community[patch]: Upgrade pydantic extra (#25185)

Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.

This works correctly also in pydantic v1.

```python
from pydantic.v1 import BaseModel

class Foo(BaseModel, extra="forbid"):
    x: int

Foo(x=5, y=1)
```

And 


```python
from pydantic.v1 import BaseModel

class Foo(BaseModel):
    x: int

    class Config:
      extra = "forbid"

Foo(x=5, y=1)
```


## Enum -> literal using grit pattern:

```
engine marzano(0.1)
language python
or {
    `extra=Extra.allow` => `extra="allow"`,
    `extra=Extra.forbid` => `extra="forbid"`,
    `extra=Extra.ignore` => `extra="ignore"`
}
```

Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)


## Sort attributes in Config:

```
engine marzano(0.1)
language python


function sort($values) js {
    return $values.text.split(',').sort().join("\n");
}


class_definition($name, $body) as $C where {
    $name <: `Config`,
    $body <: block($statements),
    $values = [],
    $statements <: some bubble($values) assignment() as $A where {
        $values += $A
    },
    $body => sort($values),
}

```

libs/community/langchain_community/embeddings/ipex_llm.py

@@ -4,7 +4,7 @@
 from typing import Any, Dict, List, Optional
 
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Extra, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 
 DEFAULT_BGE_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
@@ -107,9 +107,7 @@ class IpexLLMBgeEmbeddings(BaseModel, Embeddings):
             self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
 
     class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
+        extra = "forbid"
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a HuggingFace transformer model.