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用法模式#

估算LLM和嵌入式标记计数#

为了测量LLM和嵌入式标记计数，您需要

  1. 设置 MockLLM 和 MockEmbedding 对象
from llama_index.core.llms import MockLLM
from llama_index.core import MockEmbedding

llm = MockLLM(max_tokens=256)
embed_model = MockEmbedding(embed_dim=1536)
  1. 设置 TokenCountingCallback 处理程序
import tiktoken
from llama_index.core.callbacks import CallbackManager, TokenCountingHandler

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

callback_manager = CallbackManager([token_counter])
  1. 将它们添加到全局 Settings
from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model
Settings.callback_manager = callback_manager
  1. 构建索引
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

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

index = VectorStoreIndex.from_documents(documents)
  1. 测量计数！
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",
)

# 重置计数
token_counter.reset_counts()
  1. 运行查询，再次测量
query_engine = index.as_query_engine()

response = query_engine.query("query")

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",
)