Inconsistent pydantic versions breaking with LangChain/LiteLLM/OpenAI

### Category

Compatibility Issue

### 🐛 Describe the bug

I ran in to the [issue described here](https://github.com/langchain-ai/langchain/discussions/16985) today where trying to use `litellm>=1.35.8,<2` on SageMaker Studio Distribution v1.8 fails with:

```
[Rest of stack trace abbreviated]

File /opt/conda/lib/python3.10/site-packages/openai/_models.py:21
7 from typing_extensions import (
8 Unpack,
9 Literal,
(...)
17 runtime_checkable,
18 )
20 import pydantic
---> 21 import pydantic.generics
22 from pydantic.fields import FieldInfo
24 from ._types import (
25 Body,
26 IncEx,
(...)
33 HttpxRequestFiles,
34 )

File /opt/conda/lib/python3.10/site-packages/pydantic/generics.py:2
1 """The `generics` module is a backport module from V1."""
----> 2 from ._migration import getattr_migration
4 __getattr__ = getattr_migration(__name__)

File /opt/conda/lib/python3.10/site-packages/pydantic/_migration.py:4
1 import sys
2 from typing import Any, Callable, Dict
----> 4 from .version import version_short
6 MOVED_IN_V2 = {
7 'pydantic.utils:version_info': 'pydantic.version:version_info',
8 'pydantic.error_wrappers:ValidationError': 'pydantic:ValidationError',
(...)
13 'pydantic.generics:GenericModel': 'pydantic.BaseModel',
14 }
16 DEPRECATED_MOVED_IN_V2 = {
17 'pydantic.tools:schema_of': 'pydantic.deprecated.tools:schema_of',
18 'pydantic.tools:parse_obj_as': 'pydantic.deprecated.tools:parse_obj_as',
(...)
28 'pydantic.config:Extra': 'pydantic.deprecated.config:Extra',
29 }

ImportError: cannot import name 'version_short' from 'pydantic.version' (/opt/conda/lib/python3.10/site-packages/pydantic/version.cpython-310-x86_64-linux-gnu.so)
```

Weirdly, I get different results in SageMaker for `%pip show pydantic` (v1.10.14) versus `%conda list pydantic` (v2.7.0).

Sure enough, trying to `import pydantic.generics` from a notebook fails with the above-mentioned error - including the Pydantic source location under `/opt/conda`.

From the stack trace it *must* be picking up v2.7.0 (Compare [pydantic/generics.py @ 2.7.0](https://github.com/pydantic/pydantic/blob/v2.7.0/pydantic/generics.py) vs [pydantic/generics.py @ 1.10.14](https://github.com/pydantic/pydantic/blob/v1.10.14/pydantic/generics.py)) - but `version.version_short` [should actually exist @ 2.7.0](https://github.com/pydantic/pydantic/blob/7af856a1098406aea84bcadfd0f3de6b7901526c/pydantic/version.py#L10).

...And if I run the following from the same notebook:

```python
import pydantic
pydantic.__version__
```

...It reports `'1.10.14'`!

If I `%pip install pydantic==1.10.14`, pip detects that the version is installed so there's nothing to do. If I restart the kernel, I get the same (reporting 1.10.14, erroring on import) behaviour as above.

...But if I run `%pip install --force-reinstall pydantic==1.10.14` and restart the kernel, the ImportError gets resolved.

Can you guess what happens if I `%pip install pydantic==2.7.0` and restart the kernel?

Well `pydantic.__version__ == '2.7.0'`... **but**, `import pydantic.generics` works just fine! 😭 This is true even on a fresh container where I hadn't run the force-reinstall of 1.10.14.

I even tried `%conda install pydantic==2.7.0 --force-reinstall`, but it just spins forever and then fails with `PackagesNotFoundError: The following packages are not available from current channels` for a looooong list of packages.

Based on all this - particularly the fact that installing either 1.10.14 or 2.7.0 seems to work - it really seems like something is wrong with the pydantic installation in SM Distribution: Maybe the two versions got installed on top of each other in the same location somehow and have conflicting files?

### 🐛 Describe the expected behavior

It'd be really useful if there was be exactly one version of pydantic visible to the Python 3 kernel, with installation mechanics that made some sense, and ideally if it's a version that correctly supports `import pydantic.generics` so that LangChain/LiteLLM/OpenAI libraries can work properly. 😅

It could just be that I'm missing something about how conda & pip are meant to work together in this environment? In which case would love to learn how to deal with it properly or if it could be documented somewhere easy to find!

### Image Tags

SageMaker Studio Distribution v1.8 (2024-06-11)

Start by reproducing the import failure in a SageMaker Studio Distribution v1.8 Python 3 notebook, then compare the pip and conda package metadata with the loaded Pydantic source location. Done means the kernel exposes one consistent Pydantic version and the supported installation or compatibility steps are documented clearly.

Written by the indexing model from the issue text.