mixedbread 重排序模型使用指南¶

mixedbread.ai 已发布三款完全开源的 Apache 2.0 许可重排序模型。如需深入了解，可查阅其详细的博客文章。以下是三款模型的具体型号：

1. mxbai-rerank-xsmall-v1
2. mxbai-rerank-base-v1
3. mxbai-rerank-large-v1

本指南将展示如何在 LlamaIndex 中通过 SentenceTransformerRerank 模块使用 mxbai-rerank-base-v1 模型。该配置方案允许您通过 SentenceTransformerRerank 模块自由切换任意重排序模型，从而增强 RAG 流程的效果。

安装¶

In [ ]:

!pip install llama-index
!pip install sentence-transformers

!pip install llama-index !pip install sentence-transformers

设置 API 密钥¶

In [ ]:

import os

os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"

import os os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"

In [ ]:

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
)

from llama_index.core.postprocessor import SentenceTransformerRerank

from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, ) from llama_index.core.postprocessor import SentenceTransformerRerank

下载数据¶

In [ ]:

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

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

--2024-03-01 09:52:09--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.007s  

2024-03-01 09:52:09 (9.86 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

加载文档¶

In [ ]:

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

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

构建索引¶

In [ ]:

index = VectorStoreIndex.from_documents(documents=documents)

index = VectorStoreIndex.from_documents(documents=documents)

为 mxbai-rerank-base-v1 重排序器定义后处理器¶

In [ ]:

from llama_index.core.postprocessor import SentenceTransformerRerank

postprocessor = SentenceTransformerRerank(
    model="mixedbread-ai/mxbai-rerank-base-v1", top_n=2
)

from llama_index.core.postprocessor import SentenceTransformerRerank postprocessor = SentenceTransformerRerank( model="mixedbread-ai/mxbai-rerank-base-v1", top_n=2 )

创建查询引擎¶

我们将首先检索10个相关节点，然后使用定义的后处理器筛选出前2个节点。

In [ ]:

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[postprocessor],
)

query_engine = index.as_query_engine( similarity_top_k=10, node_postprocessors=[postprocessor], )

测试查询¶

In [ ]:

response = query_engine.query(
    "Why did Sam Altman decline the offer of becoming president of Y Combinator?",
)

print(response)

response = query_engine.query( "Why did Sam Altman decline the offer of becoming president of Y Combinator?", ) print(response)

Sam Altman initially declined the offer of becoming president of Y Combinator because he wanted to start a startup focused on making nuclear reactors.

In [ ]:

response = query_engine.query(
    "Why did Paul Graham start YC?",
)

print(response)

response = query_engine.query( "Why did Paul Graham start YC?", ) print(response)

Paul Graham started YC because he and his partners wanted to create an investment firm where they could implement their own ideas and provide the kind of support to startups that they felt was lacking when they were founders themselves. They aimed to not only make seed investments but also assist startups with various aspects of setting up a company, similar to the help they had received from others in the past.