From 6635b9f0aba6c397644ce9a35eacde7919d49071 Mon Sep 17 00:00:00 2001 From: sgurunat Date: Mon, 11 Nov 2024 09:28:40 +0000 Subject: [PATCH] Add langchain vllm support for DocSum along with authentication support for vllm endpoints --- .../summarization/vllm/langchain/Dockerfile | 28 +++++ .../summarization/vllm/langchain/README.md | 109 ++++++++++++++++++ .../summarization/vllm/langchain/__init__.py | 2 + .../vllm/langchain/docker_compose_llm.yaml | 35 ++++++ .../vllm/langchain/entrypoint.sh | 8 ++ .../llms/summarization/vllm/langchain/llm.py | 104 +++++++++++++++++ .../vllm/langchain/requirements-runtime.txt | 1 + .../vllm/langchain/requirements.txt | 15 +++ 8 files changed, 302 insertions(+) create mode 100644 comps/llms/summarization/vllm/langchain/Dockerfile create mode 100644 comps/llms/summarization/vllm/langchain/README.md create mode 100644 comps/llms/summarization/vllm/langchain/__init__.py create mode 100644 comps/llms/summarization/vllm/langchain/docker_compose_llm.yaml create mode 100644 comps/llms/summarization/vllm/langchain/entrypoint.sh create mode 100644 comps/llms/summarization/vllm/langchain/llm.py create mode 100644 comps/llms/summarization/vllm/langchain/requirements-runtime.txt create mode 100644 comps/llms/summarization/vllm/langchain/requirements.txt diff --git a/comps/llms/summarization/vllm/langchain/Dockerfile b/comps/llms/summarization/vllm/langchain/Dockerfile new file mode 100644 index 000000000..3a1cd5a8f --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/Dockerfile @@ -0,0 +1,28 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +FROM python:3.11-slim + +ARG ARCH="cpu" + +RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ + libgl1-mesa-glx \ + libjemalloc-dev + +RUN useradd -m -s /bin/bash user && \ + mkdir -p /home/user && \ + chown -R user /home/user/ + +USER user + +COPY comps /home/user/comps + +RUN pip install --no-cache-dir --upgrade pip setuptools && \ + if [ ${ARCH} = "cpu" ]; then pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \ + pip install --no-cache-dir -r /home/user/comps/llms/summarization/vllm/langchain/requirements.txt + +ENV PYTHONPATH=$PYTHONPATH:/home/user + +WORKDIR /home/user/comps/llms/summarization/vllm/langchain + +ENTRYPOINT ["bash", "entrypoint.sh"] diff --git a/comps/llms/summarization/vllm/langchain/README.md b/comps/llms/summarization/vllm/langchain/README.md new file mode 100644 index 000000000..b2c8a9999 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/README.md @@ -0,0 +1,109 @@ +# Document Summary vLLM Microservice + +This microservice leverages LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors. +[Text Generation Inference](https://github.com/huggingface/text-generation-inference) (vLLM) is a toolkit for deploying and serving Large Language Models (LLMs). vLLM enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more. + +## 🚀1. Start Microservice with Python 🐍 (Option 1) + +To start the LLM microservice, you need to install python packages first. + +### 1.1 Install Requirements + +```bash +pip install -r requirements.txt +``` + +### 1.2 Start LLM Service + +```bash +export HF_TOKEN=${your_hf_api_token} +docker run -p 8008:80 -v ./data:/data --name llm-docsum-vllm --shm-size 1g ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id ${your_hf_llm_model} +``` + +### 1.3 Verify the vLLM Service + +```bash +curl http://${your_ip}:8008/generate \ + -X POST \ + -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ + -H 'Content-Type: application/json' +``` + +### 1.4 Start LLM Service with Python Script + +```bash +export vLLM_ENDPOINT="http://${your_ip}:8008" +python llm.py +``` + +## 🚀2. Start Microservice with Docker 🐳 (Option 2) + +If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a vLLM/vLLM service with docker. + +### 2.1 Setup Environment Variables + +In order to start vLLM and LLM services, you need to setup the following environment variables first. + +```bash +export HF_TOKEN=${your_hf_api_token} +export vLLM_ENDPOINT="http://${your_ip}:8008" +export LLM_MODEL_ID=${your_hf_llm_model} +``` + +### 2.2 Build Docker Image + +```bash +cd ../../../../../ +docker build -t opea/llm-docsum-vllm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/summarization/vllm/langchain/Dockerfile . +``` + +To start a docker container, you have two options: + +- A. Run Docker with CLI +- B. Run Docker with Docker Compose + +You can choose one as needed. + +### 2.3 Run Docker with CLI (Option A) + +```bash +docker run -d --name="llm-docsum-vllm-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e vLLM_ENDPOINT=$vLLM_ENDPOINT -e HF_TOKEN=$HF_TOKEN opea/llm-docsum-vllm:latest +``` + +### 2.4 Run Docker with Docker Compose (Option B) + +```bash +docker compose -f docker_compose_llm.yaml up -d +``` + +## 🚀3. Consume LLM Service + +### 3.1 Check Service Status + +```bash +curl http://${your_ip}:9000/v1/health_check\ + -X GET \ + -H 'Content-Type: application/json' +``` + +### 3.2 Consume LLM Service + +```bash +# Enable streaming to receive a streaming response. By default, this is set to True. +curl http://${your_ip}:9000/v1/chat/docsum \ + -X POST \ + -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.", "max_tokens":32, "language":"en"}' \ + -H 'Content-Type: application/json' + +# Disable streaming to receive a non-streaming response. +curl http://${your_ip}:9000/v1/chat/docsum \ + -X POST \ + -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.", "max_tokens":32, "language":"en", "streaming":false}' \ + -H 'Content-Type: application/json' + +# Use Chinese mode. By default, language is set to "en" +curl http://${your_ip}:9000/v1/chat/docsum \ + -X POST \ + -d '{"query":"2024年9月26日，北京——今日，英特尔正式发布英特尔® 至强® 6性能核处理器（代号Granite Rapids），为AI、数据分析、科学计算等计算密集型业务提供卓越性能。", "max_tokens":32, "language":"zh", "streaming":false}' \ + -H 'Content-Type: application/json' +``` diff --git a/comps/llms/summarization/vllm/langchain/__init__.py b/comps/llms/summarization/vllm/langchain/__init__.py new file mode 100644 index 000000000..916f3a44b --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/__init__.py @@ -0,0 +1,2 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 diff --git a/comps/llms/summarization/vllm/langchain/docker_compose_llm.yaml b/comps/llms/summarization/vllm/langchain/docker_compose_llm.yaml new file mode 100644 index 000000000..4d5b44333 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/docker_compose_llm.yaml @@ -0,0 +1,35 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +version: "3.8" + +services: + vllm_service: + image: ghcr.io/huggingface/text-generation-inference:2.1.0 + container_name: vllm-service + ports: + - "8008:80" + volumes: + - "./data:/data" + environment: + HF_TOKEN: ${HF_TOKEN} + shm_size: 1g + command: --model-id ${LLM_MODEL_ID} + llm: + image: opea/llm-docsum-vllm:latest + container_name: llm-docsum-vllm-server + ports: + - "9000:9000" + ipc: host + environment: + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + vLLM_ENDPOINT: ${vLLM_ENDPOINT} + HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN} + LLM_MODEL_ID: ${LLM_MODEL_ID} + restart: unless-stopped + +networks: + default: + driver: bridge diff --git a/comps/llms/summarization/vllm/langchain/entrypoint.sh b/comps/llms/summarization/vllm/langchain/entrypoint.sh new file mode 100644 index 000000000..d60eddd36 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/entrypoint.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +pip --no-cache-dir install -r requirements-runtime.txt + +python llm.py diff --git a/comps/llms/summarization/vllm/langchain/llm.py b/comps/llms/summarization/vllm/langchain/llm.py new file mode 100644 index 000000000..e0d69c373 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/llm.py @@ -0,0 +1,104 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import os + +from fastapi.responses import StreamingResponse +from langchain.chains.summarize import load_summarize_chain +from langchain.docstore.document import Document +from langchain.prompts import PromptTemplate +from langchain.text_splitter import CharacterTextSplitter +from langchain_community.llms import VLLMOpenAI + +from comps import CustomLogger, GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice +from comps.cores.mega.utils import get_access_token + +logger = CustomLogger("llm_docsum") +logflag = os.getenv("LOGFLAG", False) + +# Environment variables +TOKEN_URL = os.getenv("TOKEN_URL") +CLIENTID = os.getenv("CLIENTID") +CLIENT_SECRET = os.getenv("CLIENT_SECRET") +MODEL_ID = os.getenv("LLM_MODEL_ID", None) + +templ_en = """Write a concise summary of the following: +"{text}" +CONCISE SUMMARY:""" + +templ_zh = """请简要概括以下内容: +"{text}" +概况:""" + +def post_process_text(text: str): + if text == " ": + return "data: @#$\n\n" + if text == "\n": + return "data:
\n\n" + if text.isspace(): + return None + new_text = text.replace(" ", "@#$") + return f"data: {new_text}\n\n" + +@register_microservice( + name="opea_service@llm_docsum", + service_type=ServiceType.LLM, + endpoint="/v1/chat/docsum", + host="0.0.0.0", + port=9000, +) +async def llm_generate(input: LLMParamsDoc): + if logflag: + logger.info(input) + if input.language in ["en", "auto"]: + templ = templ_en + elif input.language in ["zh"]: + templ = templ_zh + else: + raise NotImplementedError('Please specify the input language in "en", "zh", "auto"') + + PROMPT = PromptTemplate.from_template(templ) + + if logflag: + logger.info("After prompting:") + logger.info(PROMPT) + + access_token = get_access_token(TOKEN_URL, CLIENTID, CLIENT_SECRET) if TOKEN_URL and CLIENTID and CLIENT_SECRET else None + headers = {} + if access_token: + headers = {"Authorization": f"Bearer {access_token}"} + llm_endpoint = os.getenv("vLLM_ENDPOINT", "http://localhost:8080") + model = input.model if input.model else os.getenv("LLM_MODEL_ID") + llm = VLLMOpenAI(openai_api_key="EMPTY", openai_api_base=llm_endpoint + "/v1", model_name=model, default_headers=headers, max_tokens=input.max_tokens, top_p=input.top_p, streaming=input.streaming, temperature=input.temperature, presence_penalty=input.repetition_penalty) + llm_chain = load_summarize_chain(llm=llm, prompt=PROMPT) + texts = text_splitter.split_text(input.query) + + # Create multiple documents + docs = [Document(page_content=t) for t in texts] + + if input.streaming: + + async def stream_generator(): + from langserve.serialization import WellKnownLCSerializer + + _serializer = WellKnownLCSerializer() + async for chunk in llm_chain.astream_log(docs): + data = _serializer.dumps({"ops": chunk.ops}).decode("utf-8") + if logflag: + logger.info(data) + yield f"data: {data}\n\n" + yield "data: [DONE]\n\n" + + return StreamingResponse(stream_generator(), media_type="text/event-stream") + else: + response = await llm_chain.ainvoke(docs) + response = response["output_text"] + if logflag: + logger.info(response) + return GeneratedDoc(text=response, prompt=input.query) + + +if __name__ == "__main__": + # Split text + text_splitter = CharacterTextSplitter() + opea_microservices["opea_service@llm_docsum"].start() diff --git a/comps/llms/summarization/vllm/langchain/requirements-runtime.txt b/comps/llms/summarization/vllm/langchain/requirements-runtime.txt new file mode 100644 index 000000000..225adde27 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/requirements-runtime.txt @@ -0,0 +1 @@ +langserve diff --git a/comps/llms/summarization/vllm/langchain/requirements.txt b/comps/llms/summarization/vllm/langchain/requirements.txt new file mode 100644 index 000000000..e074ba8c8 --- /dev/null +++ b/comps/llms/summarization/vllm/langchain/requirements.txt @@ -0,0 +1,15 @@ +docarray[full] +fastapi +huggingface_hub +langchain #==0.1.12 +langchain-huggingface +langchain-openai +langchain_community +langchainhub +opentelemetry-api +opentelemetry-exporter-otlp +opentelemetry-sdk +prometheus-fastapi-instrumentator +shortuuid +transformers +uvicorn