docs: fix unrecognized relative links across user guide and ffi page

Replace trailing-slash URL-style links left over from the Sphinx site
with explicit `.md` paths so mkdocs can resolve and validate them. Also
point the DataFusion 52 upgrade-guide reference at the FFI contributor
page (the bare `[ffi](ffi)` link no longer pointed anywhere).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: timsaucer

docs/source/contributor-guide/ffi.md

@@ -28,9 +28,9 @@ when doing these integrations and the approach our project uses.
 ## The Primary Issue
 
 Suppose you wish to use DataFusion and you have a custom data source that can produce tables that
-can then be queried against, similar to how you can register a [CSV](../../user-guide/io/csv/) or
-[Parquet](../../user-guide/io/parquet/) file. In DataFusion terminology, you likely want to implement a
-[Custom Table Provider](../../user-guide/io/table_provider/). In an effort to make your data source
+can then be queried against, similar to how you can register a [CSV](../user-guide/io/csv.md) or
+[Parquet](../user-guide/io/parquet.md) file. In DataFusion terminology, you likely want to implement a
+[Custom Table Provider](../user-guide/io/table_provider.md). In an effort to make your data source
 as performant as possible and to utilize the features of DataFusion, you may decide to write
 your source in Rust and then expose it through [PyO3](https://pyo3.rs) as a Python library.
 

docs/source/user-guide/common-operations/windows.md

@@ -65,7 +65,7 @@ print(df.select(
 ### Partitions
 
 A window function can take a list of `partition_by` columns similar to an
-[Aggregation Function](../aggregations/). This will cause the window values to be evaluated
+[Aggregation Function](aggregations.md). This will cause the window values to be evaluated
 independently for each of the partitions. In the example above, we found the rank of each
 Pokemon per `Type 1` partitions. We can see the first couple of each partition if we do
 the following:
@@ -166,7 +166,7 @@ print(df.filter(col('"Type 1"') == lit("Bug")).select(
 
 ## Aggregate Functions
 
-You can use any [Aggregation Function](../aggregations/) as a window function. Here
+You can use any [Aggregation Function](aggregations.md) as a window function. Here
 is an example that shows how to compare each pokemons’s attack power with the average attack
 power in its `"Type 1"` using the [`avg`][datafusion.functions.avg] function.
 
@@ -202,7 +202,7 @@ The possible window functions are:
      - [`lag`][datafusion.functions.lag]
      - [`lead`][datafusion.functions.lead]
 3. Aggregate Functions
-   : - All [Aggregation Functions](../aggregations/) can be used as window functions.
+   : - All [Aggregation Functions](aggregations.md) can be used as window functions.
 
 ## User-Defined Window Functions
 

docs/source/user-guide/concepts.md

@@ -76,7 +76,7 @@ For more details on working with DataFrames, including visualization options and
 
 ## Expressions
 
-The third statement uses [Expressions](../common-operations/expressions/) to build up a query definition. You can find
+The third statement uses [Expressions](common-operations/expressions.md) to build up a query definition. You can find
 explanations for what the functions below do in the user documentation for
 [`col`][datafusion.col.col], [`lit`][datafusion.lit], [`round`][datafusion.functions.round],
 and [`alias`][datafusion.expr.Expr.alias].

docs/source/user-guide/data-sources.md

@@ -24,8 +24,8 @@ DataFusion provides a wide variety of ways to get data into a DataFrame to perfo
 
 ## Local file
 
-DataFusion has the ability to read from a variety of popular file formats, such as [Parquet](../io/parquet/),
-[CSV](../io/csv/), [JSON](../io/json/), and [AVRO](../io/avro/).
+DataFusion has the ability to read from a variety of popular file formats, such as [Parquet](io/parquet.md),
+[CSV](io/csv.md), [JSON](io/json.md), and [AVRO](io/avro.md).
 
 ```python exec="1" source="material-block" result="text" session="data-sources"
 ctx = SessionContext()
@@ -208,7 +208,7 @@ Features that are available in PyIceberg but not yet in Iceberg Rust will not be
 ## Custom Table Provider
 
 You can implement a custom Data Provider in Rust and expose it to DataFusion through the
-the interface as describe in the [Custom Table Provider](../io/table_provider/)
+the interface as describe in the [Custom Table Provider](io/table_provider.md)
 section. This is an advanced topic, but a
 [user example](https://github.com/apache/datafusion-python/tree/main/examples/datafusion-ffi-example)
 is provided in the DataFusion repository.

docs/source/user-guide/dataframe/index.md

@@ -77,7 +77,7 @@ DataFrames can be created in several ways:
   ```
 
 For detailed information about reading from different data sources, see the [I/O Guide](../io/index.md).
-For custom data sources, see [io_custom_table_provider](../../io/table_provider/).
+For custom data sources, see [io_custom_table_provider](../../user-guide/io/table_provider.md).
 
 ## Common DataFrame Operations
 

docs/source/user-guide/sql.md

@@ -115,7 +115,7 @@ object. Those objects will be cast into a
 [PyArrow Scalar Value](https://arrow.apache.org/docs/python/generated/pyarrow.Scalar.html).
 
 Using `param_values` will rely on the SQL dialect you have configured
-for your session. This can be set using the [configuration options](../configuration/)
+for your session. This can be set using the [configuration options](configuration.md)
 of your [`SessionContext`][datafusion.context.SessionContext]. Similar to how
 [prepared statements](https://datafusion.apache.org/user-guide/sql/prepared_statements.html)
 work, these parameters are limited to places where you would pass in a

docs/source/user-guide/upgrade-guides.md

@@ -91,7 +91,7 @@ let codec = unsafe { data.as_ref() };
 
 ## DataFusion 52.0.0
 
-This version includes a major update to the [ffi](ffi) due to upgrades
+This version includes a major update to the [ffi](../contributor-guide/ffi.md) due to upgrades
 to the [Foreign Function Interface](https://doc.rust-lang.org/nomicon/ffi.html).
 Users who contribute their own `CatalogProvider`, `SchemaProvider`,
 `TableProvider` or `TableFunction` via FFI must now provide access to a