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Use Langchain's LLM usage stats for token counting rather than explicit tokenizer #1321

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Use Langchain's LLM usage stats for token counting rather than explicit tokenizer #1321

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2 changes: 0 additions & 2 deletions pydatalab/pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -86,8 +86,6 @@ chat = [
"langchain >= 0.2.6, < 0.3",
"langchain-openai ~= 0.1",
"langchain-anthropic ~= 0.1",
"tiktoken ~= 0.7",
"transformers ~= 4.42",
]
deploy = ["gunicorn ~= 23.0"]
all = ["datalab-server[apps,server,chat]"]
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21 changes: 3 additions & 18 deletions pydatalab/src/pydatalab/apps/chat/blocks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@

from langchain_anthropic import ChatAnthropic
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI

from pydatalab.blocks.base import DataBlock
Expand Down Expand Up @@ -165,9 +166,7 @@ def render(self):

try:
model_name = self.data["model"]

model_dict = self.data["available_models"][model_name]
LOGGER.warning(f"Initializing chatblock with model: {model_name}")
LOGGER.debug(f"Initializing chatblock with model: {model_name}")

if model_name.startswith("claude"):
self.chat_client = ChatAnthropic(
Expand All @@ -183,8 +182,6 @@ def render(self):
LOGGER.debug(
f'submitting request to API for completion with last message role "{self.data["messages"][-1]["role"]}" (message = {self.data["messages"][-1:]}). Temperature = {self.data["temperature"]} (type {type(self.data["temperature"])})'
)
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

# Convert your messages to the required format
langchain_messages = []
for message in self.data["messages"]:
Expand All @@ -195,24 +192,12 @@ def render(self):
else:
langchain_messages.append(AIMessage(content=message["content"]))

token_count = self.chat_client.get_num_tokens_from_messages(langchain_messages)

self.data["token_count"] = token_count

if token_count >= model_dict["context_window"]:
self.data["error_message"] = (
f"""This conversation has reached its maximum context size and the chatbot won't be able to respond further ({token_count} tokens, max: {model_dict["context_window"]}). Please make a new chat block to start fresh, or use a model with a larger context window"""
)
return

# Call the chat client with the invoke method
response = self.chat_client.invoke(langchain_messages)
self.data["token_count"] = response.usage_metadata["total_tokens"]

langchain_messages.append(response)

token_count = self.chat_client.get_num_tokens_from_messages(langchain_messages)

self.data["token_count"] = token_count
self.data["messages"].append({"role": "assistant", "content": response.content})
self.data["error_message"] = None

Expand Down
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