llama-index-postprocessor-nvidia-rerank 0.4.0

llama-index postprocessor nvidia_rerank integration

NVIDIA NIMs

The llama-index-postprocessor-nvidia-rerank package contains LlamaIndex integrations building applications with models on NVIDIA NIM inference microservice. NIM supports models across domains like chat, embedding, and re-ranking models from the community as well as NVIDIA. These models are optimized by NVIDIA to deliver the best performance on NVIDIA accelerated infrastructure and deployed as a NIM, an easy-to-use, prebuilt containers that deploy anywhere using a single command on NVIDIA accelerated infrastructure.

NVIDIA hosted deployments of NIMs are available to test on the NVIDIA API catalog. After testing, NIMs can be exported from NVIDIA’s API catalog using the NVIDIA AI Enterprise license and run on-premises or in the cloud, giving enterprises ownership and full control of their IP and AI application.

NIMs are packaged as container images on a per model basis and are distributed as NGC container images through the NVIDIA NGC Catalog. At their core, NIMs provide easy, consistent, and familiar APIs for running inference on an AI model.

LlamaIndex Postprocessor Integration: Nvidia_Rerank

Below is an example on how to use some common functionality surrounding text-generative and embedding models

Installation

pip install --upgrade llama-index llama-index-core llama-index-postprocessor-nvidia-rerank

Setup

To get started:

1. Create a free account with NVIDIA, which hosts NVIDIA AI Foundation models.

2. Select the Retrieval tab, then select your model of choice.

3. Under Input select the Python tab, and click Get API Key. Then click Generate Key.

4. Copy and save the generated key as NVIDIA_API_KEY. From there, you should have access to the endpoints.

import getpass
import os

if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
else:
    nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
    assert nvapi_key.startswith(
        "nvapi-"
    ), f"{nvapi_key[:5]}... is not a valid key"
    os.environ["NVIDIA_API_KEY"] = nvapi_key

Working with NVIDIA API Catalog

from llama_index.postprocessor.nvidia_rerank import NVIDIARerank

rerank = NVIDIARerank()

Working with NVIDIA NIMs

from llama_index.postprocessor.nvidia_rerank import NVIDIARerank

# connect to an reranking NIM running at localhost:1976
rerank = NVIDIARerank(base_url="http://localhost:1976/v1")

Supported models

Querying available_models will still give you all of the other models offered by your API credentials.

from llama_index.postprocessor.nvidia_rerank import NVIDIARerank

rerank.available_models

To find out more about a specific model, please navigate to the NVIDIA NIM section of ai.nvidia.com as linked here.

Reranking

Below is an example:

from llama_index.postprocessor.nvidia_rerank import NVIDIARerank

from llama_index.core import Document
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter, SimpleFileNodeParser


# load documents
documents = SimpleDirectoryReader("/path_to_your_data_folder").load_data()

# use API Catalog's reranker model
my_key = os.environ["NVIDIA_API_KEY"]
rerank = NVIDIARerank()

# parse nodes
parser = SentenceSplitter(separator="\n", chunk_size=200, chunk_overlap=0)
nodes = parser.get_nodes_from_documents(documents)
# rerank
rerank.postprocess_nodes(nodes, query_str=query)