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4 Commits

Author SHA1 Message Date
NeuralChatBot
bbb4e231d0 Freeze OPEA images tag
Signed-off-by: NeuralChatBot <grp_neural_chat_bot@intel.com>
2024-11-21 14:24:16 +00:00
bjzhjing
da10068964 Adjustments for helm release change (#1173)
Signed-off-by: Cathy Zhang <cathy.zhang@intel.com>
(cherry picked from commit ef2047b070)
2024-11-21 16:57:30 +08:00
Letong Han
188b568467 Fix Translation Manifest CI with MODEL_ID (#1169)
Signed-off-by: letonghan <letong.han@intel.com>
(cherry picked from commit 94231584aa)
2024-11-21 16:57:29 +08:00
minmin-intel
9e9af9766f Fix DocIndexRetriever CI error on Xeon (#1167)
Signed-off-by: minmin-intel <minmin.hou@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
(cherry picked from commit c5177c5e2f)
2024-11-21 16:57:28 +08:00
1206 changed files with 49524 additions and 90362 deletions

37
.github/CODEOWNERS vendored
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@@ -1,26 +1,17 @@
# Code owners will review PRs within their respective folders.
* liang1.lv@intel.com feng.tian@intel.com suyue.chen@intel.com kaokao.lv@intel.com minmin.hou@intel.com rita.brugarolas.brufau@intel.com
/.github/ suyue.chen@intel.com ze.pan@intel.com
/AgentQnA/ abolfazl.shahbazi@intel.com kaokao.lv@intel.com minmin.hou@intel.com
/AudioQnA/ sihan.chen@intel.com wenjiao.yue@intel.com
/AvatarChatbot/ chun.tao@intel.com kaokao.lv@intel.com
/ChatQnA/ liang1.lv@intel.com letong.han@intel.com
/AgentQnA/ kaokao.lv@intel.com
/AudioQnA/ sihan.chen@intel.com
/ChatQnA/ liang1.lv@intel.com
/CodeGen/ liang1.lv@intel.com
/CodeTrans/ sihan.chen@intel.com
/DBQnA/ supriya.krishnamurthi@intel.com liang1.lv@intel.com
/DocIndexRetriever/ abolfazl.shahbazi@intel.com kaokao.lv@intel.com chendi.xue@intel.com
/DocSum/ letong.han@intel.com
/EdgeCraftRAG/ yongbo.zhu@intel.com mingyuan.qi@intel.com
/FinanceAgent/ abolfazl.shahbazi@intel.com kaokao.lv@intel.com minmin.hou@intel.com rita.brugarolas.brufau@intel.com
/GraphRAG/ rita.brugarolas.brufau@intel.com abolfazl.shahbazi@intel.com
/InstructionTuning/ xinyu.ye@intel.com kaokao.lv@intel.com
/MultimodalQnA/ melanie.h.buehler@intel.com tiep.le@intel.com
/ProductivitySuite/ jaswanth.karani@intel.com hoong.tee.yeoh@intel.com
/RerankFinetuning/ xinyu.ye@intel.com kaokao.lv@intel.com
/SearchQnA/ sihan.chen@intel.com letong.han@intel.com
/Text2Image/ wenjiao.yue@intel.com xinyu.ye@intel.com
/Translation/ liang1.lv@intel.com sihan.chen@intel.com
/VideoQnA/ huiling.bao@intel.com
/VisualQnA/ liang1.lv@intel.com sihan.chen@intel.com
/WorkflowExecAgent/ joshua.jian.ern.liew@intel.com kaokao.lv@intel.com
/DocIndexRetriever/ kaokao.lv@intel.com chendi.xue@intel.com
/InstructionTuning xinyu.ye@intel.com
/RerankFinetuning xinyu.ye@intel.com
/MultimodalQnA tiep.le@intel.com
/FaqGen/ xinyao.wang@intel.com
/SearchQnA/ sihan.chen@intel.com
/Translation/ liang1.lv@intel.com
/VisualQnA/ liang1.lv@intel.com
/ProductivitySuite/ hoong.tee.yeoh@intel.com
/VideoQnA huiling.bao@intel.com
/*/ liang1.lv@intel.com

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@@ -4,7 +4,6 @@
name: Report Bug
description: Used to report bug
title: "[Bug]"
labels: ["bug"]
body:
- type: dropdown
id: priority
@@ -32,7 +31,6 @@ body:
- Mac
- BSD
- Other (Please let us know in description)
- N/A
validations:
required: true
@@ -57,7 +55,6 @@ body:
- GPU-Nvidia
- GPU-AMD
- GPU-other (Please let us know in description)
- N/A
validations:
required: true
@@ -68,8 +65,6 @@ body:
options:
- label: Pull docker images from hub.docker.com
- label: Build docker images from source
- label: Other
- label: N/A
validations:
required: true
@@ -78,12 +73,10 @@ body:
attributes:
label: Deploy method
options:
- label: Docker compose
- label: Docker
- label: Docker Compose
- label: Kubernetes Helm Charts
- label: Kubernetes GMC
- label: Other
- label: N/A
- label: Kubernetes
- label: Helm
validations:
required: true
@@ -94,8 +87,6 @@ body:
options:
- Single Node
- Multiple Nodes
- Other
- N/A
default: 0
validations:
required: true
@@ -135,12 +126,3 @@ body:
render: shell
validations:
required: false
- type: textarea
id: attachments
attributes:
label: Attachments
description: Attach any relevant files or screenshots.
validations:
required: false

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@@ -4,7 +4,6 @@
name: Report Feature
description: Used to report feature
title: "[Feature]"
labels: ["feature"]
body:
- type: dropdown
id: priority
@@ -32,7 +31,6 @@ body:
- Mac
- BSD
- Other (Please let us know in description)
- N/A
validations:
required: true
@@ -57,7 +55,6 @@ body:
- GPU-Nvidia
- GPU-AMD
- GPU-other (Please let us know in description)
- N/A
validations:
required: true
@@ -68,8 +65,6 @@ body:
options:
- Single Node
- Multiple Nodes
- Other
- N/A
default: 0
validations:
required: true

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@@ -1,3 +1,2 @@
ModelIn
modelin
pressEnter

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@@ -1,5 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
export VLLM_VER=v0.8.3
export VLLM_FORK_VER=v0.6.6.post1+Gaudi-1.20.0

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@@ -1,2 +1,2 @@
Copyright (C) 2025 Intel Corporation
Copyright (C) 2024 Intel Corporation
SPDX-License-Identifier: Apache-2.0

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@@ -1,65 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Build Comps Base Image
permissions: read-all
on:
workflow_call:
inputs:
node:
required: true
type: string
build:
default: true
required: false
type: boolean
tag:
default: "latest"
required: false
type: string
opea_branch:
default: "main"
required: false
type: string
inject_commit:
default: false
required: false
type: boolean
jobs:
pre-build-image-check:
runs-on: ubuntu-latest
outputs:
should_skip: ${{ steps.check-skip.outputs.should_skip }}
steps:
- name: Check if job should be skipped
id: check-skip
run: |
should_skip=true
if [[ "${{ inputs.node }}" == "gaudi" || "${{ inputs.node }}" == "xeon" ]]; then
should_skip=false
fi
echo "should_skip=$should_skip"
echo "should_skip=$should_skip" >> $GITHUB_OUTPUT
build-images:
needs: [ pre-build-image-check ]
if: ${{ needs.pre-build-image-check.outputs.should_skip == 'false' && fromJSON(inputs.build) }}
runs-on: "docker-build-${{ inputs.node }}"
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Clone Required Repo
run: |
git clone --depth 1 --branch ${{ inputs.opea_branch }} https://github.com/opea-project/GenAIComps.git
cd GenAIComps && git rev-parse HEAD && cd ../ && ls -l
- name: Build Image
uses: opea-project/validation/actions/image-build@main
with:
work_dir: ${{ github.workspace }}/GenAIComps
docker_compose_path: ${{ github.workspace }}/GenAIComps/.github/workflows/docker/compose/base-compose.yaml
registry: ${OPEA_IMAGE_REPO}opea
inject_commit: ${{ inputs.inject_commit }}
tag: ${{ inputs.tag }}

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@@ -1,96 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Build Images
permissions: read-all
on:
workflow_call:
inputs:
node:
required: true
type: string
build:
default: true
required: false
type: boolean
example:
required: true
type: string
services:
default: ""
required: false
type: string
tag:
default: "latest"
required: false
type: string
opea_branch:
default: "main"
required: false
type: string
inject_commit:
default: false
required: false
type: boolean
jobs:
pre-build-image-check:
runs-on: ubuntu-latest
outputs:
should_skip: ${{ steps.check-skip.outputs.should_skip }}
steps:
- name: Check if job should be skipped
id: check-skip
run: |
should_skip=true
if [[ "${{ inputs.node }}" == "gaudi" || "${{ inputs.node }}" == "xeon" ]]; then
should_skip=false
fi
echo "should_skip=$should_skip"
echo "should_skip=$should_skip" >> $GITHUB_OUTPUT
build-images:
needs: [ pre-build-image-check ]
if: ${{ needs.pre-build-image-check.outputs.should_skip == 'false' && fromJSON(inputs.build) }}
runs-on: "docker-build-${{ inputs.node }}"
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Get Checkout Ref
run: |
if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
echo "CHECKOUT_REF=refs/pull/${{ github.event.number }}/merge" >> $GITHUB_ENV
else
echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
fi
- name: Checkout out GenAIExamples
uses: actions/checkout@v4
with:
ref: ${{ env.CHECKOUT_REF }}
fetch-depth: 0
- name: Clone Required Repo
run: |
cd ${{ github.workspace }}/${{ inputs.example }}/docker_image_build
docker_compose_path=${{ github.workspace }}/${{ inputs.example }}/docker_image_build/build.yaml
source ${{ github.workspace }}/.github/env/_build_image.sh
if [[ $(grep -c "vllm:" ${docker_compose_path}) != 0 ]]; then
git clone -b ${VLLM_VER} --single-branch https://github.com/vllm-project/vllm.git
fi
if [[ $(grep -c "vllm-gaudi:" ${docker_compose_path}) != 0 ]]; then
git clone -b ${VLLM_FORK_VER} --single-branch https://github.com/HabanaAI/vllm-fork.git
fi
git clone --depth 1 --branch ${{ inputs.opea_branch }} https://github.com/opea-project/GenAIComps.git
cd GenAIComps && git rev-parse HEAD && cd ../
- name: Build Image
uses: opea-project/validation/actions/image-build@main
with:
work_dir: ${{ github.workspace }}/${{ inputs.example }}/docker_image_build
docker_compose_path: ${{ github.workspace }}/${{ inputs.example }}/docker_image_build/build.yaml
service_list: ${{ inputs.services }}
registry: ${OPEA_IMAGE_REPO}opea
inject_commit: ${{ inputs.inject_commit }}
tag: ${{ inputs.tag }}

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@@ -28,7 +28,7 @@ on:
default: false
required: false
type: boolean
test_helmchart:
test_k8s:
default: false
required: false
type: boolean
@@ -43,54 +43,83 @@ on:
inject_commit:
default: false
required: false
type: boolean
use_model_cache:
default: false
required: false
type: boolean
type: string
jobs:
####################################################################################################
# Image Build
####################################################################################################
build-images:
uses: ./.github/workflows/_build_image.yml
with:
node: ${{ inputs.node }}
build: ${{ fromJSON(inputs.build) }}
example: ${{ inputs.example }}
services: ${{ inputs.services }}
tag: ${{ inputs.tag }}
opea_branch: ${{ inputs.opea_branch }}
inject_commit: ${{ inputs.inject_commit }}
runs-on: "docker-build-${{ inputs.node }}"
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Get Checkout Ref
run: |
if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
echo "CHECKOUT_REF=refs/pull/${{ github.event.number }}/merge" >> $GITHUB_ENV
else
echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
fi
- name: Checkout out GenAIExamples
uses: actions/checkout@v4
with:
ref: ${{ env.CHECKOUT_REF }}
fetch-depth: 0
- name: Clone Required Repo
run: |
cd ${{ github.workspace }}/${{ inputs.example }}/docker_image_build
docker_compose_path=${{ github.workspace }}/${{ inputs.example }}/docker_image_build/build.yaml
if [[ $(grep -c "vllm:" ${docker_compose_path}) != 0 ]]; then
git clone https://github.com/vllm-project/vllm.git
cd vllm && git rev-parse HEAD && cd ../
fi
if [[ $(grep -c "vllm-gaudi:" ${docker_compose_path}) != 0 ]]; then
git clone https://github.com/HabanaAI/vllm-fork.git
cd vllm-fork && git checkout 3c39626 && cd ../
fi
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps && git checkout ${{ inputs.opea_branch }} && git rev-parse HEAD && cd ../
- name: Build Image
if: ${{ fromJSON(inputs.build) }}
uses: opea-project/validation/actions/image-build@main
with:
work_dir: ${{ github.workspace }}/${{ inputs.example }}/docker_image_build
docker_compose_path: ${{ github.workspace }}/${{ inputs.example }}/docker_image_build/build.yaml
service_list: ${{ inputs.services }}
registry: ${OPEA_IMAGE_REPO}opea
inject_commit: ${{ inputs.inject_commit }}
tag: ${{ inputs.tag }}
####################################################################################################
# Docker Compose Test
####################################################################################################
test-example-compose:
needs: [build-images]
if: ${{ inputs.test_compose }}
if: ${{ fromJSON(inputs.test_compose) }}
uses: ./.github/workflows/_run-docker-compose.yml
with:
tag: ${{ inputs.tag }}
example: ${{ inputs.example }}
hardware: ${{ inputs.node }}
use_model_cache: ${{ inputs.use_model_cache }}
opea_branch: ${{ inputs.opea_branch }}
secrets: inherit
####################################################################################################
# helmchart Test
# K8S Test
####################################################################################################
test-helmchart:
if: ${{ fromJSON(inputs.test_helmchart) }}
uses: ./.github/workflows/_helm-e2e.yml
test-k8s-manifest:
needs: [build-images]
if: ${{ fromJSON(inputs.test_k8s) }}
uses: ./.github/workflows/_manifest-e2e.yml
with:
example: ${{ inputs.example }}
hardware: ${{ inputs.node }}
tag: ${{ inputs.tag }}
mode: "CD"
secrets: inherit
####################################################################################################
@@ -98,7 +127,7 @@ jobs:
####################################################################################################
test-gmc-pipeline:
needs: [build-images]
if: false # ${{ fromJSON(inputs.test_gmc) }}
if: ${{ fromJSON(inputs.test_gmc) }}
uses: ./.github/workflows/_gmc-e2e.yml
with:
example: ${{ inputs.example }}

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@@ -42,12 +42,6 @@ jobs:
ref: ${{ env.CHECKOUT_REF }}
fetch-depth: 0
- name: Check Dangerous Command Injection
if: github.event_name == 'pull_request' || github.event_name == 'pull_request_target'
uses: opea-project/validation/actions/check-cmd@main
with:
work_dir: ${{ github.workspace }}
- name: Get test matrix
id: get-test-matrix
run: |
@@ -60,11 +54,9 @@ jobs:
base_commit=$(git rev-parse HEAD~1) # push event
fi
merged_commit=$(git log -1 --format='%H')
echo "print all changed files..."
git diff --name-only ${base_commit} ${merged_commit}
changed_files="$(git diff --name-only ${base_commit} ${merged_commit} | \
grep -vE '${{ inputs.diff_excluded_files }}')" || true
echo "filtered changed_files=$changed_files"
echo "changed_files=$changed_files"
export changed_files=$changed_files
export test_mode=${{ inputs.test_mode }}
export WORKSPACE=${{ github.workspace }}

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@@ -3,8 +3,7 @@
# This workflow will only test GMC pipeline and will not install GMC any more
name: Single GMC E2e Test For CD Workflow Call
permissions:
contents: read
on:
workflow_call:
inputs:

View File

@@ -2,8 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
name: Build and deploy GMC system on call and manual
permissions:
contents: read
on:
workflow_dispatch:
inputs:
@@ -68,6 +67,36 @@ jobs:
make docker.build
make docker.push
- name: Scan gmcmanager
if: ${{ inputs.node == 'gaudi' }}
uses: opea-project/validation/actions/trivy-scan@main
with:
image-ref: ${{ env.DOCKER_REGISTRY }}/gmcmanager:${{ env.VERSION }}
output: gmcmanager-scan.txt
- name: Upload gmcmanager scan result
if: ${{ inputs.node == 'gaudi' }}
uses: actions/upload-artifact@v4.3.4
with:
name: gmcmanager-scan
path: gmcmanager-scan.txt
overwrite: true
- name: Scan gmcrouter
if: ${{ inputs.node == 'gaudi' }}
uses: opea-project/validation/actions/trivy-scan@main
with:
image-ref: ${{ env.DOCKER_REGISTRY }}/gmcrouter:${{ env.VERSION }}
output: gmcrouter-scan.txt
- name: Upload gmcrouter scan result
if: ${{ inputs.node == 'gaudi' }}
uses: actions/upload-artifact@v4.3.4
with:
name: gmcrouter-scan
path: gmcrouter-scan.txt
overwrite: true
- name: Clean up images
if: always()
run: |

View File

@@ -1,252 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Helm Chart E2e Test For Call
permissions:
contents: read
on:
workflow_call:
inputs:
example:
default: "chatqna"
required: true
type: string
description: "example to test, chatqna or common/asr"
hardware:
default: "xeon"
required: true
type: string
dockerhub:
default: "false"
required: false
type: string
description: "Set to true if you want to use released docker images at dockerhub. By default using internal docker registry."
mode:
default: "CD"
description: "Whether the test range is CI, CD or CICD"
required: false
type: string
tag:
default: "latest"
required: false
type: string
version:
default: "0-latest"
required: false
type: string
jobs:
get-test-case:
runs-on: ubuntu-latest
outputs:
value_files: ${{ steps.get-test-files.outputs.value_files }}
CHECKOUT_REF: ${{ steps.get-checkout-ref.outputs.CHECKOUT_REF }}
steps:
- name: Get checkout ref
id: get-checkout-ref
run: |
if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
CHECKOUT_REF=refs/pull/${{ github.event.number }}/merge
else
CHECKOUT_REF=${{ github.ref }}
fi
echo "CHECKOUT_REF=${CHECKOUT_REF}" >> $GITHUB_OUTPUT
echo "checkout ref ${CHECKOUT_REF}"
- name: Checkout Repo
uses: actions/checkout@v4
with:
ref: ${{ steps.get-checkout-ref.outputs.CHECKOUT_REF }}
fetch-depth: 0
- name: Get test Services
id: get-test-files
run: |
set -x
if [ "${{ inputs.mode }}" = "CI" ]; then
base_commit=${{ github.event.pull_request.base.sha }}
merged_commit=$(git log -1 --format='%H')
values_files=$(git diff --name-only ${base_commit} ${merged_commit} | \
grep "${{ inputs.example }}/kubernetes/helm" | \
grep "values.yaml" |\
sort -u)
echo $values_files
elif [ "${{ inputs.mode }}" = "CD" ]; then
values_files=$(ls ${{ inputs.example }}/kubernetes/helm/*values.yaml || true)
fi
value_files="["
for file in ${values_files}; do
if [ -f "$file" ]; then
filename=$(basename "$file")
if [[ "$filename" == *"gaudi"* ]]; then
if [[ "${{ inputs.hardware }}" == "gaudi" ]]; then
value_files="${value_files}\"${filename}\","
fi
elif [[ "$filename" == *"rocm"* ]]; then
if [[ "${{ inputs.hardware }}" == "rocm" ]]; then
value_files="${value_files}\"${filename}\","
fi
elif [[ "$filename" == *"nv"* ]]; then
continue
else
if [[ "${{ inputs.hardware }}" == "xeon" ]]; then
value_files="${value_files}\"${filename}\","
fi
fi
fi
done
value_files="${value_files%,}]"
echo "value_files=${value_files}"
echo "value_files=${value_files}" >> $GITHUB_OUTPUT
helm-test:
needs: [get-test-case]
if: ${{ needs.get-test-case.outputs.value_files != '[]' }}
strategy:
matrix:
value_file: ${{ fromJSON(needs.get-test-case.outputs.value_files) }}
fail-fast: false
runs-on: k8s-${{ inputs.hardware }}
continue-on-error: true
steps:
- name: Clean Up Working Directory
run: |
echo "value_file=${{ matrix.value_file }}"
sudo rm -rf ${{github.workspace}}/*
- name: Get checkout ref
id: get-checkout-ref
run: |
if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
CHECKOUT_REF=refs/pull/${{ github.event.number }}/merge
else
CHECKOUT_REF=${{ github.ref }}
fi
echo "CHECKOUT_REF=${CHECKOUT_REF}" >> $GITHUB_OUTPUT
echo "checkout ref ${CHECKOUT_REF}"
- name: Checkout Repo
uses: actions/checkout@v4
with:
ref: ${{ steps.get-checkout-ref.outputs.CHECKOUT_REF }}
fetch-depth: 0
- name: Set variables
env:
example: ${{ inputs.example }}
run: |
if [[ ! "$example" =~ ^[a-zA-Z0-9]{1,20}$ ]] || [[ "$example" =~ \.\. ]] || [[ "$example" == -* || "$example" == *- ]]; then
echo "Error: Invalid input - only lowercase alphanumeric and internal hyphens allowed"
exit 1
fi
# SAFE_PREFIX="kb-"
CHART_NAME="${SAFE_PREFIX}$(echo "$example" | tr '[:upper:]' '[:lower:]')"
RAND_SUFFIX=$(openssl rand -hex 2 | tr -dc 'a-f0-9')
cat <<EOF >> $GITHUB_ENV
CHART_NAME=${CHART_NAME}
RELEASE_NAME=${CHART_NAME}-$(date +%s)
NAMESPACE=ns-${CHART_NAME}-${RAND_SUFFIX}
ROLLOUT_TIMEOUT_SECONDS=600s
TEST_TIMEOUT_SECONDS=600s
KUBECTL_TIMEOUT_SECONDS=60s
should_cleanup=false
skip_validate=false
CHART_FOLDER=${example}/kubernetes/helm
EOF
echo "Generated safe variables:" >> $GITHUB_STEP_SUMMARY
echo "- CHART_NAME: ${CHART_NAME}" >> $GITHUB_STEP_SUMMARY
- name: Helm install
id: install
env:
GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
HFTOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
value_file: ${{ matrix.value_file }}
run: |
set -xe
echo "should_cleanup=true" >> $GITHUB_ENV
if [[ ! -f ${{ github.workspace }}/${{ env.CHART_FOLDER }}/${value_file} ]]; then
echo "No value file found, exiting test!"
echo "skip_validate=true" >> $GITHUB_ENV
echo "should_cleanup=false" >> $GITHUB_ENV
exit 0
fi
for img in `helm template -n $NAMESPACE $RELEASE_NAME oci://ghcr.io/opea-project/charts/${CHART_NAME} -f ${{ inputs.example }}/kubernetes/helm/${value_file} --version ${{ inputs.version }} | grep 'image:' | grep 'opea/' | awk '{print $2}' | xargs`;
do
# increase helm install wait for for vllm-gaudi case
if [[ $img == *"vllm-gaudi"* ]]; then
ROLLOUT_TIMEOUT_SECONDS=900s
fi
done
if ! helm install \
--create-namespace \
--namespace $NAMESPACE \
$RELEASE_NAME \
oci://ghcr.io/opea-project/charts/${CHART_NAME} \
--set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} \
--set global.modelUseHostPath=/data2/hf_model \
--set GOOGLE_API_KEY=${{ env.GOOGLE_API_KEY}} \
--set GOOGLE_CSE_ID=${{ env.GOOGLE_CSE_ID}} \
--set web-retriever.GOOGLE_API_KEY=${{ env.GOOGLE_API_KEY}} \
--set web-retriever.GOOGLE_CSE_ID=${{ env.GOOGLE_CSE_ID}} \
-f ${{ inputs.example }}/kubernetes/helm/${value_file} \
--version ${{ inputs.version }} \
--wait --timeout "$ROLLOUT_TIMEOUT_SECONDS"; then
echo "Failed to install chart ${{ inputs.example }}"
echo "skip_validate=true" >> $GITHUB_ENV
.github/workflows/scripts/k8s-utils.sh dump_pods_status $NAMESPACE
exit 1
fi
- name: Validate e2e test
if: always()
run: |
set -xe
if $skip_validate; then
echo "Skip validate"
else
LOG_PATH=/home/$(whoami)/helm-logs
chart=${{ env.CHART_NAME }}
helm test -n $NAMESPACE $RELEASE_NAME --logs --timeout "$TEST_TIMEOUT_SECONDS" | tee ${LOG_PATH}/charts-${chart}.log
exit_code=$?
if [ $exit_code -ne 0 ]; then
echo "Chart ${chart} test failed, please check the logs in ${LOG_PATH}!"
exit 1
fi
echo "Checking response results, make sure the output is reasonable. "
teststatus=false
if [[ -f $LOG_PATH/charts-${chart}.log ]] && \
[[ $(grep -c "^Phase:.*Failed" $LOG_PATH/charts-${chart}.log) != 0 ]]; then
teststatus=false
${{ github.workspace }}/.github/workflows/scripts/k8s-utils.sh dump_all_pod_logs $NAMESPACE
else
teststatus=true
fi
if [ $teststatus == false ]; then
echo "Response check failed, please check the logs in artifacts!"
exit 1
else
echo "Response check succeeded!"
exit 0
fi
fi
- name: Helm uninstall
if: always()
run: |
if $should_cleanup; then
helm uninstall $RELEASE_NAME --namespace $NAMESPACE
if ! kubectl delete ns $NAMESPACE --timeout=$KUBECTL_TIMEOUT_SECONDS; then
kubectl delete pods --namespace $NAMESPACE --force --grace-period=0 --all
kubectl delete ns $NAMESPACE --force --grace-period=0 --timeout=$KUBECTL_TIMEOUT_SECONDS
fi
fi

111
.github/workflows/_manifest-e2e.yml vendored Normal file
View File

@@ -0,0 +1,111 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Single Kubernetes Manifest E2e Test For Call
on:
workflow_call:
inputs:
example:
default: "ChatQnA"
description: "The example to test on K8s"
required: true
type: string
hardware:
default: "xeon"
description: "Nodes to run the test, xeon or gaudi"
required: true
type: string
tag:
default: "latest"
description: "Tag to apply to images, default is latest"
required: false
type: string
jobs:
manifest-test:
runs-on: "k8s-${{ inputs.hardware }}"
continue-on-error: true
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Get checkout ref
run: |
if [ "${{ github.event_name }}" == "pull_request" ] || [ "${{ github.event_name }}" == "pull_request_target" ]; then
echo "CHECKOUT_REF=refs/pull/${{ github.event.number }}/merge" >> $GITHUB_ENV
else
echo "CHECKOUT_REF=${{ github.ref }}" >> $GITHUB_ENV
fi
echo "checkout ref ${{ env.CHECKOUT_REF }}"
- name: Checkout out Repo
uses: actions/checkout@v4
with:
ref: ${{ env.CHECKOUT_REF }}
fetch-depth: 0
- name: Set variables
run: |
echo "IMAGE_REPO=${OPEA_IMAGE_REPO}opea" >> $GITHUB_ENV
echo "IMAGE_TAG=${{ inputs.tag }}" >> $GITHUB_ENV
lower_example=$(echo "${{ inputs.example }}" | tr '[:upper:]' '[:lower:]')
echo "NAMESPACE=$lower_example-$(tr -dc a-z0-9 </dev/urandom | head -c 16)" >> $GITHUB_ENV
echo "ROLLOUT_TIMEOUT_SECONDS=1800s" >> $GITHUB_ENV
echo "KUBECTL_TIMEOUT_SECONDS=60s" >> $GITHUB_ENV
echo "continue_test=true" >> $GITHUB_ENV
echo "should_cleanup=false" >> $GITHUB_ENV
echo "skip_validate=true" >> $GITHUB_ENV
echo "NAMESPACE=$NAMESPACE"
- name: Kubectl install
id: install
run: |
if [[ ! -f ${{ github.workspace }}/${{ inputs.example }}/tests/test_manifest_on_${{ inputs.hardware }}.sh ]]; then
echo "No test script found, exist test!"
exit 0
else
${{ github.workspace }}/${{ inputs.example }}/tests/test_manifest_on_${{ inputs.hardware }}.sh init_${{ inputs.example }}
echo "should_cleanup=true" >> $GITHUB_ENV
kubectl create ns $NAMESPACE
${{ github.workspace }}/${{ inputs.example }}/tests/test_manifest_on_${{ inputs.hardware }}.sh install_${{ inputs.example }} $NAMESPACE
echo "Testing ${{ inputs.example }}, waiting for pod ready..."
if kubectl rollout status deployment --namespace "$NAMESPACE" --timeout "$ROLLOUT_TIMEOUT_SECONDS"; then
echo "Testing manifests ${{ inputs.example }}, waiting for pod ready done!"
echo "skip_validate=false" >> $GITHUB_ENV
else
echo "Timeout waiting for pods in namespace $NAMESPACE to be ready!"
.github/workflows/scripts/k8s-utils.sh dump_pods_status $NAMESPACE
exit 1
fi
sleep 60
fi
- name: Validate e2e test
if: always()
run: |
if $skip_validate; then
echo "Skip validate"
else
if ${{ github.workspace }}/${{ inputs.example }}/tests/test_manifest_on_${{ inputs.hardware }}.sh validate_${{ inputs.example }} $NAMESPACE ; then
echo "Validate ${{ inputs.example }} successful!"
else
echo "Validate ${{ inputs.example }} failure!!!"
echo "Check the logs in 'Dump logs when e2e test failed' step!!!"
exit 1
fi
fi
- name: Dump logs when e2e test failed
if: failure()
run: |
.github/workflows/scripts/k8s-utils.sh dump_all_pod_logs $NAMESPACE
- name: Kubectl uninstall
if: always()
run: |
if $should_cleanup; then
if ! kubectl delete ns $NAMESPACE --timeout=$KUBECTL_TIMEOUT_SECONDS; then
kubectl delete pods --namespace $NAMESPACE --force --grace-period=0 --all
kubectl delete ns $NAMESPACE --force --grace-period=0 --timeout=$KUBECTL_TIMEOUT_SECONDS
fi
fi

View File

@@ -28,14 +28,6 @@ on:
required: false
type: string
default: ""
use_model_cache:
required: false
type: boolean
default: false
opea_branch:
default: "main"
required: false
type: string
jobs:
get-test-case:
runs-on: ubuntu-latest
@@ -68,16 +60,9 @@ jobs:
cd ${{ github.workspace }}/${{ inputs.example }}/tests
run_test_cases=""
if [[ "${{ inputs.hardware }}" == "gaudi"* ]]; then
hardware="gaudi"
elif [[ "${{ inputs.hardware }}" == "xeon"* ]]; then
hardware="xeon"
else
hardware="${{ inputs.hardware }}"
fi
default_test_case=$(find . -type f -name "test_compose_on_$hardware.sh" | cut -d/ -f2)
default_test_case=$(find . -type f -name "test_compose_on_${{ inputs.hardware }}.sh" | cut -d/ -f2)
if [ "$default_test_case" ]; then run_test_cases="$default_test_case"; fi
other_test_cases=$(find . -type f -name "test_compose_*_on_$hardware.sh" | cut -d/ -f2)
other_test_cases=$(find . -type f -name "test_compose_*_on_${{ inputs.hardware }}.sh" | cut -d/ -f2)
echo "default_test_case=$default_test_case"
echo "other_test_cases=$other_test_cases"
@@ -100,17 +85,12 @@ jobs:
fi
done
if [ -z "$run_test_cases" ] && [[ $(printf '%s\n' "${changed_files[@]}" | grep ${{ inputs.example }} | grep /tests/) ]]; then
run_test_cases=$other_test_cases
fi
test_cases=$(echo $run_test_cases | tr ' ' '\n' | sort -u | jq -R '.' | jq -sc '.')
echo "test_cases=$test_cases"
echo "test_cases=$test_cases" >> $GITHUB_OUTPUT
compose-test:
run-test:
needs: [get-test-case]
if: ${{ needs.get-test-case.outputs.test_cases != '[""]' }}
strategy:
matrix:
test_case: ${{ fromJSON(needs.get-test-case.outputs.test_cases) }}
@@ -121,18 +101,9 @@ jobs:
- name: Clean up Working Directory
run: |
sudo rm -rf ${{github.workspace}}/* || true
echo "Cleaning up containers using ports..."
cid=$(docker ps --format '{{.Names}} : {{.Ports}}' | grep -v ' : $' | grep -v 0.0.0.0:5000 | awk -F' : ' '{print $1}')
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
docker system prune -f
echo "Cleaning up images ..."
docker images --filter reference="*/*/*:latest" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="*/*:ci" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="*:5000/*/*" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="opea/comps-base" -q | xargs -r docker rmi && sleep 1s
docker images
docker rmi $(docker images --filter reference="*/*/*:latest" -q) || true
docker rmi $(docker images --filter reference="*/*:ci" -q) || true
- name: Checkout out Repo
uses: actions/checkout@v4
@@ -140,94 +111,47 @@ jobs:
ref: ${{ needs.get-test-case.outputs.CHECKOUT_REF }}
fetch-depth: 0
- name: Clean up container before test
shell: bash
run: |
docker ps
cd ${{ github.workspace }}/${{ inputs.example }}
export test_case=${{ matrix.test_case }}
export hardware=${{ inputs.hardware }}
bash ${{ github.workspace }}/.github/workflows/scripts/docker_compose_clean_up.sh "containers"
bash ${{ github.workspace }}/.github/workflows/scripts/docker_compose_clean_up.sh "ports"
docker ps
- name: Log in DockerHub
uses: docker/login-action@v3.2.0
with:
username: ${{ secrets.DOCKERHUB_USER }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Run test
shell: bash
env:
HUGGINGFACEHUB_API_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
HF_TOKEN: ${{ secrets.HUGGINGFACEHUB_API_TOKEN }}
GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
PINECONE_KEY: ${{ secrets.PINECONE_KEY }}
PINECONE_KEY_LANGCHAIN_TEST: ${{ secrets.PINECONE_KEY_LANGCHAIN_TEST }}
SDK_BASE_URL: ${{ secrets.SDK_BASE_URL }}
SERVING_TOKEN: ${{ secrets.SERVING_TOKEN }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
FINNHUB_API_KEY: ${{ secrets.FINNHUB_API_KEY }}
FINANCIAL_DATASETS_API_KEY: ${{ secrets.FINANCIAL_DATASETS_API_KEY }}
IMAGE_REPO: ${{ inputs.registry }}
IMAGE_TAG: ${{ inputs.tag }}
opea_branch: ${{ inputs.opea_branch }}
example: ${{ inputs.example }}
hardware: ${{ inputs.hardware }}
test_case: ${{ matrix.test_case }}
use_model_cache: ${{ inputs.use_model_cache }}
run: |
cd ${{ github.workspace }}/$example/tests
if [[ "$IMAGE_REPO" == "" ]]; then export IMAGE_REPO="${OPEA_IMAGE_REPO}opea"; fi
if [[ "$use_model_cache" == "true" ]]; then
if [ -d "/data2/hf_model" ]; then
export model_cache="/data2/hf_model"
else
echo "Model cache directory /data2/hf_model does not exist"
export model_cache="$HOME/.cache/huggingface/hub"
fi
if [[ "$test_case" == *"rocm"* ]]; then
export model_cache="/var/lib/GenAI/data"
fi
fi
if [ -f "${test_case}" ]; then timeout 60m bash "${test_case}"; else echo "Test script {${test_case}} not found, skip test!"; fi
if [ -f ${test_case} ]; then timeout 30m bash ${test_case}; else echo "Test script {${test_case}} not found, skip test!"; fi
- name: Clean up container after test
if: always()
- name: Clean up container
shell: bash
if: cancelled() || failure()
run: |
set -x
echo "Cleaning up containers using ports..."
cid=$(docker ps --format '{{.Names}} : {{.Ports}}' | grep -v ' : $' | grep -v 0.0.0.0:5000 | awk -F' : ' '{print $1}')
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
echo "Cleaning up images ..."
df -h
sleep 1
docker system df
sleep 1
if [[ "${{ inputs.hardware }}" == "xeon"* ]]; then
docker system prune -a -f
else
docker images --filter reference="*/*/*:latest" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="*/*:ci" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="*:5000/*/*" -q | xargs -r docker rmi && sleep 1s
docker images --filter reference="opea/comps-base" -q | xargs -r docker rmi && sleep 1s
docker system prune -f
fi
sleep 5
docker images
sleep 1
df -h
sleep 1
docker system df
sleep 1
cd ${{ github.workspace }}/${{ inputs.example }}/docker_compose
test_case=${{ matrix.test_case }}
flag=${test_case%_on_*}
flag=${flag#test_}
yaml_file=$(find . -type f -wholename "*${{ inputs.hardware }}/${flag}.yaml")
echo $yaml_file
container_list=$(cat $yaml_file | grep container_name | cut -d':' -f2)
for container_name in $container_list; do
cid=$(docker ps -aq --filter "name=$container_name")
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
done
docker system prune -f
docker rmi $(docker images --filter reference="*:5000/*/*" -q) || true
- name: Publish pipeline artifact
if: ${{ !cancelled() }}
uses: actions/upload-artifact@v4
with:
name: ${{ inputs.hardware }}_${{ inputs.example }}_${{ matrix.test_case }}
name: ${{ inputs.example }}_${{ matrix.test_case }}
path: ${{ github.workspace }}/${{ inputs.example }}/tests/*.log

View File

@@ -13,7 +13,7 @@ on:
jobs:
build:
runs-on: ubuntu-22.04
runs-on: ubuntu-latest
steps:
- name: Checkout

View File

@@ -1,94 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Daily update vLLM & vLLM-fork version
on:
schedule:
- cron: "30 22 * * *"
workflow_dispatch:
env:
BRANCH_NAME: "update"
USER_NAME: "CICD-at-OPEA"
USER_EMAIL: "CICD@opea.dev"
jobs:
freeze-tag:
runs-on: ubuntu-latest
strategy:
matrix:
include:
- repo: vLLM
repo_name: vllm-project/vllm
ver_name: VLLM_VER
- repo: vLLM-fork
repo_name: HabanaAI/vllm-fork
ver_name: VLLM_FORK_VER
fail-fast: false
permissions:
contents: write
pull-requests: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
ref: ${{ github.ref }}
- name: Set up Git
run: |
git config --global user.name ${{ env.USER_NAME }}
git config --global user.email ${{ env.USER_EMAIL }}
git remote set-url origin https://${{ env.USER_NAME }}:"${{ secrets.ACTION_TOKEN }}"@github.com/${{ github.repository }}.git
git fetch
if git ls-remote https://github.com/${{ github.repository }}.git "refs/heads/${{ env.BRANCH_NAME }}_${{ matrix.repo }}" | grep -q "refs/heads/${{ env.BRANCH_NAME }}_${{ matrix.repo }}"; then
echo "branch ${{ env.BRANCH_NAME }}_${{ matrix.repo }} exists"
git checkout ${{ env.BRANCH_NAME }}_${{ matrix.repo }}
else
echo "branch ${{ env.BRANCH_NAME }}_${{ matrix.repo }} not exists"
git checkout -b ${{ env.BRANCH_NAME }}_${{ matrix.repo }}
git push origin ${{ env.BRANCH_NAME }}_${{ matrix.repo }}
echo "branch ${{ env.BRANCH_NAME }}_${{ matrix.repo }} created successfully"
fi
- name: Run script
run: |
latest_vllm_ver=$(curl -s "https://api.github.com/repos/${{ matrix.repo_name }}/tags" | jq '.[0].name' -)
latest_vllm_ver=$(echo "$latest_vllm_ver" | sed 's/"//g')
echo "latest_vllm_ver=${latest_vllm_ver}" >> "$GITHUB_ENV"
find . -type f -name "*.sh" -exec sed -i "s/${{ matrix.ver_name }}=.*/${{ matrix.ver_name }}=${latest_vllm_ver}/" {} \;
- name: Commit changes
run: |
git add .
if git diff-index --quiet HEAD --; then
echo "No changes detected, skipping commit."
exit 1
else
git commit -s -m "Update ${{ matrix.repo }} version to ${latest_vllm_ver}"
git push --set-upstream origin ${{ env.BRANCH_NAME }}_${{ matrix.repo }}
fi
- name: Create Pull Request
env:
GH_TOKEN: ${{ secrets.ACTION_TOKEN }}
run: |
pr_count=$(curl -H "Authorization: token ${{ secrets.ACTION_TOKEN }}" -s "https://api.github.com/repos/${{ github.repository }}/pulls?state=all&head=${{ env.USER_NAME }}:${{ env.BRANCH_NAME }}_${{ matrix.repo }}" | jq '. | length')
if [ $pr_count -gt 0 ]; then
echo "Pull Request exists"
pr_number=$(curl -H "Authorization: token ${{ secrets.ACTION_TOKEN }}" -s "https://api.github.com/repos/${{ github.repository }}/pulls?state=all&head=${{ env.USER_NAME }}:${{ env.BRANCH_NAME }}_${{ matrix.repo }}" | jq '.[0].number')
gh pr edit ${pr_number} \
--title "Update ${{ matrix.repo }} version to ${latest_vllm_ver}" \
--body "Update ${{ matrix.repo }} version to ${latest_vllm_ver}"
echo "Pull Request updated successfully"
else
echo "Pull Request does not exists..."
gh pr create \
-B main \
-H ${{ env.BRANCH_NAME }}_${{ matrix.repo }} \
--title "Update ${{ matrix.repo }} version to ${latest_vllm_ver}" \
--body "Update ${{ matrix.repo }} version to ${latest_vllm_ver}"
echo "Pull Request created successfully"
fi

View File

@@ -1,29 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Check stale issue and pr
on:
schedule:
- cron: "30 22 * * *"
jobs:
close-issues:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@v9
with:
days-before-issue-stale: 30
days-before-pr-stale: 30
days-before-issue-close: 7
days-before-pr-close: 7
stale-issue-message: "This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
stale-pr-message: "This PR is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
close-issue-message: "This issue was closed because it has been stalled for 7 days with no activity."
close-pr-message: "This PR was closed because it has been stalled for 7 days with no activity."
repo-token: ${{ secrets.ACTION_TOKEN }}
start-date: "2025-03-01T00:00:00Z"
exempt-issue-labels: "Backlog"

View File

@@ -1,119 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Update Docker Hub Description
permissions:
contents: read
on:
schedule:
- cron: "0 0 * * 0"
workflow_dispatch:
jobs:
get-images-matrix:
runs-on: ubuntu-latest
outputs:
examples_json: ${{ steps.extract.outputs.examples_json }}
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Extract images info and generate JSON matrix
id: extract
run: |
#!/bin/bash
set -e
images=$(awk -F'|' '/^\| *\[opea\// {
gsub(/^ +| +$/, "", $2);
gsub(/^ +| +$/, "", $4);
gsub(/^ +| +$/, "", $5);
# Extract the path portion of the dockerHub link from the Example Images column
match($2, /\(https:\/\/hub\.docker\.com\/r\/[^)]*\)/);
repository = substr($2, RSTART, RLENGTH);
# Remove the prefix and the trailing right bracket
sub(/^\(https:\/\/hub\.docker\.com\/r\//, "", repository);
sub(/\)$/, "", repository);
# Description Direct assignment
description = $4;
# Extract the content of the github link from the Readme column
match($5, /\(https:\/\/github\.com\/[^)]*\)/);
readme_url = substr($5, RSTART, RLENGTH);
# Remove the prefix and the trailing right bracket
sub(/^\(https:\/\/github\.com\//, "", readme_url);
sub(/\)$/, "", readme_url);
# Remove blob information, such as "blob/main/" or "blob/habana_main/"
gsub(/blob\/[^/]+\//, "", readme_url);
# Remove the organization name and keep only the file path, such as changing "opea-project/GenAIExamples/AudioQnA/README.md" to "GenAIExamples/AudioQnA/README.md"
sub(/^[^\/]+\//, "", readme_url);
# Generate JSON object string
printf "{\"repository\":\"%s\",\"short-description\":\"%s\",\"readme-filepath\":\"%s\"}\n", repository, description, readme_url;
}' docker_images_list.md)
# Concatenate all JSON objects into a JSON array, using paste to separate them with commas
json="[$(echo "$images" | paste -sd, -)]"
echo "$json"
# Set as output variable for subsequent jobs to use
echo "::set-output name=examples_json::$json"
check-images-matrix:
runs-on: ubuntu-latest
needs: get-images-matrix
if: ${{ needs.get-images-matrix.outputs.examples_json != '' }}
strategy:
matrix:
image: ${{ fromJSON(needs.get-images-matrix.outputs.examples_json) }}
fail-fast: false
steps:
- name: Check dockerhub description
run: |
echo "dockerhub description for ${{ matrix.image.repository }}"
echo "short-description: ${{ matrix.image.short-description }}"
echo "readme-filepath: ${{ matrix.image.readme-filepath }}"
dockerHubDescription:
runs-on: ubuntu-latest
needs: get-images-matrix
if: ${{ needs.get-images-matrix.outputs.examples_json != '' }}
strategy:
matrix:
image: ${{ fromJSON(needs.get-images-matrix.outputs.examples_json) }}
fail-fast: false
steps:
- name: Checkout GenAIExamples
uses: actions/checkout@v4
with:
repository: opea-project/GenAIExamples
path: GenAIExamples
- name: Checkout GenAIComps
uses: actions/checkout@v4
with:
repository: opea-project/GenAIComps
path: GenAIComps
- name: Checkout vllm-openvino
uses: actions/checkout@v4
with:
repository: vllm-project/vllm
path: vllm
- name: Checkout vllm-gaudi
uses: actions/checkout@v4
with:
repository: HabanaAI/vllm-fork
ref: habana_main
path: vllm-fork
- name: add dockerhub description
uses: peter-evans/dockerhub-description@v4
with:
username: ${{ secrets.DOCKERHUB_USER }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
repository: ${{ matrix.image.repository }}
short-description: ${{ matrix.image.short-description }}
readme-filepath: ${{ matrix.image.readme-filepath }}
enable-url-completion: false

View File

@@ -1,33 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Clean up container on manual event
permissions:
contents: read
on:
workflow_dispatch:
inputs:
node:
default: "rocm"
description: "Hardware to clean"
required: true
type: string
clean_list:
default: ""
description: "docker command to clean"
required: false
type: string
jobs:
clean:
runs-on: "${{ inputs.node }}"
steps:
- name: Clean up container
run: |
docker ps
if [ "${{ inputs.clean_list }}" ]; then
echo "----------stop and remove containers----------"
docker stop ${{ inputs.clean_list }} && docker rm ${{ inputs.clean_list }}
echo "----------container removed----------"
docker ps
fi

View File

@@ -41,11 +41,9 @@ jobs:
publish:
needs: [get-image-list]
if: ${{ needs.get-image-list.outputs.matrix != '' }}
strategy:
matrix:
image: ${{ fromJSON(needs.get-image-list.outputs.matrix) }}
fail-fast: false
runs-on: "docker-build-${{ inputs.node }}"
steps:
- uses: docker/login-action@v3.2.0

View File

@@ -12,7 +12,7 @@ on:
type: string
examples:
default: ""
description: 'List of examples to publish "AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA"'
description: 'List of examples to publish "AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,FaqGen,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA"'
required: false
type: string
images:
@@ -47,7 +47,6 @@ jobs:
scan-docker:
needs: get-image-list
runs-on: "docker-build-${{ inputs.node }}"
if: ${{ needs.get-image-list.outputs.matrix != '' }}
strategy:
matrix:
image: ${{ fromJson(needs.get-image-list.outputs.matrix) }}

View File

@@ -7,12 +7,12 @@ on:
inputs:
nodes:
default: "gaudi,xeon"
description: "Hardware to run test gaudi,xeon,rocm,arc,gaudi3,xeon-gnr"
description: "Hardware to run test"
required: true
type: string
examples:
default: "ChatQnA"
description: 'List of examples to test [AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,FaqGen,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA,AvatarChatbot,Text2Image,WorkflowExecAgent,DBQnA,EdgeCraftRAG,GraphRAG]'
description: 'List of examples to test [AudioQnA,ChatQnA,CodeGen,CodeTrans,DocSum,FaqGen,SearchQnA,Translation]'
required: true
type: string
tag:
@@ -20,6 +20,11 @@ on:
description: "Tag to apply to images"
required: true
type: string
deploy_gmc:
default: false
description: 'Whether to deploy gmc'
required: true
type: boolean
build:
default: true
description: 'Build test required images for Examples'
@@ -30,9 +35,14 @@ on:
description: 'Test examples with docker compose'
required: false
type: boolean
test_helmchart:
default: true
description: 'Test examples with helm charts'
test_k8s:
default: false
description: 'Test examples with k8s'
required: false
type: boolean
test_gmc:
default: false
description: 'Test examples with gmc'
required: false
type: boolean
opea_branch:
@@ -41,15 +51,10 @@ on:
required: false
type: string
inject_commit:
default: false
description: "inject commit to docker images"
default: true
description: "inject commit to docker images true or false"
required: false
type: boolean
use_model_cache:
default: false
description: "use model cache"
required: false
type: boolean
type: string
permissions: read-all
jobs:
@@ -69,20 +74,23 @@ jobs:
nodes_json=$(printf '%s\n' "${nodes[@]}" | sort -u | jq -R '.' | jq -sc '.')
echo "nodes=$nodes_json" >> $GITHUB_OUTPUT
build-comps-base:
build-deploy-gmc:
needs: [get-test-matrix]
if: ${{ fromJSON(inputs.deploy_gmc) }}
strategy:
matrix:
node: ${{ fromJson(needs.get-test-matrix.outputs.nodes) }}
uses: ./.github/workflows/_build_comps_base_image.yml
fail-fast: false
uses: ./.github/workflows/_gmc-workflow.yml
with:
node: ${{ matrix.node }}
build: ${{ fromJSON(inputs.build) }}
tag: ${{ inputs.tag }}
opea_branch: ${{ inputs.opea_branch }}
secrets: inherit
run-examples:
needs: [get-test-matrix, build-comps-base]
needs: [get-test-matrix, build-deploy-gmc]
if: always()
strategy:
matrix:
example: ${{ fromJson(needs.get-test-matrix.outputs.examples) }}
@@ -95,8 +103,8 @@ jobs:
tag: ${{ inputs.tag }}
build: ${{ fromJSON(inputs.build) }}
test_compose: ${{ fromJSON(inputs.test_compose) }}
test_helmchart: ${{ fromJSON(inputs.test_helmchart) }}
test_k8s: ${{ fromJSON(inputs.test_k8s) }}
test_gmc: ${{ fromJSON(inputs.test_gmc) }}
opea_branch: ${{ inputs.opea_branch }}
inject_commit: ${{ inputs.inject_commit }}
use_model_cache: ${{ inputs.use_model_cache }}
secrets: inherit

View File

@@ -2,8 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
name: Freeze OPEA images release tag
permissions:
contents: read
on:
workflow_dispatch:
inputs:
@@ -26,9 +25,9 @@ jobs:
- name: Set up Git
run: |
git config --global user.name "CICD-at-OPEA"
git config --global user.email "CICD@opea.dev"
git remote set-url origin https://CICD-at-OPEA:"${{ secrets.ACTION_TOKEN }}"@github.com/opea-project/GenAIExamples.git
git config --global user.name "NeuralChatBot"
git config --global user.email "grp_neural_chat_bot@intel.com"
git remote set-url origin https://NeuralChatBot:"${{ secrets.ACTION_TOKEN }}"@github.com/opea-project/GenAIExamples.git
- name: Run script
run: |

View File

@@ -2,8 +2,6 @@
# SPDX-License-Identifier: Apache-2.0
name: Build specific images on manual event
permissions:
contents: read
on:
workflow_dispatch:
inputs:
@@ -14,7 +12,7 @@ on:
type: string
example:
default: "ChatQnA"
description: 'Build images belong to which example? [AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,FaqGen,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA,AvatarChatbot,Text2Image,WorkflowExecAgent,DBQnA,EdgeCraftRAG,GraphRAG]'
description: 'Build images belong to which example?'
required: true
type: string
services:
@@ -33,10 +31,10 @@ on:
required: false
type: string
inject_commit:
default: false
description: "inject commit to docker images"
default: true
description: "inject commit to docker images true or false"
required: false
type: boolean
type: string
jobs:
get-test-matrix:
@@ -53,7 +51,6 @@ jobs:
image-build:
needs: get-test-matrix
if: ${{ needs.get-test-matrix.outputs.nodes != '' }}
strategy:
matrix:
node: ${{ fromJson(needs.get-test-matrix.outputs.nodes) }}

View File

@@ -1,63 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Clean up Local Registry on manual event
permissions:
contents: read
on:
workflow_dispatch:
inputs:
nodes:
default: "gaudi,xeon"
description: "Hardware to clean up"
required: true
type: string
env:
EXAMPLES: ${{ vars.NIGHTLY_RELEASE_EXAMPLES }}
jobs:
get-build-matrix:
runs-on: ubuntu-latest
outputs:
examples: ${{ steps.get-matrix.outputs.examples }}
nodes: ${{ steps.get-matrix.outputs.nodes }}
steps:
- name: Create Matrix
id: get-matrix
run: |
examples=($(echo ${EXAMPLES} | tr ',' ' '))
examples_json=$(printf '%s\n' "${examples[@]}" | sort -u | jq -R '.' | jq -sc '.')
echo "examples=$examples_json" >> $GITHUB_OUTPUT
nodes=($(echo ${{ inputs.nodes }} | tr ',' ' '))
nodes_json=$(printf '%s\n' "${nodes[@]}" | sort -u | jq -R '.' | jq -sc '.')
echo "nodes=$nodes_json" >> $GITHUB_OUTPUT
clean-up:
needs: get-build-matrix
if: ${{ needs.get-image-list.outputs.matrix != '' }}
strategy:
matrix:
node: ${{ fromJson(needs.get-build-matrix.outputs.nodes) }}
fail-fast: false
runs-on: "docker-build-${{ matrix.node }}"
steps:
- name: Clean Up Local Registry
run: |
echo "Cleaning up local registry on ${{ matrix.node }}"
bash /home/sdp/workspace/fully_registry_cleanup.sh
docker ps | grep registry
build:
needs: [get-build-matrix, clean-up]
if: ${{ needs.get-image-list.outputs.matrix != '' }}
strategy:
matrix:
example: ${{ fromJson(needs.get-build-matrix.outputs.examples) }}
node: ${{ fromJson(needs.get-build-matrix.outputs.nodes) }}
fail-fast: false
uses: ./.github/workflows/_example-workflow.yml
with:
node: ${{ matrix.node }}
example: ${{ matrix.example }}
secrets: inherit

View File

@@ -2,8 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
name: Trellix Command Line Scanner
permissions:
contents: read
on:
workflow_dispatch:
schedule:

View File

@@ -2,15 +2,14 @@
# SPDX-License-Identifier: Apache-2.0
name: Nightly build/publish latest docker images
permissions:
contents: read
on:
schedule:
- cron: "30 14 * * 1-5" # UTC time
- cron: "30 13 * * *" # UTC time
workflow_dispatch:
env:
EXAMPLES: ${{ vars.NIGHTLY_RELEASE_EXAMPLES }}
EXAMPLES: "AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,FaqGen,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA"
TAG: "latest"
PUBLISH_TAGS: "latest"
@@ -33,54 +32,29 @@ jobs:
echo "TAG=$TAG" >> $GITHUB_OUTPUT
echo "PUBLISH_TAGS=$PUBLISH_TAGS" >> $GITHUB_OUTPUT
build-comps-base:
needs: [get-build-matrix]
uses: ./.github/workflows/_build_comps_base_image.yml
with:
node: gaudi
build-images:
needs: [get-build-matrix, build-comps-base]
strategy:
matrix:
example: ${{ fromJSON(needs.get-build-matrix.outputs.examples_json) }}
fail-fast: false
uses: ./.github/workflows/_build_image.yml
with:
node: gaudi
example: ${{ matrix.example }}
inject_commit: true
secrets: inherit
test-example:
needs: [get-build-matrix]
if: ${{ needs.get-build-matrix.outputs.examples_json != '' }}
build:
needs: get-build-matrix
strategy:
matrix:
example: ${{ fromJSON(needs.get-build-matrix.outputs.examples_json) }}
fail-fast: false
uses: ./.github/workflows/_example-workflow.yml
with:
node: xeon
build: false
node: gaudi
example: ${{ matrix.example }}
test_compose: true
inject_commit: true
secrets: inherit
get-image-list:
needs: [get-build-matrix]
needs: get-build-matrix
uses: ./.github/workflows/_get-image-list.yml
with:
examples: ${{ needs.get-build-matrix.outputs.EXAMPLES }}
publish:
needs: [get-build-matrix, get-image-list, build-images]
if: always()
needs: [get-build-matrix, get-image-list, build]
strategy:
matrix:
image: ${{ fromJSON(needs.get-image-list.outputs.matrix) }}
fail-fast: false
runs-on: "docker-build-gaudi"
steps:
- uses: docker/login-action@v3.2.0

View File

@@ -1,82 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: E2E Test with Helm Charts
permissions:
contents: read
on:
pull_request_target:
branches: [main]
types: [opened, reopened, ready_for_review, synchronize] # added `ready_for_review` since draft is skipped
paths:
- "!**.md"
- "**/helm/**"
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
job1:
name: Get-Test-Matrix
permissions:
contents: read
pull-requests: read
runs-on: ubuntu-latest
outputs:
run_matrix: ${{ steps.get-test-matrix.outputs.run_matrix }}
steps:
- name: Checkout Repo
uses: actions/checkout@v4
with:
ref: "refs/pull/${{ github.event.number }}/merge"
fetch-depth: 0
- name: Get Test Matrix
id: get-test-matrix
run: |
set -x
echo "base_commit=${{ github.event.pull_request.base.sha }}"
base_commit=${{ github.event.pull_request.base.sha }}
merged_commit=$(git log -1 --format='%H')
values_files=$(git diff --name-only ${base_commit} ${merged_commit} | \
grep "values.yaml" | \
sort -u ) #CodeGen/kubernetes/helm/cpu-values.yaml
run_matrix="{\"include\":["
for values_file in ${values_files}; do
if [ -f "$values_file" ]; then
valuefile=$(basename "$values_file") # cpu-values.yaml
example=$(echo "$values_file" | cut -d'/' -f1) # CodeGen
if [[ "$valuefile" == *"gaudi"* ]]; then
hardware="gaudi"
elif [[ "$valuefile" == *"rocm"* ]]; then
hardware="rocm"
elif [[ "$valuefile" == *"nv"* ]]; then
continue
else
hardware="xeon"
fi
echo "example=${example}, hardware=${hardware}, valuefile=${valuefile}"
if [[ $(echo ${run_matrix} | grep -c "{\"example\":\"${example}\",\"hardware\":\"${hardware}\"},") == 0 ]]; then
run_matrix="${run_matrix}{\"example\":\"${example}\",\"hardware\":\"${hardware}\"},"
echo "------------------ add one values file ------------------"
fi
fi
done
run_matrix="${run_matrix%,}"
run_matrix=$run_matrix"]}"
echo "run_matrix="${run_matrix}""
echo "run_matrix="${run_matrix}"" >> $GITHUB_OUTPUT
helm-chart-test:
needs: [job1]
if: always() && ${{ fromJSON(needs.job1.outputs.run_matrix).length != 0 }}
uses: ./.github/workflows/_helm-e2e.yml
strategy:
matrix: ${{ fromJSON(needs.job1.outputs.run_matrix) }}
with:
example: ${{ matrix.example }}
hardware: ${{ matrix.hardware }}
mode: "CI"
secrets: inherit

View File

@@ -1,41 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Check Duplicated Images
permissions:
contents: read
on:
pull_request:
branches: [main]
types: [opened, reopened, ready_for_review, synchronize]
paths:
- "**/docker_image_build/*.yaml"
- ".github/workflows/pr-check-duplicated-image.yml"
- ".github/workflows/scripts/check_duplicated_image.py"
workflow_dispatch:
# 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:
check-duplicated-image:
runs-on: ubuntu-latest
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Checkout Repo
uses: actions/checkout@v4
- name: Check all the docker image build files
run: |
pip install PyYAML
cd ${{github.workspace}}
build_files=""
for f in `find . -path "*/docker_image_build/build.yaml"`; do
build_files="$build_files $f"
done
python3 .github/workflows/scripts/check_duplicated_image.py $build_files
shell: bash

View File

@@ -2,9 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
name: Code Scan
permissions:
contents: read
security-events: write
on:
pull_request:
branches: [main]
@@ -36,11 +34,6 @@ jobs:
- name: Checkout out Repo
uses: actions/checkout@v4
- name: Check Dangerous Command Injection
uses: opea-project/validation/actions/check-cmd@main
with:
work_dir: ${{ github.workspace }}
- name: Docker Build
run: |
docker build -f ${{ github.workspace }}/.github/workflows/docker/${{ env.DOCKER_FILE_NAME }}.dockerfile -t ${{ env.REPO_NAME }}:${{ env.REPO_TAG }} .

View File

@@ -2,7 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
name: "Dependency Review"
on: [pull_request_target]
on: [pull_request]
permissions:
contents: read

View File

@@ -3,9 +3,6 @@
name: E2E test with docker compose
permissions:
contents: read
on:
pull_request_target:
branches: ["main", "*rc"]
@@ -31,20 +28,19 @@ jobs:
if: ${{ !github.event.pull_request.draft }}
uses: ./.github/workflows/_get-test-matrix.yml
with:
diff_excluded_files: '\.github|\.md|\.txt|kubernetes|gmc|assets|benchmark'
diff_excluded_files: '.github|*.md|*.txt|kubernetes|manifest|gmc|assets|benchmark'
example-test:
needs: [get-test-matrix]
if: ${{ needs.get-test-matrix.outputs.run_matrix != '' }}
strategy:
matrix: ${{ fromJSON(needs.get-test-matrix.outputs.run_matrix) }}
fail-fast: false
if: ${{ !github.event.pull_request.draft }}
uses: ./.github/workflows/_run-docker-compose.yml
with:
registry: "opea"
tag: "ci"
example: ${{ matrix.example }}
hardware: ${{ matrix.hardware }}
use_model_cache: true
diff_excluded_files: '\.github|\.md|\.txt|kubernetes|gmc|assets|benchmark'
diff_excluded_files: '.github|*.md|*.txt|kubernetes|manifest|gmc|assets|benchmark'
secrets: inherit

View File

@@ -1,110 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Compose file and dockerfile path checking
permissions:
contents: read
on:
pull_request:
branches: [main]
types: [opened, reopened, ready_for_review, synchronize]
jobs:
check-dockerfile-paths-in-README:
runs-on: ubuntu-latest
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Checkout Repo GenAIExamples
uses: actions/checkout@v4
- name: Clone Repo GenAIComps
run: |
cd ..
git clone --depth 1 https://github.com/opea-project/GenAIComps.git
- name: Check for Missing Dockerfile Paths in GenAIComps
run: |
cd ${{github.workspace}}
miss="FALSE"
while IFS=: read -r file line content; do
dockerfile_path=$(echo "$content" | awk -F '-f ' '{print $2}' | awk '{print $1}')
if [[ ! -f "../GenAIComps/${dockerfile_path}" ]]; then
miss="TRUE"
echo "Missing Dockerfile: GenAIComps/${dockerfile_path} (Referenced in GenAIExamples/${file}:${line})"
fi
done < <(grep -Ern 'docker build .* -f comps/.+/Dockerfile' --include='*.md' .)
if [[ "$miss" == "TRUE" ]]; then
exit 1
fi
shell: bash
check-Dockerfile-in-build-yamls:
runs-on: ubuntu-latest
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Checkout Repo GenAIExamples
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Check Dockerfile path included in image build yaml
if: always()
run: |
set -e
shopt -s globstar
no_add="FALSE"
cd ${{github.workspace}}
Dockerfiles=$(realpath $(find ./ -name '*Dockerfile*' ! -path '*/tests/*'))
if [ -n "$Dockerfiles" ]; then
for dockerfile in $Dockerfiles; do
service=$(echo "$dockerfile" | awk -F '/GenAIExamples/' '{print $2}' | awk -F '/' '{print $2}')
cd ${{github.workspace}}/$service/docker_image_build
all_paths=$(realpath $(awk ' /context:/ { context = $2 } /dockerfile:/ { dockerfile = $2; combined = context "/" dockerfile; gsub(/\/+/, "/", combined); if (index(context, ".") > 0) {print combined}}' build.yaml) 2> /dev/null || true )
if ! echo "$all_paths" | grep -q "$dockerfile"; then
echo "AR: Update $dockerfile to GenAIExamples/$service/docker_image_build/build.yaml. The yaml is used for release images build."
no_add="TRUE"
fi
done
fi
if [[ "$no_add" == "TRUE" ]]; then
exit 1
fi
check-image-and-service-names-in-build-yaml:
runs-on: ubuntu-latest
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Checkout Repo GenAIExamples
uses: actions/checkout@v4
- name: Check name agreement in build.yaml
run: |
pip install ruamel.yaml
cd ${{github.workspace}}
consistency="TRUE"
build_yamls=$(find . -name 'build.yaml')
for build_yaml in $build_yamls; do
message=$(python3 .github/workflows/scripts/check-name-agreement.py "$build_yaml")
if [[ "$message" != *"consistent"* ]]; then
consistency="FALSE"
echo "Inconsistent service name and image name found in file $build_yaml."
echo "$message"
fi
done
if [[ "$consistency" == "FALSE" ]]; then
echo "Please ensure that the service and image names are consistent in build.yaml, otherwise we cannot guarantee that your image will be published correctly."
exit 1
fi
shell: bash

View File

@@ -8,10 +8,11 @@ on:
branches: ["main", "*rc"]
types: [opened, reopened, ready_for_review, synchronize] # added `ready_for_review` since draft is skipped
paths:
- "**/kubernetes/gmc/**"
- "**/kubernetes/**/gmc/**"
- "**/tests/test_gmc**"
- "!**.md"
- "!**.txt"
- "!**/kubernetes/**/manifest/**"
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
@@ -21,7 +22,7 @@ jobs:
job1:
uses: ./.github/workflows/_get-test-matrix.yml
with:
diff_excluded_files: '\.github|docker_compose|assets|\.md|\.txt'
diff_excluded_files: '.github|docker_compose|manifest|assets|*.md|*.txt'
test_mode: "gmc"
gmc-test:

42
.github/workflows/pr-manifest-e2e.yml vendored Normal file
View File

@@ -0,0 +1,42 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: E2E test with manifests
on:
pull_request_target:
branches: ["main", "*rc"]
types: [opened, reopened, ready_for_review, synchronize] # added `ready_for_review` since draft is skipped
paths:
- "**/Dockerfile**"
- "**.py"
- "**/kubernetes/**/manifest/**"
- "**/tests/test_manifest**"
- "!**.md"
- "!**.txt"
- "!**/kubernetes/**/gmc/**"
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
job1:
uses: ./.github/workflows/_get-test-matrix.yml
with:
diff_excluded_files: '.github|docker_compose|gmc|assets|*.md|*.txt|benchmark'
test_mode: "manifest"
run-example:
needs: job1
strategy:
matrix: ${{ fromJSON(needs.job1.outputs.run_matrix) }}
fail-fast: false
uses: ./.github/workflows/_example-workflow.yml
with:
node: ${{ matrix.hardware }}
example: ${{ matrix.example }}
tag: ${{ github.event.pull_request.head.sha }}
test_k8s: true
secrets: inherit

View File

@@ -1,10 +1,7 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Check hyperlinks and relative path validity
permissions:
contents: read
name: Check Paths and Hyperlinks
on:
pull_request:
@@ -12,6 +9,39 @@ on:
types: [opened, reopened, ready_for_review, synchronize]
jobs:
check-dockerfile-paths:
runs-on: ubuntu-latest
steps:
- name: Clean Up Working Directory
run: sudo rm -rf ${{github.workspace}}/*
- name: Checkout Repo GenAIExamples
uses: actions/checkout@v4
- name: Clone Repo GenAIComps
run: |
cd ..
git clone https://github.com/opea-project/GenAIComps.git
- name: Check for Missing Dockerfile Paths in GenAIComps
run: |
cd ${{github.workspace}}
miss="FALSE"
while IFS=: read -r file line content; do
dockerfile_path=$(echo "$content" | awk -F '-f ' '{print $2}' | awk '{print $1}')
if [[ ! -f "../GenAIComps/${dockerfile_path}" ]]; then
miss="TRUE"
echo "Missing Dockerfile: GenAIComps/${dockerfile_path} (Referenced in GenAIExamples/${file}:${line})"
fi
done < <(grep -Ern 'docker build .* -f comps/.+/Dockerfile' --include='*.md' .)
if [[ "$miss" == "TRUE" ]]; then
exit 1
fi
shell: bash
check-the-validity-of-hyperlinks-in-README:
runs-on: ubuntu-latest
steps:
@@ -26,7 +56,6 @@ jobs:
- name: Check the Validity of Hyperlinks
run: |
cd ${{github.workspace}}
delay=15
fail="FALSE"
merged_commit=$(git log -1 --format='%H')
changed_files="$(git diff --name-status --diff-filter=ARM ${{ github.event.pull_request.base.sha }} ${merged_commit} | awk '/\.md$/ {print $NF}')"
@@ -39,20 +68,15 @@ jobs:
# echo $url_line
url=$(echo "$url_line"|cut -d '(' -f2 | cut -d ')' -f1|sed 's/\.git$//')
path=$(echo "$url_line"|cut -d':' -f1 | cut -d'/' -f2-)
if [[ "$url" == "https://platform.openai.com/api-keys"* ]]; then
echo "Link "$url" from ${{github.workspace}}/$path needs to be verified by a real person."
else
sleep $delay
response=$(curl -L -s -o /dev/null -w "%{http_code}" "$url")|| true
if [ "$response" -ne 200 ]; then
echo "**********Validation failed ($response), try again**********"
response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url")
if [ "$response_retry" -eq 200 ]; then
echo "*****Retry successfully*****"
else
echo "Invalid link ($response_retry) from ${{github.workspace}}/$path: $url"
fail="TRUE"
fi
response=$(curl -L -s -o /dev/null -w "%{http_code}" "$url")|| true
if [ "$response" -ne 200 ]; then
echo "**********Validation failed, try again**********"
response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url")
if [ "$response_retry" -eq 200 ]; then
echo "*****Retry successfully*****"
else
echo "Invalid link from ${{github.workspace}}/$path: $url"
fail="TRUE"
fi
fi
done
@@ -83,10 +107,15 @@ jobs:
- name: Checking Relative Path Validity
run: |
cd ${{github.workspace}}
delay=15
fail="FALSE"
repo_name=${{ github.event.pull_request.head.repo.full_name }}
branch="https://github.com/$repo_name/blob/${{ github.event.pull_request.head.ref }}"
if [ "$(echo "$repo_name"|cut -d'/' -f1)" != "opea-project" ]; then
owner=$(echo "${{ github.event.pull_request.head.repo.full_name }}" |cut -d'/' -f1)
branch="https://github.com/$owner/GenAIExamples/tree/${{ github.event.pull_request.head.ref }}"
else
branch="https://github.com/opea-project/GenAIExamples/blob/${{ github.event.pull_request.head.ref }}"
fi
link_head="https://github.com/opea-project/GenAIExamples/blob/main"
merged_commit=$(git log -1 --format='%H')
changed_files="$(git diff --name-status --diff-filter=ARM ${{ github.event.pull_request.base.sha }} ${merged_commit} | awk '/\.md$/ {print $NF}')"
@@ -115,15 +144,14 @@ jobs:
if [[ "$png_line" == *#* ]]; then
if [ -n "changed_files" ] && echo "$changed_files" | grep -q "^${refer_path}$"; then
url_dev=$branch$(echo "$real_path" | sed 's|.*/GenAIExamples||')$png_path
sleep $delay
response=$(curl -I -L -s -o /dev/null -w "%{http_code}" "$url_dev")
if [ "$response" -ne 200 ]; then
echo "**********Validation failed ($response), try again**********"
echo "**********Validation failed, try again**********"
response_retry=$(curl -s -o /dev/null -w "%{http_code}" "$url_dev")
if [ "$response_retry" -eq 200 ]; then
echo "*****Retry successfully*****"
else
echo "Invalid path ($response_retry) from ${{github.workspace}}/$refer_path: $png_path"
echo "Invalid path from ${{github.workspace}}/$refer_path: $png_path"
fail="TRUE"
fi
else

View File

@@ -3,9 +3,6 @@
# Test
name: Build latest images on push event
permissions:
contents: read
on:
push:
branches: [ 'main' ]
@@ -13,7 +10,6 @@ on:
- "**.py"
- "**Dockerfile*"
- "**docker_image_build/build.yaml"
- "**/ui/**"
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}-on-push
@@ -27,7 +23,6 @@ jobs:
image-build:
needs: job1
if: ${{ needs.job1.outputs.run_matrix != '{"include":[]}' }}
strategy:
matrix: ${{ fromJSON(needs.job1.outputs.run_matrix) }}
fail-fast: false

View File

@@ -3,12 +3,10 @@
name: Check the validity of links in docker_images_list.
permissions:
contents: read
on:
push:
branches: [main]
types: [opened, reopened, ready_for_review, synchronize]
jobs:
check-dockerfile-paths:

View File

@@ -8,10 +8,6 @@ on:
- "**/docker_compose/**/compose*.yaml"
name: Create an issue to GenAIInfra on push
permissions:
contents: read
jobs:
job1:
name: Create issue
@@ -44,7 +40,7 @@ jobs:
- name: Create Issue
uses: daisy-ycguo/create-issue-action@stable
with:
token: ${{ secrets.ACTION_TOKEN }}
token: ${{ secrets.Infra_Issue_Token }}
owner: opea-project
repo: GenAIInfra
title: |
@@ -58,6 +54,6 @@ jobs:
${{ env.changed_files }}
Please verify if the helm charts need to be changed accordingly.
Please verify if the helm charts and manifests need to be changed accordingly.
> This issue was created automatically by CI.

View File

@@ -1,46 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
from ruamel.yaml import YAML
def parse_yaml_file(file_path):
yaml = YAML()
with open(file_path, "r") as file:
data = yaml.load(file)
return data
def check_service_image_consistency(data):
inconsistencies = []
for service_name, service_details in data.get("services", {}).items():
image_name = service_details.get("image", "")
# Extract the image name part after the last '/'
image_name_part = image_name.split("/")[-1].split(":")[0]
# Check if the service name is a substring of the image name part
if service_name not in image_name_part:
# Get the line number of the service name
line_number = service_details.lc.line + 1
inconsistencies.append((service_name, image_name, line_number))
return inconsistencies
def main():
parser = argparse.ArgumentParser(description="Check service name and image name consistency in a YAML file.")
parser.add_argument("file_path", type=str, help="The path to the YAML file.")
args = parser.parse_args()
data = parse_yaml_file(args.file_path)
inconsistencies = check_service_image_consistency(data)
if inconsistencies:
for service_name, image_name, line_number in inconsistencies:
print(f"Service name: {service_name}, Image name: {image_name}, Line number: {line_number}")
else:
print("All consistent")
if __name__ == "__main__":
main()

View File

@@ -1,79 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import os.path
import subprocess
import sys
import yaml
images = {}
dockerfiles = {}
errors = []
def check_docker_compose_build_definition(file_path):
with open(file_path, "r") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
for service in data["services"]:
if "build" in data["services"][service] and "image" in data["services"][service]:
bash_command = "echo " + data["services"][service]["image"]
image = (
subprocess.run(["bash", "-c", bash_command], check=True, capture_output=True)
.stdout.decode("utf-8")
.strip()
)
build = data["services"][service]["build"]
context = build.get("context", "")
dockerfile = os.path.normpath(
os.path.join(os.path.dirname(file_path), context, build.get("dockerfile", ""))
)
if not os.path.isfile(dockerfile):
# dockerfile not exists in the current repo context, assume it's in 3rd party context
dockerfile = os.path.normpath(os.path.join(context, build.get("dockerfile", "")))
item = {"file_path": file_path, "service": service, "dockerfile": dockerfile, "image": image}
if image in images and dockerfile != images[image]["dockerfile"]:
errors.append(
f"ERROR: !!! Found Conflicts !!!\n"
f"Image: {image}, Dockerfile: {dockerfile}, defined in Service: {service}, File: {file_path}\n"
f"Image: {image}, Dockerfile: {images[image]['dockerfile']}, defined in Service: {images[image]['service']}, File: {images[image]['file_path']}"
)
else:
# print(f"Add Image: {image} Dockerfile: {dockerfile}")
images[image] = item
if dockerfile in dockerfiles and image != dockerfiles[dockerfile]["image"]:
errors.append(
f"WARNING: Different images using the same Dockerfile\n"
f"Dockerfile: {dockerfile}, Image: {image}, defined in Service: {service}, File: {file_path}\n"
f"Dockerfile: {dockerfile}, Image: {dockerfiles[dockerfile]['image']}, defined in Service: {dockerfiles[dockerfile]['service']}, File: {dockerfiles[dockerfile]['file_path']}"
)
else:
dockerfiles[dockerfile] = item
def parse_arg():
parser = argparse.ArgumentParser(
description="Check for conflicts in image build definition in docker-compose.yml files"
)
parser.add_argument("files", nargs="+", help="list of files to be checked")
return parser.parse_args()
def main():
args = parse_arg()
for file_path in args.files:
check_docker_compose_build_definition(file_path)
print("SUCCESS: No Conlicts Found.")
if errors:
for error in errors:
print(error)
sys.exit(1)
else:
print("SUCCESS: No Conflicts Found.")
return 0
if __name__ == "__main__":
main()

View File

@@ -7,7 +7,7 @@ source /GenAIExamples/.github/workflows/scripts/change_color
log_dir=/GenAIExamples/.github/workflows/scripts/codeScan
ERROR_WARN=false
find . -type f \( -name "Dockerfile*" \) -print -exec hadolint --ignore DL3006 --ignore DL3007 --ignore DL3008 --ignore DL3013 --ignore DL3018 --ignore DL3016 {} \; > ${log_dir}/hadolint.log
find . -type f \( -name "Dockerfile*" \) -print -exec hadolint --ignore DL3006 --ignore DL3007 --ignore DL3008 --ignore DL3013 {} \; > ${log_dir}/hadolint.log
if [[ $(grep -c "error" ${log_dir}/hadolint.log) != 0 ]]; then
$BOLD_RED && echo "Error!! Please Click on the artifact button to download and check error details." && $RESET

View File

@@ -1,55 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# The test machine used by several opea projects, so the test scripts can't use `docker compose down` to clean up
# the all the containers, ports and networks directly.
# So we need to use the following script to minimize the impact of the clean up.
test_case=${test_case:-"test_compose_on_gaudi.sh"}
hardware=${hardware:-"gaudi"}
flag=${test_case%_on_*}
flag=${flag#test_}
yaml_file=$(find . -type f -wholename "*${hardware}/${flag}.yaml")
echo $yaml_file
case "$1" in
containers)
echo "Stop and remove all containers used by the services in $yaml_file ..."
containers=$(cat $yaml_file | grep container_name | cut -d':' -f2)
for container_name in $containers; do
cid=$(docker ps -aq --filter "name=$container_name")
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
done
;;
ports)
echo "Release all ports used by the services in $yaml_file ..."
pip install jq yq
ports=$(yq '.services[].ports[] | split(":")[0]' $yaml_file | grep -o '[0-9a-zA-Z_-]\+')
echo "All ports list..."
echo "$ports"
for port in $ports; do
if [[ $port =~ [a-zA-Z_-] ]]; then
echo "Search port value $port from the test case..."
port_fix=$(grep -E "export $port=" tests/$test_case | cut -d'=' -f2)
if [[ "$port_fix" == "" ]]; then
echo "Can't find the port value from the test case, use the default value in yaml..."
port_fix=$(yq '.services[].ports[]' $yaml_file | grep $port | cut -d':' -f2 | grep -o '[0-9a-zA-Z]\+')
fi
port=$port_fix
fi
if [[ $port =~ [0-9] ]]; then
if [[ $port == 5000 ]]; then
echo "Error: Port 5000 is used by local docker registry, please DO NOT use it in docker compose deployment!!!"
exit 1
fi
echo "Check port $port..."
cid=$(docker ps --filter "publish=${port}" --format "{{.ID}}")
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && echo "release $port"; fi
fi
done
;;
*)
echo "Unknown function: $1"
;;
esac

View File

@@ -12,25 +12,21 @@ run_matrix="{\"include\":["
examples=$(printf '%s\n' "${changed_files[@]}" | grep '/' | cut -d'/' -f1 | sort -u)
for example in ${examples}; do
if [[ ! -d $WORKSPACE/$example ]]; then continue; fi
cd $WORKSPACE/$example
if [[ ! $(find . -type f | grep ${test_mode}) ]]; then continue; fi
cd tests
ls -l
if [[ "$test_mode" == "docker_image_build" ]]; then
hardware_list="gaudi xeon"
find_name="test_manifest_on_*.sh"
else
find_name="test_${test_mode}*_on_*.sh"
hardware_list=$(find . -type f -name "${find_name}" | cut -d/ -f2 | cut -d. -f1 | awk -F'_on_' '{print $2}'| sort -u)
fi
hardware_list=$(find . -type f -name "${find_name}" | cut -d/ -f2 | cut -d. -f1 | awk -F'_on_' '{print $2}'| sort -u)
echo -e "Test supported hardware list: \n${hardware_list}"
run_hardware=""
if [[ $(printf '%s\n' "${changed_files[@]}" | grep ${example} | cut -d'/' -f2 | grep -E '\.py|Dockerfile*|ui|docker_image_build' ) ]]; then
echo "run test on all hardware if megaservice or ui code change..."
run_hardware=$hardware_list
elif [[ $(printf '%s\n' "${changed_files[@]}" | grep ${example} | grep 'tests'| cut -d'/' -f3 | grep -vE '^test_|^_test' ) ]]; then
echo "run test on all hardware if common test scripts change..."
if [[ $(printf '%s\n' "${changed_files[@]}" | grep ${example} | cut -d'/' -f2 | grep -E '*.py|Dockerfile*|ui|docker_image_build' ) ]]; then
# run test on all hardware if megaservice or ui code change
run_hardware=$hardware_list
else
for hardware in ${hardware_list}; do

View File

@@ -2,7 +2,7 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
#set -xe
function dump_pod_log() {
pod_name=$1
@@ -12,7 +12,7 @@ function dump_pod_log() {
kubectl describe pod $pod_name -n $namespace
echo "-----------------------------------"
echo "#kubectl logs $pod_name -n $namespace"
kubectl logs $pod_name -n $namespace --all-containers --prefix=true
kubectl logs $pod_name -n $namespace
echo "-----------------------------------"
}
@@ -44,13 +44,8 @@ function dump_pods_status() {
function dump_all_pod_logs() {
namespace=$1
echo "------SUMMARY of POD STATUS in NS $namespace------"
kubectl get pods -n $namespace -o wide
echo "------SUMMARY of SVC STATUS in NS $namespace------"
kubectl get services -n $namespace -o wide
echo "------SUMMARY of endpoint STATUS in NS $namespace------"
kubectl get endpoints -n $namespace -o wide
echo "-----DUMP POD STATUS AND LOG in NS $namespace------"
pods=$(kubectl get pods -n $namespace -o jsonpath='{.items[*].metadata.name}')
for pod_name in $pods
do

View File

@@ -1,57 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Weekly test all examples on multiple HWs
permissions: read-all
on:
schedule:
- cron: "30 2 * * 6" # UTC time
workflow_dispatch:
env:
EXAMPLES: "CodeTrans" #${{ vars.NIGHTLY_RELEASE_EXAMPLES }}
NODES: "gaudi,xeon,rocm,arc"
jobs:
get-test-matrix:
runs-on: ubuntu-latest
outputs:
examples: ${{ steps.get-matrix.outputs.examples }}
nodes: ${{ steps.get-matrix.outputs.nodes }}
steps:
- name: Create Matrix
id: get-matrix
run: |
examples=($(echo ${EXAMPLES} | tr ',' ' '))
examples_json=$(printf '%s\n' "${examples[@]}" | sort -u | jq -R '.' | jq -sc '.')
echo "examples=$examples_json" >> $GITHUB_OUTPUT
nodes=($(echo ${NODES} | tr ',' ' '))
nodes_json=$(printf '%s\n' "${nodes[@]}" | sort -u | jq -R '.' | jq -sc '.')
echo "nodes=$nodes_json" >> $GITHUB_OUTPUT
build-comps-base:
needs: [get-test-matrix]
strategy:
matrix:
node: ${{ fromJson(needs.get-test-matrix.outputs.nodes) }}
uses: ./.github/workflows/_build_comps_base_image.yml
with:
node: ${{ matrix.node }}
run-examples:
needs: [get-test-matrix, build-comps-base]
strategy:
matrix:
example: ${{ fromJson(needs.get-test-matrix.outputs.examples) }}
node: ${{ fromJson(needs.get-test-matrix.outputs.nodes) }}
fail-fast: false
uses: ./.github/workflows/_example-workflow.yml
with:
node: ${{ matrix.node }}
example: ${{ matrix.example }}
build: true
test_compose: true
test_helmchart: true
secrets: inherit

View File

@@ -1,9 +1,11 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Weekly update 3rd party images
name: Weekly update base images and 3rd party images
on:
schedule:
- cron: "0 0 * * 0"
workflow_dispatch:
permissions:
@@ -14,8 +16,8 @@ jobs:
freeze-images:
runs-on: ubuntu-latest
env:
USER_NAME: "CICD-at-OPEA"
USER_EMAIL: "CICD@opea.dev"
USER_NAME: "NeuralChatBot"
USER_EMAIL: "grp_neural_chat_bot@intel.com"
BRANCH_NAME: "update_images_tag"
steps:
- name: Checkout repository

View File

@@ -7,7 +7,7 @@ ci:
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
rev: v4.6.0
hooks:
- id: end-of-file-fixer
files: (.*\.(py|md|rst|yaml|yml|json|ts|js|html|svelte|sh))$
@@ -74,7 +74,7 @@ repos:
name: Unused noqa
- repo: https://github.com/pycqa/isort
rev: 6.0.1
rev: 5.13.2
hooks:
- id: isort
@@ -100,21 +100,21 @@ repos:
- prettier@3.2.5
- repo: https://github.com/psf/black.git
rev: 25.1.0
rev: 24.4.2
hooks:
- id: black
files: (.*\.py)$
- repo: https://github.com/asottile/blacken-docs
rev: 1.19.1
rev: 1.18.0
hooks:
- id: blacken-docs
args: [--line-length=120, --skip-errors]
additional_dependencies:
- black==24.10.0
- black==24.4.2
- repo: https://github.com/codespell-project/codespell
rev: v2.4.1
rev: v2.3.0
hooks:
- id: codespell
args: [-w]
@@ -122,7 +122,7 @@ repos:
- tomli
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.11.4
rev: v0.5.0
hooks:
- id: ruff
args: [--fix, --exit-non-zero-on-fix, --no-cache]

View File

@@ -1,18 +1,9 @@
# Agents for Question Answering
## Table of contents
1. [Overview](#overview)
2. [Deploy with Docker](#deploy-with-docker)
3. [How to interact with the agent system with UI](#how-to-interact-with-the-agent-system-with-ui)
4. [Validate Services](#validate-services)
5. [Register Tools](#how-to-register-other-tools-with-the-ai-agent)
6. [Monitoring and Tracing](#monitor-and-tracing)
## Overview
This example showcases a hierarchical multi-agent system for question-answering applications. The architecture diagram below shows a supervisor agent that interfaces with the user and dispatches tasks to two worker agents to gather information and come up with answers. The worker RAG agent uses the retrieval tool to retrieve relevant documents from a knowledge base - a vector database. The worker SQL agent retrieves relevant data from a SQL database. Although not included in this example by default, other tools such as a web search tool or a knowledge graph query tool can be used by the supervisor agent to gather information from additional sources.
![Architecture Overview](assets/img/agent_qna_arch.png)
This example showcases a hierarchical multi-agent system for question-answering applications. The architecture diagram is shown below. The supervisor agent interfaces with the user and dispatch tasks to the worker agent and other tools to gather information and come up with answers. The worker agent uses the retrieval tool to generate answers to the queries posted by the supervisor agent. Other tools used by the supervisor agent may include APIs to interface knowledge graphs, SQL databases, external knowledge bases, etc.
![Architecture Overview](assets/agent_qna_arch.png)
The AgentQnA example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
@@ -47,7 +38,6 @@ flowchart LR
end
AG_REACT([Agent MicroService - react]):::blue
AG_RAG([Agent MicroService - rag]):::blue
AG_SQL([Agent MicroService - sql]):::blue
LLM_gen{{LLM Service <br>}}
DP([Data Preparation MicroService]):::blue
TEI_RER{{Reranking service<br>}}
@@ -61,7 +51,6 @@ flowchart LR
direction LR
a[User Input Query] --> AG_REACT
AG_REACT --> AG_RAG
AG_REACT --> AG_SQL
AG_RAG --> DocIndexRetriever-MegaService
EM ==> RET
RET ==> RER
@@ -70,7 +59,6 @@ flowchart LR
%% Embedding service flow
direction LR
AG_RAG <-.-> LLM_gen
AG_SQL <-.-> LLM_gen
AG_REACT <-.-> LLM_gen
EM <-.-> TEI_EM
RET <-.-> R_RET
@@ -84,219 +72,152 @@ flowchart LR
```
### Why should AI Agents be used for question-answering?
### Why Agent for question answering?
1. **Improve relevancy of retrieved context.**
RAG agents can rephrase user queries, decompose user queries, and iterate to get the most relevant context for answering a user's question. Compared to conventional RAG, RAG agents significantly improve the correctness and relevancy of the answer because of the iterations it goes through.
2. **Expand scope of skills.**
The supervisor agent interacts with multiple worker agents that specialize in different skills (e.g., retrieve documents, write SQL queries, etc.). Thus, it can answer questions with different methods.
3. **Hierarchical multi-agents improve performance.**
Expert worker agents, such as RAG agents and SQL agents, can provide high-quality output for different aspects of a complex query, and the supervisor agent can aggregate the information to provide a comprehensive answer. If only one agent is used and all tools are provided to this single agent, it can lead to large overhead or not use the best tool to provide accurate answers.
1. Improve relevancy of retrieved context.
Agent can rephrase user queries, decompose user queries, and iterate to get the most relevant context for answering user's questions. Compared to conventional RAG, RAG agent can significantly improve the correctness and relevancy of the answer.
2. Use tools to get additional knowledge.
For example, knowledge graphs and SQL databases can be exposed as APIs for Agents to gather knowledge that may be missing in the retrieval vector database.
3. Hierarchical agent can further improve performance.
Expert worker agents, such as retrieval agent, knowledge graph agent, SQL agent, etc., can provide high-quality output for different aspects of a complex query, and the supervisor agent can aggregate the information together to provide a comprehensive answer.
## Deploy with docker
## Deployment with docker
### 1. Set up environment </br>
1. Build agent docker image [Optional]
#### First, clone the `GenAIExamples` repo.
> [!NOTE]
> the step is optional. The docker images will be automatically pulled when running the docker compose commands. This step is only needed if pulling images failed.
```bash
First, clone the opea GenAIComps repo.
```
export WORKDIR=<your-work-directory>
cd $WORKDIR
git clone https://github.com/opea-project/GenAIExamples.git
git clone https://github.com/opea-project/GenAIComps.git
```
#### Second, set up environment variables.
Then build the agent docker image. Both the supervisor agent and the worker agent will use the same docker image, but when we launch the two agents we will specify different strategies and register different tools.
##### For proxy environments only
```bash
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
```
cd GenAIComps
docker build -t opea/agent-langchain:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/agent/langchain/Dockerfile .
```
##### For using open-source llms
2. Set up environment for this example </br>
Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token).
First, clone this repo.
Then set an environment variable with the token and another for a directory to download the models:
```
export WORKDIR=<your-work-directory>
cd $WORKDIR
git clone https://github.com/opea-project/GenAIExamples.git
```
Second, set up env vars.
```
# Example: host_ip="192.168.1.1" or export host_ip="External_Public_IP"
export host_ip=$(hostname -I | awk '{print $1}')
# if you are in a proxy environment, also set the proxy-related environment variables
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
# for using open-source llms
export HUGGINGFACEHUB_API_TOKEN=<your-HF-token>
export HF_CACHE_DIR=<directory-where-llms-are-downloaded> #so that no need to redownload every time
# optional: OPANAI_API_KEY if you want to use OpenAI models
export OPENAI_API_KEY=<your-openai-key>
```
3. Deploy the retrieval tool (i.e., DocIndexRetriever mega-service)
First, launch the mega-service.
```
cd $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool
bash launch_retrieval_tool.sh
```
Then, ingest data into the vector database. Here we provide an example. You can ingest your own data.
```
bash run_ingest_data.sh
```
4. Launch other tools. </br>
In this example, we will use some of the mock APIs provided in the Meta CRAG KDD Challenge to demonstrate the benefits of gaining additional context from mock knowledge graphs.
```
docker run -d -p=8080:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
```
5. Launch agent services</br>
We provide two options for `llm_engine` of the agents: 1. open-source LLMs, 2. OpenAI models via API calls.
Deploy it on Gaudi or Xeon respectively
::::{tab-set}
:::{tab-item} Gaudi
:sync: Gaudi
To use open-source LLMs on Gaudi2, run commands below.
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_tgi_gaudi.sh
bash launch_agent_service_tgi_gaudi.sh
```
:::
:::{tab-item} Xeon
:sync: Xeon
To use OpenAI models, run commands below.
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
bash launch_agent_service_openai.sh
```
:::
::::
## Validate services
First look at logs of the agent docker containers:
```bash
export HUGGINGFACEHUB_API_TOKEN=<your-HF-token>
export HF_CACHE_DIR=<directory-where-llms-are-downloaded> # to avoid redownloading models
```
##### [Optional] OPENAI_API_KEY to use OpenAI models or Intel® AI for Enterprise Inference
To use OpenAI models, generate a key following these [instructions](https://platform.openai.com/api-keys).
To use a remote server running Intel® AI for Enterprise Inference, contact the cloud service provider or owner of the on-prem machine for a key to access the desired model on the server.
Then set the environment variable `OPENAI_API_KEY` with the key contents:
```bash
export OPENAI_API_KEY=<your-openai-key>
```
#### Third, set up environment variables for the selected hardware using the corresponding `set_env.sh`
##### Gaudi
```bash
source $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/set_env.sh
```
##### Xeon
```bash
source $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon/set_env.sh
```
For running
### 2. Launch the multi-agent system. </br>
We make it convenient to launch the whole system with docker compose, which includes microservices for LLM, agents, UI, retrieval tool, vector database, dataprep, and telemetry. There are 3 docker compose files, which make it easy for users to pick and choose. Users can choose a different retrieval tool other than the `DocIndexRetriever` example provided in our GenAIExamples repo. Users can choose not to launch the telemetry containers.
#### Launch on Gaudi
On Gaudi, `meta-llama/Meta-Llama-3.3-70B-Instruct` will be served using vllm. The command below will launch the multi-agent system with the `DocIndexRetriever` as the retrieval tool for the Worker RAG agent.
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml up -d
```
> **Note**: To enable the web search tool, skip this step and proceed to the "[Optional] Web Search Tool Support" section.
To enable Open Telemetry Tracing, compose.telemetry.yaml file need to be merged along with default compose.yaml file.
Gaudi example with Open Telemetry feature:
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml -f compose.telemetry.yaml up -d
```
##### [Optional] Web Search Tool Support
<details>
<summary> Instructions </summary>
A web search tool is supported in this example and can be enabled by running docker compose with the `compose.webtool.yaml` file.
The Google Search API is used. Follow the [instructions](https://python.langchain.com/docs/integrations/tools/google_search) to create an API key and enable the Custom Search API on a Google account. The environment variables `GOOGLE_CSE_ID` and `GOOGLE_API_KEY` need to be set.
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
export GOOGLE_CSE_ID="YOUR_ID"
export GOOGLE_API_KEY="YOUR_API_KEY"
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml -f compose.webtool.yaml up -d
```
</details>
#### Launch on Xeon
On Xeon, OpenAI models and models deployed on a remote server are supported. Both methods require an API key.
```bash
export OPENAI_API_KEY=<your-openai-key>
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
```
##### OpenAI Models
The command below will launch the multi-agent system with the `DocIndexRetriever` as the retrieval tool for the Worker RAG agent.
```bash
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose_openai.yaml up -d
```
##### Models on Remote Server
When models are deployed on a remote server with Intel® AI for Enterprise Inference, a base URL and an API key are required to access them. To run the Agent microservice on Xeon while using models deployed on a remote server, add `compose_remote.yaml` to the `docker compose` command and set additional environment variables.
###### Notes
- `OPENAI_API_KEY` is already set in a previous step.
- `model` is used to overwrite the value set for this environment variable in `set_env.sh`.
- `LLM_ENDPOINT_URL` is the base URL given from the owner of the on-prem machine or cloud service provider. It will follow this format: "https://<DNS>". Here is an example: "https://api.inference.example.com".
```bash
export model=<name-of-model-card>
export LLM_ENDPOINT_URL=<http-endpoint-of-remote-server>
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose_openai.yaml -f compose_remote.yaml up -d
```
### 3. Ingest Data into the vector database
The `run_ingest_data.sh` script will use an example jsonl file to ingest example documents into a vector database. Other ways to ingest data and other types of documents supported can be found in the OPEA dataprep microservice located in the opea-project/GenAIComps repo.
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool/
bash run_ingest_data.sh
```
> **Note**: This is a one-time operation.
## How to interact with the agent system with UI
The UI microservice is launched in the previous step with the other microservices.
To see the UI, open a web browser to `http://${ip_address}:5173` to access the UI. Note the `ip_address` here is the host IP of the UI microservice.
1. Click on the arrow above `Get started`. Create an admin account with a name, email, and password.
2. Add an OpenAI-compatible API endpoint. In the upper right, click on the circle button with the user's initial, go to `Admin Settings`->`Connections`. Under `Manage OpenAI API Connections`, click on the `+` to add a connection. Fill in these fields:
- **URL**: `http://${ip_address}:9090/v1`, do not forget the `v1`
- **Key**: any value
- **Model IDs**: any name i.e. `opea-agent`, then press `+` to add it
Click "Save".
![opea-agent-setting](assets/img/opea-agent-setting.png)
3. Test OPEA agent with UI. Return to `New Chat` and ensure the model (i.e. `opea-agent`) is selected near the upper left. Enter in any prompt to interact with the agent.
![opea-agent-test](assets/img/opea-agent-test.png)
## [Optional] Deploy using Helm Charts
Refer to the [AgentQnA helm chart](./kubernetes/helm/README.md) for instructions on deploying AgentQnA on Kubernetes.
## Validate Services
1. First look at logs for each of the agent docker containers:
```bash
# worker RAG agent
# worker agent
docker logs rag-agent-endpoint
```
# worker SQL agent
docker logs sql-agent-endpoint
```
# supervisor agent
docker logs react-agent-endpoint
```
Look for the message "HTTP server setup successful" to confirm the agent docker container has started successfully.</p>
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
2. Use python to validate each agent is working properly:
Second, validate worker agent:
```bash
# RAG worker agent
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "Tell me about Michael Jackson song Thriller" --agent_role "worker" --ext_port 9095
# SQL agent
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "How many employees in company" --agent_role "worker" --ext_port 9096
# supervisor agent: this will test a two-turn conversation
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port 9090
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
## How to register other tools with the AI agent
Third, validate supervisor agent:
The [tools](./tools) folder contains YAML and Python files for additional tools for the supervisor and worker agents. Refer to the "Provide your own tools" section in the instructions [here](https://github.com/opea-project/GenAIComps/tree/main/comps/agent/src/README.md) to add tools and customize the AI agents.
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
## Monitor and Tracing
## How to register your own tools with agent
Follow [OpenTelemetry OPEA Guide](https://opea-project.github.io/latest/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.html) to understand how to use OpenTelemetry tracing and metrics in OPEA.
For AgentQnA specific tracing and metrics monitoring, follow [OpenTelemetry on AgentQnA](https://opea-project.github.io/latest/tutorial/OpenTelemetry/deploy/AgentQnA.html) section.
You can take a look at the tools yaml and python files in this example. For more details, please refer to the "Provide your own tools" section in the instructions [here](https://github.com/opea-project/GenAIComps/tree/main/comps/agent/langchain/README.md).

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# Build Mega Service of AgentQnA on AMD ROCm GPU
## Build Docker Images
### 1. Build Docker Image
- #### Create application install directory and go to it:
```bash
mkdir ~/agentqna-install && cd agentqna-install
```
- #### Clone the repository GenAIExamples (the default repository branch "main" is used here):
```bash
git clone https://github.com/opea-project/GenAIExamples.git
```
If you need to use a specific branch/tag of the GenAIExamples repository, then (v1.3 replace with its own value):
```bash
git clone https://github.com/opea-project/GenAIExamples.git && cd GenAIExamples && git checkout v1.3
```
We remind you that when using a specific version of the code, you need to use the README from this version:
- #### Go to build directory:
```bash
cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_image_build
```
- Cleaning up the GenAIComps repository if it was previously cloned in this directory.
This is necessary if the build was performed earlier and the GenAIComps folder exists and is not empty:
```bash
echo Y | rm -R GenAIComps
```
- #### Clone the repository GenAIComps (the default repository branch "main" is used here):
```bash
git clone https://github.com/opea-project/GenAIComps.git
```
We remind you that when using a specific version of the code, you need to use the README from this version.
- #### Setting the list of images for the build (from the build file.yaml)
If you want to deploy a vLLM-based or TGI-based application, then the set of services is installed as follows:
#### vLLM-based application
```bash
service_list="vllm-rocm agent agent-ui"
```
#### TGI-based application
```bash
service_list="agent agent-ui"
```
- #### Optional. Pull TGI Docker Image (Do this if you want to use TGI)
```bash
docker pull ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
```
- #### Build Docker Images
```bash
docker compose -f build.yaml build ${service_list} --no-cache
```
- #### Build DocIndexRetriever Docker Images
```bash
cd ~/agentqna-install/GenAIExamples/DocIndexRetriever/docker_image_build/
git clone https://github.com/opea-project/GenAIComps.git
service_list="doc-index-retriever dataprep embedding retriever reranking"
docker compose -f build.yaml build ${service_list} --no-cache
```
- #### Pull DocIndexRetriever Docker Images
```bash
docker pull redis/redis-stack:7.2.0-v9
docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
```
After the build, we check the list of images with the command:
```bash
docker image ls
```
The list of images should include:
##### vLLM-based application:
- opea/vllm-rocm:latest
- opea/agent:latest
- redis/redis-stack:7.2.0-v9
- ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
- opea/embedding:latest
- opea/retriever:latest
- opea/reranking:latest
- opea/doc-index-retriever:latest
##### TGI-based application:
- ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
- opea/agent:latest
- redis/redis-stack:7.2.0-v9
- ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
- opea/embedding:latest
- opea/retriever:latest
- opea/reranking:latest
- opea/doc-index-retriever:latest
---
## Deploy the AgentQnA Application
### Docker Compose Configuration for AMD GPUs
To enable GPU support for AMD GPUs, the following configuration is added to the Docker Compose file:
- compose_vllm.yaml - for vLLM-based application
- compose.yaml - for TGI-based
```yaml
shm_size: 1g
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
cap_add:
- SYS_PTRACE
group_add:
- video
security_opt:
- seccomp:unconfined
```
This configuration forwards all available GPUs to the container. To use a specific GPU, specify its `cardN` and `renderN` device IDs. For example:
```yaml
shm_size: 1g
devices:
- /dev/kfd:/dev/kfd
- /dev/dri/card0:/dev/dri/card0
- /dev/dri/render128:/dev/dri/render128
cap_add:
- SYS_PTRACE
group_add:
- video
security_opt:
- seccomp:unconfined
```
**How to Identify GPU Device IDs:**
Use AMD GPU driver utilities to determine the correct `cardN` and `renderN` IDs for your GPU.
### Set deploy environment variables
#### Setting variables in the operating system environment:
```bash
### Replace the string 'server_address' with your local server IP address
export host_ip='server_address'
### Replace the string 'your_huggingfacehub_token' with your HuggingFacehub repository access token.
export HUGGINGFACEHUB_API_TOKEN='your_huggingfacehub_token'
### Replace the string 'your_langchain_api_key' with your LANGCHAIN API KEY.
export LANGCHAIN_API_KEY='your_langchain_api_key'
export LANGCHAIN_TRACING_V2=""
```
### Start the services:
#### If you use vLLM
```bash
cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_vllm_rocm.sh
```
#### If you use TGI
```bash
cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_tgi_rocm.sh
```
All containers should be running and should not restart:
##### If you use vLLM:
- dataprep-redis-server
- doc-index-retriever-server
- embedding-server
- rag-agent-endpoint
- react-agent-endpoint
- redis-vector-db
- reranking-tei-xeon-server
- retriever-redis-server
- sql-agent-endpoint
- tei-embedding-server
- tei-reranking-server
- vllm-service
##### If you use TGI:
- dataprep-redis-server
- doc-index-retriever-server
- embedding-server
- rag-agent-endpoint
- react-agent-endpoint
- redis-vector-db
- reranking-tei-xeon-server
- retriever-redis-server
- sql-agent-endpoint
- tei-embedding-server
- tei-reranking-server
- tgi-service
---
## Validate the Services
### 1. Validate the vLLM/TGI Service
#### If you use vLLM:
```bash
DATA='{"model": "Intel/neural-chat-7b-v3-3t", '\
'"messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 256}'
curl http://${HOST_IP}:${VLLM_SERVICE_PORT}/v1/chat/completions \
-X POST \
-d "$DATA" \
-H 'Content-Type: application/json'
```
Checking the response from the service. The response should be similar to JSON:
```json
{
"id": "chatcmpl-142f34ef35b64a8db3deedd170fed951",
"object": "chat.completion",
"created": 1742270316,
"model": "Intel/neural-chat-7b-v3-3",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "",
"tool_calls": []
},
"logprobs": null,
"finish_reason": "length",
"stop_reason": null
}
],
"usage": { "prompt_tokens": 66, "total_tokens": 322, "completion_tokens": 256, "prompt_tokens_details": null },
"prompt_logprobs": null
}
```
If the service response has a meaningful response in the value of the "choices.message.content" key,
then we consider the vLLM service to be successfully launched
#### If you use TGI:
```bash
DATA='{"inputs":"What is Deep Learning?",'\
'"parameters":{"max_new_tokens":256,"do_sample": true}}'
curl http://${HOST_IP}:${TGI_SERVICE_PORT}/generate \
-X POST \
-d "$DATA" \
-H 'Content-Type: application/json'
```
Checking the response from the service. The response should be similar to JSON:
```json
{
"generated_text": " "
}
```
If the service response has a meaningful response in the value of the "generated_text" key,
then we consider the TGI service to be successfully launched
### 2. Validate Agent Services
#### Validate Rag Agent Service
```bash
export agent_port=${WORKER_RAG_AGENT_PORT}
prompt="Tell me about Michael Jackson song Thriller"
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port
```
The response must contain the meaningful text of the response to the request from the "prompt" variable
#### Validate Sql Agent Service
```bash
export agent_port=${WORKER_SQL_AGENT_PORT}
prompt="How many employees are there in the company?"
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port
```
The answer should make sense - "8 employees in the company"
#### Validate React (Supervisor) Agent Service
```bash
export agent_port=${SUPERVISOR_REACT_AGENT_PORT}
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream
```
The response should contain "Iron Maiden"
### 3. Stop application
#### If you use vLLM
```bash
cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash stop_agent_service_vllm_rocm.sh
```
#### If you use TGI
```bash
cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash stop_agent_service_tgi_rocm.sh
```

View File

@@ -1,124 +0,0 @@
# Copyright (C) 2025 Advanced Micro Devices, Inc.
services:
tgi-service:
image: ghcr.io/huggingface/text-generation-inference:3.0.0-rocm
container_name: tgi-service
ports:
- "${TGI_SERVICE_PORT-8085}:80"
volumes:
- "${MODEL_CACHE:-./data}:/data"
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
TGI_LLM_ENDPOINT: "http://${ip_address}:${TGI_SERVICE_PORT}"
HUGGING_FACE_HUB_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
shm_size: 32g
devices:
- /dev/kfd:/dev/kfd
- /dev/dri:/dev/dri
cap_add:
- SYS_PTRACE
group_add:
- video
security_opt:
- seccomp:unconfined
ipc: host
command: --model-id ${LLM_MODEL_ID} --max-input-length 4096 --max-total-tokens 8192
worker-rag-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: rag-agent-endpoint
volumes:
- "${TOOLSET_PATH}:/home/user/tools/"
ports:
- "${WORKER_RAG_AGENT_PORT:-9095}:9095"
ipc: host
environment:
ip_address: ${ip_address}
strategy: rag_agent_llama
with_memory: false
recursion_limit: ${recursion_limit_worker}
llm_engine: tgi
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
tools: /home/user/tools/worker_agent_tools.yaml
require_human_feedback: false
RETRIEVAL_TOOL_URL: ${RETRIEVAL_TOOL_URL}
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-worker-agent-service"
port: 9095
worker-sql-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: sql-agent-endpoint
volumes:
- "${WORKDIR}/tests/Chinook_Sqlite.sqlite:/home/user/chinook-db/Chinook_Sqlite.sqlite:rw"
ports:
- "${WORKER_SQL_AGENT_PORT:-9096}:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent_llama
with_memory: false
db_name: ${db_name}
db_path: ${db_path}
use_hints: false
recursion_limit: ${recursion_limit_worker}
llm_engine: vllm
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
port: 9096
supervisor-react-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: react-agent-endpoint
depends_on:
- worker-rag-agent
volumes:
- "${TOOLSET_PATH}:/home/user/tools/"
ports:
- "${SUPERVISOR_REACT_AGENT_PORT:-9090}:9090"
ipc: host
environment:
ip_address: ${ip_address}
strategy: react_llama
with_memory: true
recursion_limit: ${recursion_limit_supervisor}
llm_engine: tgi
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: true
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-supervisor-agent-service"
CRAG_SERVER: ${CRAG_SERVER}
WORKER_AGENT_URL: ${WORKER_AGENT_URL}
SQL_AGENT_URL: ${SQL_AGENT_URL}
port: 9090

View File

@@ -1,128 +0,0 @@
# Copyright (C) 2025 Advanced Micro Devices, Inc.
services:
vllm-service:
image: ${REGISTRY:-opea}/vllm-rocm:${TAG:-latest}
container_name: vllm-service
ports:
- "${VLLM_SERVICE_PORT:-8081}:8011"
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
WILM_USE_TRITON_FLASH_ATTENTION: 0
PYTORCH_JIT: 0
volumes:
- "${MODEL_CACHE:-./data}:/data"
shm_size: 20G
devices:
- /dev/kfd:/dev/kfd
- /dev/dri/:/dev/dri/
cap_add:
- SYS_PTRACE
group_add:
- video
security_opt:
- seccomp:unconfined
- apparmor=unconfined
command: "--model ${VLLM_LLM_MODEL_ID} --swap-space 16 --disable-log-requests --dtype float16 --tensor-parallel-size 4 --host 0.0.0.0 --port 8011 --num-scheduler-steps 1 --distributed-executor-backend \"mp\""
ipc: host
worker-rag-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: rag-agent-endpoint
volumes:
- ${TOOLSET_PATH}:/home/user/tools/
ports:
- "${WORKER_RAG_AGENT_PORT:-9095}:9095"
ipc: host
environment:
ip_address: ${ip_address}
strategy: rag_agent_llama
with_memory: false
recursion_limit: ${recursion_limit_worker}
llm_engine: vllm
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
tools: /home/user/tools/worker_agent_tools.yaml
require_human_feedback: false
RETRIEVAL_TOOL_URL: ${RETRIEVAL_TOOL_URL}
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-worker-agent-service"
port: 9095
worker-sql-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: sql-agent-endpoint
volumes:
- "${WORKDIR}/tests/Chinook_Sqlite.sqlite:/home/user/chinook-db/Chinook_Sqlite.sqlite:rw"
ports:
- "${WORKER_SQL_AGENT_PORT:-9096}:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent_llama
with_memory: false
db_name: ${db_name}
db_path: ${db_path}
use_hints: false
recursion_limit: ${recursion_limit_worker}
llm_engine: vllm
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
port: 9096
supervisor-react-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: react-agent-endpoint
depends_on:
- worker-rag-agent
volumes:
- ${TOOLSET_PATH}:/home/user/tools/
ports:
- "${SUPERVISOR_REACT_AGENT_PORT:-9090}:9090"
ipc: host
environment:
ip_address: ${ip_address}
strategy: react_llama
with_memory: true
recursion_limit: ${recursion_limit_supervisor}
llm_engine: vllm
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: true
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-supervisor-agent-service"
CRAG_SERVER: ${CRAG_SERVER}
WORKER_AGENT_URL: ${WORKER_AGENT_URL}
SQL_AGENT_URL: ${SQL_AGENT_URL}
port: 9090

View File

@@ -1,87 +0,0 @@
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0
# Before start script:
# export host_ip="your_host_ip_or_host_name"
# export HUGGINGFACEHUB_API_TOKEN="your_huggingface_api_token"
# export LANGCHAIN_API_KEY="your_langchain_api_key"
# export LANGCHAIN_TRACING_V2=""
# Set server hostname or IP address
export ip_address=${host_ip}
# Set services IP ports
export TGI_SERVICE_PORT="18110"
export WORKER_RAG_AGENT_PORT="18111"
export WORKER_SQL_AGENT_PORT="18112"
export SUPERVISOR_REACT_AGENT_PORT="18113"
export CRAG_SERVER_PORT="18114"
export WORKPATH=$(dirname "$PWD")
export WORKDIR=${WORKPATH}/../../../
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export HF_CACHE_DIR="./data"
export MODEL_CACHE="./data"
export TOOLSET_PATH=${WORKPATH}/../../../tools/
export recursion_limit_worker=12
export LLM_ENDPOINT_URL=http://${ip_address}:${TGI_SERVICE_PORT}
export temperature=0.01
export max_new_tokens=512
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
export LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2}
export db_name=Chinook
export db_path="sqlite:////home/user/chinook-db/Chinook_Sqlite.sqlite"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export CRAG_SERVER=http://${ip_address}:${CRAG_SERVER_PORT}
export WORKER_AGENT_URL="http://${ip_address}:${WORKER_RAG_AGENT_PORT}/v1/chat/completions"
export SQL_AGENT_URL="http://${ip_address}:${WORKER_SQL_AGENT_PORT}/v1/chat/completions"
export HF_CACHE_DIR=${HF_CACHE_DIR}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
echo ${WORKER_RAG_AGENT_PORT} > ${WORKPATH}/WORKER_RAG_AGENT_PORT_tmp
echo ${WORKER_SQL_AGENT_PORT} > ${WORKPATH}/WORKER_SQL_AGENT_PORT_tmp
echo ${SUPERVISOR_REACT_AGENT_PORT} > ${WORKPATH}/SUPERVISOR_REACT_AGENT_PORT_tmp
echo ${CRAG_SERVER_PORT} > ${WORKPATH}/CRAG_SERVER_PORT_tmp
echo "Downloading chinook data..."
echo Y | rm -R chinook-database
git clone https://github.com/lerocha/chinook-database.git
echo Y | rm -R ../../../../../AgentQnA/tests/Chinook_Sqlite.sqlite
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite ../../../../../AgentQnA/tests
docker compose -f ../../../../../DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml up -d
docker compose -f compose.yaml up -d
n=0
until [[ "$n" -ge 100 ]]; do
docker logs tgi-service > ${WORKPATH}/tgi_service_start.log
if grep -q Connected ${WORKPATH}/tgi_service_start.log; then
break
fi
sleep 10s
n=$((n+1))
done
echo "Starting CRAG server"
docker run -d --runtime=runc --name=kdd-cup-24-crag-service -p=${CRAG_SERVER_PORT}:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0

View File

@@ -1,88 +0,0 @@
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0
# Before start script:
# export host_ip="your_host_ip_or_host_name"
# export HUGGINGFACEHUB_API_TOKEN="your_huggingface_api_token"
# export LANGCHAIN_API_KEY="your_langchain_api_key"
# export LANGCHAIN_TRACING_V2=""
# Set server hostname or IP address
export ip_address=${host_ip}
# Set services IP ports
export VLLM_SERVICE_PORT="18110"
export WORKER_RAG_AGENT_PORT="18111"
export WORKER_SQL_AGENT_PORT="18112"
export SUPERVISOR_REACT_AGENT_PORT="18113"
export CRAG_SERVER_PORT="18114"
export WORKPATH=$(dirname "$PWD")
export WORKDIR=${WORKPATH}/../../../
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export VLLM_LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export HF_CACHE_DIR="./data"
export MODEL_CACHE="./data"
export TOOLSET_PATH=${WORKPATH}/../../../tools/
export recursion_limit_worker=12
export LLM_ENDPOINT_URL=http://${ip_address}:${VLLM_SERVICE_PORT}
export LLM_MODEL_ID=${VLLM_LLM_MODEL_ID}
export temperature=0.01
export max_new_tokens=512
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
export LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2}
export db_name=Chinook
export db_path="sqlite:////home/user/chinook-db/Chinook_Sqlite.sqlite"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export CRAG_SERVER=http://${ip_address}:${CRAG_SERVER_PORT}
export WORKER_AGENT_URL="http://${ip_address}:${WORKER_RAG_AGENT_PORT}/v1/chat/completions"
export SQL_AGENT_URL="http://${ip_address}:${WORKER_SQL_AGENT_PORT}/v1/chat/completions"
export HF_CACHE_DIR=${HF_CACHE_DIR}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
echo ${WORKER_RAG_AGENT_PORT} > ${WORKPATH}/WORKER_RAG_AGENT_PORT_tmp
echo ${WORKER_SQL_AGENT_PORT} > ${WORKPATH}/WORKER_SQL_AGENT_PORT_tmp
echo ${SUPERVISOR_REACT_AGENT_PORT} > ${WORKPATH}/SUPERVISOR_REACT_AGENT_PORT_tmp
echo ${CRAG_SERVER_PORT} > ${WORKPATH}/CRAG_SERVER_PORT_tmp
echo "Downloading chinook data..."
echo Y | rm -R chinook-database
git clone https://github.com/lerocha/chinook-database.git
echo Y | rm -R ../../../../../AgentQnA/tests/Chinook_Sqlite.sqlite
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite ../../../../../AgentQnA/tests
docker compose -f ../../../../../DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml up -d
docker compose -f compose_vllm.yaml up -d
n=0
until [[ "$n" -ge 500 ]]; do
docker logs vllm-service >& "${WORKPATH}"/vllm-service_start.log
if grep -q "Application startup complete" "${WORKPATH}"/vllm-service_start.log; then
break
fi
sleep 20s
n=$((n+1))
done
echo "Starting CRAG server"
docker run -d --runtime=runc --name=kdd-cup-24-crag-service -p=${CRAG_SERVER_PORT}:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0

View File

@@ -1,62 +0,0 @@
#!/usr/bin/env bash
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0
WORKPATH=$(dirname "$PWD")/..
export ip_address=${host_ip}
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export AGENTQNA_TGI_IMAGE=ghcr.io/huggingface/text-generation-inference:2.4.1-rocm
export AGENTQNA_TGI_SERVICE_PORT="19001"
# LLM related environment variables
export AGENTQNA_CARD_ID="card1"
export AGENTQNA_RENDER_ID="renderD136"
export HF_CACHE_DIR=${HF_CACHE_DIR}
ls $HF_CACHE_DIR
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export NUM_SHARDS=4
export LLM_ENDPOINT_URL="http://${ip_address}:${AGENTQNA_TGI_SERVICE_PORT}"
export temperature=0.01
export max_new_tokens=512
# agent related environment variables
export AGENTQNA_WORKER_AGENT_SERVICE_PORT="9095"
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export WORKER_AGENT_URL="http://${ip_address}:${AGENTQNA_WORKER_AGENT_SERVICE_PORT}/v1/chat/completions"
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export CRAG_SERVER=http://${ip_address}:18881
export AGENTQNA_FRONTEND_PORT="15557"
#retrieval_tool
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:26379"
export INDEX_NAME="rag-redis"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/delete"
echo "Removing chinook data..."
echo Y | rm -R chinook-database
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
echo "Stopping CRAG server"
docker rm kdd-cup-24-crag-service --force
echo "Stopping Agent services"
docker compose -f compose.yaml down
echo "Stopping Retrieval services"
docker compose -f ../../../../../DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml down

View File

@@ -1,84 +0,0 @@
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0
# Before start script:
# export host_ip="your_host_ip_or_host_name"
# export HUGGINGFACEHUB_API_TOKEN="your_huggingface_api_token"
# export LANGCHAIN_API_KEY="your_langchain_api_key"
# export LANGCHAIN_TRACING_V2=""
# Set server hostname or IP address
export ip_address=${host_ip}
# Set services IP ports
export VLLM_SERVICE_PORT="18110"
export WORKER_RAG_AGENT_PORT="18111"
export WORKER_SQL_AGENT_PORT="18112"
export SUPERVISOR_REACT_AGENT_PORT="18113"
export CRAG_SERVER_PORT="18114"
export WORKPATH=$(dirname "$PWD")
export WORKDIR=${WORKPATH}/../../../
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export VLLM_LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
export HF_CACHE_DIR="./data"
export MODEL_CACHE="./data"
export TOOLSET_PATH=${WORKPATH}/../../../tools/
export recursion_limit_worker=12
export LLM_ENDPOINT_URL=http://${ip_address}:${VLLM_SERVICE_PORT}
export LLM_MODEL_ID=${VLLM_LLM_MODEL_ID}
export temperature=0.01
export max_new_tokens=512
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
export LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2}
export db_name=Chinook
export db_path="sqlite:////home/user/chinook-db/Chinook_Sqlite.sqlite"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export CRAG_SERVER=http://${ip_address}:${CRAG_SERVER_PORT}
export WORKER_AGENT_URL="http://${ip_address}:${WORKER_RAG_AGENT_PORT}/v1/chat/completions"
export SQL_AGENT_URL="http://${ip_address}:${WORKER_SQL_AGENT_PORT}/v1/chat/completions"
export HF_CACHE_DIR=${HF_CACHE_DIR}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
echo ${WORKER_RAG_AGENT_PORT} > ${WORKPATH}/WORKER_RAG_AGENT_PORT_tmp
echo ${WORKER_SQL_AGENT_PORT} > ${WORKPATH}/WORKER_SQL_AGENT_PORT_tmp
echo ${SUPERVISOR_REACT_AGENT_PORT} > ${WORKPATH}/SUPERVISOR_REACT_AGENT_PORT_tmp
echo ${CRAG_SERVER_PORT} > ${WORKPATH}/CRAG_SERVER_PORT_tmp
echo "Removing chinook data..."
echo Y | rm -R chinook-database
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
echo "Stopping CRAG server"
docker rm kdd-cup-24-crag-service --force
echo "Stopping Agent services"
docker compose -f compose_vllm.yaml down
echo "Stopping Retrieval services"
docker compose -f ../../../../../DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml down

View File

@@ -1,3 +1,100 @@
# Single node on-prem deployment with Docker Compose on Xeon Scalable processors
This example showcases a hierarchical multi-agent system for question-answering applications. To deploy the example on Xeon, OpenAI LLM models via API calls are used. For instructions, refer to the deployment guide [here](../../../../README.md).
This example showcases a hierarchical multi-agent system for question-answering applications. We deploy the example on Xeon. For LLMs, we use OpenAI models via API calls. For instructions on using open-source LLMs, please refer to the deployment guide [here](../../../../README.md).
## Deployment with docker
1. First, clone this repo.
```
export WORKDIR=<your-work-directory>
cd $WORKDIR
git clone https://github.com/opea-project/GenAIExamples.git
```
2. Set up environment for this example </br>
```
# Example: host_ip="192.168.1.1" or export host_ip="External_Public_IP"
export host_ip=$(hostname -I | awk '{print $1}')
# if you are in a proxy environment, also set the proxy-related environment variables
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
#OPANAI_API_KEY if you want to use OpenAI models
export OPENAI_API_KEY=<your-openai-key>
```
3. Deploy the retrieval tool (i.e., DocIndexRetriever mega-service)
First, launch the mega-service.
```
cd $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool
bash launch_retrieval_tool.sh
```
Then, ingest data into the vector database. Here we provide an example. You can ingest your own data.
```
bash run_ingest_data.sh
```
4. Launch Tool service
In this example, we will use some of the mock APIs provided in the Meta CRAG KDD Challenge to demonstrate the benefits of gaining additional context from mock knowledge graphs.
```
docker run -d -p=8080:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
```
5. Launch `Agent` service
The configurations of the supervisor agent and the worker agent are defined in the docker-compose yaml file. We currently use openAI GPT-4o-mini as LLM, and llama3.1-70B-instruct (served by TGI-Gaudi) in Gaudi example. To use openai llm, run command below.
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
bash launch_agent_service_openai.sh
```
6. [Optional] Build `Agent` docker image if pulling images failed.
```
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/agent-langchain:latest -f comps/agent/langchain/Dockerfile .
```
## Validate services
First look at logs of the agent docker containers:
```
# worker agent
docker logs rag-agent-endpoint
```
```
# supervisor agent
docker logs react-agent-endpoint
```
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
Second, validate worker agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
Third, validate supervisor agent:
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
## How to register your own tools with agent
You can take a look at the tools yaml and python files in this example. For more details, please refer to the "Provide your own tools" section in the instructions [here](https://github.com/opea-project/GenAIComps/tree/main/comps/agent/langchain/README.md).

View File

@@ -3,7 +3,7 @@
services:
worker-rag-agent:
image: opea/agent:latest
image: opea/agent-langchain:latest
container_name: rag-agent-endpoint
volumes:
- ${TOOLSET_PATH}:/home/user/tools/
@@ -13,14 +13,13 @@ services:
environment:
ip_address: ${ip_address}
strategy: rag_agent
with_memory: false
recursion_limit: ${recursion_limit_worker}
llm_engine: openai
OPENAI_API_KEY: ${OPENAI_API_KEY}
model: ${model}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
streaming: false
tools: /home/user/tools/worker_agent_tools.yaml
require_human_feedback: false
RETRIEVAL_TOOL_URL: ${RETRIEVAL_TOOL_URL}
@@ -32,40 +31,12 @@ services:
LANGCHAIN_PROJECT: "opea-worker-agent-service"
port: 9095
worker-sql-agent:
image: opea/agent:latest
container_name: sql-agent-endpoint
volumes:
- ${WORKDIR}/GenAIExamples/AgentQnA/tests:/home/user/chinook-db # SQL database
ports:
- "9096:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent
with_memory: false
db_name: ${db_name}
db_path: ${db_path}
use_hints: false
recursion_limit: ${recursion_limit_worker}
llm_engine: openai
OPENAI_API_KEY: ${OPENAI_API_KEY}
model: ${model}
temperature: 0
max_new_tokens: ${max_new_tokens}
stream: false
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
port: 9096
supervisor-react-agent:
image: opea/agent:latest
image: opea/agent-langchain:latest
container_name: react-agent-endpoint
depends_on:
- worker-rag-agent
- worker-sql-agent
volumes:
- ${TOOLSET_PATH}:/home/user/tools/
ports:
@@ -73,15 +44,14 @@ services:
ipc: host
environment:
ip_address: ${ip_address}
strategy: react_llama
with_memory: true
strategy: react_langgraph
recursion_limit: ${recursion_limit_supervisor}
llm_engine: openai
OPENAI_API_KEY: ${OPENAI_API_KEY}
model: ${model}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: true
streaming: false
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}
@@ -92,21 +62,4 @@ services:
LANGCHAIN_PROJECT: "opea-supervisor-agent-service"
CRAG_SERVER: $CRAG_SERVER
WORKER_AGENT_URL: $WORKER_AGENT_URL
SQL_AGENT_URL: $SQL_AGENT_URL
port: 9090
mock-api:
image: docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
container_name: mock-api
ports:
- "8080:8000"
ipc: host
agent-ui:
image: opea/agent-ui
container_name: agent-ui
ports:
- "5173:8080"
ipc: host
networks:
default:
driver: bridge

View File

@@ -1,18 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
services:
worker-rag-agent:
environment:
llm_endpoint_url: ${LLM_ENDPOINT_URL}
api_key: ${OPENAI_API_KEY}
worker-sql-agent:
environment:
llm_endpoint_url: ${LLM_ENDPOINT_URL}
api_key: ${OPENAI_API_KEY}
supervisor-react-agent:
environment:
llm_endpoint_url: ${LLM_ENDPOINT_URL}
api_key: ${OPENAI_API_KEY}

View File

@@ -0,0 +1,19 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
pushd "../../../../../" > /dev/null
source .set_env.sh
popd > /dev/null
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
export ip_address=$(hostname -I | awk '{print $1}')
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export model="gpt-4o-mini-2024-07-18"
export temperature=0
export max_new_tokens=4096
export OPENAI_API_KEY=${OPENAI_API_KEY}
export WORKER_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export CRAG_SERVER=http://${ip_address}:8080
docker compose -f compose_openai.yaml up -d

View File

@@ -1,57 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
pushd "../../../../../" > /dev/null
source .set_env.sh
popd > /dev/null
if [[ -z "${WORKDIR}" ]]; then
echo "Please set WORKDIR environment variable"
exit 0
fi
echo "WORKDIR=${WORKDIR}"
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
export ip_address=$(hostname -I | awk '{print $1}')
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export model="gpt-4o-mini-2024-07-18"
export temperature=0
export max_new_tokens=4096
export OPENAI_API_KEY=${OPENAI_API_KEY}
export WORKER_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
export SQL_AGENT_URL="http://${ip_address}:9096/v1/chat/completions"
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export CRAG_SERVER=http://${ip_address}:8080
export db_name=Chinook
export db_path="sqlite:////home/user/chinook-db/Chinook_Sqlite.sqlite"
if [ ! -f $WORKDIR/GenAIExamples/AgentQnA/tests/Chinook_Sqlite.sqlite ]; then
echo "Download Chinook_Sqlite!"
wget -O $WORKDIR/GenAIExamples/AgentQnA/tests/Chinook_Sqlite.sqlite https://github.com/lerocha/chinook-database/releases/download/v1.4.5/Chinook_Sqlite.sqlite
fi
# retriever
export host_ip=$(hostname -I | awk '{print $1}')
export HF_CACHE_DIR=${HF_CACHE_DIR}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
export no_proxy="$no_proxy,rag-agent-endpoint,sql-agent-endpoint,react-agent-endpoint,agent-ui"

View File

@@ -1,3 +1,105 @@
# Single node on-prem deployment AgentQnA on Gaudi
This example showcases a hierarchical multi-agent system for question-answering applications. To deploy the example on Gaudi using open-source LLMs, refer to the deployment guide [here](../../../../README.md).
This example showcases a hierarchical multi-agent system for question-answering applications. We deploy the example on Gaudi using open-source LLMs,
For more details, please refer to the deployment guide [here](../../../../README.md).
## Deployment with docker
1. First, clone this repo.
```
export WORKDIR=<your-work-directory>
cd $WORKDIR
git clone https://github.com/opea-project/GenAIExamples.git
```
2. Set up environment for this example </br>
```
# Example: host_ip="192.168.1.1" or export host_ip="External_Public_IP"
export host_ip=$(hostname -I | awk '{print $1}')
# if you are in a proxy environment, also set the proxy-related environment variables
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
# for using open-source llms
export HUGGINGFACEHUB_API_TOKEN=<your-HF-token>
# Example export HF_CACHE_DIR=$WORKDIR so that no need to redownload every time
export HF_CACHE_DIR=<directory-where-llms-are-downloaded>
```
3. Deploy the retrieval tool (i.e., DocIndexRetriever mega-service)
First, launch the mega-service.
```
cd $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool
bash launch_retrieval_tool.sh
```
Then, ingest data into the vector database. Here we provide an example. You can ingest your own data.
```
bash run_ingest_data.sh
```
4. Launch Tool service
In this example, we will use some of the mock APIs provided in the Meta CRAG KDD Challenge to demonstrate the benefits of gaining additional context from mock knowledge graphs.
```
docker run -d -p=8080:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
```
5. Launch `Agent` service
To use open-source LLMs on Gaudi2, run commands below.
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_tgi_gaudi.sh
bash launch_agent_service_tgi_gaudi.sh
```
6. [Optional] Build `Agent` docker image if pulling images failed.
```
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/agent-langchain:latest -f comps/agent/langchain/Dockerfile .
```
## Validate services
First look at logs of the agent docker containers:
```
# worker agent
docker logs rag-agent-endpoint
```
```
# supervisor agent
docker logs react-agent-endpoint
```
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
Second, validate worker agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
Third, validate supervisor agent:
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Most recent album by Taylor Swift"
}'
```
## How to register your own tools with agent
You can take a look at the tools yaml and python files in this example. For more details, please refer to the "Provide your own tools" section in the instructions [here](https://github.com/opea-project/GenAIComps/tree/main/comps/agent/langchain/README.md).

View File

@@ -1,93 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
services:
tei-embedding-service:
command: --model-id ${EMBEDDING_MODEL_ID} --auto-truncate --otlp-endpoint $OTEL_EXPORTER_OTLP_TRACES_ENDPOINT
tei-reranking-service:
command: --model-id ${RERANK_MODEL_ID} --auto-truncate --otlp-endpoint $OTEL_EXPORTER_OTLP_TRACES_ENDPOINT
jaeger:
image: jaegertracing/all-in-one:1.67.0
container_name: jaeger
ports:
- "16686:16686"
- "4317:4317"
- "4318:4318"
- "9411:9411"
ipc: host
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
COLLECTOR_ZIPKIN_HOST_PORT: 9411
restart: unless-stopped
prometheus:
image: prom/prometheus:v2.52.0
container_name: prometheus
user: root
volumes:
- ./prometheus.yaml:/etc/prometheus/prometheus.yaml
- ./prometheus_data:/prometheus
command:
- '--config.file=/etc/prometheus/prometheus.yaml'
ports:
- '9091:9090'
ipc: host
restart: unless-stopped
grafana:
image: grafana/grafana:11.0.0
container_name: grafana
volumes:
- ./grafana_data:/var/lib/grafana
- ./grafana/dashboards:/var/lib/grafana/dashboards
- ./grafana/provisioning:/etc/grafana/provisioning
user: root
environment:
GF_SECURITY_ADMIN_PASSWORD: admin
GF_RENDERING_CALLBACK_URL: http://grafana:3000/
GF_LOG_FILTERS: rendering:debug
depends_on:
- prometheus
ports:
- '3000:3000'
ipc: host
restart: unless-stopped
node-exporter:
image: prom/node-exporter
container_name: node-exporter
volumes:
- /proc:/host/proc:ro
- /sys:/host/sys:ro
- /:/rootfs:ro
command:
- '--path.procfs=/host/proc'
- '--path.sysfs=/host/sys'
- --collector.filesystem.ignored-mount-points
- "^/(sys|proc|dev|host|etc|rootfs/var/lib/docker/containers|rootfs/var/lib/docker/overlay2|rootfs/run/docker/netns|rootfs/var/lib/docker/aufs)($$|/)"
ports:
- 9100:9100
restart: always
deploy:
mode: global
gaudi-exporter:
image: vault.habana.ai/gaudi-metric-exporter/metric-exporter:1.19.2-32
container_name: gaudi-exporter
volumes:
- /proc:/host/proc:ro
- /sys:/host/sys:ro
- /:/rootfs:ro
- /dev:/dev
ports:
- 41612:41611
restart: always
deploy:
mode: global
worker-rag-agent:
environment:
- TELEMETRY_ENDPOINT=${TELEMETRY_ENDPOINT}
worker-sql-agent:
environment:
- TELEMETRY_ENDPOINT=${TELEMETRY_ENDPOINT}
supervisor-react-agent:
environment:
- TELEMETRY_ENDPOINT=${TELEMETRY_ENDPOINT}

View File

@@ -1,9 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
services:
supervisor-react-agent:
environment:
- tools=/home/user/tools/supervisor_agent_webtools.yaml
- GOOGLE_CSE_ID=${GOOGLE_CSE_ID}
- GOOGLE_API_KEY=${GOOGLE_API_KEY}

View File

@@ -3,9 +3,10 @@
services:
worker-rag-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
image: opea/agent-langchain:latest
container_name: rag-agent-endpoint
volumes:
# - ${WORKDIR}/GenAIExamples/AgentQnA/docker_image_build/GenAIComps/comps/agent/langchain/:/home/user/comps/agent/langchain/
- ${TOOLSET_PATH}:/home/user/tools/
ports:
- "9095:9095"
@@ -13,15 +14,14 @@ services:
environment:
ip_address: ${ip_address}
strategy: rag_agent_llama
with_memory: false
recursion_limit: ${recursion_limit_worker}
llm_engine: vllm
llm_engine: tgi
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
streaming: false
tools: /home/user/tools/worker_agent_tools.yaml
require_human_feedback: false
RETRIEVAL_TOOL_URL: ${RETRIEVAL_TOOL_URL}
@@ -33,42 +33,14 @@ services:
LANGCHAIN_PROJECT: "opea-worker-agent-service"
port: 9095
worker-sql-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
container_name: sql-agent-endpoint
volumes:
- ${WORKDIR}/GenAIExamples/AgentQnA/tests:/home/user/chinook-db # test db
ports:
- "9096:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent_llama
with_memory: false
db_name: ${db_name}
db_path: ${db_path}
use_hints: false
recursion_limit: ${recursion_limit_worker}
llm_engine: vllm
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
require_human_feedback: false
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
port: 9096
supervisor-react-agent:
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
image: opea/agent-langchain:latest
container_name: react-agent-endpoint
depends_on:
- worker-rag-agent
- worker-sql-agent
volumes:
# - ${WORKDIR}/GenAIExamples/AgentQnA/docker_image_build/GenAIComps/comps/agent/langchain/:/home/user/comps/agent/langchain/
- ${TOOLSET_PATH}:/home/user/tools/
ports:
- "9090:9090"
@@ -76,15 +48,14 @@ services:
environment:
ip_address: ${ip_address}
strategy: react_llama
with_memory: true
recursion_limit: ${recursion_limit_supervisor}
llm_engine: vllm
llm_engine: tgi
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
llm_endpoint_url: ${LLM_ENDPOINT_URL}
model: ${LLM_MODEL_ID}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: true
streaming: false
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}
@@ -95,47 +66,4 @@ services:
LANGCHAIN_PROJECT: "opea-supervisor-agent-service"
CRAG_SERVER: $CRAG_SERVER
WORKER_AGENT_URL: $WORKER_AGENT_URL
SQL_AGENT_URL: $SQL_AGENT_URL
port: 9090
mock-api:
image: docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
container_name: mock-api
ports:
- "8080:8000"
ipc: host
agent-ui:
image: ${REGISTRY:-opea}/agent-ui:${TAG:-latest}
container_name: agent-ui
environment:
host_ip: ${host_ip}
ports:
- "5173:8080"
ipc: host
vllm-service:
image: ${REGISTRY:-opea}/vllm-gaudi:${TAG:-latest}
container_name: vllm-gaudi-server
ports:
- "8086:8000"
volumes:
- "${MODEL_CACHE:-./data}:/data"
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HF_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HABANA_VISIBLE_DEVICES: all
OMPI_MCA_btl_vader_single_copy_mechanism: none
LLM_MODEL_ID: ${LLM_MODEL_ID}
VLLM_TORCH_PROFILER_DIR: "/mnt"
VLLM_SKIP_WARMUP: true
PT_HPU_ENABLE_LAZY_COLLECTIVES: true
healthcheck:
test: ["CMD-SHELL", "curl -f http://$host_ip:8086/health || exit 1"]
interval: 10s
timeout: 10s
retries: 100
runtime: habana
cap_add:
- SYS_NICE
ipc: host
command: --model $LLM_MODEL_ID --tensor-parallel-size 4 --host 0.0.0.0 --port 8000 --block-size 128 --max-num-seqs 256 --max-seq-len-to-capture 16384

View File

@@ -1,10 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
rm *.json
wget https://raw.githubusercontent.com/opea-project/GenAIEval/refs/heads/main/evals/benchmark/grafana/chatqna_megaservice_grafana.json
mv chatqna_megaservice_grafana.json agentqna_microervices_grafana.json
wget https://raw.githubusercontent.com/opea-project/GenAIEval/refs/heads/main/evals/benchmark/grafana/vllm_grafana.json
wget https://raw.githubusercontent.com/opea-project/GenAIEval/refs/heads/main/evals/benchmark/grafana/tgi_grafana.json
wget https://raw.githubusercontent.com/opea-project/GenAIEval/refs/heads/main/evals/benchmark/grafana/node_grafana.json
wget https://raw.githubusercontent.com/opea-project/GenAIEval/refs/heads/main/evals/benchmark/grafana/gaudi_grafana.json

View File

@@ -1,14 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
apiVersion: 1
providers:
- name: 'default'
orgId: 1
folder: ''
type: file
disableDeletion: false
updateIntervalSeconds: 10 #how often Grafana will scan for changed dashboards
options:
path: /var/lib/grafana/dashboards

View File

@@ -1,54 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# config file version
apiVersion: 1
# list of datasources that should be deleted from the database
deleteDatasources:
- name: Prometheus
orgId: 1
# list of datasources to insert/update depending
# what's available in the database
datasources:
# <string, required> name of the datasource. Required
- name: Prometheus
# <string, required> datasource type. Required
type: prometheus
# <string, required> access mode. direct or proxy. Required
access: proxy
# <int> org id. will default to orgId 1 if not specified
orgId: 1
# <string> url
url: http://prometheus:9090
# <string> database password, if used
password:
# <string> database user, if used
user:
# <string> database name, if used
database:
# <bool> enable/disable basic auth
basicAuth: false
# <string> basic auth username, if used
basicAuthUser:
# <string> basic auth password, if used
basicAuthPassword:
# <bool> enable/disable with credentials headers
withCredentials:
# <bool> mark as default datasource. Max one per org
isDefault: true
# <map> fields that will be converted to json and stored in json_data
jsonData:
httpMethod: GET
graphiteVersion: "1.1"
tlsAuth: false
tlsAuthWithCACert: false
# <string> json object of data that will be encrypted.
secureJsonData:
tlsCACert: "..."
tlsClientCert: "..."
tlsClientKey: "..."
version: 1
# <bool> allow users to edit datasources from the UI.
editable: true

View File

@@ -0,0 +1,32 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
pushd "../../../../../" > /dev/null
source .set_env.sh
popd > /dev/null
WORKPATH=$(dirname "$PWD")/..
# export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
# LLM related environment variables
export HF_CACHE_DIR=${HF_CACHE_DIR}
ls $HF_CACHE_DIR
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="meta-llama/Meta-Llama-3.1-70B-Instruct"
export NUM_SHARDS=4
export LLM_ENDPOINT_URL="http://${ip_address}:8085"
export temperature=0.01
export max_new_tokens=4096
# agent related environment variables
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
echo "TOOLSET_PATH=${TOOLSET_PATH}"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export WORKER_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export CRAG_SERVER=http://${ip_address}:8080
docker compose -f compose.yaml up -d

View File

@@ -0,0 +1,25 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# LLM related environment variables
export HF_CACHE_DIR=${HF_CACHE_DIR}
ls $HF_CACHE_DIR
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="meta-llama/Meta-Llama-3.1-70B-Instruct"
export NUM_SHARDS=4
docker compose -f tgi_gaudi.yaml up -d
sleep 5s
echo "Waiting tgi gaudi ready"
n=0
until [[ "$n" -ge 100 ]] || [[ $ready == true ]]; do
docker logs tgi-server &> tgi-gaudi-service.log
n=$((n+1))
if grep -q Connected tgi-gaudi-service.log; then
break
fi
sleep 5s
done
sleep 5s
echo "Service started successfully"

View File

@@ -1,55 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
global:
scrape_interval: 5s
external_labels:
monitor: "my-monitor"
scrape_configs:
- job_name: "prometheus"
static_configs:
- targets: ["prometheus:9090"]
- job_name: "vllm"
metrics_path: /metrics
static_configs:
- targets: ["vllm-gaudi-server:8000"]
- job_name: "tgi"
metrics_path: /metrics
static_configs:
- targets: ["tgi-gaudi-server:80"]
- job_name: "tei-embedding"
metrics_path: /metrics
static_configs:
- targets: ["tei-embedding-server:80"]
- job_name: "tei-reranking"
metrics_path: /metrics
static_configs:
- targets: ["tei-reranking-server:80"]
- job_name: "retriever"
metrics_path: /metrics
static_configs:
- targets: ["retriever:7000"]
- job_name: "dataprep-redis-service"
metrics_path: /metrics
static_configs:
- targets: ["dataprep-redis-service:5000"]
- job_name: "prometheus-node-exporter"
metrics_path: /metrics
static_configs:
- targets: ["node-exporter:9100"]
- job_name: "prometheus-gaudi-exporter"
metrics_path: /metrics
static_configs:
- targets: ["gaudi-exporter:41611"]
- job_name: "supervisor-react-agent"
metrics_path: /metrics
static_configs:
- targets: ["react-agent-endpoint:9090"]
- job_name: "worker-rag-agent"
metrics_path: /metrics
static_configs:
- targets: ["rag-agent-endpoint:9095"]
- job_name: "worker-sql-agent"
metrics_path: /metrics
static_configs:
- targets: ["sql-agent-endpoint:9096"]

View File

@@ -1,72 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
pushd "../../../../../" > /dev/null
source .set_env.sh
popd > /dev/null
WORKPATH=$(dirname "$PWD")/..
# export WORKDIR=$WORKPATH/../../
if [[ -z "${WORKDIR}" ]]; then
echo "Please set WORKDIR environment variable"
exit 0
fi
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
# LLM related environment variables
export HF_CACHE_DIR=${HF_CACHE_DIR}
ls $HF_CACHE_DIR
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
export NUM_SHARDS=4
export LLM_ENDPOINT_URL="http://${ip_address}:8086"
export temperature=0
export max_new_tokens=4096
# agent related environment variables
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
echo "TOOLSET_PATH=${TOOLSET_PATH}"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export WORKER_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
export SQL_AGENT_URL="http://${ip_address}:9096/v1/chat/completions"
export RETRIEVAL_TOOL_URL="http://${ip_address}:8889/v1/retrievaltool"
export CRAG_SERVER=http://${ip_address}:8080
export db_name=Chinook
export db_path="sqlite:////home/user/chinook-db/Chinook_Sqlite.sqlite"
if [ ! -f $WORKDIR/GenAIExamples/AgentQnA/tests/Chinook_Sqlite.sqlite ]; then
echo "Download Chinook_Sqlite!"
wget -O $WORKDIR/GenAIExamples/AgentQnA/tests/Chinook_Sqlite.sqlite https://github.com/lerocha/chinook-database/releases/download/v1.4.5/Chinook_Sqlite.sqlite
fi
# configure agent ui
# echo "AGENT_URL = 'http://$ip_address:9090/v1/chat/completions'" | tee ${WORKDIR}/GenAIExamples/AgentQnA/ui/svelte/.env
# retriever
export host_ip=$(hostname -I | awk '{print $1}')
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
# Set OpenTelemetry Tracing Endpoint
export JAEGER_IP=$(ip route get 8.8.8.8 | grep -oP 'src \K[^ ]+')
export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
export TELEMETRY_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
export no_proxy="$no_proxy,rag-agent-endpoint,sql-agent-endpoint,react-agent-endpoint,agent-ui,vllm-gaudi-server,jaeger,grafana,prometheus,node-exporter,gaudi-exporter,127.0.0.1,localhost,0.0.0.0,$host_ip,,$JAEGER_IP"

View File

@@ -3,7 +3,7 @@
services:
tgi-server:
image: ghcr.io/huggingface/tgi-gaudi:2.3.1
image: ghcr.io/huggingface/tgi-gaudi:2.0.6
container_name: tgi-server
ports:
- "8085:80"

View File

@@ -2,30 +2,12 @@
# SPDX-License-Identifier: Apache-2.0
services:
agent:
agent-langchain:
build:
context: GenAIComps
dockerfile: comps/agent/src/Dockerfile
dockerfile: comps/agent/langchain/Dockerfile
args:
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
no_proxy: ${no_proxy}
image: ${REGISTRY:-opea}/agent:${TAG:-latest}
agent-ui:
build:
context: ../ui
dockerfile: ./docker/Dockerfile
extends: agent
image: ${REGISTRY:-opea}/agent-ui:${TAG:-latest}
vllm-gaudi:
build:
context: vllm-fork
dockerfile: Dockerfile.hpu
extends: agent
image: ${REGISTRY:-opea}/vllm-gaudi:${TAG:-latest}
vllm-rocm:
build:
context: GenAIComps
dockerfile: comps/third_parties/vllm/src/Dockerfile.amd_gpu
extends: agent
image: ${REGISTRY:-opea}/vllm-rocm:${TAG:-latest}
image: ${REGISTRY:-opea}/agent-langchain:${TAG:-latest}

View File

@@ -1,11 +0,0 @@
# Deploy AgentQnA on Kubernetes cluster
- You should have Helm (version >= 3.15) installed. Refer to the [Helm Installation Guide](https://helm.sh/docs/intro/install/) for more information.
- For more deploy options, refer to [helm charts README](https://github.com/opea-project/GenAIInfra/tree/main/helm-charts#readme).
## Deploy on Gaudi
```
export HFTOKEN="insert-your-huggingface-token-here"
helm install agentqna oci://ghcr.io/opea-project/charts/agentqna --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} -f gaudi-values.yaml
```

View File

@@ -1,22 +0,0 @@
# Copyright (C) 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
tgi:
enabled: false
vllm:
enabled: true
LLM_MODEL_ID: "meta-llama/Meta-Llama-3-8B-Instruct"
extraCmdArgs: ["--max-seq-len-to-capture", "16384", "--enable-auto-tool-choice", "--tool-call-parser", "llama3_json"]
supervisor:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Meta-Llama-3-8B-Instruct"
ragagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Meta-Llama-3-8B-Instruct"
sqlagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Meta-Llama-3-8B-Instruct"

View File

@@ -1,35 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# Accelerate inferencing in heaviest components to improve performance
# by overriding their subchart values
tgi:
enabled: false
vllm:
enabled: true
accelDevice: "gaudi"
image:
repository: opea/vllm-gaudi
resources:
limits:
habana.ai/gaudi: 4
LLM_MODEL_ID: "meta-llama/Llama-3.3-70B-Instruct"
OMPI_MCA_btl_vader_single_copy_mechanism: none
PT_HPU_ENABLE_LAZY_COLLECTIVES: true
VLLM_SKIP_WARMUP: true
shmSize: 16Gi
extraCmdArgs: ["--tensor-parallel-size", "4", "--max-seq-len-to-capture", "16384", "--enable-auto-tool-choice", "--tool-call-parser", "llama3_json"]
supervisor:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Llama-3.3-70B-Instruct"
ragagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Llama-3.3-70B-Instruct"
sqlagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
llm_engine: vllm
model: "meta-llama/Llama-3.3-70B-Instruct"

View File

@@ -53,7 +53,7 @@ def main():
host_ip = args.host_ip
port = args.port
proxies = {"http": ""}
url = "http://{host_ip}:{port}/v1/dataprep/ingest".format(host_ip=host_ip, port=port)
url = "http://{host_ip}:{port}/v1/dataprep".format(host_ip=host_ip, port=port)
# Split jsonl file into json files
files = split_jsonl_into_txts(os.path.join(args.filedir, args.filename))

View File

@@ -13,14 +13,13 @@ export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get_file"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete_file"
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml up -d

View File

@@ -1,22 +1,7 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
host_ip=$(hostname -I | awk '{print $1}')
port=6007
FILEDIR=${WORKDIR}/GenAIExamples/AgentQnA/example_data/
FILENAME=test_docs_music.jsonl
# AgentQnA ingestion script requires following packages
packages=("requests" "tqdm")
# Check if packages are installed
for package in "${packages[@]}"; do
if pip freeze | grep -q "$package="; then
echo "$package is installed"
else
echo "$package is not installed"
pip install --no-cache-dir "$package"
fi
done
python3 index_data.py --filedir ${FILEDIR} --filename ${FILENAME} --host_ip $host_ip --port $port
python3 index_data.py --filedir ${FILEDIR} --filename ${FILENAME} --host_ip $host_ip

View File

@@ -15,35 +15,28 @@ function stop_agent_and_api_server() {
echo "Stopping CRAG server"
docker stop $(docker ps -q --filter ancestor=docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0)
echo "Stopping Agent services"
docker stop $(docker ps -q --filter ancestor=opea/agent:latest)
docker stop $(docker ps -q --filter ancestor=opea/agent-langchain:latest)
}
function stop_retrieval_tool() {
echo "Stopping Retrieval tool"
local RETRIEVAL_TOOL_PATH=$WORKPATH/../DocIndexRetriever
cd $RETRIEVAL_TOOL_PATH/docker_compose/intel/cpu/xeon/
container_list=$(cat compose.yaml | grep container_name | cut -d':' -f2)
for container_name in $container_list; do
cid=$(docker ps -aq --filter "name=$container_name")
echo "Stopping container $container_name"
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
done
docker compose -f $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool/docker/docker-compose-retrieval-tool.yaml down
}
echo "=================== #1 Building docker images===================="
bash step1_build_images.sh xeon
bash 1_build_images.sh
echo "=================== #1 Building docker images completed===================="
echo "=================== #2 Start retrieval tool===================="
bash step2_start_retrieval_tool.sh
bash 2_start_retrieval_tool.sh
echo "=================== #2 Retrieval tool started===================="
echo "=================== #3 Ingest data and validate retrieval===================="
bash step3_ingest_data_and_validate_retrieval.sh
bash 3_ingest_data_and_validate_retrieval.sh
echo "=================== #3 Data ingestion and validation completed===================="
echo "=================== #4 Start agent and API server===================="
bash step4_launch_and_validate_agent_openai.sh
bash 4_launch_and_validate_agent_openai.sh
echo "=================== #4 Agent test passed ===================="
echo "=================== #5 Stop agent and API server===================="

View File

@@ -1,6 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
DATAPATH=$WORKDIR/TAG-Bench/tag_queries.csv
OUTFOLDER=$WORKDIR/TAG-Bench/query_by_db
python3 split_data.py --path $DATAPATH --output $OUTFOLDER

View File

@@ -1,27 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import pandas as pd
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--path", type=str, required=True)
parser.add_argument("--output", type=str, required=True)
args = parser.parse_args()
# if output folder does not exist, create it
if not os.path.exists(args.output):
os.makedirs(args.output)
# Load the data
data = pd.read_csv(args.path)
# Split the data by domain
domains = data["DB used"].unique()
for domain in domains:
domain_data = data[data["DB used"] == domain]
out = os.path.join(args.output, f"query_{domain}.csv")
domain_data.to_csv(out, index=False)

View File

@@ -11,90 +11,38 @@ export ip_address=$(hostname -I | awk '{print $1}')
function get_genai_comps() {
if [ ! -d "GenAIComps" ] ; then
git clone --depth 1 --branch ${opea_branch:-"main"} https://github.com/opea-project/GenAIComps.git
git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
fi
}
function build_docker_images_for_retrieval_tool(){
cd $WORKDIR/GenAIExamples/DocIndexRetriever/docker_image_build/
# git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
get_genai_comps
echo "Build all the images with --no-cache..."
docker compose -f build.yaml build --no-cache
service_list="doc-index-retriever dataprep-redis embedding-tei retriever-redis reranking-tei"
docker compose -f build.yaml build ${service_list} --no-cache
docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
docker images && sleep 1s
}
function build_agent_docker_image_xeon() {
function build_agent_docker_image() {
cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/
get_genai_comps
echo "Build agent image with --no-cache..."
service_list="agent agent-ui"
docker compose -f build.yaml build ${service_list} --no-cache
docker compose -f build.yaml build --no-cache
}
function build_agent_docker_image_gaudi_vllm() {
cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/
get_genai_comps
git clone https://github.com/HabanaAI/vllm-fork.git && cd vllm-fork
VLLM_FORK_VER=v0.6.6.post1+Gaudi-1.20.0
git checkout ${VLLM_FORK_VER} &> /dev/null && cd ../
echo "Build agent image with --no-cache..."
service_list="agent agent-ui vllm-gaudi"
docker compose -f build.yaml build ${service_list} --no-cache
}
function build_agent_docker_image_rocm() {
cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/
get_genai_comps
echo "Build agent image with --no-cache..."
service_list="agent agent-ui"
docker compose -f build.yaml build ${service_list} --no-cache
}
function build_agent_docker_image_rocm_vllm() {
cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/
get_genai_comps
echo "Build agent image with --no-cache..."
service_list="agent agent-ui vllm-rocm"
docker compose -f build.yaml build ${service_list} --no-cache
}
function main() {
echo "==================== Build docker images for retrieval tool ===================="
build_docker_images_for_retrieval_tool
echo "==================== Build docker images for retrieval tool completed ===================="
sleep 3s
case $1 in
"rocm")
echo "==================== Build agent docker image for ROCm ===================="
build_agent_docker_image_rocm
;;
"rocm_vllm")
echo "==================== Build agent docker image for ROCm VLLM ===================="
build_agent_docker_image_rocm_vllm
;;
"gaudi_vllm")
echo "==================== Build agent docker image for Gaudi ===================="
build_agent_docker_image_gaudi_vllm
;;
"xeon")
echo "==================== Build agent docker image for Xeon ===================="
build_agent_docker_image_xeon
;;
*)
echo "Invalid argument"
exit 1
;;
esac
docker image ls | grep vllm
echo "==================== Build agent docker image ===================="
build_agent_docker_image
echo "==================== Build agent docker image completed ===================="
}
main $1
main

View File

@@ -7,9 +7,8 @@ WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=${ip_address}
export HF_CACHE_DIR=${model_cache:-"$WORKDIR/hf_cache"}
export HF_CACHE_DIR=$WORKDIR/hf_cache
if [ ! -d "$HF_CACHE_DIR" ]; then
echo "Creating HF_CACHE directory"
mkdir -p "$HF_CACHE_DIR"

View File

@@ -1,49 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=${ip_address}
export HF_CACHE_DIR=$WORKPATH/hf_cache
if [ ! -d "$HF_CACHE_DIR" ]; then
echo "Creating HF_CACHE directory"
mkdir -p "$HF_CACHE_DIR"
fi
function start_retrieval_tool() {
echo "Starting Retrieval tool"
cd $WORKPATH/../DocIndexRetriever/docker_compose/intel/cpu/xeon
host_ip=$(hostname -I | awk '{print $1}')
export HF_CACHE_DIR=${HF_CACHE_DIR}
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export no_proxy=${no_proxy}
export http_proxy=${http_proxy}
export https_proxy=${https_proxy}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export RERANK_TYPE="tei"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8889/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep/ingest"
export DATAPREP_GET_FILE_ENDPOINT="http://${host_ip}:6008/v1/dataprep/get"
export DATAPREP_DELETE_FILE_ENDPOINT="http://${host_ip}:6009/v1/dataprep/delete"
docker compose -f compose.yaml up -d
}
echo "==================== Start retrieval tool ===================="
start_retrieval_tool
sleep 20 # needed for downloading the models
echo "==================== Retrieval tool started ===================="

View File

@@ -1,68 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=$ip_address
echo "ip_address=${ip_address}"
function validate() {
local CONTENT="$1"
local EXPECTED_RESULT="$2"
local SERVICE_NAME="$3"
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
echo 0
else
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
echo 1
fi
}
function ingest_data_and_validate() {
echo "Ingesting data"
cd $WORKPATH/retrieval_tool/
echo $PWD
local CONTENT=$(bash run_ingest_data.sh)
local EXIT_CODE=$(validate "$CONTENT" "Data preparation succeeded" "dataprep-redis-server")
echo "$EXIT_CODE"
local EXIT_CODE="${EXIT_CODE:0-1}"
echo "return value is $EXIT_CODE"
if [ "$EXIT_CODE" == "1" ]; then
docker logs dataprep-redis-server
return 1
fi
}
function validate_retrieval_tool() {
echo "----------------Test retrieval tool ----------------"
local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "Who sang Thriller"
}')
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "retrieval-tool")
if [ "$EXIT_CODE" == "1" ]; then
docker logs retrievaltool-xeon-backend-server
exit 1
fi
}
function main(){
echo "==================== Ingest data ===================="
ingest_data_and_validate
echo "==================== Data ingestion completed ===================="
echo "==================== Validate retrieval tool ===================="
validate_retrieval_tool
echo "==================== Retrieval tool validated ===================="
}
main

View File

@@ -1,197 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=$ip_address
echo "ip_address=${ip_address}"
export TOOLSET_PATH=$WORKPATH/tools/
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
model="meta-llama/Llama-3.3-70B-Instruct" #"meta-llama/Meta-Llama-3.1-70B-Instruct"
export HF_CACHE_DIR=${model_cache:-"/data2/huggingface"}
if [ ! -d "$HF_CACHE_DIR" ]; then
HF_CACHE_DIR=$WORKDIR/hf_cache
mkdir -p "$HF_CACHE_DIR"
fi
echo "HF_CACHE_DIR=$HF_CACHE_DIR"
ls $HF_CACHE_DIR
vllm_port=8086
vllm_volume=${HF_CACHE_DIR}
function start_agent_service() {
echo "Starting agent service"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
source set_env.sh
docker compose -f compose.yaml up -d
}
function start_all_services() {
echo "token is ${HF_TOKEN}"
echo "start vllm gaudi service"
echo "**************model is $model**************"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
source set_env.sh
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml -f compose.telemetry.yaml up -d
sleep 5s
echo "Waiting vllm gaudi ready"
n=0
LOG_PATH=$PWD
until [[ "$n" -ge 100 ]] || [[ $ready == true ]]; do
docker logs vllm-gaudi-server
docker logs vllm-gaudi-server &> ${LOG_PATH}/vllm-gaudi-service.log
n=$((n+1))
if grep -q "Uvicorn running on" ${LOG_PATH}/vllm-gaudi-service.log; then
break
fi
if grep -q "No such container" ${LOG_PATH}/vllm-gaudi-service.log; then
echo "container vllm-gaudi-server not found"
exit 1
fi
sleep 5s
done
sleep 5s
echo "Service started successfully"
}
function download_chinook_data(){
echo "Downloading chinook data..."
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite $WORKDIR/GenAIExamples/AgentQnA/tests/
}
function validate() {
local CONTENT="$1"
local EXPECTED_RESULT="$2"
local SERVICE_NAME="$3"
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
echo 0
else
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
echo 1
fi
}
function validate_agent_service() {
# # test worker rag agent
echo "======================Testing worker rag agent======================"
export agent_port="9095"
export agent_ip="127.0.0.1"
prompt="Tell me about Michael Jackson song Thriller"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ip_addr $agent_ip --ext_port $agent_port)
# echo $CONTENT
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs rag-agent-endpoint
exit 1
fi
# # test worker sql agent
echo "======================Testing worker sql agent======================"
export agent_port="9096"
prompt="How many employees are there in the company?"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ip_addr $agent_ip --ext_port $agent_port)
local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint")
echo $CONTENT
# echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs sql-agent-endpoint
exit 1
fi
# test supervisor react agent
echo "======================Testing supervisor react agent======================"
export agent_port="9090"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ip_addr $agent_ip --ext_port $agent_port --stream)
local EXIT_CODE=$(validate "$CONTENT" "Iron" "react-agent-endpoint")
# echo $CONTENT
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs react-agent-endpoint
exit 1
fi
}
function remove_chinook_data(){
echo "Removing chinook data..."
cd $WORKDIR
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
}
function ingest_data_and_validate() {
echo "Ingesting data"
cd $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool/
echo $PWD
local CONTENT=$(bash run_ingest_data.sh)
local EXIT_CODE=$(validate "$CONTENT" "Data preparation succeeded" "dataprep-redis-server")
echo "$EXIT_CODE"
local EXIT_CODE="${EXIT_CODE:0-1}"
echo "return value is $EXIT_CODE"
if [ "$EXIT_CODE" == "1" ]; then
docker logs dataprep-redis-server
return 1
fi
}
function validate_retrieval_tool() {
echo "----------------Test retrieval tool ----------------"
local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "Who sang Thriller"
}')
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "retrieval-tool")
if [ "$EXIT_CODE" == "1" ]; then
docker logs retrievaltool-xeon-backend-server
exit 1
fi
}
function main() {
echo "==================== Prepare data ===================="
download_chinook_data
echo "==================== Data prepare done ===================="
echo "==================== Start all services ===================="
start_all_services
echo "==================== all services started ===================="
echo "==================== Ingest data ===================="
ingest_data_and_validate
echo "==================== Data ingestion completed ===================="
echo "==================== Validate retrieval tool ===================="
validate_retrieval_tool
echo "==================== Retrieval tool validated ===================="
echo "==================== Validate agent service ===================="
validate_agent_service
echo "==================== Agent service validated ===================="
}
remove_chinook_data
main
remove_chinook_data

View File

@@ -11,22 +11,13 @@ echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
function download_chinook_data(){
echo "Downloading chinook data..."
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite $WORKDIR/GenAIExamples/AgentQnA/tests/
}
function start_agent_and_api_server() {
echo "Starting CRAG server"
docker run -d --runtime=runc --name=kdd-cup-24-crag-service -p=8080:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
echo "Starting Agent services"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon/
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
bash launch_agent_service_openai.sh
sleep 2m
}
function validate() {
@@ -44,64 +35,19 @@ function validate() {
}
function validate_agent_service() {
# # test worker rag agent
echo "======================Testing worker rag agent======================"
export agent_port="9095"
prompt="Tell me about Michael Jackson song Thriller"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
# echo $CONTENT
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
echo "----------------Test agent ----------------"
local CONTENT=$(http_proxy="" curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Tell me about Michael Jackson song thriller"
}')
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "react-agent-endpoint")
docker logs react-agent-endpoint
if [ "$EXIT_CODE" == "1" ]; then
docker logs rag-agent-endpoint
exit 1
fi
# # test worker sql agent
echo "======================Testing worker sql agent======================"
export agent_port="9096"
prompt="How many employees are there in the company?"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint")
echo $CONTENT
# echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs sql-agent-endpoint
exit 1
fi
# test supervisor react agent
echo "======================Testing supervisor react agent======================"
export agent_port="9090"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream)
local EXIT_CODE=$(validate "$CONTENT" "Iron" "react-agent-endpoint")
# echo $CONTENT
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs react-agent-endpoint
exit 1
fi
}
function remove_chinook_data(){
echo "Removing chinook data..."
cd $WORKDIR
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
}
function main() {
echo "==================== Prepare data ===================="
download_chinook_data
echo "==================== Data prepare done ===================="
echo "==================== Start agent ===================="
start_agent_and_api_server
echo "==================== Agent started ===================="
@@ -111,9 +57,4 @@ function main() {
echo "==================== Agent service validated ===================="
}
remove_chinook_data
main
remove_chinook_data

View File

@@ -1,120 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export LOG_PATH=${WORKPATH}
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=${ip_address}
export TOOLSET_PATH=$WORKPATH/tools/
export HF_CACHE_DIR=$WORKPATH/data2/huggingface
if [ ! -d "$HF_CACHE_DIR" ]; then
HF_CACHE_DIR=$WORKDIR/hf_cache
mkdir -p "$HF_CACHE_DIR"
fi
function download_chinook_data(){
echo "Downloading chinook data..."
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite ${WORKPATH}/tests/
}
function start_agent_and_api_server() {
echo "Starting Agent services"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_vllm_rocm.sh
}
function validate() {
local CONTENT="$1"
local EXPECTED_RESULT="$2"
local SERVICE_NAME="$3"
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
echo 0
else
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
echo 1
fi
}
function validate_agent_service() {
# # test worker rag agent
echo "======================Testing worker rag agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/WORKER_RAG_AGENT_PORT_tmp)
prompt="Tell me about Michael Jackson song Thriller"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
# echo $CONTENT
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs rag-agent-endpoint
exit 1
fi
# test worker sql agent
echo "======================Testing worker sql agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/WORKER_SQL_AGENT_PORT_tmp)
prompt="How many employees are there in the company?"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint")
echo $CONTENT
# echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs sql-agent-endpoint
exit 1
fi
# test supervisor react agent
echo "======================Testing supervisor react agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/SUPERVISOR_REACT_AGENT_PORT_tmp)
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream)
local EXIT_CODE=$(validate "$CONTENT" "Iron" "react-agent-endpoint")
# echo $CONTENT
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs react-agent-endpoint
exit 1
fi
}
function remove_chinook_data(){
echo "Removing chinook data..."
cd $WORKDIR
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
}
function main() {
echo "==================== Prepare data ===================="
download_chinook_data
echo "==================== Data prepare done ===================="
echo "==================== Start agent ===================="
start_agent_and_api_server
echo "==================== Agent started ===================="
echo "==================== Validate agent service ===================="
validate_agent_service
echo "==================== Agent service validated ===================="
}
remove_chinook_data
main
remove_chinook_data

View File

@@ -0,0 +1,91 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export HF_CACHE_DIR=$WORKDIR/hf_cache
if [ ! -d "$HF_CACHE_DIR" ]; then
mkdir -p "$HF_CACHE_DIR"
fi
ls $HF_CACHE_DIR
function start_tgi(){
echo "Starting tgi-gaudi server"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_tgi_gaudi.sh
}
function start_agent_and_api_server() {
echo "Starting CRAG server"
docker run -d --runtime=runc --name=kdd-cup-24-crag-service -p=8080:8000 docker.io/aicrowd/kdd-cup-24-crag-mock-api:v0
echo "Starting Agent services"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_agent_service_tgi_gaudi.sh
sleep 10
}
function validate() {
local CONTENT="$1"
local EXPECTED_RESULT="$2"
local SERVICE_NAME="$3"
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
echo 0
else
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
echo 1
fi
}
function validate_agent_service() {
echo "----------------Test agent ----------------"
# local CONTENT=$(http_proxy="" curl http://${ip_address}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
# "query": "Tell me about Michael Jackson song thriller"
# }')
export agent_port="9095"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py)
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
docker logs rag-agent-endpoint
if [ "$EXIT_CODE" == "1" ]; then
exit 1
fi
# local CONTENT=$(http_proxy="" curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
# "query": "Tell me about Michael Jackson song thriller"
# }')
export agent_port="9090"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py)
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "react-agent-endpoint")
docker logs react-agent-endpoint
if [ "$EXIT_CODE" == "1" ]; then
exit 1
fi
}
function main() {
echo "==================== Start TGI ===================="
start_tgi
echo "==================== TGI started ===================="
echo "==================== Start agent ===================="
start_agent_and_api_server
echo "==================== Agent started ===================="
echo "==================== Validate agent service ===================="
validate_agent_service
echo "==================== Agent service validated ===================="
}
main

View File

@@ -1,120 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -e
WORKPATH=$(dirname "$PWD")
export LOG_PATH=${WORKPATH}
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export host_ip=${ip_address}
export TOOLSET_PATH=$WORKPATH/tools/
export HF_CACHE_DIR=$WORKPATH/data2/huggingface
if [ ! -d "$HF_CACHE_DIR" ]; then
HF_CACHE_DIR=$WORKDIR/hf_cache
mkdir -p "$HF_CACHE_DIR"
fi
function download_chinook_data(){
echo "Downloading chinook data..."
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite ${WORKPATH}/tests/
}
function start_agent_and_api_server() {
echo "Starting Agent services"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_tgi_rocm.sh
}
function validate() {
local CONTENT="$1"
local EXPECTED_RESULT="$2"
local SERVICE_NAME="$3"
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
echo 0
else
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
echo 1
fi
}
function validate_agent_service() {
# # test worker rag agent
echo "======================Testing worker rag agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/WORKER_RAG_AGENT_PORT_tmp)
prompt="Tell me about Michael Jackson song Thriller"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
# echo $CONTENT
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs rag-agent-endpoint
exit 1
fi
# test worker sql agent
echo "======================Testing worker sql agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/WORKER_SQL_AGENT_PORT_tmp)
prompt="How many employees are there in the company?"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint")
echo $CONTENT
# echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs sql-agent-endpoint
exit 1
fi
# test supervisor react agent
echo "======================Testing supervisor react agent======================"
export agent_port=$(cat ${WORKPATH}/docker_compose/amd/gpu/SUPERVISOR_REACT_AGENT_PORT_tmp)
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream)
local EXIT_CODE=$(validate "$CONTENT" "Iron" "react-agent-endpoint")
# echo $CONTENT
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs react-agent-endpoint
exit 1
fi
}
function remove_chinook_data(){
echo "Removing chinook data..."
cd $WORKDIR
if [ -d "chinook-database" ]; then
rm -rf chinook-database
fi
echo "Chinook data removed!"
}
function main() {
echo "==================== Prepare data ===================="
download_chinook_data
echo "==================== Data prepare done ===================="
echo "==================== Start agent ===================="
start_agent_and_api_server
echo "==================== Agent started ===================="
echo "==================== Validate agent service ===================="
validate_agent_service
echo "==================== Agent service validated ===================="
}
remove_chinook_data
main
remove_chinook_data

View File

@@ -1,77 +1,25 @@
# Copyright (C) 2025 Intel Corporation
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import json
import uuid
import os
import requests
def process_request(url, query, is_stream=False):
def generate_answer_agent_api(url, prompt):
proxies = {"http": ""}
content = json.dumps(query) if query is not None else None
try:
resp = requests.post(url=url, data=content, proxies=proxies, stream=is_stream)
if not is_stream:
ret = resp.json()["text"]
else:
for line in resp.iter_lines(decode_unicode=True):
print(line)
ret = None
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
return ret
except requests.exceptions.RequestException as e:
ret = f"An error occurred:{e}"
return None
def test_worker_agent(args):
url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions"
query = {"role": "user", "messages": args.prompt, "stream": "false"}
ret = process_request(url, query)
print("Response: ", ret)
def add_message_and_run(url, user_message, thread_id, stream=False):
print("User message: ", user_message)
query = {"role": "user", "messages": user_message, "thread_id": thread_id, "stream": stream}
ret = process_request(url, query, is_stream=stream)
print("Response: ", ret)
def test_chat_completion_multi_turn(args):
url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions"
thread_id = f"{uuid.uuid4()}"
# first turn
print("===============First turn==================")
user_message = "Which artist has the most albums in the database?"
add_message_and_run(url, user_message, thread_id, stream=args.stream)
print("===============End of first turn==================")
# second turn
print("===============Second turn==================")
user_message = "Give me a few examples of the artist's albums?"
add_message_and_run(url, user_message, thread_id, stream=args.stream)
print("===============End of second turn==================")
payload = {
"query": prompt,
}
response = requests.post(url, json=payload, proxies=proxies)
answer = response.json()["text"]
return answer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ip_addr", type=str, default="127.0.0.1", help="endpoint ip address")
parser.add_argument("--ext_port", type=str, default="9090", help="endpoint port")
parser.add_argument("--stream", action="store_true", help="streaming mode")
parser.add_argument("--prompt", type=str, help="prompt message")
parser.add_argument("--agent_role", type=str, default="supervisor", help="supervisor or worker")
args, _ = parser.parse_known_args()
print(args)
if args.agent_role == "supervisor":
test_chat_completion_multi_turn(args)
elif args.agent_role == "worker":
test_worker_agent(args)
else:
raise ValueError("Invalid agent role")
ip_address = os.getenv("ip_address", "localhost")
agent_port = os.getenv("agent_port", "9095")
url = f"http://{ip_address}:{agent_port}/v1/chat/completions"
prompt = "Tell me about Michael Jackson song thriller"
answer = generate_answer_agent_api(url, prompt)
print(answer)

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