add workflow test for microservice

.github/workflows/microservice-test.yml

@@ -0,0 +1,87 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+name: MicroService-test
+
+on:
+  pull_request:
+    branches: [main]
+    types: [opened, reopened, ready_for_review, synchronize] # added `ready_for_review` since draft is skipped
+    paths:
+      - comps/**
+      - "!**.md"
+      - "!**.txt"
+      - .github/workflows/microservice.yml
+
+# If there is a new commit, the previous jobs will be canceled
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  job1:
+    name: Get-test-matrix
+    runs-on: ubuntu-latest
+    outputs:
+      run_matrix: ${{ steps.get-test-matrix.outputs.run_matrix }}
+    steps:
+      - name: Checkout out Repo
+        uses: actions/checkout@v4
+        with:
+          ref: "refs/pull/${{ github.event.number }}/merge"
+          fetch-depth: 0
+      - name: Get test matrix
+        id: get-test-matrix
+        run: |
+          set -xe
+          changed_files=$(git diff --name-only ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }} \
+          | grep 'comps/' | grep -vE '*.md|*.txt')
+          services=$(printf '%s\n' "${changed_files[@]}" | grep '/' | cut -d'/' -f2 | sort -u)
+          run_matrix="{\"include\":["
+          for service in ${services}; do
+              hardware="gaudi" # default hardware, set based on the changed files
+              run_matrix="${run_matrix}{\"service\":\"${service}\",\"hardware\":\"${hardware}\"},"
+          done
+          run_matrix=$run_matrix"]}"
+          echo "run_matrix=${run_matrix}" >> $GITHUB_OUTPUT
+
+  Microservice-test:
+    needs: job1
+    strategy:
+      matrix: ${{ fromJSON(needs.job1.outputs.run_matrix) }}
+    runs-on: ${{ matrix.hardware }}
+    continue-on-error: true
+    steps:
+      - name: Clean Up Working Directory
+        run: sudo rm -rf ${{github.workspace}}/*
+
+      - name: Checkout out Repo
+        uses: actions/checkout@v4
+        with:
+          ref: "refs/pull/${{ github.event.number }}/merge"
+
+      - name: Run microservice test
+        env:
+          HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
+          service: ${{ matrix.service }}
+          hardware: ${{ matrix.hardware }}
+        run: |
+          cd tests
+          if [ -f test_${service}.sh ]; then timeout 10m bash test_${service}.sh; else echo "Test script not found, skip test!"; fi
+
+      - name: Clean up container
+        env:
+          service: ${{ matrix.service }}
+          hardware: ${{ matrix.hardware }}
+        if: cancelled() || failure()
+        run: |
+          cid=$(docker ps -aq --filter "name=test-comps-*")
+          if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+          echo y | docker system prune
+
+      - name: Publish pipeline artifact
+        if: ${{ !cancelled() }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.service }}-${{ matrix.hardware }}
+          path: ${{ github.workspace }}/tests/*.log

comps/asr/README.md

@@ -2,43 +2,45 @@
 
 ASR (Audio-Speech-Recognition) microservice helps users convert speech to text. When building a talking bot with LLM, users will need to convert their audio inputs (What they talk, or Input audio from other sources) to text, so the LLM is able to tokenize the text and generate an answer. This microservice is built for that conversion stage.
 
-# 🚀Start Microservice with Python
+# 🚀1. Start Microservice with Python (Option 1)
 
 To start the ASR microservice with Python, you need to first install python packages.
 
-## Install Requirements
+## 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Start ASR Service with Python Script
+## 1.2 Start ASR Service with Python Script
 
 ```bash
 python asr.py
 ```
 
-# 🚀Start Microservice with Docker
+# 🚀2. Start Microservice with Docker (Option 2)
 
 Alternatively, you can also start the ASR microservice with Docker.
 
-## Build Docker Image
+## 2.1 Build Docker Image
 
 ```bash
 cd ../../
-docker build -t opea/gen-ai-comps:asr --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/Dockerfile .
+docker build -t opea/asr:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/Dockerfile .
 ```
 
-## Run Docker with CLI
+## 2.2 Run Docker with CLI
 
 ```bash
-docker run -p 9099:9099 --network=host --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/gen-ai-comps:asr
+docker run -p 9099:9099 --network=host --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/asr:latest
 ```
 
-# Test
+# 🚀3. Consume ASR Service
 
 You can use the following `curl` command to test whether the service is up. Notice that the first request can be slow because it needs to download the models.
 
 ```bash
-curl http://localhost:9099/v1/audio/transcriptions -H "Content-Type: application/json" -d '{"url": "https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample_2.wav"}'
+curl http://localhost:9099/v1/audio/transcriptions \
+    -H "Content-Type: application/json" \
+    -d '{"url": "https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample_2.wav"}'
 ```

comps/dataprep/redis/README.md

@@ -1,18 +1,18 @@
 # Dataprep Microservice with Redis
 
-# 🚀Start Microservice with Python
+# 🚀1. Start Microservice with Python（Option 1）
 
-## Install Requirements
+## 1.1 Install Requirements
 
 ```bash
 pip install -r requirements.txt
 ```
 
-## Start Redis Stack Server
+## 1.2 Start Redis Stack Server
 
 Please refer to this [readme](../../../vectorstores/langchain/redis/README.md).
 
-## Setup Environment Variables
+## 1.3 Setup Environment Variables
 
 ```bash
 export REDIS_URL="redis://${your_ip}:6379"
@@ -22,46 +22,57 @@ export LANGCHAIN_API_KEY=${your_langchain_api_key}
 export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
 ```
 
-## Start Document Preparation Microservice for Redis with Python Script
+## 1.4 Start Document Preparation Microservice for Redis with Python Script
 
 Start document preparation microservice for Redis with below command.
 
 ```bash
 python prepare_doc_redis.py
 ```
 
-# 🚀Start Microservice with Docker
+# 🚀2. Start Microservice with Docker (Option 2)
 
-## Build Docker Image
+## 2.1 Start Redis Stack Server
 
-```bash
-cd ../../../../
-docker build -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/docker/Dockerfile .
-```
+Please refer to this [readme](../../../vectorstores/langchain/redis/README.md).
 
-## Run Docker with CLI
+## 2.2 Setup Environment Variables
 
 ```bash
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
 export LANGCHAIN_TRACING_V2=true
 export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
+export LANGCHAIN_PROJECT="opea/dataprep"
+```
+
+## 2.3 Build Docker Image
 
-docker run -d --name="dataprep-redis-server" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME opea/dataprep-redis:latest
+```bash
+cd ../../../../
+docker build -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/docker/Dockerfile .
+```
+
+## 2.4 Run Docker with CLI (Option A)
+
+```bash
+docker run -d --name="dataprep-redis-server" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-redis:latest
 ```
 
-## Run Docker with Docker Compose
+## 2.5 Run with Docker Compose (Option B)
 
 ```bash
 cd comps/dataprep/redis/docker
 docker compose -f docker-compose-dataprep-redis.yaml up -d
 ```
 
-# Invoke Microservice
+# 🚀3. Consume Microservice
 
 Once document preparation microservice for Redis is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
 ```bash
-curl -X POST -H "Content-Type: application/json" -d '{"path":"/path/to/document"}' http://localhost:6007/v1/dataprep
+curl -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"path":"/path/to/document"}' \
+    http://localhost:6007/v1/dataprep
 ```