Open In Colab

Token Counting Handler#

This notebook walks through how to use the TokenCountingHandler and how it can be used to track your prompt, completion, and embedding token usage over time.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

!pip install llama-index

import tiktoken
from llama_index.llms import Anthropic

from llama_index import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    ServiceContext,
    set_global_service_context,
)
from llama_index.callbacks import CallbackManager, TokenCountingHandler

import os

os.environ["ANTHROPIC_API_KEY"] = "YOUR_API_KEY"

Setup#

Here, we setup the callback and the serivce context. We set a global service context so that we don’t have to worry about passing it into indexes and queries.

token_counter = TokenCountingHandler(
    tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode
)

callback_manager = CallbackManager([token_counter])

llm = Anthropic()

service_context = ServiceContext.from_defaults(
    llm=llm, callback_manager=callback_manager, embed_model="local"
)

# set the global default!
set_global_service_context(service_context)

/Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Token Counting#

The token counter will track embedding, prompt, and completion token usage. The token counts are cummulative and are only reset when you choose to do so, with token_counter.reset_counts().

Embedding Token Usage#

Now that the service context is setup, let’s track our embedding token usage.

Download Data#

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

documents = SimpleDirectoryReader("./data/paul_graham").load_data()

index = VectorStoreIndex.from_documents(documents)

print(token_counter.total_embedding_token_count)

16852

That looks right! Before we go any further, lets reset the counts

token_counter.reset_counts()

LLM + Embedding Token Usage#

Next, let’s test a query and see what the counts look like.

query_engine = index.as_query_engine(similarity_top_k=4)
response = query_engine.query("What did the author do growing up?")

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

print(
    "Embedding Tokens: ",
    token_counter.total_embedding_token_count,
    "\n",
    "LLM Prompt Tokens: ",
    token_counter.prompt_llm_token_count,
    "\n",
    "LLM Completion Tokens: ",
    token_counter.completion_llm_token_count,
    "\n",
    "Total LLM Token Count: ",
    token_counter.total_llm_token_count,
    "\n",
)

Embedding Tokens:  8 
 LLM Prompt Tokens:  3527 
 LLM Completion Tokens:  214 
 Total LLM Token Count:  3741

Token Counting + Streaming!#

The token counting handler also handles token counting during streaming.

Here, token counting will only happen once the stream is completed.

token_counter.reset_counts()

query_engine = index.as_query_engine(similarity_top_k=4, streaming=True)
response = query_engine.query("What happened at Interleaf?")

# finish the stream
for token in response.response_gen:
    # print(token, end="", flush=True)
    continue

print(
    "Embedding Tokens: ",
    token_counter.total_embedding_token_count,
    "\n",
    "LLM Prompt Tokens: ",
    token_counter.prompt_llm_token_count,
    "\n",
    "LLM Completion Tokens: ",
    token_counter.completion_llm_token_count,
    "\n",
    "Total LLM Token Count: ",
    token_counter.total_llm_token_count,
    "\n",
)

Embedding Tokens:  6 
 LLM Prompt Tokens:  3631 
 LLM Completion Tokens:  214 
 Total LLM Token Count:  3845

Advanced Usage#

The token counter tracks each token usage event in an object called a TokenCountingEvent. This object has the following attributes:

  • prompt -> The prompt string sent to the LLM or Embedding model

  • prompt_token_count -> The token count of the LLM prompt

  • completion -> The string completion received from the LLM (not used for embeddings)

  • completion_token_count -> The token count of the LLM completion (not used for embeddings)

  • total_token_count -> The total prompt + completion tokens for the event

  • event_id -> A string ID for the event, which aligns with other callback handlers

These events are tracked on the token counter in two lists:

  • llm_token_counts

  • embedding_token_counts

Let’s explore what these look like!

print("Num LLM token count events: ", len(token_counter.llm_token_counts))
print(
    "Num Embedding token count events: ",
    len(token_counter.embedding_token_counts),
)

Num LLM token count events:  1
Num Embedding token count events:  1

This makes sense! The previous query embedded the query text, and then made 2 LLM calls (since the top k was 4, and the default chunk size is 1024, two seperate calls need to be made so the LLM can read all the retrieved text).

Next, let’s quickly see what these events look like for a single event.

print("prompt: ", token_counter.llm_token_counts[0].prompt[:100], "...\n")
print(
    "prompt token count: ",
    token_counter.llm_token_counts[0].prompt_token_count,
    "\n",
)

print(
    "completion: ", token_counter.llm_token_counts[0].completion[:100], "...\n"
)
print(
    "completion token count: ",
    token_counter.llm_token_counts[0].completion_token_count,
    "\n",
)

print("total token count", token_counter.llm_token_counts[0].total_token_count)

prompt:  user: Context information is below.
---------------------
a web app, is common now, but at the time  ...

prompt token count:  3631 

completion:  assistant:  Based on the context, a few key things happened at Interleaf:

- It was a software compa ...

completion token count:  199 

total token count 3830