From 0175906437d5ede529d55e6de62aaa3b1122ed39 Mon Sep 17 00:00:00 2001 From: lvliang-intel Date: Sat, 30 Mar 2024 05:37:32 +0800 Subject: [PATCH] templates: add RAG template for Intel Xeon Scalable Processors (#18424) **Description:** This template utilizes Chroma and TGI (Text Generation Inference) to execute RAG on the Intel Xeon Scalable Processors. It serves as a demonstration for users, illustrating the deployment of the RAG service on the Intel Xeon Scalable Processors and showcasing the resulting performance enhancements. **Issue:** None **Dependencies:** The template contains the poetry project requirements to run this template. CPU TGI batching is WIP. **Twitter handle:** None --------- Signed-off-by: lvliang-intel Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur --- templates/intel-rag-xeon/README.md | 97 + .../intel-rag-xeon/data/nke-10k-2023.pdf | Bin 0 -> 2397936 bytes templates/intel-rag-xeon/ingest.py | 49 + templates/intel-rag-xeon/intel_rag_xeon.ipynb | 62 + .../intel-rag-xeon/intel_rag_xeon/__init__.py | 3 + .../intel-rag-xeon/intel_rag_xeon/chain.py | 72 + templates/intel-rag-xeon/poetry.lock | 5693 +++++++++++++++++ templates/intel-rag-xeon/pyproject.toml | 51 + templates/intel-rag-xeon/tests/__init__.py | 0 9 files changed, 6027 insertions(+) create mode 100644 templates/intel-rag-xeon/README.md create mode 100644 templates/intel-rag-xeon/data/nke-10k-2023.pdf create mode 100644 templates/intel-rag-xeon/ingest.py create mode 100644 templates/intel-rag-xeon/intel_rag_xeon.ipynb create mode 100644 templates/intel-rag-xeon/intel_rag_xeon/__init__.py create mode 100644 templates/intel-rag-xeon/intel_rag_xeon/chain.py create mode 100644 templates/intel-rag-xeon/poetry.lock create mode 100644 templates/intel-rag-xeon/pyproject.toml create mode 100644 templates/intel-rag-xeon/tests/__init__.py diff --git a/templates/intel-rag-xeon/README.md b/templates/intel-rag-xeon/README.md new file mode 100644 index 0000000000..0ecd696b36 --- /dev/null +++ b/templates/intel-rag-xeon/README.md @@ -0,0 +1,97 @@ +# 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") +``` diff --git a/templates/intel-rag-xeon/data/nke-10k-2023.pdf b/templates/intel-rag-xeon/data/nke-10k-2023.pdf new file mode 100644 index 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chunk_size=1500, chunk_overlap=100, add_start_index=True + ) + loader = UnstructuredFileLoader(doc, mode="single", strategy="fast") + chunks = loader.load_and_split(text_splitter) + + print("Done preprocessing. Created", len(chunks), "chunks of the original pdf") + + # Create vectorstore + embedder = HuggingFaceEmbeddings( + model_name="sentence-transformers/all-MiniLM-L6-v2" + ) + + documents = [] + for chunk in chunks: + doc = Document(page_content=chunk.page_content, metadata=chunk.metadata) + documents.append(doc) + + # Add to vectorDB + _ = Chroma.from_documents( + documents=documents, + collection_name="xeon-rag", + embedding=embedder, + persist_directory="/tmp/xeon_rag_db", + ) + + +if __name__ == "__main__": + ingest_documents() diff --git a/templates/intel-rag-xeon/intel_rag_xeon.ipynb b/templates/intel-rag-xeon/intel_rag_xeon.ipynb new file mode 100644 index 0000000000..29cf2b45cd --- /dev/null +++ b/templates/intel-rag-xeon/intel_rag_xeon.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Connect to RAG App\n", + "\n", + "Assuming you are already running this server:\n", + "```bash\n", + "langserve start\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d774be2a", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "\n", + "gaudi_rag = RemoteRunnable(\"http://localhost:8000/intel-rag-xeon\")\n", + "\n", + "print(gaudi_rag.invoke(\"What was Nike's revenue in 2023?\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07ae0005", + "metadata": {}, + "outputs": [], + "source": [ + "print(gaudi_rag.invoke(\"How many employees work at Nike?\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/templates/intel-rag-xeon/intel_rag_xeon/__init__.py b/templates/intel-rag-xeon/intel_rag_xeon/__init__.py new file mode 100644 index 0000000000..16f967a438 --- /dev/null +++ b/templates/intel-rag-xeon/intel_rag_xeon/__init__.py @@ -0,0 +1,3 @@ +from intel_rag_xeon.chain import chain + +__all__ = ["chain"] diff --git a/templates/intel-rag-xeon/intel_rag_xeon/chain.py b/templates/intel-rag-xeon/intel_rag_xeon/chain.py new file mode 100644 index 0000000000..f968abaa28 --- /dev/null +++ b/templates/intel-rag-xeon/intel_rag_xeon/chain.py @@ -0,0 +1,72 @@ +from langchain.callbacks import streaming_stdout +from langchain_community.embeddings import HuggingFaceEmbeddings +from langchain_community.llms import HuggingFaceEndpoint +from langchain_community.vectorstores import Chroma +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.runnables import RunnableParallel, RunnablePassthrough +from langchain_core.vectorstores import VectorStoreRetriever + + +# Make this look better in the docs. +class Question(BaseModel): + __root__: str + + +# Init Embeddings +embedder = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") + +knowledge_base = Chroma( + persist_directory="/tmp/xeon_rag_db", + embedding_function=embedder, + collection_name="xeon-rag", +) +query = "What was Nike's revenue in 2023?" +docs = knowledge_base.similarity_search(query) +print(docs[0].page_content) +retriever = VectorStoreRetriever( + vectorstore=knowledge_base, search_type="mmr", search_kwargs={"k": 1, "fetch_k": 5} +) + +# Define our prompt +template = """ +Use the following pieces of context from retrieved +dataset to answer the question. Do not make up an answer if there is no +context provided to help answer it. + +Context: +--------- +{context} + +--------- +Question: {question} +--------- + +Answer: +""" + + +prompt = ChatPromptTemplate.from_template(template) + + +ENDPOINT_URL = "http://localhost:8080" +callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()] +model = HuggingFaceEndpoint( + endpoint_url=ENDPOINT_URL, + max_new_tokens=512, + top_k=10, + top_p=0.95, + typical_p=0.95, + temperature=0.01, + repetition_penalty=1.03, + streaming=True, +) + +# RAG Chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +).with_types(input_type=Question) diff --git a/templates/intel-rag-xeon/poetry.lock b/templates/intel-rag-xeon/poetry.lock new file mode 100644 index 0000000000..a5baee8a70 --- /dev/null +++ b/templates/intel-rag-xeon/poetry.lock @@ -0,0 +1,5693 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not 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