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RAG example on Intel Xeon

This template performs RAG using Chroma and Text Generation Inference on Intel® Xeon® Scalable Processors. Intel® Xeon® Scalable processors feature built-in accelerators for more performance-per-core and unmatched AI performance, with advanced security technologies for the most in-demand workload requirements—all while offering the greatest cloud choice and application portability, please check Intel® Xeon® Scalable Processors.

Environment Setup

To use 🤗 text-generation-inference on Intel® Xeon® Scalable Processors, please follow these steps:

Launch a local server instance on Intel Xeon Server:

volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --shm-size 1g -p 8080:80 -v $volume:/data --model-id $model

For gated models such as LLAMA-2, you will have to pass -e HUGGING_FACE_HUB_TOKEN=\<token> to the docker run command above with a valid Hugging Face Hub read token.

Please follow this link huggingface token to get the access token ans export HUGGINGFACEHUB_API_TOKEN environment with the token.


Send a request to check if the endpoint is wokring:

curl localhost:8080/generate -X POST -d '{"inputs":"Which NFL team won the Super Bowl in the 2010 season?","parameters":{"max_new_tokens":128, "do_sample": true}}'   -H 'Content-Type: application/json'

More details please refer to text-generation-inference.

Populating with data

If you want to populate the DB with some example data, you can run the below commands:

poetry install
poetry run python

The script process and stores sections from Edgar 10k filings data for Nike nke-10k-2023.pdf into a Chroma database.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package intel-rag-xeon

If you want to add this to an existing project, you can just run:

langchain app add intel-rag-xeon

And add the following code to your file:

from intel_rag_xeon import chain as xeon_rag_chain

add_routes(app, xeon_rag_chain, path="/intel-rag-xeon")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/intel-rag-xeon")

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