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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
100 lines
3.7 KiB
Markdown
100 lines
3.7 KiB
Markdown
# RAG - Intel Xeon
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This template performs RAG using `Chroma` and `Hugging Face Text Generation Inference`
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on `Intel® Xeon® Scalable` Processors.
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`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).
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## Environment Setup
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To use [🤗 text-generation-inference](https://github.com/huggingface/text-generation-inference) on Intel® Xeon® Scalable Processors, please follow these steps:
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### Launch a local server instance on Intel Xeon Server:
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```bash
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model=Intel/neural-chat-7b-v3-3
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
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```
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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.
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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.
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```bash
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export HUGGINGFACEHUB_API_TOKEN=<token>
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```
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Send a request to check if the endpoint is working:
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```bash
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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'
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```
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More details please refer to [text-generation-inference](https://github.com/huggingface/text-generation-inference).
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## Populating with data
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If you want to populate the DB with some example data, you can run the below commands:
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```shell
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poetry install
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poetry run python ingest.py
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```
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The script process and stores sections from Edgar 10k filings data for Nike `nke-10k-2023.pdf` into a Chroma database.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package intel-rag-xeon
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add intel-rag-xeon
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```
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And add the following code to your `server.py` file:
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```python
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from intel_rag_xeon import chain as xeon_rag_chain
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add_routes(app, xeon_rag_chain, path="/intel-rag-xeon")
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```
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(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
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/intel-rag-xeon")
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```
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