allow tokentextsplitters to use model name to select encoder (#2963)

Fixes a bug I was seeing when the `TokenTextSplitter` was correctly
splitting text under the gpt3.5-turbo token limit, but when firing the
prompt off too openai, it'd come back with an error that we were over
the context limit.

gpt3.5-turbo and gpt-4 use `cl100k_base` tokenizer, and so the counts
are just always off with the default `gpt-2` encoder.

It's possible to pass along the encoding to the `TokenTextSplitter`, but
it's much simpler to pass the model name of the LLM. No more concern
about keeping the tokenizer and llm model in sync :)

langchain/text_splitter.py

@@ -139,6 +139,7 @@ class TextSplitter(ABC):
     def from_tiktoken_encoder(
         cls,
         encoding_name: str = "gpt2",
+        model_name: Optional[str] = None,
         allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
         disallowed_special: Union[Literal["all"], Collection[str]] = "all",
         **kwargs: Any,
@@ -153,8 +154,10 @@ class TextSplitter(ABC):
                 "Please install it with `pip install tiktoken`."
             )
 
-        # create a GPT-3 encoder instance
-        enc = tiktoken.get_encoding(encoding_name)
+        if model_name is not None:
+            enc = tiktoken.encoding_for_model(model_name)
+        else:
+            enc = tiktoken.get_encoding(encoding_name)
 
         def _tiktoken_encoder(text: str, **kwargs: Any) -> int:
             return len(
@@ -193,6 +196,7 @@ class TokenTextSplitter(TextSplitter):
     def __init__(
         self,
         encoding_name: str = "gpt2",
+        model_name: Optional[str] = None,
         allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
         disallowed_special: Union[Literal["all"], Collection[str]] = "all",
         **kwargs: Any,
@@ -207,8 +211,12 @@ class TokenTextSplitter(TextSplitter):
                 "This is needed in order to for TokenTextSplitter. "
                 "Please install it with `pip install tiktoken`."
             )
-        # create a GPT-3 encoder instance
-        self._tokenizer = tiktoken.get_encoding(encoding_name)
+
+        if model_name is not None:
+            enc = tiktoken.encoding_for_model(model_name)
+        else:
+            enc = tiktoken.get_encoding(encoding_name)
+        self._tokenizer = enc
         self._allowed_special = allowed_special
         self._disallowed_special = disallowed_special