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WatsonxRerank

WatsonxRerank is a wrapper for IBM watsonx.ai foundation models.

This notebook shows how to use watsonx's rerank endpoint in a retriever. This builds on top of ideas in the ContextualCompressionRetriever.

Overview​

Integration details​

ClassPackageJS supportPackage downloadsPackage latest
WatsonxReranklangchain-ibmâś…PyPI - DownloadsPyPI - Version

Setup​

To access IBM watsonx.ai models you'll need to create an IBM watsonx.ai account, get an API key, and install the langchain-ibm integration package.

Credentials​

The cell below defines the credentials required to work with watsonx Foundation Model inferencing.

Action: Provide the IBM Cloud user API key. For details, see Managing user API keys.

import os
from getpass import getpass

watsonx_api_key = getpass()
os.environ["WATSONX_APIKEY"] = watsonx_api_key

Additionally you are able to pass additional secrets as an environment variable.

import os

os.environ["WATSONX_URL"] = "your service instance url"
os.environ["WATSONX_TOKEN"] = "your token for accessing the CPD cluster"
os.environ["WATSONX_PASSWORD"] = "your password for accessing the CPD cluster"
os.environ["WATSONX_USERNAME"] = "your username for accessing the CPD cluster"
os.environ["WATSONX_INSTANCE_ID"] = "your instance_id for accessing the CPD cluster"

Installation​

The LangChain IBM integration lives in the langchain-ibm package:

!pip install -qU langchain-ibm
!pip install -qU langchain-community
!pip install -qU langchain_text_splitters

For experiment purpose please also install faiss or faiss-cpu package:

!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)]
        )
    )

Instantiation​

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.

Initialize the WatsonxEmbeddings. For more details see WatsonxEmbeddings.

Note:

  • To provide context for the API call, you must add project_id or space_id. For more information see documentation.
  • Depending on the region of your provisioned service instance, use one of the urls described here.

In this example, we’ll use the project_id and Dallas url.

You need to specify model_id that will be used for embedding. All available models you can find in documentation.

from langchain_ibm import WatsonxEmbeddings

wx_embeddings = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
)
API Reference:WatsonxEmbeddings
from langchain_community.document_loaders import TextLoader
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)
retriever = FAISS.from_documents(texts, wx_embeddings).as_retriever(
    search_kwargs={"k": 20}
)

query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.invoke(query)
pretty_print_docs(docs[:5])  # Printing the first 5 documents
Document 1:

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
----------------------------------------------------------------------------------------------------
Document 2:

I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. 

I’ve worked on these issues a long time. 

I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. 

So let’s not abandon our streets. Or choose between safety and equal justice.
----------------------------------------------------------------------------------------------------
Document 3:

He met the Ukrainian people. 

From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. 

Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. 

In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.
----------------------------------------------------------------------------------------------------
Document 4:

As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. 

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.
----------------------------------------------------------------------------------------------------
Document 5:

To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. 

And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. 

Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.

Usage​

Doing reranking with WatsonxRerank​

Now let's wrap our base retriever with a ContextualCompressionRetriever. We'll add an WatsonxRerank, uses the watsonx rerank endpoint to rerank the returned results. Do note that it is mandatory to specify the model name in WatsonxRerank!

from langchain_ibm import WatsonxRerank

wx_rerank = WatsonxRerank(
    model_id="ibm/slate-125m-english-rtrvr",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
)
API Reference:WatsonxRerank
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever

compression_retriever = ContextualCompressionRetriever(
    base_compressor=wx_rerank, base_retriever=retriever
)

compressed_docs = compression_retriever.invoke(
    "What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs[:5])  # Printing the first 5 compressed documents
Document 1:

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
----------------------------------------------------------------------------------------------------
Document 2:

As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. 

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.
----------------------------------------------------------------------------------------------------
Document 3:

To all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. 

And I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. 

Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.
----------------------------------------------------------------------------------------------------
Document 4:

I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. 

I’ve worked on these issues a long time. 

I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. 

So let’s not abandon our streets. Or choose between safety and equal justice.
----------------------------------------------------------------------------------------------------
Document 5:

He met the Ukrainian people. 

From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. 

Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. 

In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.

Use within a chain​

You can of course use this retriever within a QA pipeline

Initialize the ChatWatsonx. For more details see ChatWatsonx.

from langchain_ibm import ChatWatsonx

wx_chat = ChatWatsonx(
    model_id="meta-llama/llama-3-1-70b-instruct",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="PASTE YOUR PROJECT_ID HERE",
)
API Reference:ChatWatsonx
from langchain.chains import RetrievalQA

chain = RetrievalQA.from_chain_type(llm=wx_chat, retriever=compression_retriever)
API Reference:RetrievalQA
chain.invoke(query)
{'query': 'What did the president say about Ketanji Brown Jackson',
 'result': 'The President said that he had nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court, and described her as "one of our nation\'s top legal minds" who will continue Justice Breyer\'s legacy of excellence.'}

API reference​

For detailed documentation of all WatsonxRerank features and configurations head to the API reference.

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