vLLM support for DocSum (#885)

* Add model parameter for DocSumGateway in gateway.py file

Signed-off-by: sgurunat <gurunath.s@intel.com>

* Add langchain vllm support for DocSum along with authentication support for vllm endpoints

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Updated docker_compose_llm.yaml and README file with vLLM information

Signed-off-by: sgurunat <gurunath.s@intel.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Updated docsum-vllm Dockerfile into llm-compose-cd.yaml under github workflows

Signed-off-by: sgurunat <gurunath.s@intel.com>

* Updated llm-compose.yaml file to include vllm sumarization docker build

Signed-off-by: sgurunat <gurunath.s@intel.com>

---------

Signed-off-by: sgurunat <gurunath.s@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: lvliang-intel <liang1.lv@intel.com>

comps/cores/mega/gateway.py

@@ -433,6 +433,8 @@ class DocSumGateway(Gateway):
             presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
             repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
             streaming=stream_opt,
+            language=chat_request.language if chat_request.language else "auto",
+            model=chat_request.model if chat_request.model else None,
         )
         result_dict, runtime_graph = await self.megaservice.schedule(
             initial_inputs={data["type"]: prompt}, llm_parameters=parameters

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"]

comps/llms/summarization/vllm/langchain/README.md

@@ -0,0 +1,112 @@
+# Document Summary vLLM Microservice
+
+This microservice leverages LangChain to implement summarization strategies and facilitate LLM inference using vLLM.
+[vLLM](https://github.com/vllm-project/vllm) is a fast and easy-to-use library for LLM inference and serving, it delivers state-of-the-art serving throughput with a set of advanced features such as PagedAttention, Continuous batching and etc.. Besides GPUs, vLLM already supported [Intel CPUs](https://www.intel.com/content/www/us/en/products/overview.html) and [Gaudi accelerators](https://habana.ai/products).
+
+## 🚀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=${your_hf_api_token}
+export LLM_MODEL_ID=${your_hf_llm_model}
+docker run -p 8008:80 -v ./data:/data --name llm-docsum-vllm --shm-size 1g opea/vllm:hpu --model-id ${LLM_MODEL_ID}
+```
+
+### 1.3 Verify the vLLM Service
+
+```bash
+curl http://${your_ip}:8008/v1/chat/completions \
+    -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning? "}]}'
+```
+
+### 1.4 Start LLM Service with Python Script
+
+```bash
+export vLLM_ENDPOINT="http://${your_ip}:8008"
+python llm.py
+```
+
+## 🚀2. Start Microservice with Docker 🐳 (Option 2)
+
+If you start an LLM microservice with docker, the `docker_compose_llm.yaml` file will automatically start a vLLM/vLLM service with docker.
+
+To setup or build the vLLM image follow the instructions provided in [vLLM Gaudi](https://github.com/opea-project/GenAIComps/tree/main/comps/llms/text-generation/vllm/langchain#22-vllm-on-gaudi)
+
+### 2.1 Setup Environment Variables
+
+In order to start vLLM and LLM services, you need to setup the following environment variables first.
+
+```bash
+export HF_TOKEN=${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'
+```

comps/llms/summarization/vllm/langchain/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

comps/llms/summarization/vllm/langchain/docker_compose_llm.yaml

@@ -0,0 +1,44 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3.8"
+
+services:
+  vllm-service:
+    image: opea/vllm:hpu
+    container_name: vllm-gaudi-server
+    ports:
+      - "8008:80"
+    volumes:
+      - "./data:/data"
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      HF_TOKEN: ${HF_TOKEN}
+      HABANA_VISIBLE_DEVICES: all
+      OMPI_MCA_btl_vader_single_copy_mechanism: none
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+    runtime: habana
+    cap_add:
+      - SYS_NICE
+    ipc: host
+    command: --enforce-eager --model $LLM_MODEL_ID --tensor-parallel-size 1 --host 0.0.0.0 --port 80
+  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

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

comps/llms/summarization/vllm/langchain/llm.py

@@ -0,0 +1,118 @@
+# 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: @#$\n\n"
+    if text == "\n":
+        return "data: <br/>\n\n"
+    if text.isspace():
+        return None
+    new_text = text.replace(" ", "@#$")
+    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()

comps/llms/summarization/vllm/langchain/requirements-runtime.txt

@@ -0,0 +1 @@
+langserve

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