link

* fix one link

---------

Co-authored-by: Andrei Fajardo <andrei@nerdai.io>

Author: houtaroy

docs/docs/examples/ingestion/document_management_pipeline.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "If we do not attach a vector store, we can only check for and remove duplicate inputs.\n",
     "\n",
-    "If a vector store is attached, we can also handle upserts! We have [another guide](/examples/ingestion/redis_ingestion_pipeline.ipynb) for upserts and vector stores."
+    "If a vector store is attached, we can also handle upserts! We have [another guide](/en/stable/examples/ingestion/redis_ingestion_pipeline) for upserts and vector stores."
    ]
   },
   {

docs/docs/module_guides/loading/node_parsers/modules.md

@@ -130,7 +130,7 @@ A full example can be found [here in combination with the `MetadataReplacementNo
 
 ### SemanticSplitterNodeParser
 
-"Semantic chunking" is a new concept proposed Greg Kamradt in his video tutorial on 5 levels of embedding chunking: https://youtu.be/8OJC21T2SL4?t=1933.
+"Semantic chunking" is a new concept proposed Greg Kamradt in his video tutorial on 5 levels of embedding chunking: [https://youtu.be/8OJC21T2SL4?t=1933](https://youtu.be/8OJC21T2SL4?t=1933).
 
 Instead of chunking text with a **fixed** chunk size, the semantic splitter adaptively picks the breakpoint in-between sentences using embedding similarity. This ensures that a "chunk" contains sentences that are semantically related to each other.
 

docs/docs/understanding/loading/loading.md

@@ -28,7 +28,7 @@ documents = SimpleDirectoryReader("./data").load_data()
 
 Because there are so many possible places to get data, they are not all built-in. Instead, you download them from our registry of data connectors, [LlamaHub](llamahub.md).
 
-In this example LlamaIndex downloads and installs the connector called [DatabaseReader](https://llamahub.ai/l/database), which runs a query against a SQL database and returns every row of the results as a `Document`:
+In this example LlamaIndex downloads and installs the connector called [DatabaseReader](https://llamahub.ai/l/readers/llama-index-readers-database), which runs a query against a SQL database and returns every row of the results as a `Document`:
 
 ```python
 from llama_index.core import download_loader

docs/docs/understanding/using_llms/using_llms.md

@@ -65,4 +65,4 @@ See the [custom LLM's How-To](../../module_guides/models/llms/usage_custom.md) f
 
 ## Prompts
 
-By default LlamaIndex comes with a great set of built-in, battle-tested prompts that handle the tricky work of getting a specific LLM to correctly handle and format data. This is one of the biggest benefits of using LlamaIndex. If you want to, you can [customize the prompts](../../module_guides/models/prompts/index.md)
+By default LlamaIndex comes with a great set of built-in, battle-tested prompts that handle the tricky work of getting a specific LLM to correctly handle and format data. This is one of the biggest benefits of using LlamaIndex. If you want to, you can [customize the prompts](../../module_guides/models/prompts/index.md).