# 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](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html). ## Environment Setup To use [🤗 text-generation-inference](https://github.com/huggingface/text-generation-inference) on Intel® Xeon® Scalable Processors, please follow these steps: ### Launch a local server instance on Intel Xeon Server: ```bash model=Intel/neural-chat-7b-v3-3 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 ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model ``` For gated models such as `LLAMA-2`, you will have to pass -e HUGGING_FACE_HUB_TOKEN=\ to the docker run command above with a valid Hugging Face Hub read token. Please follow this link [huggingface token](https://huggingface.co/docs/hub/security-tokens) to get the access token ans export `HUGGINGFACEHUB_API_TOKEN` environment with the token. ```bash export HUGGINGFACEHUB_API_TOKEN= ``` Send a request to check if the endpoint is wokring: ```bash 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](https://github.com/huggingface/text-generation-inference). ## Populating with data If you want to populate the DB with some example data, you can run the below commands: ```shell poetry install poetry run python ingest.py ``` The script process and stores sections from Edgar 10k filings data for Nike `nke-10k-2023.pdf` into a Chroma database. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package intel-rag-xeon ``` If you want to add this to an existing project, you can just run: ```shell langchain app add intel-rag-xeon ``` And add the following code to your `server.py` file: ```python 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. LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/intel-rag-xeon/playground](http://127.0.0.1:8000/intel-rag-xeon/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/intel-rag-xeon") ```