Jina Reranker

本笔记展示了如何使用 Jina Reranker 进行文档压缩和检索。

%pip install -qU langchain langchain-openai langchain-community langchain-text-splitters langchainhub

%pip install --upgrade --quiet  faiss

# OR  (depending on Python version)

%pip install --upgrade --quiet  faiss_cpu

# Helper function for printing docs


def pretty_print_docs(docs):
    print(
        f"\n{'-' * 100}\n".join(
            [f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
        )
    )

设置基础向量存储检索器

让我们开始初始化一个简单的向量存储检索器，并存储2023年国情咨文（分块）。我们可以设置检索器以检索大量（20个）文档。

设置Jina和OpenAI API密钥

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["JINA_API_KEY"] = getpass.getpass()

<!--IMPORTS:[{"imported": "TextLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.text.TextLoader.html", "title": "Jina Reranker"}, {"imported": "JinaEmbeddings", "source": "langchain_community.embeddings", "docs": "https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.jina.JinaEmbeddings.html", "title": "Jina Reranker"}, {"imported": "FAISS", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html", "title": "Jina Reranker"}, {"imported": "RecursiveCharacterTextSplitter", "source": "langchain_text_splitters", "docs": "https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html", "title": "Jina Reranker"}]-->
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import JinaEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter

documents = TextLoader(
    "../../how_to/state_of_the_union.txt",
).load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)

embedding = JinaEmbeddings(model_name="jina-embeddings-v2-base-en")
retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={"k": 20})

query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)

使用JinaRerank进行重新排序

现在让我们用ContextualCompressionRetriever包装我们的基础检索器，使用Jina Reranker作为压缩器。

<!--IMPORTS:[{"imported": "ContextualCompressionRetriever", "source": "langchain.retrievers", "docs": "https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.contextual_compression.ContextualCompressionRetriever.html", "title": "Jina Reranker"}, {"imported": "JinaRerank", "source": "langchain_community.document_compressors", "docs": "https://python.langchain.com/api_reference/community/document_compressors/langchain_community.document_compressors.jina_rerank.JinaRerank.html", "title": "Jina Reranker"}]-->
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.document_compressors import JinaRerank

compressor = JinaRerank()
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)

compressed_docs = compression_retriever.get_relevant_documents(
    "What did the president say about Ketanji Jackson Brown"
)

pretty_print_docs(compressed_docs)

使用Jina Reranker进行问答重新排序

<!--IMPORTS:[{"imported": "create_retrieval_chain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html", "title": "Jina Reranker"}, {"imported": "create_stuff_documents_chain", "source": "langchain.chains.combine_documents", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html", "title": "Jina Reranker"}]-->
from langchain import hub
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
retrieval_qa_chat_prompt.pretty_print()

================================ System Message ================================

Answer any use questions based solely on the context below:

<context>
{context}
</context>

============================= Messages Placeholder =============================

{chat_history}

================================ Human Message =================================

{input}

<!--IMPORTS:[{"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Jina Reranker"}]-->
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)
chain = create_retrieval_chain(compression_retriever, combine_docs_chain)

chain.invoke({"input": query})