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Jina Reranker

This notebook shows how to use Jina Reranker for document compression and retrieval.

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

Set up the base vector store retriever

Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs.

Set the Jina and OpenAI API keys
import getpass
import os

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

Doing reranking with JinaRerank

Now let's wrap our base retriever with a ContextualCompressionRetriever, using Jina Reranker as a compressor.

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"

QA reranking with 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")
================================ System Message ================================

Answer any use questions based solely on the context below:


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


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

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
combine_docs_chain = create_stuff_documents_chain(llm, retrieval_qa_chat_prompt)
chain = create_retrieval_chain(compression_retriever, combine_docs_chain)

API Reference:

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

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