Add OpenAIEmbeddings special token params for tiktoken (#2682)

#2681 

Original type hints
```python
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),  # noqa: B006
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
```
from

46287bfa49/tiktoken/core.py (L79-L80)
are not compatible with pydantic

<img width="718" alt="image"
src="https://user-images.githubusercontent.com/5096640/230993236-c744940e-85fb-4baa-b9da-8b00fb60a2a8.png">

I think we could use
```python
allowed_special: Union[Literal["all"], Set[str]] = set()
disallowed_special: Union[Literal["all"], Set[str], Tuple[()]] = "all"
```

Please let me know if you would like to implement it differently.

langchain/embeddings/openai.py

@@ -2,7 +2,17 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Callable, Dict, List, Optional
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
 
 import numpy as np
 from pydantic import BaseModel, Extra, root_validator
@@ -99,6 +109,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     embedding_ctx_length: int = 8191
     openai_api_key: Optional[str] = None
     openai_organization: Optional[str] = None
+    allowed_special: Union[Literal["all"], Set[str]] = set()
+    disallowed_special: Union[Literal["all"], Set[str], Tuple[()]] = "all"
     chunk_size: int = 1000
     """Maximum number of texts to embed in each batch"""
     max_retries: int = 6
@@ -195,7 +207,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             for i, text in enumerate(texts):
                 # replace newlines, which can negatively affect performance.
                 text = text.replace("\n", " ")
-                token = encoding.encode(text)
+                token = encoding.encode(
+                    text,
+                    allowed_special=self.allowed_special,
+                    disallowed_special=self.disallowed_special,
+                )
                 for j in range(0, len(token), self.embedding_ctx_length):
                     tokens += [token[j : j + self.embedding_ctx_length]]
                     indices += [i]