# 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=\<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=<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. You can sign up for LangSmith [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=<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:

```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")
```