LlamaIndex Postprocessor Integration: IBM

This package integrates the LlamaIndex Postprocessor API with the IBM watsonx.ai Rerank API by leveraging ibm-watsonx-ai SDK.

Installation

pip install llama-index-postprocessor-ibm

Usage

Setting up

Install other required packages:

pip install -qU llama-index
pip install -qU llama-index-llms-ibm
pip install -qU llama-index-embeddings-ibm

To use IBM's Foundation Models, Embeddings and Rerank, you must have an IBM Cloud user API key. Here's how to obtain and set up your API key:

1. Obtain an API Key: For more details on how to create and manage an API key, refer to Managing user API keys.
2. Set the API Key as an Environment Variable: For security reasons, it's recommended to not hard-code your API key directly in your scripts. Instead, set it up as an environment variable. You can use the following code to prompt for the API key and set it as an environment variable:

import os
from getpass import getpass

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

Alternatively, you can set the environment variable in your terminal.

• Linux/macOS: Open your terminal and execute the following command:

  bashCopy

  export WATSONX_APIKEY='your_ibm_api_key'
  

  To make this environment variable persistent across terminal sessions, add the above line to your ~/.bashrc, ~/.bash_profile, or ~/.zshrc file.

• Windows: For Command Prompt, use:

  cmdCopy

  set WATSONX_APIKEY=your_ibm_api_key
  

Note:

• To provide context for the API call, you must pass the project_id or space_id. To get your project or space ID, open your project or space, go to the Manage tab, and click General. For more information see: Project documentation or Deployment space documentation.
• Depending on the region of your provisioned service instance, use one of the urls listed in watsonx.ai API Authentication.

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

Provide PROJECT_ID that will be used for initialize each watsonx integration instance.

PROJECT_ID = "PASTE YOUR PROJECT_ID HERE"
URL = "https://us-south.ml.cloud.ibm.com"

Download data and load documents

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

from llama_index.core import SimpleDirectoryReader

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

Load the Rerank

You might need to adjust rerank parameters for different tasks:

truncate_input_tokens = 512

Initialize WatsonxRerank instance.

You need to specify the model_id that will be used for rerank. You can find the list of all the available models in Supported reranker models.

from llama_index.postprocessor.ibm import WatsonxRerank

watsonx_rerank = WatsonxRerank(
    model_id="cross-encoder/ms-marco-minilm-l-12-v2",
    top_n=2,
    url=URL,
    project_id=PROJECT_ID,
    truncate_input_tokens=truncate_input_tokens,
)

Alternatively, you can use Cloud Pak for Data credentials. For details, see watsonx.ai software setup.

from llama_index.postprocessor.ibm import WatsonxRerank

watsonx_rerank = WatsonxRerank(
    model_id="cross-encoder/ms-marco-minilm-l-12-v2",
    url=URL,
    username="PASTE YOUR USERNAME HERE",
    password="PASTE YOUR PASSWORD HERE",
    instance_id="openshift",
    version="5.1",
    project_id=PROJECT_ID,
    truncate_input_tokens=truncate_input_tokens,
)

Load the embedding model

Initialize the WatsonxEmbeddings instance.

For more information about WatsonxEmbeddings please refer to the llama-index-embeddings-ibm package description.

You might need to adjust embedding parameters for different tasks:

truncate_input_tokens = 512

You need to specify the model_id that will be used for embedding. You can find the list of all the available models in Supported embedding models.

from llama_index.embeddings.ibm import WatsonxEmbeddings

watsonx_embedding = WatsonxEmbeddings(
    model_id="ibm/slate-30m-english-rtrvr",
    url=URL,
    project_id=PROJECT_ID,
    truncate_input_tokens=truncate_input_tokens,
)

Change default settings

from llama_index.core import Settings

Settings.chunk_size = 512

Build index

from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(
    documents=documents, embed_model=watsonx_embedding
)

Load the LLM

Initialize the WatsonxLLM instance.

For more information about WatsonxLLM please refer to the llama-index-llms-ibm package description.

You need to specify the model_id that will be used for inferencing. You can find the list of all the available models in Supported foundation models.

You might need to adjust model parameters for different models or tasks. For details, refer to Available MetaNames.

max_new_tokens = 128

from llama_index.llms.ibm import WatsonxLLM

watsonx_llm = WatsonxLLM(
    model_id="meta-llama/llama-3-3-70b-instruct",
    url=URL,
    project_id=PROJECT_ID,
    max_new_tokens=max_new_tokens,
)

Send a query

Retrieve top 10 most relevant nodes, then filter with WatsonxRerank

query_engine = index.as_query_engine(
    llm=watsonx_llm,
    similarity_top_k=10,
    node_postprocessors=[watsonx_rerank],
)
response = query_engine.query(
    "What did Sam Altman do in this essay?",
)

from llama_index.core.response.pprint_utils import pprint_response

pprint_response(response, show_source=True)

Directly retrieve top 2 most similar nodes

query_engine = index.as_query_engine(
    llm=watsonx_llm,
    similarity_top_k=2,
)
response = query_engine.query(
    "What did Sam Altman do in this essay?",
)

Retrieved context is irrelevant and response is hallucinated.

pprint_response(response, show_source=True)