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28 Commits

Author SHA1 Message Date
archer
2f6fca1a6d doc 2025-02-04 23:23:40 +08:00
archer
9ac8908e25 doc 2025-02-04 23:12:58 +08:00
archer
38f180070e update compose 2025-02-04 22:57:41 +08:00
archer
085a522d70 update doc 2025-02-04 22:50:51 +08:00
archer
b65cd4de55 animation 2025-02-04 21:51:21 +08:00
archer
239dd5b48a lock 2025-02-04 17:26:22 +08:00
Archer
b00dc933f5 doc (#3685)
* perf: supplement assistant empty response

* check array

* doc
2025-02-04 17:25:45 +08:00
Archer
2a209d43af fix: reasoning response (#3684)
* perf: supplement assistant empty response

* check array

* fix: reasoning response

* fix: reasoning response
2025-02-04 17:25:45 +08:00
Archer
9e100957eb fix: stream response (#3682)
* perf: supplement assistant empty response

* check array

* fix: stream response

* fix: model config cannot set to null
2025-02-04 17:25:44 +08:00
Archer
54defd8a3c perf: max_token count;feat: support resoner output;fix: member scroll (#3681)
* perf: supplement assistant empty response

* check array

* perf: max_token count

* feat: support resoner output

* member scroll

* update provider order

* i18n
2025-02-04 17:25:44 +08:00
Archer
9e0379382f perf: supplement assistant empty response (#3669)
* perf: supplement assistant empty response

* check array
2025-02-04 17:25:43 +08:00
archer
c3d55d5c8f fix: chat model select 2025-02-04 17:25:43 +08:00
archer
383fe66cd7 add gemini model 2025-02-04 17:25:43 +08:00
archer
0b392073b6 comment 2025-02-04 17:25:43 +08:00
heheer
b79d7e4015 fix interactive edge (#3659)
* fix interactive edge

* fix
2025-02-04 17:25:42 +08:00
Archer
7407912bb8 fix: err tip (#3666)
* fix: err tip

* perf: training queue

* doc
2025-02-04 17:25:42 +08:00
Archer
c8e2e0283b reload buffer (#3665)
* reload buffer

* reload buffer

* tts selector
2025-02-04 17:25:41 +08:00
Archer
4ada33e7e6 feat: markdown extension (#3663)
* feat: markdown extension

* media cros

* rerank test

* default price

* perf: default model

* fix: cannot custom provider

* fix: default model select

* update bg

* perf: default model selector

* fix: usage export

* i18n

* fix: rerank

* update init extension

* perf: ip limit check

* doubao model order

* web default modle

* perf: tts selector

* perf: tts error

* qrcode package
2025-02-04 17:25:37 +08:00
heheer
3683ac4003 export usage csv i18n (#3660)
* export usage csv i18n

* fix build
2025-02-04 17:25:11 +08:00
a.e.
10b3e16b8b fix: false triggerd org selection (#3661) 2025-02-04 17:25:10 +08:00
Archer
51fac7431f feat: default model (#3662)
* move model config

* feat: default model
2025-02-04 17:25:09 +08:00
a.e.
2015bbe9a9 fix: POST 500 error on dingtalk bot (#3655) 2025-02-04 17:25:08 +08:00
Archer
e48df175d7 model perf (#3657)
* fix: model

* dataset quote

* perf: model config

* model tag

* doubao model config

* perf: config model

* feat: model test
2025-02-04 17:25:07 +08:00
Archer
f2be9ae32d 4.8.20 test (#3656)
* provider

* perf: model config
2025-02-04 17:25:06 +08:00
heheer
28cbe3e24e add default model config (#3653) 2025-02-04 17:25:05 +08:00
Archer
5a04d015f9 perf: usages list;perf: move components (#3654)
* perf: usages list

* team sub plan load

* perf: usage dashboard code

* perf: dashboard ui

* perf: move components
2025-02-04 17:25:01 +08:00
heheer
e4b85ffada feat: usage filter & export & dashbord (#3538)
* feat: usage filter & export & dashbord

* adjust ui

* fix tmb scroll

* fix code & selecte all

* merge
2025-02-04 17:24:31 +08:00
Archer
12c6ecb987 Aiproxy (#3649)
* model config

* feat: model config ui

* perf: rename variable

* feat: custom request url

* perf: model buffer

* perf: init model

* feat: json model config

* auto login

* fix: ts

* update packages

* package

* fix: dockerfile
2025-02-04 17:23:46 +08:00
1086 changed files with 18370 additions and 50158 deletions

30
.github/gh-bot.yml vendored Normal file
View File

@@ -0,0 +1,30 @@
version: v1
debug: true
action:
printConfig: false
release:
retry: 15s
actionName: Release
allowOps:
- cuisongliu
bot:
prefix: /
spe: _
allowOps:
- sealos-ci-robot
- sealos-release-robot
email: sealos-ci-robot@sealos.io
username: sealos-ci-robot
repo:
org: false
message:
success: |
🤖 says: Hooray! The action {{.Body}} has been completed successfully. 🎉
format_error: |
🤖 says: ‼️ There is a formatting issue with the action, kindly verify the action's format.
permission_error: |
🤖 says: ‼️ The action doesn't have permission to trigger.
release_error: |
🤖 says: ‼️ Release action failed.
Error details: {{.Error}}

View File

@@ -1,4 +1,4 @@
yangchuansheng/fastgpt-imgs:
- source: docSite/assets/imgs/
dest: imgs/
deleteOrphaned: true
deleteOrphaned: true

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@@ -0,0 +1,96 @@
name: Build fastgpt-sandbox images
on:
workflow_dispatch:
push:
paths:
- 'projects/sandbox/**'
tags:
- 'v*'
jobs:
build-fastgpt-sandbox-images:
runs-on: ubuntu-20.04
steps:
# install env
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Install Dependencies
run: |
sudo apt update && sudo apt install -y nodejs npm
- name: Set up QEMU (optional)
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v2
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Login to Ali Hub
uses: docker/login-action@v2
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
# Set tag
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
fi
- name: Build and publish image for main branch or tag push event
env:
Git_Tag: ${{ env.Git_Tag }}
Git_Latest: ${{ env.Git_Latest }}
Ali_Tag: ${{ env.Ali_Tag }}
Ali_Latest: ${{ env.Ali_Latest }}
Docker_Hub_Tag: ${{ env.Docker_Hub_Tag }}
Docker_Hub_Latest: ${{ env.Docker_Hub_Latest }}
run: |
docker buildx build \
-f projects/sandbox/Dockerfile \
--platform linux/amd64,linux/arm64 \
--label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/fastgpt-sandbox" \
--label "org.opencontainers.image.description=fastgpt-sandbox image" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${Git_Tag} \
-t ${Git_Latest} \
-t ${Ali_Tag} \
-t ${Ali_Latest} \
-t ${Docker_Hub_Tag} \
-t ${Docker_Hub_Latest} \
.

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@@ -6,17 +6,12 @@ on:
- 'docSite/**'
branches:
- 'main'
tags:
- 'v*.*.*'
jobs:
build-fastgpt-docs-images:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
attestations: write
id-token: write
steps:
- name: Checkout
uses: actions/checkout@v4
@@ -34,6 +29,7 @@ jobs:
with:
# list of Docker images to use as base name for tags
images: |
${{ secrets.DOCKER_HUB_NAME }}/fastgpt-docs
ghcr.io/${{ github.repository_owner }}/fastgpt-docs
registry.cn-hangzhou.aliyuncs.com/${{ secrets.ALI_HUB_USERNAME }}/fastgpt-docs
tags: |
@@ -46,12 +42,18 @@ jobs:
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to DockerHub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Login to ghcr.io
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Login to Aliyun
uses: docker/login-action@v3
@@ -70,7 +72,6 @@ jobs:
labels: ${{ steps.meta.outputs.labels }}
outputs:
tags: ${{ steps.datetime.outputs.datetime }}
update-docs-image:
needs: build-fastgpt-docs-images
runs-on: ubuntu-20.04

View File

@@ -1,4 +1,4 @@
name: Deploy doc image to cf
name: Deploy doc image to vercel
on:
workflow_dispatch:
@@ -7,6 +7,8 @@ on:
- 'docSite/**'
branches:
- 'main'
tags:
- 'v*.*.*'
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
@@ -20,11 +22,6 @@ jobs:
# The type of runner that the job will run on
runs-on: ubuntu-22.04
permissions:
contents: write
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
# Job outputs
outputs:
docs: ${{ steps.filter.outputs.docs }}
@@ -61,11 +58,22 @@ jobs:
# Step 4 - Builds the site using Hugo
- name: Build
run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
# Step 5 - Push our generated site to Vercel
- name: Deploy to Vercel
uses: amondnet/vercel-action@v25
id: vercel-action
with:
vercel-token: ${{ secrets.VERCEL_TOKEN }} # Required
vercel-org-id: ${{ secrets.VERCEL_ORG_ID }} #Required
vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }} #Required
github-comment: false
vercel-args: '--prod --local-config ../vercel.json' # Optional
working-directory: docSite/public
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v4
if: github.ref == 'refs/heads/main'
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
github_token: ${{ secrets.GH_PAT }}
publish_dir: docSite/public

View File

@@ -4,18 +4,14 @@ on:
pull_request_target:
paths:
- 'docSite/**'
branches:
- 'main'
workflow_dispatch:
# A workflow run is made up of one or more jobs that can run sequentially or in parallel
jobs:
# This workflow contains jobs "deploy-production"
deploy-preview:
permissions:
contents: read
packages: write
attestations: write
id-token: write
pull-requests: write
# The environment this job references
environment:
name: Preview
@@ -38,7 +34,6 @@ jobs:
repository: ${{ github.event.pull_request.head.repo.full_name }}
submodules: recursive # Fetch submodules
fetch-depth: 0 # Fetch all history for .GitInfo and .Lastmod
token: ${{ secrets.GITHUB_TOKEN }}
# Step 2 Detect changes to Docs Content
- name: Detect changes in doc content
@@ -50,6 +45,10 @@ jobs:
- 'docSite/content/docs/**'
base: main
- name: Add cdn for images
run: |
sed -i "s#\](/imgs/#\](https://cdn.jsdelivr.net/gh/yangchuansheng/fastgpt-imgs@main/imgs/#g" $(grep -rl "\](/imgs/" docSite/content/zh-cn/docs)
# Step 3 - Install Hugo (specific version)
- name: Install Hugo
uses: peaceiris/actions-hugo@v2
@@ -59,37 +58,41 @@ jobs:
# Step 4 - Builds the site using Hugo
- name: Build
run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
# Step 5 - Push our generated site to Cloudflare
- name: Deploy to Cloudflare Pages
id: deploy
uses: cloudflare/wrangler-action@v3
# Step 5 - Push our generated site to Vercel
- name: Deploy to Vercel
uses: amondnet/vercel-action@v25
id: vercel-action
with:
apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }}
accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
command: pages deploy ./docSite/public --project-name=fastgpt-doc
packageManager: npm
- name: Create deployment status comment
if: always()
vercel-token: ${{ secrets.VERCEL_TOKEN }} # Required
vercel-org-id: ${{ secrets.VERCEL_ORG_ID }} #Required
vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }} #Required
github-comment: false
vercel-args: '--local-config ../vercel.json' # Optional
working-directory: docSite/public
alias-domains: | #Optional
fastgpt-staging.vercel.app
docsOutput:
needs: [deploy-preview]
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
with:
ref: ${{ github.event.pull_request.head.ref }}
repository: ${{ github.event.pull_request.head.repo.full_name }}
- name: Write md
run: |
echo "# 🤖 Generated by deploy action" > report.md
echo "[👀 Visit Preview](${{ needs.deploy-preview.outputs.url }})" >> report.md
cat report.md
- name: Gh Rebot for Sealos
uses: labring/gh-rebot@v0.0.6
if: ${{ (github.event_name == 'pull_request_target') }}
with:
version: v0.0.6
env:
JOB_STATUS: ${{ job.status }}
PREVIEW_URL: ${{ steps.deploy.outputs.deployment-url }}
uses: actions/github-script@v6
with:
token: ${{ secrets.GITHUB_TOKEN }}
script: |
const success = process.env.JOB_STATUS === 'success';
const deploymentUrl = `${process.env.PREVIEW_URL}`;
const status = success ? '✅ Success' : '❌ Failed';
console.log(process.env.JOB_STATUS);
const commentBody = `**Deployment Status: ${status}**
${success ? `🔗 Preview URL: ${deploymentUrl}` : ''}`;
await github.rest.issues.createComment({
...context.repo,
issue_number: context.payload.pull_request.number,
body: commentBody
});
GH_TOKEN: '${{ secrets.GH_PAT }}'
SEALOS_TYPE: 'pr_comment'
SEALOS_FILENAME: 'report.md'
SEALOS_REPLACE_TAG: 'DEFAULT_REPLACE_DEPLOY'

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@@ -1,171 +0,0 @@
name: Build FastGPT images
on:
workflow_dispatch:
push:
paths:
- "projects/app/**"
- "packages/**"
tags:
- "v*"
jobs:
build-fastgpt-images:
permissions:
packages: write
contents: read
attestations: write
id-token: write
strategy:
matrix:
sub_routes:
- repo: fastgpt
base_url: ""
- repo: fastgpt-sub-route
base_url: "/fastai"
- repo: fastgpt-sub-route-gchat
base_url: "/gchat"
archs:
- arch: amd64
- arch: arm64
runs-on: ubuntu-24.04-arm
runs-on: ${{ matrix.archs.runs-on || 'ubuntu-24.04' }}
steps:
# install env
- name: Checkout
uses: actions/checkout@v4
with:
fetch-depth: 1
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-${{ matrix.sub_routes.repo }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-${{ matrix.sub_routes.repo }}-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Login to Ali Hub
uses: docker/login-action@v3
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Build for ${{ matrix.archs.arch }}
id: build
uses: docker/build-push-action@v6
with:
context: .
file: projects/app/Dockerfile
platforms: linux/${{ matrix.archs.arch }}
build-args: |
${{ matrix.sub_routes.base_url && format('base_url={0}', matrix.sub_routes.base_url) || '' }}
labels: |
org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}
org.opencontainers.image.description=${{ matrix.sub_routes.repo }} image
outputs: type=image,"name=ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }},${{ secrets.ALI_IMAGE_NAME }}/${{ matrix.sub_routes.repo }},${{ secrets.DOCKER_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}",push-by-digest=true,push=true
cache-from: type=local,src=/tmp/.buildx-cache
cache-to: type=local,dest=/tmp/.buildx-cache
- name: Export digest
run: |
mkdir -p ${{ runner.temp }}/digests/${{ matrix.sub_routes.repo }}
digest="${{ steps.build.outputs.digest }}"
touch "${{ runner.temp }}/digests/${{ matrix.sub_routes.repo }}/${digest#sha256:}"
- name: Upload digest
uses: actions/upload-artifact@v4
with:
name: digests-${{ matrix.sub_routes.repo }}-${{ github.sha }}-${{ matrix.archs.arch }}
path: ${{ runner.temp }}/digests/${{ matrix.sub_routes.repo }}/*
if-no-files-found: error
retention-days: 1
release-fastgpt-images:
permissions:
packages: write
contents: read
attestations: write
id-token: write
needs: build-fastgpt-images
strategy:
matrix:
sub_routes:
- repo: fastgpt
- repo: fastgpt-sub-route
- repo: fastgpt-sub-route-gchat
runs-on: ubuntu-24.04
steps:
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Login to Ali Hub
uses: docker/login-action@v3
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Download digests
uses: actions/download-artifact@v4
with:
path: ${{ runner.temp }}/digests
pattern: digests-${{ matrix.sub_routes.repo }}-${{ github.sha }}-*
merge-multiple: true
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/${{ matrix.sub_routes.repo }}:latest" >> $GITHUB_ENV
fi
- name: Create manifest list and push
working-directory: ${{ runner.temp }}/digests
run: |
TAGS="$(echo -e "${Git_Tag}\n${Git_Latest}\n${Ali_Tag}\n${Ali_Latest}\n${Docker_Hub_Tag}\n${Docker_Hub_Latest}")"
for TAG in $TAGS; do
docker buildx imagetools create -t $TAG \
$(printf 'ghcr.io/${{ github.repository_owner }}/${{ matrix.sub_routes.repo }}@sha256:%s ' *)
sleep 5
done

View File

@@ -9,11 +9,6 @@ on:
- 'main'
jobs:
build-fastgpt-images:
permissions:
packages: write
contents: read
attestations: write
id-token: write
runs-on: ubuntu-20.04
if: github.repository != 'labring/FastGPT'
steps:
@@ -37,7 +32,7 @@ jobs:
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
password: ${{ secrets.GH_PAT }}
- name: Set DOCKER_REPO_TAGGED based on branch or tag
run: |
echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt:latest" >> $GITHUB_ENV

258
.github/workflows/fastgpt-image.yml vendored Normal file
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@@ -0,0 +1,258 @@
name: Build FastGPT images
on:
workflow_dispatch:
push:
paths:
- 'projects/app/**'
- 'packages/**'
tags:
- 'v*'
jobs:
build-fastgpt-images:
runs-on: ubuntu-20.04
steps:
# install env
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 1
- name: Install Dependencies
run: |
sudo apt update && sudo apt install -y nodejs npm
- name: Set up QEMU (optional)
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v2
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Login to Ali Hub
uses: docker/login-action@v2
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
# Set tag
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt:latest" >> $GITHUB_ENV
fi
- name: Build and publish image for main branch or tag push event
env:
DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
run: |
docker buildx build \
-f projects/app/Dockerfile \
--platform linux/amd64,linux/arm64 \
--label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/FastGPT" \
--label "org.opencontainers.image.description=fastgpt image" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${Git_Tag} \
-t ${Git_Latest} \
-t ${Ali_Tag} \
-t ${Ali_Latest} \
-t ${Docker_Hub_Tag} \
-t ${Docker_Hub_Latest} \
.
build-fastgpt-images-sub-route:
runs-on: ubuntu-20.04
steps:
# install env
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 1
- name: Install Dependencies
run: |
sudo apt update && sudo apt install -y nodejs npm
- name: Set up QEMU (optional)
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v2
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Login to Ali Hub
uses: docker/login-action@v2
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
# Set tag
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route:latest" >> $GITHUB_ENV
fi
- name: Build and publish image for main branch or tag push event
env:
DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
run: |
docker buildx build \
-f projects/app/Dockerfile \
--platform linux/amd64,linux/arm64 \
--build-arg base_url=/fastai \
--label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/FastGPT" \
--label "org.opencontainers.image.description=fastgpt image" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${Git_Tag} \
-t ${Git_Latest} \
-t ${Ali_Tag} \
-t ${Ali_Latest} \
-t ${Docker_Hub_Tag} \
-t ${Docker_Hub_Latest} \
.
build-fastgpt-images-sub-route-gchat:
runs-on: ubuntu-20.04
steps:
# install env
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 1
- name: Install Dependencies
run: |
sudo apt update && sudo apt install -y nodejs npm
- name: Set up QEMU (optional)
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v2
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Login to Ali Hub
uses: docker/login-action@v2
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
# Set tag
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route-gchat:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route-gchat:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route-gchat:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sub-route-gchat:latest" >> $GITHUB_ENV
fi
- name: Build and publish image for main branch or tag push event
env:
DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
run: |
docker buildx build \
-f projects/app/Dockerfile \
--platform linux/amd64,linux/arm64 \
--build-arg base_url=/gchat \
--label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/FastGPT" \
--label "org.opencontainers.image.description=fastgpt-sub-route-gchat image" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${Git_Tag} \
-t ${Git_Latest} \
-t ${Ali_Tag} \
-t ${Ali_Latest} \
-t ${Docker_Hub_Tag} \
-t ${Docker_Hub_Latest} \
.

View File

@@ -1,32 +0,0 @@
name: 'FastGPT-Test'
on:
pull_request:
workflow_dispatch:
jobs:
test:
runs-on: ubuntu-latest
permissions:
# Required to checkout the code
contents: read
# Required to put a comment into the pull-request
pull-requests: write
steps:
- uses: actions/checkout@v4
with:
ref: ${{ github.event.pull_request.head.ref }}
repository: ${{ github.event.pull_request.head.repo.full_name }}
- uses: pnpm/action-setup@v4
with:
version: 10
- name: 'Install Deps'
run: pnpm install
- name: 'Test'
run: pnpm run test
- name: 'Report Coverage'
# Set if: always() to also generate the report if tests are failing
# Only works if you set `reportOnFailure: true` in your vite config as specified above
if: always()
uses: davelosert/vitest-coverage-report-action@v2

View File

@@ -8,11 +8,6 @@ on:
jobs:
helm:
permissions:
packages: write
contents: read
attestations: write
id-token: write
runs-on: ubuntu-20.04
steps:
- name: Checkout
@@ -25,10 +20,10 @@ jobs:
run: echo "tag=$(git describe --tags)" >> $GITHUB_OUTPUT
- name: Release Helm
run: |
echo ${{ secrets.GITHUB_TOKEN }} | helm registry login ghcr.io -u ${{ github.repository_owner }} --password-stdin
echo ${{ secrets.GH_PAT }} | helm registry login ghcr.io -u ${{ github.repository_owner }} --password-stdin
export APP_VERSION=${{ steps.vars.outputs.tag }}
export HELM_VERSION=${{ steps.vars.outputs.tag }}
export HELM_REPO=ghcr.io/${{ github.repository_owner }}
helm dependency update deploy/helm/fastgpt
helm package deploy/helm/fastgpt --version ${HELM_VERSION}-helm --app-version ${APP_VERSION} -d bin
helm dependency update files/helm/fastgpt
helm package files/helm/fastgpt --version ${HELM_VERSION}-helm --app-version ${APP_VERSION} -d bin
helm push bin/fastgpt-${HELM_VERSION}-helm.tgz oci://${HELM_REPO}

View File

@@ -1,17 +1,13 @@
name: Preview FastGPT images
on:
pull_request_target:
paths:
- 'projects/app/**'
- 'packages/**'
workflow_dispatch:
jobs:
preview-fastgpt-images:
permissions:
contents: read
packages: write
attestations: write
id-token: write
pull-requests: write
runs-on: ubuntu-20.04
steps:
- name: Checkout
@@ -19,9 +15,8 @@ jobs:
with:
ref: ${{ github.event.pull_request.head.ref }}
repository: ${{ github.event.pull_request.head.repo.full_name }}
submodules: recursive # Fetch submodules
fetch-depth: 0 # Fetch all history for .GitInfo and .Lastmod
token: ${{ secrets.GITHUB_TOKEN }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
with:
@@ -33,18 +28,15 @@ jobs:
key: ${{ runner.os }}-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-buildx-
- name: Login to GitHub Container Registry
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
password: ${{ secrets.GH_PAT }}
- name: Set DOCKER_REPO_TAGGED based on branch or tag
run: |
echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt-pr:${{ github.event.pull_request.head.sha }}" >> $GITHUB_ENV
- name: Build image for PR
env:
DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
@@ -59,13 +51,31 @@ jobs:
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${DOCKER_REPO_TAGGED} \
.
- uses: actions/github-script@v7
# Add write md step after build
- name: Write md
run: |
echo "# 🤖 Generated by deploy action" > report.md
echo "📦 Preview Image: \`${DOCKER_REPO_TAGGED}\`" >> report.md
cat report.md
- name: Gh Rebot for Sealos
uses: labring/gh-rebot@v0.0.6
if: ${{ (github.event_name == 'pull_request_target') }}
with:
github-token: ${{secrets.GITHUB_TOKEN}}
script: |
github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: 'Preview Image: `${{ env.DOCKER_REPO_TAGGED }}`'
})
version: v0.0.6
env:
GH_TOKEN: '${{ secrets.GH_PAT }}'
SEALOS_TYPE: 'pr_comment'
SEALOS_FILENAME: 'report.md'
SEALOS_REPLACE_TAG: 'DEFAULT_REPLACE_DEPLOY'
helm-check:
runs-on: ubuntu-20.04
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Helm Check
run: |
helm dependency update files/helm/fastgpt
helm lint files/helm/fastgpt
helm package files/helm/fastgpt

View File

@@ -1,151 +0,0 @@
name: Build fastgpt-sandbox images
on:
workflow_dispatch:
push:
paths:
- 'projects/sandbox/**'
tags:
- 'v*'
jobs:
build-fastgpt-sandbox-images:
permissions:
packages: write
contents: read
attestations: write
id-token: write
strategy:
matrix:
include:
- arch: amd64
- arch: arm64
runs-on: ubuntu-24.04-arm
runs-on: ${{ matrix.runs-on || 'ubuntu-24.04' }}
steps:
# install env
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
driver-opts: network=host
- name: Cache Docker layers
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-sandbox-buildx-${{ github.sha }}
restore-keys: |
${{ runner.os }}-sandbox-buildx-
# login docker
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Login to Ali Hub
uses: docker/login-action@v3
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Build for ${{ matrix.arch }}
id: build
uses: docker/build-push-action@v6
with:
context: .
file: projects/sandbox/Dockerfile
platforms: linux/${{ matrix.arch }}
labels: |
org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/fastgpt-sandbox
org.opencontainers.image.description=fastgpt-sandbox image
outputs: type=image,"name=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox,${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox,${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox",push-by-digest=true,push=true
cache-from: type=local,src=/tmp/.buildx-cache
cache-to: type=local,dest=/tmp/.buildx-cache
- name: Export digest
run: |
mkdir -p ${{ runner.temp }}/digests
digest="${{ steps.build.outputs.digest }}"
touch "${{ runner.temp }}/digests/${digest#sha256:}"
- name: Upload digest
uses: actions/upload-artifact@v4
with:
name: digests-fastgpt-sandbox-${{ github.sha }}-${{ matrix.arch }}
path: ${{ runner.temp }}/digests/*
if-no-files-found: error
retention-days: 1
release-fastgpt-sandbox-images:
permissions:
packages: write
contents: read
attestations: write
id-token: write
needs: build-fastgpt-sandbox-images
runs-on: ubuntu-24.04
steps:
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Login to Ali Hub
uses: docker/login-action@v3
with:
registry: registry.cn-hangzhou.aliyuncs.com
username: ${{ secrets.ALI_HUB_USERNAME }}
password: ${{ secrets.ALI_HUB_PASSWORD }}
- name: Login to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Download digests
uses: actions/download-artifact@v4
with:
path: ${{ runner.temp }}/digests
pattern: digests-fastgpt-sandbox-${{ github.sha }}-*
merge-multiple: true
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Set image name and tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
else
echo "Git_Tag=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Git_Latest=ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Ali_Tag=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Ali_Latest=${{ secrets.ALI_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
echo "Docker_Hub_Tag=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:${{ github.ref_name }}" >> $GITHUB_ENV
echo "Docker_Hub_Latest=${{ secrets.DOCKER_IMAGE_NAME }}/fastgpt-sandbox:latest" >> $GITHUB_ENV
fi
- name: Create manifest list and push
working-directory: ${{ runner.temp }}/digests
run: |
TAGS="$(echo -e "${Git_Tag}\n${Git_Latest}\n${Ali_Tag}\n${Ali_Latest}\n${Docker_Hub_Tag}\n${Docker_Hub_Latest}")"
for TAG in $TAGS; do
docker buildx imagetools create -t $TAG \
$(printf 'ghcr.io/${{ github.repository_owner }}/fastgpt-sandbox@sha256:%s ' *)
sleep 5
done

View File

@@ -1,6 +1,6 @@
name: Sync images
on:
pull_request:
pull_request_target:
branches:
- main
paths:
@@ -15,6 +15,13 @@ jobs:
sync:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
if: ${{ (github.event_name == 'pull_request_target') }}
with:
ref: ${{ github.event.pull_request.head.ref }}
repository: ${{ github.event.pull_request.head.repo.full_name }}
- name: Checkout
uses: actions/checkout@v3
@@ -25,4 +32,4 @@ jobs:
CONFIG_PATH: .github/sync_imgs.yml
ORIGINAL_MESSAGE: true
SKIP_PR: true
COMMIT_EACH_FILE: false
COMMIT_EACH_FILE: false

1
.gitignore vendored
View File

@@ -44,4 +44,3 @@ files/helm/fastgpt/fastgpt-0.1.0.tgz
files/helm/fastgpt/charts/*.tgz
tmp/
coverage

View File

@@ -5,6 +5,4 @@ node_modules
docSite/
*.md
pnpm-lock.yaml
cl100l_base.ts
dict.json
cl100l_base.ts

View File

@@ -17,8 +17,15 @@ usageMatchRegex:
# you can ignore it and use your own matching rules as well
- "[^\\w\\d]t\\(['\"`]({key})['\"`]"
- "[^\\w\\d]commonT\\(['\"`]({key})['\"`]"
# 支持 appT("your.i18n.keys")
- "[^\\w\\d]appT\\(['\"`]({key})['\"`]"
# 支持 datasetT("your.i18n.keys")
- "[^\\w\\d]datasetT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]fileT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]publishT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]workflowT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]userT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]chatT\\(['\"`]({key})['\"`]"
- "[^\\w\\d]i18nT\\(['\"`]({key})['\"`]"
# A RegEx to set a custom scope range. This scope will be used as a prefix when detecting keys

39
.vscode/launch.json vendored
View File

@@ -1,39 +0,0 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "Next.js: debug server-side",
"type": "node-terminal",
"request": "launch",
"command": "pnpm run dev",
"cwd": "${workspaceFolder}/projects/app"
},
{
"name": "Next.js: debug client-side",
"type": "chrome",
"request": "launch",
"url": "http://localhost:3000"
},
{
"name": "Next.js: debug client-side (Edge)",
"type": "msedge",
"request": "launch",
"url": "http://localhost:3000"
},
{
"name": "Next.js: debug full stack",
"type": "node-terminal",
"request": "launch",
"command": "pnpm run dev",
"cwd": "${workspaceFolder}/projects/app",
"skipFiles": ["<node_internals>/**"],
"serverReadyAction": {
"action": "debugWithEdge",
"killOnServerStop": true,
"pattern": "- Local:.+(https?://.+)",
"uriFormat": "%s",
"webRoot": "${workspaceFolder}/projects/app"
}
}
]
}

View File

@@ -58,7 +58,7 @@
"body": [
"import '@/pages/api/__mocks__/base';",
"import { root } from '@/pages/api/__mocks__/db/init';",
"import { getTestRequest } from '@fastgpt/service/test/utils'; ;",
"import { getTestRequest } from '@/test/utils';",
"import { AppErrEnum } from '@fastgpt/global/common/error/code/app';",
"import handler from './demo';",
"",

View File

@@ -27,5 +27,7 @@
},
"markdown.copyFiles.destination": {
"/docSite/content/**/*": "${documentWorkspaceFolder}/docSite/assets/imgs/"
}
},
"markdown.copyFiles.overwriteBehavior": "nameIncrementally",
"markdown.copyFiles.transformPath": "const filename = uri.path.split('/').pop(); return `/imgs/${filename}`;"
}

View File

@@ -10,7 +10,7 @@
<a href="./README_ja.md">日语</a>
</p>
FastGPT 是一个 AI Agent 构建平台,提供开箱即用的数据处理、模型调用等能力同时可以通过 Flow 可视化进行工作流编排,从而实现复杂的应用场景!
FastGPT 是一个基于 LLM 大语言模型的知识库问答系统,提供开箱即用的数据处理、模型调用等能力同时可以通过 Flow 可视化进行工作流编排,从而实现复杂的问答场景!
</div>
@@ -83,7 +83,6 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
- [x] 统一查阅对话记录,并对数据进行标注
`6` 其他
- [x] 可视化模型配置。
- [x] 支持语音输入和输出 (可配置语音输入语音回答)
- [x] 模糊输入提示
- [x] 模板市场
@@ -114,6 +113,16 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
<img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
</a>
## 🏘️ 社区交流群
扫码加入飞书话题群:
![](https://oss.laf.run/otnvvf-imgs/fastgpt-feishu1.png)
<a href="#readme">
<img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
</a>
## 🏘️ 加入我们
我们正在寻找志同道合的小伙伴,加速 FastGPT 的发展。你可以通过 [FastGPT 2025 招聘](https://fael3z0zfze.feishu.cn/wiki/P7FOwEmPziVcaYkvVaacnVX1nvg)了解 FastGPT 的招聘信息。
@@ -123,26 +132,17 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
- [Laf3 分钟快速接入三方应用](https://github.com/labring/laf)
- [Sealos快速部署集群应用](https://github.com/labring/sealos)
- [One API多模型管理支持 Azure、文心一言等](https://github.com/songquanpeng/one-api)
- [TuShan5 分钟搭建后台管理系统](https://github.com/msgbyte/tushan)
<a href="#readme">
<img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
</a>
## 🌿 第三方生态
- [PPIO 派欧云:一键调用高性价比的开源模型 API 和 GPU 容器](https://ppinfra.com/user/register?invited_by=VITYVU&utm_source=github_fastgpt)
- [AI Proxy国内模型聚合服务](https://sealos.run/aiproxy/?k=fastgpt-github/)
- [SiliconCloud (硅基流动) —— 开源模型在线体验平台](https://cloud.siliconflow.cn/i/TR9Ym0c4)
- [COW 个人微信/企微机器人](https://doc.tryfastgpt.ai/docs/use-cases/external-integration/onwechat/)
<a href="#readme">
<img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
</a>
## 🏘️ 社区交流群
扫码加入飞书话题群:
![](https://oss.laf.run/otnvvf-imgs/fastgpt-feishu1.png)
- [SiliconCloud (硅基流动) —— 开源模型在线体验平台](https://cloud.siliconflow.cn/i/TR9Ym0c4)
<a href="#readme">
<img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">

View File

@@ -69,7 +69,7 @@ Project tech stack: NextJs + TS + ChakraUI + MongoDB + PostgreSQL (PG Vector plu
> When using [Sealos](https://sealos.io) services, there is no need to purchase servers or domain names. It supports high concurrency and dynamic scaling, and the database application uses the kubeblocks database, which far exceeds the simple Docker container deployment in terms of IO performance.
<div align="center">
[![](https://cdn.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt&uid=fnWRt09fZP)
[![](https://cdn.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt)
</div>
Give it a 2-4 minute wait after deployment as it sets up the database. Initially, it might be a too slow since we're using the basic settings.

View File

@@ -94,7 +94,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
- **⚡ デプロイ**
[![](https://cdn.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt&uid=fnWRt09fZP)
[![](https://cdn.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt)
デプロイ 後、データベースをセットアップするので、24分待 ってください。基本設定 を 使 っているので、最初 は 少 し 遅 いかもしれません。

View File

@@ -1,26 +0,0 @@
# 安全策略
## 漏洞报告
如果您发现了 FastGPT 的安全漏洞,请按照以下步骤进行报告:
1. **报告方式**
发送邮件至yujinlong@sealos.io
请备注版本以及您的 GitHub 账号
3. **响应时间**
- 我们会在 48 小时内确认收到您的报告
- 一般在 3 个工作日内给出初步评估结果
4. **漏洞处理流程**
- 确认漏洞:我们会验证漏洞的存在性和影响范围
- 修复开发:针对已确认的漏洞进行修复
- 版本发布:在下一个版本更新中发布安全补丁
- 公开披露:在修复完成后,我们会在更新日志中公布相关信息
5. **注意事项**
- 在漏洞未修复前,请勿公开披露漏洞详情
- 我们欢迎负责任的漏洞披露
- 对于重大贡献者,我们会在项目致谢名单中提及
感谢您为 FastGPT 的安全性做出贡献!

View File

@@ -1,202 +0,0 @@
# 数据库的默认账号和密码仅首次运行时设置有效
# 如果修改了账号密码,记得改数据库和项目连接参数,别只改一处~
# 该配置文件只是给快速启动,测试使用。正式使用,记得务必修改账号密码,以及调整合适的知识库参数,共享内存等。
# 如何无法访问 dockerhub 和 git可以用阿里云阿里云没有arm包
version: '3.3'
services:
# vector db
ob:
image: oceanbase/oceanbase-ce # docker hub
# image: quay.io/oceanbase/oceanbase-ce:4.3.5.1-101000042025031818 # 镜像
container_name: ob
restart: always
# ports: # 生产环境建议不要暴露
# - 2881:2881
networks:
- fastgpt
environment:
# 这里的配置只有首次运行生效。修改后,重启镜像是不会生效的。需要把持久化数据删除再重启,才有效果
- OB_SYS_PASSWORD=obsyspassword
# 不同于传统数据库OceanBase 数据库的账号包含更多字段,包括用户名、租户名和集群名。经典格式为“用户名@租户名#集群名”
# 比如用mysql客户端连接时根据本文件的默认配置应该指定 “-uroot@tenantname”
- OB_TENANT_NAME=tenantname
- OB_TENANT_PASSWORD=tenantpassword
# MODE分为MINI和NORMAL 后者会最大程度使用主机资源
- MODE=NORMAL
- OB_SERVER_IP=127.0.0.1
# 更多环境变量配置见oceanbase官方文档 https://www.oceanbase.com/docs/common-oceanbase-database-cn-1000000002013494
volumes:
- ./ob/data:/root/ob
- ./ob/config:/root/.obd/cluster
- ./init.sql:/root/boot/init.d/init.sql
healthcheck:
# obclient -h127.0.0.1 -P2881 -uroot@tenantname -ptenantpassword -e "SELECT 1;"
test: ["CMD-SHELL", "obclient -h$OB_SERVER_IP -P2881 -uroot@$OB_TENANT_NAME -p$OB_TENANT_PASSWORD -e \"SELECT 1;\""]
interval: 30s
timeout: 10s
retries: 1000
start_period: 10s
mongo:
image: mongo:5.0.18 # dockerhub
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/mongo:5.0.18 # 阿里云
# image: mongo:4.4.29 # cpu不支持AVX时候使用
container_name: mongo
restart: always
# ports:
# - 27017:27017
networks:
- fastgpt
command: mongod --keyFile /data/mongodb.key --replSet rs0
environment:
- MONGO_INITDB_ROOT_USERNAME=myusername
- MONGO_INITDB_ROOT_PASSWORD=mypassword
volumes:
- ./mongo/data:/data/db
entrypoint:
- bash
- -c
- |
openssl rand -base64 128 > /data/mongodb.key
chmod 400 /data/mongodb.key
chown 999:999 /data/mongodb.key
echo 'const isInited = rs.status().ok === 1
if(!isInited){
rs.initiate({
_id: "rs0",
members: [
{ _id: 0, host: "mongo:27017" }
]
})
}' > /data/initReplicaSet.js
# 启动MongoDB服务
exec docker-entrypoint.sh "$$@" &
# 等待MongoDB服务启动
until mongo -u myusername -p mypassword --authenticationDatabase admin --eval "print('waited for connection')"; do
echo "Waiting for MongoDB to start..."
sleep 2
done
# 执行初始化副本集的脚本
mongo -u myusername -p mypassword --authenticationDatabase admin /data/initReplicaSet.js
# 等待docker-entrypoint.sh脚本执行的MongoDB服务进程
wait $$!
# fastgpt
sandbox:
container_name: sandbox
image: ghcr.io/labring/fastgpt-sandbox:v4.9.3 # git
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.9.3 # 阿里云
networks:
- fastgpt
restart: always
fastgpt:
container_name: fastgpt
image: ghcr.io/labring/fastgpt:v4.9.3 # git
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.9.3 # 阿里云
ports:
- 3000:3000
networks:
- fastgpt
depends_on:
mongo:
condition: service_started
ob:
condition: service_healthy
sandbox:
condition: service_started
restart: always
environment:
# 前端外部可访问的地址,用于自动补全文件资源路径。例如 https:fastgpt.cn不能填 localhost。这个值可以不填不填则发给模型的图片会是一个相对路径而不是全路径模型可能伪造Host。
- FE_DOMAIN=
# root 密码,用户名为: root。如果需要修改 root 密码,直接修改这个环境变量,并重启即可。
- DEFAULT_ROOT_PSW=1234
# # AI Proxy 的地址,如果配了该地址,优先使用
# - AIPROXY_API_ENDPOINT=http://aiproxy:3000
# # AI Proxy 的 Admin Token与 AI Proxy 中的环境变量 ADMIN_KEY
# - AIPROXY_API_TOKEN=aiproxy
# 模型中转地址(如果用了 AI Proxy下面 2 个就不需要了,旧版 OneAPI 用户,使用下面的变量)
- # openai 基本地址,可用作中转。
- OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
- # OpenAI API Key
- CHAT_API_KEY=sk-8990fa15a34b464a805237cfe9561f11
# 数据库最大连接数
- DB_MAX_LINK=30
# 登录凭证密钥
- TOKEN_KEY=any
# root的密钥常用于升级时候的初始化请求
- ROOT_KEY=root_key
# 文件阅读加密
- FILE_TOKEN_KEY=filetoken
# MongoDB 连接参数. 用户名myusername,密码mypassword。
- MONGODB_URI=mongodb://myusername:mypassword@mongo:27017/fastgpt?authSource=admin
# OceanBase 向量库连接参数
- OCEANBASE_URL=mysql://root%40tenantname:tenantpassword@ob:2881/test
# sandbox 地址
- SANDBOX_URL=http://sandbox:3000
# 日志等级: debug, info, warn, error
- LOG_LEVEL=info
- STORE_LOG_LEVEL=warn
# 工作流最大运行次数
- WORKFLOW_MAX_RUN_TIMES=1000
# 批量执行节点,最大输入长度
- WORKFLOW_MAX_LOOP_TIMES=100
# 自定义跨域,不配置时,默认都允许跨域(多个域名通过逗号分割)
- ALLOWED_ORIGINS=
# 是否开启IP限制默认不开启
- USE_IP_LIMIT=false
volumes:
- ./config.json:/app/data/config.json
# AI Proxy
aiproxy:
image: ghcr.io/labring/aiproxy:v0.1.5
# image: registry.cn-hangzhou.aliyuncs.com/labring/aiproxy:v0.1.3 # 阿里云
container_name: aiproxy
restart: unless-stopped
depends_on:
aiproxy_pg:
condition: service_healthy
networks:
- fastgpt
environment:
# 对应 fastgpt 里的AIPROXY_API_TOKEN
- ADMIN_KEY=aiproxy
# 错误日志详情保存时间(小时)
- LOG_DETAIL_STORAGE_HOURS=1
# 数据库连接地址
- SQL_DSN=postgres://postgres:aiproxy@aiproxy_pg:5432/aiproxy
# 最大重试次数
- RETRY_TIMES=3
# 不需要计费
- BILLING_ENABLED=false
# 不需要严格检测模型
- DISABLE_MODEL_CONFIG=true
healthcheck:
test: ['CMD', 'curl', '-f', 'http://localhost:3000/api/status']
interval: 5s
timeout: 5s
retries: 10
aiproxy_pg:
image: pgvector/pgvector:0.8.0-pg15 # docker hub
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/pgvector:v0.8.0-pg15 # 阿里云
restart: unless-stopped
container_name: aiproxy_pg
volumes:
- ./aiproxy_pg:/var/lib/postgresql/data
networks:
- fastgpt
environment:
TZ: Asia/Shanghai
POSTGRES_USER: postgres
POSTGRES_DB: aiproxy
POSTGRES_PASSWORD: aiproxy
healthcheck:
test: ['CMD', 'pg_isready', '-U', 'postgres', '-d', 'aiproxy']
interval: 5s
timeout: 5s
retries: 10
networks:
fastgpt:

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@@ -1,2 +0,0 @@
ALTER SYSTEM SET ob_vector_memory_limit_percentage = 30;

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@@ -3,7 +3,7 @@ FROM hugomods/hugo:0.117.0 AS builder
WORKDIR /app
ADD ./docSite hugo
RUN cd /app/hugo && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
RUN cd /app/hugo && hugo mod get -u github.com/colinwilson/lotusdocs@6d0568e && hugo -v --minify
FROM fholzer/nginx-brotli:latest

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@@ -13,8 +13,8 @@ weight: 707
下面配置文件示例中包含了系统参数和各个模型配置:
## 4.8.20+ 版本新配置文件示例
> 从4.8.20版本开始,模型在页面中进行配置。
## 4.6.8+ 版本新配置文件示例
```json
{
"feConfigs": {
@@ -23,54 +23,8 @@ weight: 707
"systemEnv": {
"vectorMaxProcess": 15, // 向量处理线程数量
"qaMaxProcess": 15, // 问答拆分线程数量
"vlmMaxProcess": 15, // 图片理解模型最大处理进程
"tokenWorkers": 50, // Token 计算线程保持数,会持续占用内存,不能设置太大。
"hnswEfSearch": 100, // 向量搜索参数,仅对 PG 和 OB 生效。越大搜索越精确但是速度越慢。设置为100有99%+精度。
"customPdfParse": { // 4.9.0 新增配置
"url": "", // 自定义 PDF 解析服务地址
"key": "", // 自定义 PDF 解析服务密钥
"doc2xKey": "", // doc2x 服务密钥
"price": 0 // PDF 解析服务价格
}
"pgHNSWEfSearch": 100 // 向量搜索参数。越大搜索越精确但是速度越慢。设置为100有99%+精度。
}
}
```
## 自定义 PDF 解析配置
自定义 PDF 服务解析的优先级高于 Doc2x 服务,所以如果使用 Doc2x 服务,请勿配置自定义 PDF 服务。
### 使用 Sealos PDF 解析服务
#### 1. 申请 Sealos AI proxy API Key
[点击打开 Sealos Pdf parser 官网](https://hzh.sealos.run/?uid=fnWRt09fZP&openapp=system-aiproxy),并进行对应 API Key 的申请。
#### 2. 修改 FastGPT 配置文件
`systemEnv.customPdfParse.url`填写成`https://aiproxy.hzh.sealos.run/v1/parse/pdf?model=parse-pdf`
`systemEnv.customPdfParse.key`填写成在 Sealos AI proxy 中申请的 API Key。
![](/imgs/deployconfig-aiproxy.png)
### 使用 Doc2x 解析 PDF 文件
`Doc2x`是一个国内提供专业 PDF 解析。
#### 1. 申请 Doc2x 服务
[点击打开 Doc2x 官网](https://doc2x.noedgeai.com?inviteCode=9EACN2),并进行对应 API Key 的申请。
#### 2. 修改 FastGPT 配置文件
开源版用户在 `config.json` 文件中添加 `systemEnv.customPdfParse.doc2xKey` 配置,并填写上申请到的 API Key。并重启服务。
商业版用户在 Admin 后台根据表单指引填写 Doc2x 服务密钥。
#### 3. 开始使用
在知识库导入数据或应用文件上传配置中,可以勾选`PDF 增强解析`,则在对 PDF 解析时候,会使用 Doc2x 服务进行解析。
### 使用 Marker 解析 PDF 文件
[点击查看 Marker 接入教程](/docs/development/custom-models/marker)
```

View File

@@ -31,9 +31,9 @@ weight: 920
3 个模型代码分别为:
1. [https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-base](https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-base)
2. [https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-large](https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-large)
3. [https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-v2-m3](https://github.com/labring/FastGPT/tree/main/plugins/model/rerank-bge/bge-reranker-v2-m3)
1. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-base](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-base)
2. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-large](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-large)
3. [https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-v2-m3](https://github.com/labring/FastGPT/tree/main/python/bge-rerank/bge-reranker-v2-m3)
### 3. 安装依赖

View File

@@ -46,7 +46,7 @@ ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本,
### 源码部署
1. 根据上面的环境配置配置好环境,具体教程自行 GPT
2. 下载 [python 文件](https://github.com/labring/FastGPT/blob/main/plugins/model/llm-ChatGLM2/openai_api.py)
2. 下载 [python 文件](https://github.com/labring/FastGPT/blob/main/files/models/ChatGLM2/openai_api.py)
3. 在命令行输入命令 `pip install -r requirements.txt`
4. 打开你需要启动的 py 文件,在代码的 `verify_token` 方法中配置 token这里的 token 只是加一层验证,防止接口被人盗用;
5. 执行命令 `python openai_api.py --model_name 16`。这里的数字根据上面的配置进行选择。

View File

@@ -11,51 +11,39 @@ weight: 909
PDF 是一个相对复杂的文件格式,在 FastGPT 内置的 pdf 解析器中,依赖的是 pdfjs 库解析,该库基于逻辑解析,无法有效的理解复杂的 pdf 文件。所以我们在解析 pdf 时候,如果遇到图片、表格、公式等非简单文本内容,会发现解析效果不佳。
市面上目前有多种解析 PDF 的方法,比如使用 [Marker](https://github.com/VikParuchuri/marker),该项目使用了 Surya 模型,基于视觉解析,可以有效提取图片、表格、公式等复杂内容。
市面上目前有多种解析 PDF 的方法,比如使用 [Marker](https://github.com/VikParuchuri/marker),该项目使用了 Surya 模型,基于视觉解析,可以有效提取图片、表格、公式等复杂内容。为了可以让 Marker 快速接入 FastGPT我们做了一个自定义解析的拓展 Demo。
`FastGPT v4.9.0` 版本中,开源版用户可以在`config.json`文件中添加`systemEnv.customPdfParse`配置,来使用 Marker 解析 PDF 文件。商业版用户直接在 Admin 后台根据表单指引填写即可。需重新拉取 Marker 镜像,接口格式已变动。
在 FastGPT 4.8.15 版本中,你可以通过增加一个环境变量,来替换掉 FastGPT 系统内置解析器,实现自定义的文档解析服务。该功能只是 Demo 阶段,后期配置模式和交互规则会发生改动。
## 使用教程
### 1. 安装 Marker
### 1. 按照 Marker
参考文档 [Marker 安装教程](https://github.com/labring/FastGPT/tree/main/plugins/model/pdf-marker),安装 Marker 模型。封装的 API 已经适配了 FastGPT 自定义解析服务。
参考文档 [Marker 安装教程](https://github.com/labring/FastGPT/tree/main/python/pdf-marker),安装 Marker 模型。封装的 API 已经适配了 FastGPT 自定义解析服务。
这里介绍快速 Docker 安装的方法:
```dockerfile
docker pull crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:v0.2
docker run --gpus all -itd -p 7231:7232 --name model_pdf_v2 -e PROCESSES_PER_GPU="2" crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:v0.2
```
### 2. 添加 FastGPT 文件配置
```json
{
xxx
"systemEnv": {
xxx
"customPdfParse": {
"url": "http://xxxx.com/v2/parse/file", // 自定义 PDF 解析服务地址 marker v0.2
"key": "", // 自定义 PDF 解析服务密钥
"doc2xKey": "", // doc2x 服务密钥
"price": 0 // PDF 解析服务价格
}
}
}
docker pull crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:latest
docker run --gpus all -itd -p 7231:7231 --name model_pdf_v1 crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:latest
```
需要重启服务。
### 2. 添加 FastGPT 环境变量
```
CUSTOM_READ_FILE_URL=http://xxxx.com/v1/parse/file
CUSTOM_READ_FILE_EXTENSION=pdf
```
* CUSTOM_READ_FILE_URL - 自定义解析服务的地址, host改成解析服务的访问地址path 不能变动。
* CUSTOM_READ_FILE_EXTENSION - 支持的文件后缀,多个文件类型,可用逗号隔开。
### 3. 测试效果
通过知识库上传一个 pdf 文件,并勾选上 `PDF 增强解析`
![alt text](/imgs/marker2.png)
确认上传后,可以在日志中看到 LOG LOG_LEVEL需要设置 info 或者 debug
通过知识库上传一个 pdf 文件,并确认上传,可以在日志中看到 LOG LOG_LEVEL需要设置 info 或者 debug
```
[Info] 2024-12-05 15:04:42 Parsing files from an external service
[Info] 2024-12-05 15:04:42 Parsing files from an external service
[Info] 2024-12-05 15:07:08 Custom file parsing is complete, time: 1316ms
```
@@ -63,10 +51,6 @@ docker run --gpus all -itd -p 7231:7232 --name model_pdf_v2 -e PROCESSES_PER_GPU
![alt text](/imgs/image-10.png)
同样的,在应用中,你可以在文件上传配置里,勾选上 `PDF 增强解析`
![alt text](/imgs/marker3.png)
## 效果展示
@@ -79,25 +63,4 @@ docker run --gpus all -itd -p 7231:7232 --name model_pdf_v2 -e PROCESSES_PER_GPU
上图是分块后的结果,下图是 pdf 原文。整体图片、公式、表格都可以提取出来,效果还是杠杠的。
不过要注意的是,[Marker](https://github.com/VikParuchuri/marker) 的协议是`GPL-3.0 license`,请在遵守协议的前提下使用。
## 旧版 Marker 使用方法
FastGPT V4.9.0 版本之前,可以用以下方式,试用 Marker 解析服务。
安装和运行 Marker 服务:
```dockerfile
docker pull crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:v0.1
docker run --gpus all -itd -p 7231:7231 --name model_pdf_v1 -e PROCESSES_PER_GPU="2" crpi-h3snc261q1dosroc.cn-hangzhou.personal.cr.aliyuncs.com/marker11/marker_images:v0.1
```
并修改 FastGPT 环境变量:
```
CUSTOM_READ_FILE_URL=http://xxxx.com/v1/parse/file
CUSTOM_READ_FILE_EXTENSION=pdf
```
* CUSTOM_READ_FILE_URL - 自定义解析服务的地址, host改成解析服务的访问地址path 不能变动。
* CUSTOM_READ_FILE_EXTENSION - 支持的文件后缀,多个文件类型,可用逗号隔开。
不过要注意的是,[Marker](https://github.com/VikParuchuri/marker) 的协议是`GPL-3.0 license`,请在遵守协议的前提下使用。

View File

@@ -1,184 +0,0 @@
---
title: '使用 Ollama 接入本地模型 '
description: ' 采用 Ollama 部署自己的模型'
icon: 'api'
draft: false
toc: true
weight: 950
---
[Ollama](https://ollama.com/)是一个开源的AI大模型部署工具专注于简化大语言模型的部署和使用支持一键下载和运行各种大模型。
## 安装 Ollama
Ollama 本身支持多种安装方式,但是推荐使用 Docker 拉取镜像部署。如果是个人设备上安装了 Ollama 后续需要解决如何让 Docker 中 FastGPT 容器访问宿主机 Ollama的问题较为麻烦。
### Docker 安装(推荐)
你可以使用 Ollama 官方的 Docker 镜像来一键安装和启动 Ollama 服务(确保你的机器上已经安装了 Docker命令如下
```bash
docker pull ollama/ollama
docker run --rm -d --name ollama -p 11434:11434 ollama/ollama
```
如果你的 FastGPT 是在 Docker 中进行部署的,建议在拉取 Ollama 镜像时保证和 FastGPT 镜像处于同一网络,否则可能出现 FastGPT 无法访问的问题,命令如下:
```bash
docker run --rm -d --name ollama --network (你的 Fastgpt 容器所在网络) -p 11434:11434 ollama/ollama
```
### 主机安装
如果你不想使用 Docker ,也可以采用主机安装,以下是主机安装的一些方式。
#### MacOS
如果你使用的是 macOS且系统中已经安装了 Homebrew 包管理器,可通过以下命令来安装 Ollama
```bash
brew install ollama
ollama serve #安装完成后,使用该命令启动服务
```
#### Linux
在 Linux 系统上,你可以借助包管理器来安装 Ollama。以 Ubuntu 为例,在终端执行以下命令:
```bash
curl https://ollama.com/install.sh | sh #此命令会从官方网站下载并执行安装脚本。
ollama serve #安装完成后,同样启动服务
```
#### Windows
在 Windows 系统中,你可以从 Ollama 官方网站 下载 Windows 版本的安装程序。下载完成后,运行安装程序,按照安装向导的提示完成安装。安装完成后,在命令提示符或 PowerShell 中启动服务:
```bash
ollama serve #安装完成并启动服务后,你可以在浏览器中访问 http://localhost:11434 来验证 Ollama 是否安装成功。
```
#### 补充说明
如果你是采用的主机应用 Ollama 而不是镜像,需要确保你的 Ollama 可以监听0.0.0.0。
##### 1. Linxu 系统
如果 Ollama 作为 systemd 服务运行,打开终端,编辑 Ollama 的 systemd 服务文件使用命令sudo systemctl edit ollama.service在[Service]部分添加Environment="OLLAMA_HOST=0.0.0.0"。保存并退出编辑器然后执行sudo systemctl daemon - reload和sudo systemctl restart ollama使配置生效。
##### 2. MacOS 系统
打开终端使用launchctl setenv ollama_host "0.0.0.0"命令设置环境变量,然后重启 Ollama 应用程序以使更改生效。
##### 3. Windows 系统
通过 “开始” 菜单或搜索栏打开 “编辑系统环境变量”,在 “系统属性” 窗口中点击 “环境变量”,在 “系统变量” 部分点击 “新建”创建一个名为OLLAMA_HOST的变量变量值设置为0.0.0.0,点击 “确定” 保存更改,最后从 “开始” 菜单重启 Ollama 应用程序。
### Ollama 拉取模型镜像
在安装后 Ollama 后,本地是没有模型镜像的,需要自己去拉取 Ollama 中的模型镜像。命令如下:
```bash
# Docker 部署需要先进容器,命令为: docker exec -it < Ollama 容器名 > /bin/sh
ollama pull <模型名>
```
![](/imgs/Ollama-pull.png)
### 测试通信
在安装完成后,需要进行检测测试,首先进入 FastGPT 所在的容器,尝试访问自己的 Ollama ,命令如下:
```bash
docker exec -it < FastGPT 所在的容器名 > /bin/sh
curl http://XXX.XXX.XXX.XXX:11434 #容器部署地址为“http://<容器名>:<端口>”,主机安装地址为"http://<主机IP>:<端口>"主机IP不可为localhost
```
看到访问显示自己的 Ollama 服务以及启动,说明可以正常通信。
## 将 Ollama 接入 FastGPT
### 1. 查看 Ollama 所拥有的模型
首先采用下述命令查看 Ollama 中所拥有的模型,
```bash
# Docker 部署 Ollama需要此命令 docker exec -it < Ollama 容器名 > /bin/sh
ollama ls
```
![](/imgs/Ollama-models1.png)
### 2. AI Proxy 接入
如果你采用的是 FastGPT 中的默认配置文件部署[这里](/docs/development/docker.md),即默认采用 AI Proxy 进行启动。
![](/imgs/Ollama-aiproxy1.png)
以及在确保你的 FastGPT 可以直接访问 Ollama 容器的情况下,无法访问,参考上文[点此跳转](#安装-ollama)的安装过程检测是不是主机不能监测0.0.0.0,或者容器不在同一个网络。
![](/imgs/Ollama-aiproxy2.png)
在 FastGPT 中点击账号->模型提供商->模型配置->新增模型添加自己的模型即可添加模型时需要保证模型ID和 OneAPI 中的模型名称一致。详细参考[这里](/docs/development/modelConfig/intro.md)
![](/imgs/Ollama-models2.png)
![](/imgs/Ollama-models3.png)
运行 FastGPT ,在页面中选择账号->模型提供商->模型渠道->新增渠道。之后,在渠道选择中选择 Ollama ,然后加入自己拉取的模型,填入代理地址,如果是容器中安装 Ollama 代理地址为http://地址:端口补充容器部署地址为“http://<容器名>:<端口>”,主机安装地址为"http://<主机IP>:<端口>"主机IP不可为localhost
![](/imgs/Ollama-aiproxy3.png)
在工作台中创建一个应用,选择自己之前添加的模型,此处模型名称为自己当时设置的别名。注:同一个模型无法多次添加,系统会采取最新添加时设置的别名。
![](/imgs/Ollama-models4.png)
### 3. OneAPI 接入
如果你想使用 OneAPI ,首先需要拉取 OneAPI 镜像,然后将其在 FastGPT 容器的网络中运行。具体命令如下:
```bash
# 拉取 oneAPI 镜像
docker pull intel/oneapi-hpckit
# 运行容器并指定自定义网络和容器名
docker run -it --network < FastGPT 网络 > --name 容器名 intel/oneapi-hpckit /bin/bash
```
进入 OneAPI 页面,添加新的渠道,类型选择 Ollama ,在模型中填入自己 Ollama 中的模型,需要保证添加的模型名称和 Ollama 中一致,再在下方填入自己的 Ollama 代理地址默认http://地址:端口,不需要填写/v1。添加成功后在 OneAPI 进行渠道测试,测试成功则说明添加成功。此处演示采用的是 Docker 部署 Ollama 的效果,主机 Ollama需要修改代理地址为http://<主机IP>:<端口>
![](/imgs/Ollama-oneapi1.png)
渠道添加成功后,点击令牌,点击添加令牌,填写名称,修改配置。
![](/imgs/Ollama-oneapi2.png)
修改部署 FastGPT 的 docker-compose.yml 文件,在其中将 AI Proxy 的使用注释,在 OPENAI_BASE_URL 中加入自己的 OneAPI 开放地址默认是http://地址:端口/v1v1必须填写。KEY 中填写自己在 OneAPI 的令牌。
![](/imgs/Ollama-oneapi3.png)
[直接跳转5](#5-模型添加和使用)添加模型,并使用。
### 4. 直接接入
如果你既不想使用 AI Proxy也不想使用 OneAPI也可以选择直接接入修改部署 FastGPT 的 docker-compose.yml 文件,在其中将 AI Proxy 的使用注释,采用和 OneAPI 的类似配置。注释掉 AIProxy 相关代码在OPENAI_BASE_URL中加入自己的 Ollama 开放地址默认是http://地址:端口/v1强调:v1必须填写。在KEY中随便填入因为 Ollama 默认没有鉴权,如果开启鉴权,请自行填写。其他操作和在 OneAPI 中加入 Ollama 一致,只需在 FastGPT 中加入自己的模型即可使用。此处演示采用的是 Docker 部署 Ollama 的效果,主机 Ollama需要修改代理地址为http://<主机IP>:<端口>
![](/imgs/Ollama-direct1.png)
完成后[点击这里](#5-模型添加和使用)进行模型添加并使用。
### 5. 模型添加和使用
在 FastGPT 中点击账号->模型提供商->模型配置->新增模型添加自己的模型即可添加模型时需要保证模型ID和 OneAPI 中的模型名称一致。
![](/imgs/Ollama-models2.png)
![](/imgs/Ollama-models3.png)
在工作台中创建一个应用,选择自己之前添加的模型,此处模型名称为自己当时设置的别名。注:同一个模型无法多次添加,系统会采取最新添加时设置的别名。
![](/imgs/Ollama-models4.png)
### 6. 补充
上述接入 Ollama 的代理地址中,主机安装 Ollama 的地址为“http://<主机IP>:<端口>”,容器部署 Ollama 地址为“http://<容器名>:<端口>”

View File

@@ -11,7 +11,7 @@ weight: 707
1. 基础的网络知识:端口,防火墙……
2. Docker 和 Docker Compose 基础知识
3. 大模型相关接口和参数
3. 大模型相关接口和参数
4. RAG 相关知识:向量模型,向量数据库,向量检索
## 部署架构图
@@ -30,7 +30,7 @@ weight: 707
### PgVector版本
非常轻量,适合知识库索引量在 5000 万以下。
非常轻量,适合数据量在 5000 万以下。
{{< table "table-hover table-striped-columns" >}}
| 环境 | 最低配置(单节点) | 推荐配置 |
@@ -56,7 +56,7 @@ weight: 707
### zilliz cloud版本
Zilliz Cloud 由 Milvus 原厂打造,是全托管的 SaaS 向量数据库服务,性能优于 Milvus 并提供 SLA点击使用 [Zilliz Cloud](https://zilliz.com.cn/)。
Milvus 的全托管服务,性能优于 Milvus 并提供 SLA点击使用 [Zilliz Cloud](https://zilliz.com.cn/)。
由于向量库使用了 Cloud无需占用本地资源无需太关注。
@@ -118,7 +118,7 @@ brew install orbstack
非 Linux 环境或无法访问外网环境,可手动创建一个目录,并下载配置文件和对应版本的`docker-compose.yml`在这个文件夹中依据下载的配置文件运行docker若作为本地开发使用推荐`docker-compose-pgvector`版本,并且自行拉取并运行`sandbox``fastgpt`并在docker配置文件中注释掉`sandbox``fastgpt`的部分
- [config.json](https://raw.githubusercontent.com/labring/FastGPT/refs/heads/main/projects/app/data/config.json)
- [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/deploy/docker) (注意,不同向量库版本的文件不一样)
- [docker-compose.yml](https://github.com/labring/FastGPT/blob/main/files/docker) (注意,不同向量库版本的文件不一样)
{{% alert icon="🤖" context="success" %}}
@@ -134,14 +134,11 @@ cd fastgpt
curl -O https://raw.githubusercontent.com/labring/FastGPT/main/projects/app/data/config.json
# pgvector 版本(测试推荐,简单快捷)
curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-pgvector.yml
# oceanbase 版本需要将init.sql和docker-compose.yml放在同一个文件夹方便挂载
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-oceanbase/docker-compose.yml
# curl -o init.sql https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-oceanbase/init.sql
curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-pgvector.yml
# milvus 版本
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-milvus.yml
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-milvus.yml
# zilliz 版本
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/deploy/docker/docker-compose-zilliz.yml
# curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/main/files/docker/docker-compose-zilliz.yml
```
### 2. 修改环境变量
@@ -152,21 +149,18 @@ curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/mai
{{< tab tabName="PgVector版本" >}}
{{< markdownify >}}
无需操作
{{< /markdownify >}}
{{< /tab >}}
{{< tab tabName="Oceanbase版本" >}}
{{< markdownify >}}
无需操作
```
FE_DOMAIN=你的前端你访问地址,例如 http://192.168.0.1:3000;https://cloud.fastgpt.cn
```
{{< /markdownify >}}
{{< /tab >}}
{{< tab tabName="Milvus版本" >}}
{{< markdownify >}}
无需操作
```
FE_DOMAIN=你的前端你访问地址,例如 http://192.168.0.1:3000;https://cloud.fastgpt.cn
```
{{< /markdownify >}}
{{< /tab >}}
@@ -180,6 +174,7 @@ curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/mai
{{% alert icon="🤖" context="success" %}}
1. 修改`MILVUS_ADDRESS``MILVUS_TOKEN`链接参数,分别对应 `zilliz``Public Endpoint``Api key`记得把自己ip加入白名单。
2. 修改FE_DOMAIN=你的前端你访问地址,例如 http://192.168.0.1:3000;https://cloud.fastgpt.cn
{{% /alert %}}
@@ -194,28 +189,32 @@ curl -o docker-compose.yml https://raw.githubusercontent.com/labring/FastGPT/mai
```bash
# 启动容器
docker-compose up -d
# 等待10sOneAPI第一次总是要重启几次才能连上Mysql
sleep 10
# 重启一次oneapi(由于OneAPI的默认Key有点问题不重启的话会提示找不到渠道临时手动重启一次解决等待作者修复)
docker restart oneapi
```
### 4. 访问 FastGPT
### 4. 打开 OneAPI 添加模型
目前可以通过 `ip:3000` 直接访问(注意开放防火墙)。登录用户名为 `root`密码为`docker-compose.yml`环境变量里设置的 `DEFAULT_ROOT_PSW`
可以通过`ip:3001`访问OneAPI默认账号为`root`密码为`123456`
在OneApi中添加合适的AI模型渠道。[点击查看相关教程](/docs/development/modelconfig/one-api/)
### 5. 访问 FastGPT
目前可以通过 `ip:3000` 直接访问(注意防火墙)。登录用户名为 `root`,密码为`docker-compose.yml`环境变量里设置的 `DEFAULT_ROOT_PSW`
如果需要域名访问,请自行安装并配置 Nginx。
首次运行,会自动初始化 root 用户,密码为 `1234`(与环境变量中的`DEFAULT_ROOT_PSW`一致),日志可能会提示一次`MongoServerError: Unable to read from a snapshot due to pending collection catalog changes;`可忽略。
首次运行,会自动初始化 root 用户,密码为 `1234`(与环境变量中的`DEFAULT_ROOT_PSW`一致),日志会提示一次`MongoServerError: Unable to read from a snapshot due to pending collection catalog changes;`可忽略。
### 5. 配置模型
### 6. 配置模型
- 首次登录FastGPT后系统会提示未配置`语言模型``索引模型`,并自动跳转模型配置页面。系统必须至少有这两类模型才能正常使用。
- 如果系统未正常跳转,可以在`账号-模型提供商`页面,进行模型配置。[点击查看相关教程](/docs/development/modelconfig/ai-proxy)
- 目前已知可能问题:首次进入系统后,整个浏览器 tab 无法响应。此时需要删除该tab重新打开一次即可。
[点击查看模型配置教程](/docs/development/modelConfig/intro/)
## FAQ
### 登录系统后,浏览器无法响应
无法点击任何内容刷新也无效。此时需要删除该tab重新打开一次即可。
### Mongo 副本集自动初始化失败
最新的 docker-compose 示例优化 Mongo 副本集初始化,实现了全自动。目前在 unbuntu20,22 centos7, wsl2, mac, window 均通过测试。仍无法正常启动,大部分是因为 cpu 不支持 AVX 指令集,可以切换 Mongo4.x 版本。

View File

@@ -9,31 +9,17 @@ images: []
## 一、错误排查方式
可以先找找[Issue](https://github.com/labring/FastGPT/issues),或新提 Issue私有部署错误务必提供详细的操作步骤、日志、截图否则很难排查。
### 获取后端错误
遇到问题先按下面方式排查。
1. `docker ps -a` 查看所有容器运行状态,检查是否全部 running如有异常尝试`docker logs 容器名`查看对应日志。
2. 容器都运行正常的,`docker logs 容器名` 查看报错日志
3. 带有`requestId`的,都是 OneAPI 提示错误,大部分都是因为模型接口报错。
4. 无法解决时,可以找找[Issue](https://github.com/labring/FastGPT/issues),或新提 Issue私有部署错误务必提供详细的日志否则很难排查。
### 前端错误
前端报错时,页面会出现崩溃,并提示检查控制台日志。可以打开浏览器控制台,并查看`console`中的 log 日志。还可以点击对应 log 的超链接,会提示到具体错误文件,可以把这些详细错误信息提供,方便排查。
### OneAPI 错误
带有`requestId`的,都是 OneAPI 提示错误,大部分都是因为模型接口报错。可以参考 [OneAPI 常见错误](/docs/development/faq/#三常见的-oneapi-错误)
## 二、通用问题
### 前端页面崩溃
1. 90% 情况是模型配置不正确:确保每类模型都至少有一个启用;检查模型中一些`对象`参数是否异常(数组和对象),如果为空,可以尝试给个空数组或空对象。
2. 少部分是由于浏览器兼容问题,由于项目中包含一些高阶语法,可能低版本浏览器不兼容,可以将具体操作步骤和控制台中错误信息提供 issue。
3. 关闭浏览器翻译功能,如果浏览器开启了翻译,可能会导致页面崩溃。
### 通过sealos部署的话是否没有本地部署的一些限制
![](/imgs/faq1.png)
这是索引模型的长度限制,通过任何方式部署都一样的,但不同索引模型的配置不一样,可以在后台修改参数。
@@ -142,13 +128,9 @@ OneAPI 的 API Key 配置错误,需要修改`OPENAI_API_KEY`环境变量,并
3. ....
### Tiktoken 下载失败
由于 OneAPI 会在启动时从网络下载一个 tiktoken 的依赖,如果网络异常,就会导致启动失败。可以参考[OneAPI 离线部署](https://blog.csdn.net/wanh/article/details/139039216)解决。
## 四、常见模型问题
### 如何检查模型可用性问题
### 如何检查模型问题
1. 私有部署模型,先确认部署的模型是否正常。
2. 通过 CURL 请求,直接测试上游模型是否正常运行(云端模型或私有模型均进行测试)
@@ -421,7 +403,3 @@ curl --location --request POST 'https://oneapi.xxxx/v1/chat/completions' \
"tool_choice": "auto"
}'
```
### 向量检索得分大于 1
由于模型没有归一化导致的。目前仅支持归一化的模型。

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