Install AskGPT Agent
Created：2024-05-30 Last Modified：2025-08-29
This document was translated by ChatGPT
#1. Prerequisites
When deploying DeepFlow, the AI component is not enabled by default. You need to manually add the AI component configuration in the values-custom.yaml file:
stella-agent-ce:
enabled: true
replicas: 1
hostNetwork: 'false'
dnsPolicy: ClusterFirst
imagePullSecrets: []
nameOverride: ''
fullnameOverride: ''
podAnnotations: {}
image:
repository: '{{ .Values.global.image.repository }}/deepflowio-stella-agent-ce'
pullPolicy: Always
# Overrides the image tag whose default is the chart appVersion.
tag: latest
podSecurityContext:
{}
# fsGroup: 2000
securityContext:
# privileged: true
# capabilities:
# drop:
# - ALL
# readOnlyRootFilesystem: false
# runAsNonRoot: false
# runAsUser: 0
service:
## Configuration for ClickHouse service
annotations: {}
labels: {}
clusterIP: ''
## Port for ClickHouse Service to listen on
ports:
- name: tcp
port: 20831
targetPort: 20831
nodePort:
protocol: TCP
# Additional ports to open for server service
additionalPorts: []
externalIPs: []
loadBalancerIP: ''
loadBalancerSourceRanges: []
## Denotes if this Service desires to route external traffic to node-local or cluster-wide endpoints
externalTrafficPolicy: Cluster
type: ClusterIP
readinessProbe:
httpGet:
path: /v1/health/
port: http
failureThreshold: 10
initialDelaySeconds: 15
periodSeconds: 10
successThreshold: 1
livenessProbe:
failureThreshold: 6
initialDelaySeconds: 15
periodSeconds: 20
successThreshold: 1
httpGet:
path: /v1/health/
port: http
timeoutSeconds: 1
configmap:
df-llm-agent.yaml:
daemon: true
api_timeout: 500
sql_show: 'false'
log_file: '/var/log/df-llm-agent.log'
log_level: 'info'
instance_path: '/root/df-llm-agent'
mysql:
host: '{{ if $.Values.global.externalMySQL.enabled }}{{$.Values.global.externalMySQL.ip}}{{ else }}{{ $.Release.Name }}-mysql{{end}}'
port: '{{ if $.Values.global.externalMySQL.enabled }}{{$.Values.global.externalMySQL.port}}{{ else }}30130{{end}}'
user_name: '{{ if $.Values.global.externalMySQL.enabled }}{{$.Values.global.externalMySQL.username}}{{ else }}root{{end}}'
user_password: '{{ if $.Values.global.externalMySQL.enabled }}{{$.Values.global.externalMySQL.password}}{{ else }}{{ .Values.global.password.mysql }}{{end}}'
database: 'deepflow_llm'
resources:
{}
# limits:
# cpu: 100m
# memory: 128Mi
# requests:
# cpu: 100m
# memory: 128Mi
nodeSelector: {}
tolerations: []
podAntiAffinityLabelSelector: []
podAntiAffinityTermLabelSelector: []
podAffinityLabelSelector: []
podAffinityTermLabelSelector: []
nodeAffinityLabelSelector:
[]
# - matchExpressions:
# - key: kubernetes.io/hostname
# operator: In
# values: controller
nodeAffinityTermLabelSelector: []
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#2. Configure Conversation Models
Currently, the service supports the following models, which can be enabled as needed via values-custom.yaml:
stella-agent-ce:
configmap:
df-llm-agent.yaml:
ai:
enable: False
platforms:
- platform: 'azure' # https://learn.microsoft.com/zh-cn/azure/ai-services/openai/
enable: False
model: 'gpt'
api_type: 'azure'
api_key: 'xxx' # FIXME
api_base: 'xxx' # FIXME
api_version: 'xxx' # FIXME
engine_name:
- 'xxx' # FIXME (model deployment name)
- platform: 'aliyun' # https://help.aliyun.com/zh/dashscope/create-a-chat-foundation-model
enable: False
model: 'dashscope'
api_key: 'xxx' # FIXME
engine_name:
- 'qwen-turbo'
- 'qwen-plus'
- platform: 'baidu' # https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html
enable: False
model: 'qianfan'
api_key: 'xxx' # FIXME
api_secret: 'xxx' # FIXME
engine_name:
- 'ERNIE-Bot'
- 'ERNIE-Bot-turbo'
- platform: 'zhipu' # https://open.bigmodel.cn/
enable: False
model: 'zhipuai'
api_key: 'xxx' # FIXME
engine_name:
- 'chatglm_turbo'
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Update DeepFlow:
helm upgrade deepflow -n deepflow -f values-custom.yaml deepflow/deepflow
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#3. Using in Grafana
The AI model interpretation feature (alpha version) is currently available in the DeepFlow Topo Panel and DeepFlow Tracing Panel:
DeepFlow Topo Panel
DeepFlow Tracing Panel
#4. Using via API
Method: POST
URL:
- http[s]:😕/{ip}:{port}/v1/ai/stream/{platform_name}?engine={engine_name}
- Parameter description:
- ip/port: The K8s Service of stella-agent, default port is 30831.
- platform_name: The name of the platform where the model is located, e.g., azure.
- engine_name: The engine name, e.g., DF-GPT4-32K.
Header:
content-type: application/json
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Request JSON Body:
{
"system_content": "You are an expert in web technologies", # Model role definition
"user_content": "How to optimize a web page" # Question description
}
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Response:
Web page optimization refers to various methods to improve the performance of a web page, including load speed, response time, and user experience.
Web page optimization is a complex and comprehensive task involving multiple aspects, including frontend, backend, and network. It requires analysis and optimization based on the specific situation.
Here are some common web page optimization methods:
Frontend Optimization
Reduce HTTP requests: Reducing HTTP requests can decrease network latency and browser parsing time. This can be achieved by:
Merging CSS and JavaScript files.
Using sprite images.
Implementing lazy loading.
Optimize resources: Optimizing resources can reduce their size, thus improving load speed. This can be done by:
Compressing images.
Using appropriate image formats.
Minifying CSS and JavaScript files.
Use caching effectively: Caching can reduce repeated network requests, thus improving load speed. This can be done by:
Setting appropriate cache headers.
Using browser caching.
Optimize DOM structure: A well-structured DOM can reduce browser parsing and rendering time. This can be done by:
Reducing the number and depth of DOM elements.
Avoiding complex CSS selectors.
Optimize JavaScript code: Optimizing JavaScript can improve execution efficiency. This can be done by:
Avoiding global variables.
Using caching.
Using appropriate algorithms and data structures.
Backend Optimization
Optimize database queries: Optimizing database queries can reduce the load on the database server, thus improving page response speed. This can be done by:
Using appropriate indexes.
Avoiding unnecessary queries.
Using caching.
Optimize application servers: Optimizing application servers can improve their performance. This can be done by:
Using appropriate load balancing strategies.
Using caching.
Optimizing code.
Network Optimization
Choose an appropriate CDN: A CDN can distribute content to servers closer to users, reducing network latency.
Optimize DNS resolution: Optimizing DNS resolution can improve DNS lookup speed. This can be done by:
Using a CDN.
Configuring DNS records.
Use Gzip compression: Gzip compression can reduce the amount of data transmitted, thus improving load speed.
Tools
Various tools can be used to test and analyze web page performance, such as:
Google PageSpeed Insights
Lighthouse
WebPageTest
By using these tools, you can identify performance bottlenecks in web pages and perform corresponding optimizations.
Web page optimization is an ongoing process that requires continuous testing and optimization to achieve the best performance.
Here are some additional suggestions:
Always consider performance when developing web pages.
Use performance testing tools to test web page performance.
Monitor web page performance and optimize regularly.
I hope this information is helpful to you.
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