句子嵌入优化器¶

此后处理器通过移除与查询不相关的句子（使用嵌入完成）来优化 token 使用。百分比截断（percentile cutoff）用于选取最相关的句子中的顶部百分比。也可以指定阈值截断（threshold cutoff），它使用原始相似度截断来选择要保留的句子。

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%pip install llama-index-readers-wikipedia

%pip install llama-index-readers-wikipedia

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# My OpenAI Key
import os

os.environ["OPENAI_API_KEY"] = "INSERT OPENAI KEY"

# My OpenAI Key import os os.environ["OPENAI_API_KEY"] = "INSERT OPENAI KEY"

设置¶

如果您在colab上打开此Notebook，您可能需要安装LlamaIndex 🦙。

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!pip install llama-index

!pip install llama-index

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from llama_index.core import download_loader

from llama_index.readers.wikipedia import WikipediaReader

loader = WikipediaReader()
documents = loader.load_data(pages=["Berlin"])

from llama_index.core import download_loader from llama_index.readers.wikipedia import WikipediaReader loader = WikipediaReader() documents = loader.load_data(pages=["Berlin"])

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from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents)

from llama_index.core import VectorStoreIndex index = VectorStoreIndex.from_documents(documents)

<class 'llama_index.readers.schema.base.Document'>

INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens
INFO:root:> [build_index_from_documents] Total embedding token usage: 18390 tokens

比较使用和不使用优化（针对LLM token使用、查询上的嵌入模型使用、优化器的嵌入模型使用和总时间）进行查询的效果。

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import time
from llama_index.core import VectorStoreIndex
from llama_index.core.postprocessor import SentenceEmbeddingOptimizer

print("Without optimization")
start_time = time.time()
query_engine = index.as_query_engine()
res = query_engine.query("What is the population of Berlin?")
end_time = time.time()
print("Total time elapsed: {}".format(end_time - start_time))
print("Answer: {}".format(res))

print("With optimization")
start_time = time.time()
query_engine = index.as_query_engine(
    node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)]
)
res = query_engine.query("What is the population of Berlin?")
end_time = time.time()
print("Total time elapsed: {}".format(end_time - start_time))
print("Answer: {}".format(res))

print("Alternate optimization cutoff")
start_time = time.time()
query_engine = index.as_query_engine(
    node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=0.7)]
)
res = query_engine.query("What is the population of Berlin?")
end_time = time.time()
print("Total time elapsed: {}".format(end_time - start_time))
print("Answer: {}".format(res))

import time from llama_index.core import VectorStoreIndex from llama_index.core.postprocessor import SentenceEmbeddingOptimizer print("Without optimization") start_time = time.time() query_engine = index.as_query_engine() res = query_engine.query("What is the population of Berlin?") end_time = time.time() print("Total time elapsed: {}".format(end_time - start_time)) print("Answer: {}".format(res)) print("With optimization") start_time = time.time() query_engine = index.as_query_engine( node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)] ) res = query_engine.query("What is the population of Berlin?") end_time = time.time() print("Total time elapsed: {}".format(end_time - start_time)) print("Answer: {}".format(res)) print("Alternate optimization cutoff") start_time = time.time() query_engine = index.as_query_engine( node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=0.7)] ) res = query_engine.query("What is the population of Berlin?") end_time = time.time() print("Total time elapsed: {}".format(end_time - start_time)) print("Answer: {}".format(res))

Without optimization

INFO:root:> [query] Total LLM token usage: 3545 tokens
INFO:root:> [query] Total embedding token usage: 7 tokens

Total time elapsed: 2.8928110599517822
Answer: 
The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.

With optimization

INFO:root:> [optimize] Total embedding token usage: 7 tokens
INFO:root:> [query] Total LLM token usage: 1779 tokens
INFO:root:> [query] Total embedding token usage: 7 tokens

Total time elapsed: 2.346346139907837
Answer: 
The population of Berlin is around 4.5 million.
Alternate optimization cutoff

INFO:root:> [optimize] Total embedding token usage: 7 tokens
INFO:root:> [query] Total LLM token usage: 3215 tokens
INFO:root:> [query] Total embedding token usage: 7 tokens

Total time elapsed: 2.101111888885498
Answer: 
The population of Berlin is around 4.5 million.