get_num_tokens_from_message and get_num_tokens function gives accurate tokens for a string or not.

[VedantRaj3907]

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Example Code

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.prompts import MessagesPlaceholder
from langchain_core.prompts import ChatPromptTemplate

model = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key='api_key')
ins = ChatPromptTemplate.from_messages(
            [
                ("system", "helpful assistant"),
                ("human", "{input}")
            ]
        )

chain = ins | model
for chunk in chain.stream({"input":"write code to add two numbers"}):
    print(chunk.content)
    print("---")

print(model.get_num_tokens("write code to add two numbers"))

Description

I was wondering what are the above two functions used for. Are they used to calculate the tokens for a string, Can anyone please explain them to me? Also why there is not documentation for this function on langchain.

System Info

langchain_openai==0.1.3
langchain_core==0.1.45
langchain_anthropic==0.1.11
python-dotenv==1.0.1
python==3.11

[dosubot[bot]]

The get_num_tokens and get_num_tokens_from_message functions in LangChain are designed for calculating the number of tokens in strings and sequences of messages, respectively. These functions are crucial for managing inputs relative to a model's context window limitations.

• get_num_tokens: This function calculates the number of tokens in a text string. It uses a tokenizer (defaulting to GPT-2's tokenizer if a custom one isn't specified) to convert the text into tokens and then counts these tokens. This ensures your input text doesn't exceed the model's maximum token limit.

• get_num_tokens_from_message: It calculates the total number of tokens across a list of messages by converting each message into a string, calculating the number of tokens for each, and summing these counts. This is useful for sequences of messages where the total sequence must stay within the model's context window.

These functions are model-agnostic, meaning they should accurately count tokens for any text input, including inputs for models like "gemini-pro" used in your example. The accuracy of the token count depends on the compatibility of the tokenizer with the model's tokenization process.

The lack of specific documentation for these functions in LangChain could be due to the project's ongoing development. The source code, particularly in the libs/core/langchain_core/language_models/base.py file, serves as the primary resource for understanding these functions' implementation and usage.

For the ChatGoogleGenerativeAI model, the get_num_tokens function adapts based on whether the model is anthropic, using a specific function for anthropic models or defaulting to the superclass's method otherwise.

This explanation should help clarify the purpose and functionality of the get_num_tokens and get_num_tokens_from_message functions within the LangChain framework. If you have more questions or need additional details, feel free to ask.

Details

Sources

• libs/community/langchain_community/chat_models/anthropic.py
• libs/core/langchain_core/language_models/base.py
• libs/community/langchain_community/chat_models/bedrock.py
• libs/community/langchain_community/chat_models/everlyai.py
• libs/community/langchain_community/llms/google_palm.py

About Dosu
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[VedantRaj3907]

@dosu What if I want to count the tokens for any of the models like gpt.3-5-turbo or Claude opus or Gemini, and stream the output using langchain and get the total token counts for any of the model for each prompt. Is there any way to do so?

[dosubot]

To count tokens for models like gpt.3-5-turbo, Claude opus, or Gemini, especially when streaming outputs with LangChain, you can use the following approaches:

1. Direct Token Usage Information: For models that provide token usage in their response, you can access this directly. For example, with OpenAI's models, after generating a response, you can look at the response metadata for token usage or use the get_num_tokens method for a specific input.

2. Callbacks for Token Tracking: LangChain offers callback mechanisms to track token usage across calls. This is useful for detailed tracking of token consumption. For instance, using CallbackManagerForLLMRun with OpenAI models allows you to track the total tokens used in a call.

3. Streaming Outputs: Use the _stream and _astream methods for synchronous and asynchronous streaming, respectively, in LangChain's OpenAI class. This allows for streaming outputs while keeping track of token usage.

4. Aggregating Token Usage: For multiple calls or complex interactions, aggregate token usage information across calls or steps to manage token consumption effectively.

5. Model-Specific Token Counting: The get_num_tokens method can be used for precise token counting based on the input, crucial for managing costs and optimizing language model usage.

For more specific details on token counting for models like Claude opus and Gemini, or for the ChatGoogleGenerativeAI class's handling of the gemini-pro model, you might need to consult the maintainers of the ChatGoogleGenerativeAI class or the respective model providers, as the LangChain documentation may not cover these specifics.

Refer to LangChain's documentation and source code, particularly in the libs/partners/openai/langchain_openai/llms/base.py, for implementation details and usage examples. This approach ensures you have a clear understanding of your token consumption, crucial for managing costs and optimizing the use of language models in your applications.

Details

Sources

• Generate Tokens usage while streaming an output
• Added Token Count support for Gen AI Models
• can you tell me how to calculate what will be cost for this input tokens in prompt and output tokens in prompt
• How to reduce prompt with too many tools
• libs/partners/openai/langchain_openai/llms/base.py
• docs/docs/modules/model_io/chat/token_usage_tracking.ipynb
• libs/community/langchain_community/llms/openai.py
• libs/partners/openai/tests/unit_tests/test_token_counts.py
• libs/community/langchain_community/llms/bedrock.py
• libs/community/langchain_community/chat_models/bedrock.py
• libs/community/langchain_community/chat_models/openai.py
• libs/core/langchain_core/language_models/llms.py
• libs/partners/ibm/langchain_ibm/llms.py
• docs/docs/expression_language/streaming.ipynb
• libs/community/langchain_community/llms/sparkllm.py
• libs/core/langchain_core/language_models/base.py

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[VedantRaj3907]

@dosu can you please provide example code if possible

[dosubot]

To count tokens and stream outputs for models like gpt.3-5-turbo, Claude opus, or Gemini using LangChain, you can follow this approach:

import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessageChunk, AIMessageChunk

# Initialize the ChatOpenAI class with your model of choice
chat_model = ChatOpenAI(model="your-model-name-here")

# Example messages to send to the model
messages = [
    HumanMessageChunk(content="Your first message here."),
    AIMessageChunk(content="Your second message here.")
]

# Function to stream outputs and potentially track token usage
async def stream_and_track_tokens():
    async for chunk in chat_model._astream(messages):
        print(f"Generated chunk: {chunk.message.content}")
        # Token tracking or other metrics can be implemented here

# Run the async function to stream and track tokens
asyncio.run(stream_and_track_tokens())

This example demonstrates how to initialize a chat model, prepare messages, and stream outputs asynchronously. You can adapt the ChatOpenAI class initialization by specifying the model you're working with, such as "gpt.3-5-turbo", "Claude opus", or "Gemini".

For token counting, you can use the get_num_tokens function directly if you're dealing with simple text inputs or aggregate token usage information across calls by tracking the tokens used in each response. This requires accessing the response metadata or implementing a custom tracking mechanism within your streaming function.

Remember, the specifics of token counting and streaming might vary slightly based on the model and the LangChain version you're using, so it's a good idea to refer to the latest documentation or the source code for the most accurate and up-to-date information.

Details

Sources

• Generate Tokens usage while streaming an output
• Added Token Count support for Gen AI Models
• libs/community/langchain_community/chat_models/openai.py
• docs/docs/modules/model_io/chat/token_usage_tracking.ipynb
• libs/community/langchain_community/callbacks/infino_callback.py
• libs/core/langchain_core/language_models/base.py
• libs/community/langchain_community/llms/openai.py
• libs/partners/openai/tests/unit_tests/chat_models/test_base.py
• libs/partners/openai/tests/unit_tests/test_token_counts.py
• docs/docs/integrations/chat/google_generative_ai.ipynb
• templates/gemini-functions-agent/gemini_functions_agent/agent.py

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[VedantRaj3907]

Thanks for the reply @dosu but i think I need to implement custom methods to get the tokens from each of different models.

[dosubot]

Implementing custom methods to accurately count tokens for different models can be a good approach, especially when dealing with a variety of language models that may have unique tokenization processes. Here's a basic outline on how you might proceed:

1. Identify the Tokenization Process: Each model (e.g., GPT-3.5-turbo, Claude, Gemini) may use a different tokenization process. Understanding these processes is crucial for accurate token counting.

2. Custom Token Counting Function: Develop a function that takes a string input and returns the number of tokens according to the specific model's tokenization process. This might involve using the model's tokenizer directly or implementing a similar tokenization logic.

3. Integration with LangChain: Once you have your custom token counting functions, integrate them into your LangChain workflow. You can do this by creating a wrapper or utility class that handles token counting for the specific model you're interacting with.

4. Testing and Validation: Ensure the accuracy of your token counts by comparing them against the model's reported token usage (if available) for various inputs. This step is crucial to validate your custom methods.

5. Documentation: Document your custom methods, including details on the tokenization process for each model and examples of usage. This will be helpful for future reference and for others working with your code.

Here's a very simplified example of a custom token counting function for illustrative purposes:

def count_tokens_gpt(text):
    # This is a placeholder for the actual tokenization logic
    # For GPT models, you might use the tokenizer provided by the OpenAI API or a local implementation
    tokens = text.split()  # Simplified example; real tokenization is more complex
    return len(tokens)

# Example usage
text = "This is a test string."
token_count = count_tokens_gpt(text)
print(f"Token count for '{text}': {token_count}")

Remember, the actual implementation will depend on the specific tokenization process used by each model, which can be significantly more complex than the simplified example above.

Details

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.