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4 Commits
v1.2 ... v1.1rc

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
668 changed files with 35990 additions and 19713 deletions

38
.github/CODEOWNERS vendored
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@@ -1,23 +1,17 @@
* liang1.lv@intel.com feng.tian@intel.com suyue.chen@intel.com
/.github/ suyue.chen@intel.com ze.pan@intel.com
/AgentQnA/ 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
/CodeGen/ liang1.lv@intel.com xinyao.wang@intel.com
/CodeTrans/ sihan.chen@intel.com xinyao.wang@intel.com
/DBQnA/ supriya.krishnamurthi@intel.com liang1.lv@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
/DocSum/ letong.han@intel.com
/DocIndexRetriever/ kaokao.lv@intel.com chendi.xue@intel.com
/DocSum/ letong.han@intel.com xinyao.wang@intel.com
/EdgeCraftRAG/ yongbo.zhu@intel.com mingyuan.qi@intel.com
/FaqGen/ yogesh.pandey@intel.com xinyao.wang@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 xinyao.wang@intel.com
/VisualQnA/ liang1.lv@intel.com sihan.chen@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
@@ -66,7 +65,6 @@ body:
options:
- label: Pull docker images from hub.docker.com
- label: Build docker images from source
- label: Other
validations:
required: true
@@ -75,11 +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: Kubernetes
- label: Helm
validations:
required: true
@@ -90,7 +87,6 @@ body:
options:
- Single Node
- Multiple Nodes
- Other
default: 0
validations:
required: true
@@ -130,12 +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
@@ -66,7 +65,6 @@ body:
options:
- Single Node
- Multiple Nodes
- Other
default: 0
validations:
required: true

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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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@@ -28,7 +28,7 @@ on:
default: false
required: false
type: boolean
test_helmchart:
test_k8s:
default: false
required: false
type: boolean
@@ -74,15 +74,15 @@ jobs:
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 --depth 1 https://github.com/vllm-project/vllm.git
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 --depth 1 --branch v0.6.4.post2+Gaudi-1.19.0 https://github.com/HabanaAI/vllm-fork.git
sed -i 's/triton/triton==3.1.0/g' vllm-fork/requirements-hpu.txt
git clone https://github.com/HabanaAI/vllm-fork.git
cd vllm-fork && git checkout 3c39626 && cd ../
fi
git clone --depth 1 --branch ${{ inputs.opea_branch }} https://github.com/opea-project/GenAIComps.git
cd GenAIComps && git rev-parse HEAD && cd ../
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) }}
@@ -110,16 +110,16 @@ jobs:
####################################################################################################
# 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
####################################################################################################

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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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@@ -67,6 +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: |

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@@ -1,233 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Helm Chart E2e Test For Call
permissions: read-all
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 [[ "$filename" == *"nv"* ]]; then
continue
else
if [[ "${{ 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]
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: ${{ inputs.example }}
run: |
CHART_NAME="${example,,}" # CodeGen
echo "CHART_NAME=$CHART_NAME" >> $GITHUB_ENV
echo "RELEASE_NAME=${CHART_NAME}$(date +%Y%m%d%H%M%S)" >> $GITHUB_ENV
echo "NAMESPACE=${CHART_NAME}-$(date +%Y%m%d%H%M%S)" >> $GITHUB_ENV
echo "ROLLOUT_TIMEOUT_SECONDS=600s" >> $GITHUB_ENV
echo "TEST_TIMEOUT_SECONDS=600s" >> $GITHUB_ENV
echo "KUBECTL_TIMEOUT_SECONDS=60s" >> $GITHUB_ENV
echo "should_cleanup=false" >> $GITHUB_ENV
echo "skip_validate=false" >> $GITHUB_ENV
echo "CHART_FOLDER=${example}/kubernetes/helm" >> $GITHUB_ENV
- 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=${HFTOKEN} \
--set global.modelUseHostPath=/home/sdp/.cache/huggingface/hub \
--set GOOGLE_API_KEY=${{ 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
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@@ -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

@@ -89,7 +89,7 @@ jobs:
echo "test_cases=$test_cases"
echo "test_cases=$test_cases" >> $GITHUB_OUTPUT
compose-test:
run-test:
needs: [get-test-case]
strategy:
matrix:
@@ -111,17 +111,6 @@ 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: Run test
shell: bash
env:
@@ -132,7 +121,6 @@ jobs:
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 }}
IMAGE_REPO: ${{ inputs.registry }}
IMAGE_TAG: ${{ inputs.tag }}
example: ${{ inputs.example }}
@@ -143,14 +131,21 @@ jobs:
if [[ "$IMAGE_REPO" == "" ]]; then export IMAGE_REPO="${OPEA_IMAGE_REPO}opea"; 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
- name: Clean up container
shell: bash
if: cancelled() || failure()
run: |
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"
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

View File

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

View File

@@ -1,31 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Clean up container on manual event
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

@@ -12,7 +12,7 @@ on:
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:
@@ -35,9 +35,9 @@ 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:
@@ -51,7 +51,7 @@ on:
required: false
type: string
inject_commit:
default: false
default: true
description: "inject commit to docker images true or false"
required: false
type: string
@@ -103,7 +103,7 @@ 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 }}

View File

@@ -12,7 +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:
@@ -31,7 +31,7 @@ on:
required: false
type: string
inject_commit:
default: false
default: true
description: "inject commit to docker images true or false"
required: false
type: string

View File

@@ -1,59 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Clean up Local Registry on manual event
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
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]
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

@@ -5,11 +5,11 @@ name: Nightly build/publish latest docker images
on:
schedule:
- cron: "30 14 * * *" # UTC time
- cron: "30 13 * * *" # UTC time
workflow_dispatch:
env:
EXAMPLES: ${{ vars.NIGHTLY_RELEASE_EXAMPLES }}
EXAMPLES: "AgentQnA,AudioQnA,ChatQnA,CodeGen,CodeTrans,DocIndexRetriever,DocSum,FaqGen,InstructionTuning,MultimodalQnA,ProductivitySuite,RerankFinetuning,SearchQnA,Translation,VideoQnA,VisualQnA"
TAG: "latest"
PUBLISH_TAGS: "latest"
@@ -32,7 +32,7 @@ jobs:
echo "TAG=$TAG" >> $GITHUB_OUTPUT
echo "PUBLISH_TAGS=$PUBLISH_TAGS" >> $GITHUB_OUTPUT
build-and-test:
build:
needs: get-build-matrix
strategy:
matrix:
@@ -42,7 +42,6 @@ jobs:
with:
node: gaudi
example: ${{ matrix.example }}
test_compose: true
secrets: inherit
get-image-list:
@@ -52,7 +51,7 @@ jobs:
examples: ${{ needs.get-build-matrix.outputs.EXAMPLES }}
publish:
needs: [get-build-matrix, get-image-list, build-and-test]
needs: [get-build-matrix, get-image-list, build]
strategy:
matrix:
image: ${{ fromJSON(needs.get-image-list.outputs.matrix) }}

View File

@@ -1,76 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: E2E Test with Helm Charts
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
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" == *"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() && ${{ needs.job1.outputs.run_matrix.example.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

@@ -1,40 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Check Duplicated Images
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

@@ -34,11 +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

@@ -28,7 +28,7 @@ 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]
@@ -42,5 +42,5 @@ jobs:
tag: "ci"
example: ${{ matrix.example }}
hardware: ${{ matrix.hardware }}
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

@@ -1,109 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Compose file and dockerfile path checking
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

@@ -1,7 +1,7 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
name: Check hyperlinks and relative path validity
name: Check Paths and Hyperlinks
on:
pull_request:
@@ -9,6 +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:

View File

@@ -10,7 +10,6 @@ on:
- "**.py"
- "**Dockerfile*"
- "**docker_image_build/build.yaml"
- "**/ui/**"
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}-on-push

View File

@@ -40,7 +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: |
@@ -54,6 +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

@@ -1,48 +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 [[ $port =~ [a-zA-Z_-] ]]; then
port=$(grep -E "export $port=" tests/$test_case | cut -d'=' -f2)
fi
if [[ $port =~ [0-9] ]]; then
if [[ $port == 5000 ]]; then
echo "Error: Port 5000 is used by local docker registry, please DO NOT use it in docker compose deployment!!!"
exit 1
fi
cid=$(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

@@ -17,15 +17,15 @@ for example in ${examples}; do
cd tests
ls -l
if [[ "$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
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

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

@@ -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))$
@@ -100,18 +100,18 @@ repos:
- prettier@3.2.5
- repo: https://github.com/psf/black.git
rev: 24.10.0
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: v2.3.0
@@ -122,7 +122,7 @@ repos:
- tomli
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.8.6
rev: v0.5.0
hooks:
- id: ruff
args: [--fix, --exit-non-zero-on-fix, --no-cache]

View File

@@ -2,8 +2,8 @@
## Overview
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 two worker agents to gather information and come up with answers. The worker RAG agent uses the retrieval tool to retrieve relevant documents from the knowledge base (a vector database). The worker SQL agent retrieve relevant data from the SQL database. Although not included in this example, but 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.
@@ -38,7 +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>}}
@@ -52,7 +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
@@ -61,7 +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
@@ -78,11 +75,11 @@ flowchart LR
### Why Agent for question answering?
1. Improve relevancy of retrieved context.
RAG 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. Expand scope of the agent.
The supervisor agent can interact with multiple worker agents that specialize in different domains with different skills (e.g., retrieve documents, write SQL queries, etc.), and thus can answer questions in multiple domains.
3. Hierarchical multi-agents can improve performance.
Expert worker agents, such as RAG agent and SQL agent, 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. If we only use one agent and provide all the tools to this single agent, it may get overwhelmed and not able to provide accurate answers.
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.
## Deployment with docker
@@ -103,7 +100,7 @@ Then build the agent docker image. Both the supervisor agent and the worker agen
```
cd GenAIComps
docker build -t opea/agent:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/agent/src/Dockerfile .
docker build -t opea/agent-langchain:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/agent/langchain/Dockerfile .
```
2. Set up environment for this example </br>
@@ -151,55 +148,28 @@ docker build -t opea/agent:latest --build-arg https_proxy=$https_proxy --build-a
bash run_ingest_data.sh
```
4. Prepare SQL database
In this example, we will use the Chinook SQLite database. Run the commands below.
```
# Download data
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite $WORKDIR/GenAIExamples/AgentQnA/tests/
```
5. Launch other tools. </br>
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
```
6. Launch multi-agent system. </br>
We provide two options for `llm_engine` of the agents: 1. open-source LLMs on Intel Gaudi2, 2. OpenAI models via API calls.
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
On Gaudi2 we will serve `meta-llama/Meta-Llama-3.1-70B-Instruct` using vllm.
To use open-source LLMs on Gaudi2, run commands below.
First build vllm-gaudi docker image.
```bash
cd $WORKDIR
git clone https://github.com/vllm-project/vllm.git
cd ./vllm
git checkout v0.6.6
docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
```
Then launch vllm on Gaudi2 with the command below.
```bash
vllm_port=8086
model="meta-llama/Meta-Llama-3.1-70B-Instruct"
docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=0,1,2,3 -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:latest --model ${model} --max-seq-len-to-capture 16384 --tensor-parallel-size 4
```
Then launch Agent microservices.
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
bash launch_agent_service_gaudi.sh
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_tgi_gaudi.sh
bash launch_agent_service_tgi_gaudi.sh
```
:::
@@ -209,7 +179,6 @@ docker build -t opea/agent:latest --build-arg https_proxy=$https_proxy --build-a
To use OpenAI models, run commands below.
```
export OPENAI_API_KEY=<your-openai-key>
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
bash launch_agent_service_openai.sh
```
@@ -217,20 +186,13 @@ docker build -t opea/agent:latest --build-arg https_proxy=$https_proxy --build-a
:::
::::
## Deploy using Helm Chart
Refer to the [AgentQnA helm chart](./kubernetes/helm/README.md) for instructions on deploying AgentQnA on Kubernetes.
## Validate services
First look at logs of the agent docker containers:
```
# worker RAG agent
# worker agent
docker logs rag-agent-endpoint
# worker SQL agent
docker logs sql-agent-endpoint
```
```
@@ -240,36 +202,22 @@ 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 RAG agent:
Second, validate worker agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "Michael Jackson song Thriller"
"query": "Most recent album by Taylor Swift"
}'
```
Third, validate worker SQL agent:
```
curl http://${host_ip}:9096/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many employees are in the company"
}'
```
Finally, validate supervisor agent:
Third, validate supervisor agent:
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many albums does Iron Maiden have?"
"query": "Most recent album by Taylor Swift"
}'
```
## Deploy AgentQnA UI
The AgentQnA UI can be deployed locally or using Docker.
For detailed instructions on deploying AgentQnA UI, refer to the [AgentQnA UI Guide](./ui/svelte/README.md).
## 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/src/README.md).
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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@@ -1,101 +0,0 @@
# Single node on-prem deployment with Docker Compose on AMD GPU
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>
# Set AMD GPU settings
export AGENTQNA_CARD_ID="card1"
export AGENTQNA_RENDER_ID="renderD136"
```
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
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_tgi_rocm.sh
```
6. [Optional] Build `Agent` docker image if pulling images failed.
```
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/agent:latest -f comps/agent/src/Dockerfile .
```
## Validate services
First look at logs of the agent docker containers:
```
# worker agent
docker logs rag-agent-endpoint
```
```
# supervisor agent
docker logs react-agent-endpoint
```
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
Second, validate worker agent:
```
curl http://${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/src/README.md).

View File

@@ -1,97 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
services:
agent-tgi-server:
image: ${AGENTQNA_TGI_IMAGE}
container_name: agent-tgi-server
ports:
- "${AGENTQNA_TGI_SERVICE_PORT-8085}:80"
volumes:
- /var/opea/agent-service/:/data
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
TGI_LLM_ENDPOINT: "http://${HOST_IP}:${AGENTQNA_TGI_SERVICE_PORT}"
HUGGING_FACE_HUB_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
shm_size: 1g
devices:
- /dev/kfd:/dev/kfd
- /dev/dri/${AGENTQNA_CARD_ID}:/dev/dri/${AGENTQNA_CARD_ID}
- /dev/dri/${AGENTQNA_RENDER_ID}:/dev/dri/${AGENTQNA_RENDER_ID}
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: opea/agent: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"
ipc: host
environment:
ip_address: ${ip_address}
strategy: rag_agent_llama
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
supervisor-react-agent:
image: opea/agent:latest
container_name: react-agent-endpoint
depends_on:
- agent-tgi-server
- worker-rag-agent
volumes:
# - ${WORKDIR}/GenAIExamples/AgentQnA/docker_image_build/GenAIComps/comps/agent/langchain/:/home/user/comps/agent/langchain/
- ${TOOLSET_PATH}:/home/user/tools/
ports:
- "${AGENTQNA_FRONTEND_PORT}:9090"
ipc: host
environment:
ip_address: ${ip_address}
strategy: react_langgraph
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: false
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
port: 9090

View File

@@ -1,47 +0,0 @@
# 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:2.3.1-rocm
export AGENTQNA_TGI_SERVICE_PORT="8085"
# LLM related environment variables
export AGENTQNA_CARD_ID="card1"
export AGENTQNA_RENDER_ID="renderD136"
export HF_CACHE_DIR=${HF_CACHE_DIR}
ls $HF_CACHE_DIR
export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
#export NUM_SHARDS=4
export LLM_ENDPOINT_URL="http://${ip_address}:${AGENTQNA_TGI_SERVICE_PORT}"
export temperature=0.01
export max_new_tokens=512
# agent related environment variables
export AGENTQNA_WORKER_AGENT_SERVICE_PORT="9095"
export TOOLSET_PATH=/home/huggingface/datamonsters/amd-opea/GenAIExamples/AgentQnA/tools/
echo "TOOLSET_PATH=${TOOLSET_PATH}"
export recursion_limit_worker=12
export recursion_limit_supervisor=10
export WORKER_AGENT_URL="http://${ip_address}:${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="9090"
#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"
docker compose -f compose.yaml up -d

View File

@@ -1,46 +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:2.3.1-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 $HF_CACHE_DIR
export LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
export NUM_SHARDS=4
export LLM_ENDPOINT_URL="http://${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"

View File

@@ -41,38 +41,26 @@ This example showcases a hierarchical multi-agent system for question-answering
bash run_ingest_data.sh
```
4. Prepare SQL database
In this example, we will use the SQLite database provided in the [TAG-Bench](https://github.com/TAG-Research/TAG-Bench/tree/main). Run the commands below.
```
# Download data
cd $WORKDIR
git clone https://github.com/TAG-Research/TAG-Bench.git
cd TAG-Bench/setup
chmod +x get_dbs.sh
./get_dbs.sh
```
5. Launch Tool service
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
```
6. Launch multi-agent system
5. Launch `Agent` service
The configurations of the supervisor agent and the worker agents are defined in the docker-compose yaml file. We currently use openAI GPT-4o-mini as LLM.
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 $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
bash launch_agent_service_openai.sh
```
7. [Optional] Build `Agent` docker image if pulling images failed.
6. [Optional] Build `Agent` docker image if pulling images failed.
```
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/agent:latest -f comps/agent/src/Dockerfile .
docker build -t opea/agent-langchain:latest -f comps/agent/langchain/Dockerfile .
```
## Validate services
@@ -80,11 +68,8 @@ This example showcases a hierarchical multi-agent system for question-answering
First look at logs of the agent docker containers:
```
# worker RAG agent
# worker agent
docker logs rag-agent-endpoint
# worker SQL agent
docker logs sql-agent-endpoint
```
```
@@ -94,30 +79,22 @@ docker logs react-agent-endpoint
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
Second, validate worker RAG agent:
Second, validate worker agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "Michael Jackson song Thriller"
"query": "Most recent album by Taylor Swift"
}'
```
Third, validate worker SQL agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many employees are in the company?"
}'
```
Finally, validate supervisor agent:
Third, validate supervisor agent:
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many albums does Iron Maiden have?"
"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/src/README.md).
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/
@@ -19,7 +19,7 @@ services:
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}
@@ -31,36 +31,9 @@ services:
LANGCHAIN_PROJECT: "opea-worker-agent-service"
port: 9095
worker-sql-agent:
image: opea/agent:latest
container_name: sql-agent-endpoint
volumes:
- ${WORKDIR}/TAG-Bench/:/home/user/TAG-Bench # SQL database
ports:
- "9096:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent
db_name: ${db_name}
db_path: ${db_path}
use_hints: false
hints_file: /home/user/TAG-Bench/${db_name}_hints.csv
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
@@ -78,7 +51,7 @@ services:
model: ${model}
temperature: ${temperature}
max_new_tokens: ${max_new_tokens}
stream: false
streaming: false
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}

View File

@@ -13,10 +13,7 @@ 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://${ip_address}:8080
export db_name=california_schools
export db_path="sqlite:////home/user/TAG-Bench/dev_folder/dev_databases/${db_name}/${db_name}.sqlite"
docker compose -f compose_openai.yaml up -d

View File

@@ -1,6 +1,6 @@
# Single node on-prem deployment AgentQnA on Gaudi
This example showcases a hierarchical multi-agent system for question-answering applications. We deploy the example on Gaudi using open-source LLMs.
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
@@ -45,58 +45,27 @@ For more details, please refer to the deployment guide [here](../../../../README
bash run_ingest_data.sh
```
4. Prepare SQL database
In this example, we will use the Chinook SQLite database. Run the commands below.
```
# Download data
cd $WORKDIR
git clone https://github.com/lerocha/chinook-database.git
cp chinook-database/ChinookDatabase/DataSources/Chinook_Sqlite.sqlite $WORKDIR/GenAIExamples/AgentQnA/tests/
```
5. Launch Tool service
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
```
6. Launch multi-agent system
5. Launch `Agent` service
On Gaudi2 we will serve `meta-llama/Meta-Llama-3.1-70B-Instruct` using vllm.
To use open-source LLMs on Gaudi2, run commands below.
First build vllm-gaudi docker image.
```bash
cd $WORKDIR
git clone https://github.com/vllm-project/vllm.git
cd ./vllm
git checkout v0.6.6
docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
```
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_tgi_gaudi.sh
bash launch_agent_service_tgi_gaudi.sh
```
Then launch vllm on Gaudi2 with the command below.
```bash
vllm_port=8086
model="meta-llama/Meta-Llama-3.1-70B-Instruct"
docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=0,1,2,3 -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:latest --model ${model} --max-seq-len-to-capture 16384 --tensor-parallel-size 4
```
Then launch Agent microservices.
```bash
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
bash launch_agent_service_gaudi.sh
```
7. [Optional] Build `Agent` docker image if pulling images failed.
If docker image pulling failed in Step 6 above, build the agent docker image with the commands below. After image build, try Step 6 again.
6. [Optional] Build `Agent` docker image if pulling images failed.
```
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
docker build -t opea/agent:latest -f comps/agent/src/Dockerfile .
docker build -t opea/agent-langchain:latest -f comps/agent/langchain/Dockerfile .
```
## Validate services
@@ -104,11 +73,8 @@ For more details, please refer to the deployment guide [here](../../../../README
First look at logs of the agent docker containers:
```
# worker RAG agent
# worker agent
docker logs rag-agent-endpoint
# worker SQL agent
docker logs sql-agent-endpoint
```
```
@@ -118,30 +84,22 @@ docker logs react-agent-endpoint
You should see something like "HTTP server setup successful" if the docker containers are started successfully.</p>
Second, validate worker RAG agent:
Second, validate worker agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "Michael Jackson song Thriller"
"query": "Most recent album by Taylor Swift"
}'
```
Third, validate worker SQL agent:
```
curl http://${host_ip}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many employees are in the company?"
}'
```
Finally, validate supervisor agent:
Third, validate supervisor agent:
```
curl http://${host_ip}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"messages": "How many albums does Iron Maiden have?"
"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/src/README.md).
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

@@ -3,9 +3,10 @@
services:
worker-rag-agent:
image: opea/agent: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"
@@ -14,13 +15,13 @@ services:
ip_address: ${ip_address}
strategy: rag_agent_llama
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}
@@ -32,41 +33,14 @@ 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 # test db
ports:
- "9096:9096"
ipc: host
environment:
ip_address: ${ip_address}
strategy: sql_agent_llama
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: opea/agent: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"
@@ -75,13 +49,13 @@ services:
ip_address: ${ip_address}
strategy: react_llama
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: false
streaming: false
tools: /home/user/tools/supervisor_agent_tools.yaml
require_human_feedback: false
no_proxy: ${no_proxy}
@@ -92,5 +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

View File

@@ -16,8 +16,8 @@ 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://${ip_address}:8086"
export temperature=0
export LLM_ENDPOINT_URL="http://${ip_address}:8085"
export temperature=0.01
export max_new_tokens=4096
# agent related environment variables
@@ -26,11 +26,7 @@ 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"
docker compose -f compose.yaml up -d

View File

@@ -2,18 +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}
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

@@ -1,16 +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
vllm:
enabled: true
image:
repository: opea/vllm-gaudi
supervisor:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
ragagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm
sqlagent:
llm_endpoint_url: http://{{ .Release.Name }}-vllm

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

@@ -15,7 +15,7 @@ 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() {

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

@@ -11,16 +11,17 @@ 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..."
service_list="doc-index-retriever dataprep embedding retriever reranking"
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
@@ -34,26 +35,6 @@ function build_agent_docker_image() {
docker compose -f build.yaml build --no-cache
}
function build_vllm_docker_image() {
echo "Building the vllm docker image"
cd $WORKPATH
echo $WORKPATH
if [ ! -d "./vllm-fork" ]; then
git clone https://github.com/HabanaAI/vllm-fork.git
fi
cd ./vllm-fork
git checkout v0.6.4.post2+Gaudi-1.19.0
sed -i 's/triton/triton==3.1.0/g' requirements-hpu.txt
docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:ci --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
if [ $? -ne 0 ]; then
echo "opea/vllm-gaudi:ci failed"
exit 1
else
echo "opea/vllm-gaudi:ci successful"
fi
}
function main() {
echo "==================== Build docker images for retrieval tool ===================="
build_docker_images_for_retrieval_tool
@@ -62,12 +43,6 @@ function main() {
echo "==================== Build agent docker image ===================="
build_agent_docker_image
echo "==================== Build agent docker image completed ===================="
echo "==================== Build vllm docker image ===================="
build_vllm_docker_image
echo "==================== Build vllm docker image completed ===================="
docker image ls | grep vllm
}
main

View File

@@ -7,7 +7,6 @@ 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=$WORKDIR/hf_cache
if [ ! -d "$HF_CACHE_DIR" ]; then

View File

@@ -8,22 +8,15 @@ WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export TOOLSET_PATH=$WORKPATH/tools/
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
model="meta-llama/Meta-Llama-3.1-70B-Instruct"
export HF_CACHE_DIR=/data2/huggingface
export HF_CACHE_DIR=$WORKDIR/hf_cache
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_tgi(){
echo "Starting tgi-gaudi server"
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
@@ -31,67 +24,14 @@ function start_tgi(){
}
function start_vllm_service_70B() {
echo "token is ${HF_TOKEN}"
echo "start vllm gaudi service"
echo "**************model is $model**************"
vllm_image=opea/vllm-gaudi:ci
docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=0,1,2,3 -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host $vllm_image --model ${model} --max-seq-len-to-capture 16384 --tensor-parallel-size 4
sleep 5s
echo "Waiting vllm gaudi ready"
n=0
LOG_PATH=$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 prepare_data() {
cd $WORKDIR
echo "Downloading data..."
git clone https://github.com/TAG-Research/TAG-Bench.git
cd TAG-Bench/setup
chmod +x get_dbs.sh
./get_dbs.sh
echo "Split data..."
cd $WORKPATH/tests/sql_agent_test
bash run_data_split.sh
echo "Data preparation done!"
}
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 $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi
bash launch_agent_service_gaudi.sh
sleep 2m
bash launch_agent_service_tgi_gaudi.sh
sleep 10
}
function validate() {
@@ -109,76 +49,35 @@ function validate() {
}
function validate_agent_service() {
# # test worker rag agent
echo "======================Testing worker rag agent======================"
echo "----------------Test agent ----------------"
# local CONTENT=$(http_proxy="" curl http://${ip_address}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
# "query": "Tell me about Michael Jackson song thriller"
# }')
export agent_port="9095"
prompt="Tell me about Michael Jackson song Thriller"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt")
# echo $CONTENT
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py)
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "rag-agent-endpoint")
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
docker logs rag-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")
local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint")
echo $CONTENT
# echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
if [ "$EXIT_CODE" == "1" ]; then
docker logs sql-agent-endpoint
exit 1
fi
# test supervisor react agent
echo "======================Testing supervisor react agent======================"
# local CONTENT=$(http_proxy="" curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
# "query": "Tell me about Michael Jackson song thriller"
# }')
export agent_port="9090"
prompt="How many albums does Iron Maiden have?"
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt")
local EXIT_CODE=$(validate "$CONTENT" "21" "react-agent-endpoint")
# echo $CONTENT
echo $EXIT_CODE
local EXIT_CODE="${EXIT_CODE:0-1}"
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
docker logs react-agent-endpoint
exit 1
fi
}
function remove_data() {
echo "Removing data..."
cd $WORKDIR
if [ -d "TAG-Bench" ]; then
rm -rf TAG-Bench
fi
echo "Data removed!"
}
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 VLLM service ===================="
start_vllm_service_70B
echo "==================== VLLM service started ===================="
echo "==================== Start TGI ===================="
start_tgi
echo "==================== TGI started ===================="
echo "==================== Start agent ===================="
start_agent_and_api_server
@@ -189,8 +88,4 @@ function main() {
echo "==================== Agent service validated ===================="
}
remove_data
remove_chinook_data
main
remove_data
remove_chinook_data

View File

@@ -1,76 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -ex
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_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/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: $CONTENT"
echo 1
fi
}
function validate_agent_service() {
echo "----------------Test agent ----------------"
local CONTENT=$(http_proxy="" curl http://${ip_address}:9095/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
"query": "Tell me about Michael Jackson song thriller"
}')
local EXIT_CODE=$(validate "$CONTENT" "Thriller" "react-agent-endpoint")
docker logs rag-agent-endpoint
if [ "$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"
}')
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 agent ===================="
start_agent_and_api_server
echo "==================== Agent started ===================="
echo "==================== Validate agent service ===================="
validate_agent_service
echo "==================== Agent service validated ===================="
}
main

View File

@@ -1,7 +1,6 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import argparse
import os
import requests
@@ -10,47 +9,17 @@ import requests
def generate_answer_agent_api(url, prompt):
proxies = {"http": ""}
payload = {
"messages": prompt,
"query": prompt,
}
response = requests.post(url, json=payload, proxies=proxies)
answer = response.json()["text"]
return answer
def process_request(url, query, is_stream=False):
proxies = {"http": ""}
payload = {
"messages": query,
}
try:
resp = requests.post(url=url, json=payload, proxies=proxies, stream=is_stream)
if not is_stream:
ret = resp.json()["text"]
print(ret)
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}"
print(ret)
return False
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str)
parser.add_argument("--stream", action="store_true")
args = parser.parse_args()
ip_address = os.getenv("ip_address", "localhost")
agent_port = os.getenv("agent_port", "9090")
agent_port = os.getenv("agent_port", "9095")
url = f"http://{ip_address}:{agent_port}/v1/chat/completions"
prompt = args.prompt
process_request(url, prompt, args.stream)
prompt = "Tell me about Michael Jackson song thriller"
answer = generate_answer_agent_api(url, prompt)
print(answer)

View File

@@ -1,7 +1,8 @@
#!/bin/bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
set -xe
set -e
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
@@ -26,7 +27,7 @@ function stop_agent_docker() {
done
}
function stop_llm(){
function stop_tgi(){
cd $WORKPATH/docker_compose/intel/hpu/gaudi/
container_list=$(cat tgi_gaudi.yaml | grep container_name | cut -d':' -f2)
for container_name in $container_list; do
@@ -35,14 +36,6 @@ function stop_llm(){
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
done
cid=$(docker ps -aq --filter "name=vllm-gaudi-server")
echo "Stopping container $cid"
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
cid=$(docker ps -aq --filter "name=test-comps-vllm-gaudi-service")
echo "Stopping container $cid"
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
}
function stop_retrieval_tool() {
@@ -59,7 +52,7 @@ function stop_retrieval_tool() {
echo "workpath: $WORKPATH"
echo "=================== Stop containers ===================="
stop_crag
stop_llm
stop_tgi
stop_agent_docker
stop_retrieval_tool
@@ -85,9 +78,8 @@ echo "=================== #5 Stop agent and API server===================="
stop_crag
stop_agent_docker
stop_retrieval_tool
stop_llm
echo "=================== #5 Agent and API server stopped===================="
echo y | docker system prune
echo "ALL DONE!!"
echo "ALL DONE!"

View File

@@ -1,75 +0,0 @@
#!/bin/bash
# Copyright (C) 2024 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: Apache-2.0
set -xe
WORKPATH=$(dirname "$PWD")
export WORKDIR=$WORKPATH/../../
echo "WORKDIR=${WORKDIR}"
export ip_address=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/
function stop_crag() {
cid=$(docker ps -aq --filter "name=kdd-cup-24-crag-service")
echo "Stopping container kdd-cup-24-crag-service with cid $cid"
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
}
function stop_agent_docker() {
cd $WORKPATH/docker_compose/amd/gpu/rocm
# docker compose -f compose.yaml down
container_list=$(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 $cid -f && sleep 1s; fi
done
}
function stop_retrieval_tool() {
echo "Stopping Retrieval tool"
local RETRIEVAL_TOOL_PATH=$WORKPATH/../DocIndexRetriever
cd $RETRIEVAL_TOOL_PATH/docker_compose/intel/cpu/xeon/
# docker compose -f compose.yaml down
container_list=$(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
}
echo "workpath: $WORKPATH"
echo "=================== Stop containers ===================="
stop_crag
stop_agent_docker
stop_retrieval_tool
cd $WORKPATH/tests
echo "=================== #1 Building docker images===================="
bash step1_build_images.sh
echo "=================== #1 Building docker images completed===================="
echo "=================== #2 Start retrieval tool===================="
bash step2_start_retrieval_tool.sh
echo "=================== #2 Retrieval tool started===================="
echo "=================== #3 Ingest data and validate retrieval===================="
bash step3_ingest_data_and_validate_retrieval.sh
echo "=================== #3 Data ingestion and validation completed===================="
echo "=================== #4 Start agent and API server===================="
bash step4a_launch_and_validate_agent_tgi_on_rocm.sh
echo "=================== #4 Agent test passed ===================="
echo "=================== #5 Stop agent and API server===================="
stop_crag
stop_agent_docker
stop_retrieval_tool
echo "=================== #5 Agent and API server stopped===================="
echo y | docker system prune
echo "ALL DONE!!"

View File

@@ -2,7 +2,7 @@
# SPDX-License-Identifier: Apache-2.0
search_knowledge_base:
description: Search a knowledge base for a given query. Returns text related to the query.
description: Search knowledge base for a given query. Returns text related to the query.
callable_api: tools.py:search_knowledge_base
args_schema:
query:
@@ -10,15 +10,6 @@ search_knowledge_base:
description: query
return_output: retrieved_data
search_artist_database:
description: Search a SQL database on artists and their music with a natural language query. Returns text related to the query.
callable_api: tools.py:search_sql_database
args_schema:
query:
type: str
description: natural language query
return_output: retrieved_data
get_artist_birth_place:
description: Get the birth place of an artist.
callable_api: tools.py:get_artist_birth_place

View File

@@ -8,30 +8,13 @@ from tools.pycragapi import CRAG
def search_knowledge_base(query: str) -> str:
"""Search a knowledge base about music and singers for a given query.
Returns text related to the query.
"""
"""Search the knowledge base for a specific query."""
# use worker agent (DocGrader) to search the knowledge base
url = os.environ.get("WORKER_AGENT_URL")
print(url)
proxies = {"http": ""}
payload = {
"messages": query,
}
response = requests.post(url, json=payload, proxies=proxies)
return response.json()["text"]
def search_sql_database(query: str) -> str:
"""Search a SQL database on artists and their music with a natural language query.
Returns text related to the query.
"""
url = os.environ.get("SQL_AGENT_URL")
print(url)
proxies = {"http": ""}
payload = {
"messages": query,
"query": query,
}
response = requests.post(url, json=payload, proxies=proxies)
return response.json()["text"]

View File

@@ -12,7 +12,7 @@ def search_knowledge_base(query: str) -> str:
print(url)
proxies = {"http": ""}
payload = {
"text": query,
"messages": query,
}
response = requests.post(url, json=payload, proxies=proxies)
print(response)

View File

@@ -1,26 +0,0 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
# Use node 20.11.1 as the base image
FROM node:20.11.1
# Update package manager and install Git
RUN apt-get update -y && apt-get install -y git
# Copy the front-end code repository
COPY svelte /home/user/svelte
# Set the working directory
WORKDIR /home/user/svelte
# Install front-end dependencies
RUN npm install
# Build the front-end application
RUN npm run build
# Expose the port of the front-end application
EXPOSE 5173
# Run the front-end application in preview mode
CMD ["npm", "run", "preview", "--", "--port", "5173", "--host", "0.0.0.0"]

View File

@@ -1,10 +0,0 @@
[*]
indent_style = tab
[package.json]
indent_style = space
indent_size = 2
[*.md]
indent_style = space
indent_size = 2

View File

@@ -1 +0,0 @@
AGENT_URL = '/v1/chat/completions'

View File

@@ -1,13 +0,0 @@
.DS_Store
node_modules
/build
/.svelte-kit
/package
.env
.env.*
!.env.example
# Ignore files for PNPM, NPM and YARN
pnpm-lock.yaml
package-lock.json
yarn.lock

View File

@@ -1,20 +0,0 @@
module.exports = {
root: true,
parser: "@typescript-eslint/parser",
extends: ["eslint:recommended", "plugin:@typescript-eslint/recommended", "prettier"],
plugins: ["svelte3", "@typescript-eslint", "neverthrow"],
ignorePatterns: ["*.cjs"],
overrides: [{ files: ["*.svelte"], processor: "svelte3/svelte3" }],
settings: {
"svelte3/typescript": () => require("typescript"),
},
parserOptions: {
sourceType: "module",
ecmaVersion: 2020,
},
env: {
browser: true,
es2017: true,
node: true,
},
};

View File

@@ -1,13 +0,0 @@
.DS_Store
node_modules
/build
/.svelte-kit
/package
.env
.env.*
!.env.example
# Ignore files for PNPM, NPM and YARN
pnpm-lock.yaml
package-lock.json
yarn.lock

View File

@@ -1,13 +0,0 @@
{
"pluginSearchDirs": [
"."
],
"overrides": [
{
"files": "*.svelte",
"options": {
"parser": "svelte"
}
}
]
}

View File

@@ -1,60 +0,0 @@
# AgentQnA
## 📸 Project Screenshots
![project-screenshot](../../assets/img/agent_ui.png)
![project-screenshot](../../assets/img/agent_ui_result.png)
## 🧐 Features
Here're some of the project's features:
- Create AgentProvide more precise answers based on user queries, showcase the high-quality output process of complex queries across different dimensions, and consolidate information to present comprehensive answers.
## 🛠️ Get it Running
1. Clone the repo.
2. cd command to the current folder.
```
cd AgentQnA/ui
```
3. Modify the required .env variables.
```
AGENT_URL = ''
```
4. **For Local Development:**
- Install the dependencies:
```
npm install
```
- Start the development server:
```
npm run dev
```
- The application will be available at `http://localhost:3000`.
5. **For Docker Setup:**
- Build the Docker image:
```
docker build -t opea:agent-ui .
```
- Run the Docker container:
```
docker run -d -p 3000:3000 --name agent-ui opea:agent-ui
```
- The application will be available at `http://localhost:3000`.

View File

@@ -1,60 +0,0 @@
{
"name": "agent-example",
"version": "0.0.1",
"private": true,
"scripts": {
"dev": "vite dev --host 0.0.0.0",
"build": "vite build",
"preview": "vite preview",
"check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
"check:watch": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json --watch",
"lint": "prettier --check . && eslint .",
"format": "prettier --write ."
},
"devDependencies": {
"@fortawesome/free-solid-svg-icons": "6.2.0",
"@sveltejs/adapter-auto": "1.0.0-next.75",
"@sveltejs/kit": "^1.20.1",
"@tailwindcss/typography": "0.5.7",
"@types/debug": "4.1.7",
"@typescript-eslint/eslint-plugin": "^5.27.0",
"@typescript-eslint/parser": "^5.27.0",
"autoprefixer": "^10.4.7",
"daisyui": "^2.52.0",
"debug": "4.3.4",
"eslint": "^8.16.0",
"eslint-config-prettier": "^8.3.0",
"eslint-plugin-neverthrow": "1.1.4",
"eslint-plugin-svelte3": "^4.0.0",
"neverthrow": "5.0.0",
"pocketbase": "0.7.0",
"postcss": "^8.4.23",
"postcss-load-config": "^4.0.1",
"postcss-preset-env": "^8.3.2",
"prettier": "^2.8.8",
"prettier-plugin-svelte": "^2.7.0",
"prettier-plugin-tailwindcss": "^0.3.0",
"svelte": "^3.59.1",
"svelte-check": "^2.7.1",
"svelte-fa": "3.0.3",
"svelte-preprocess": "^4.10.7",
"tailwindcss": "^3.1.5",
"ts-pattern": "4.0.5",
"tslib": "^2.3.1",
"typescript": "^4.7.4",
"vite": "^4.3.9"
},
"type": "module",
"dependencies": {
"@heroicons/vue": "^2.1.5",
"echarts": "^5.4.2",
"flowbite-svelte": "^0.38.5",
"flowbite-svelte-icons": "^0.3.6",
"fuse.js": "^6.6.2",
"marked": "^15.0.0",
"ramda": "^0.29.0",
"sjcl": "^1.0.8",
"sse.js": "^0.6.1",
"svelte-notifications": "^0.9.98"
}
}

View File

@@ -1,13 +0,0 @@
const tailwindcss = require("tailwindcss");
const autoprefixer = require("autoprefixer");
const config = {
plugins: [
//Some plugins, like tailwindcss/nesting, need to run before Tailwind,
tailwindcss(),
//But others, like autoprefixer, need to run after,
autoprefixer,
],
};
module.exports = config;

View File

@@ -1,50 +0,0 @@
// Copyright (C) 2025 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
// See: https://kit.svelte.dev/docs/types#app
// import { Result} from "neverthrow";
declare namespace App {
interface Locals {
user?: User;
}
// interface PageData { }
// interface PageError {}
// interface Platform {}
}
interface User {
id?: string;
email: string;
password?: string;
token?: string;
[key: string]: any;
}
type AuthResponse = Result<User>;
interface AuthAdapter {
login(props: { email: string; password: string }): Promise<AuthResponse>;
signup(props: { email: string; password: string; password_confirm: string }): Promise<AuthResponse>;
validate_session(props: { token: string }): Promise<AuthResponse>;
logout(props: { token: string; email: string }): Promise<Result<void>>;
forgotPassword(props: { email: string; password: string }): Promise<Result<void>>;
}
interface ChatAdapter {
modelList(props: {}): Promise<Result<void>>;
txt2img(props: {}): Promise<Result<void>>;
}
interface ChatMessage {
role: string;
content: string;
}
interface ChatMessageType {
model: string;
knowledge: string;
temperature: string;
max_new_tokens: string;
topk: string;
}

View File

@@ -1,17 +0,0 @@
<!--
Copyright (C) 2025 Intel Corporation
SPDX-License-Identifier: Apache-2.0
-->
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" href="%sveltekit.assets%/favicon.png" />
<meta name="viewport" content="width=device-width" />
%sveltekit.head%
</head>
<body>
<div>%sveltekit.body%</div>
</body>
</html>

View File

@@ -1,82 +0,0 @@
/* Write your global styles here, in PostCSS syntax */
@tailwind base;
@tailwind components;
@tailwind utilities;
.btn {
@apply flex-nowrap;
}
a.btn {
@apply no-underline;
}
.input {
@apply text-base;
}
.bg-dark-blue {
background-color: #004a86;
}
.bg-light-blue {
background-color: #0068b5;
}
.bg-turquoise {
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