core: Put Python version as a project requirement so it is considered by ruff (#26608)

Ruff doesn't know about the python version in
`[tool.poetry.dependencies]`. It can get it from
`project.requires-python`.

Notes:
* poetry seems to have issues getting the python constraints from
`requires-python` and using `python` in per dependency constraints. So I
had to duplicate the info. I will open an issue on poetry.
* `inspect.isclass()` doesn't work correctly with `GenericAlias`
(`list[...]`, `dict[..., ...]`) on Python <3.11 so I added some `not
isinstance(type, GenericAlias)` checks:

Python 3.11
```pycon
>>> import inspect
>>> inspect.isclass(list)
True
>>> inspect.isclass(list[str])
False
```

Python 3.9
```pycon
>>> import inspect
>>> inspect.isclass(list)
True
>>> inspect.isclass(list[str])
True
```

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

libs/core/langchain_core/embeddings/embeddings.py

@@ -1,7 +1,6 @@
 """**Embeddings** interface."""
 
 from abc import ABC, abstractmethod
-from typing import List
 
 from langchain_core.runnables.config import run_in_executor
 
@@ -35,7 +34,7 @@ class Embeddings(ABC):
     """
 
     @abstractmethod
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Embed search docs.
 
         Args:
@@ -46,7 +45,7 @@ class Embeddings(ABC):
         """
 
     @abstractmethod
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Embed query text.
 
         Args:
@@ -56,7 +55,7 @@ class Embeddings(ABC):
             Embedding.
         """
 
-    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+    async def aembed_documents(self, texts: list[str]) -> list[list[float]]:
         """Asynchronous Embed search docs.
 
         Args:
@@ -67,7 +66,7 @@ class Embeddings(ABC):
         """
         return await run_in_executor(None, self.embed_documents, texts)
 
-    async def aembed_query(self, text: str) -> List[float]:
+    async def aembed_query(self, text: str) -> list[float]:
         """Asynchronous Embed query text.
 
         Args:

libs/core/langchain_core/embeddings/fake.py

@@ -2,7 +2,6 @@
 
 # Please do not add additional fake embedding model implementations here.
 import hashlib
-from typing import List
 
 from pydantic import BaseModel
 
@@ -51,15 +50,15 @@ class FakeEmbeddings(Embeddings, BaseModel):
     size: int
     """The size of the embedding vector."""
 
-    def _get_embedding(self) -> List[float]:
+    def _get_embedding(self) -> list[float]:
         import numpy as np  # type: ignore[import-not-found, import-untyped]
 
         return list(np.random.normal(size=self.size))
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         return [self._get_embedding() for _ in texts]
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         return self._get_embedding()
 
 
@@ -106,7 +105,7 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
     size: int
     """The size of the embedding vector."""
 
-    def _get_embedding(self, seed: int) -> List[float]:
+    def _get_embedding(self, seed: int) -> list[float]:
         import numpy as np  # type: ignore[import-not-found, import-untyped]
 
         # set the seed for the random generator
@@ -117,8 +116,8 @@ class DeterministicFakeEmbedding(Embeddings, BaseModel):
         """Get a seed for the random generator, using the hash of the text."""
         return int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 10**8
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         return self._get_embedding(seed=self._get_seed(text))