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Sample App

This sample application takes minimal configuration to get you started on your LangStream journey.
  1. 1.
    Complete the LangStream installation steps in Get Started.​
  2. 2.
    Pass your OpenAI credentials as an ENV variable:
export OPEN_AI_ACCESS_KEY=xxxxx
  1. 3.
    Create a project folder in examples/applications:
mkdir sample-app && cd sample-app
touch secrets.yaml
mkdir application && cd application
touch chatbot.yaml gateways.yaml configuration.yaml
It should look something like this:
|- project-folder
|- application
|- chatbot.yaml
|- gateways.yaml
|- configuration.yaml
|- secrets.yaml
  1. 4.
    Populate the yaml files:
configuration.yaml contains information about external services (in this case the OpenAI API).
configuration:
resources:
- type: open-ai-configuration
name: OpenAI Azure configuration
configuration:
access-key: "${ secrets.open-ai.access-key }"
provider: openai
secrets.yaml contains the definition of secrets used by your application.
secrets:
- id: open-ai
data:
access-key: "${OPEN_AI_ACCESS_KEY:-}"
gateways.yaml contains API gateways for communicating with your application.
gateways:
- id: produce-input
type: produce
topic: input-topic
parameters:
- sessionId
produceOptions:
headers:
- key: langstream-client-session-id
valueFromParameters: sessionId
​
- id: consume-output
type: consume
topic: output-topic
parameters:
- sessionId
consumeOptions:
filters:
headers:
- key: langstream-client-session-id
valueFromParameters: sessionId
chatbot.yaml contains the chain of agents that makes up your program, and the input and output topics that they communicate with.
topics:
- name: "input-topic"
creation-mode: create-if-not-exists
- name: "output-topic"
creation-mode: create-if-not-exists
pipeline:
- name: "ai-chat-completions"
type: "ai-chat-completions"
input: "input-topic"
output: "output-topic"
errors:
on-failure: skip
configuration:
model: "gpt-3.5-turbo"
completion-field: "value"
messages:
- role: user
content: "What can you tell me about {{ value}} ?"
You may notice that this "langstream docker run" command doesn't reference an instance.yaml file to define the application's runtime environment. Instead, "langstream docker run" uses a default instance.yaml with a Kafka broker inside the Docker container. This default configuration is:
instance:
streamingCluster:
type: "kafka"
configuration:
admin:
bootstrap.servers: localhost:9092
Save all your files.
  1. 5.
    Deploy your application from project-folder, here we're calling the deployed application sample-app:
langstream docker run sample-app -app ./application -s ./secrets.yaml
  1. 6.
    You should see a docker container starting and then running the application. To ensure your app is running, open a new terminal an inspect the status of the application:
docker ps
Result:
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
421bb7e082bb ghcr.io/langstream/langstream-runtime-tester:0.0.21 "/app/entrypoint.sh" 2 minutes ago Up 2 minutes 0.0.0.0:8090-8091->8090-8091/tcp
​
And you can use the CLI to inspect the status of the application:
langstream apps get sample-app
Result:
ID STREAMING COMPUTE STATUS EXECUTORS REPLICAS
sample-app kafka kubernetes DEPLOYED 1/1 1/1
For the LangStream CLI the application appears to be running on "kubernetes", even if you are using the docker mode, this is because the docker container emulates partially the Kubernetes environment.
langstream apps get sample-app -o yaml
Result:
---
application-id: "sample-app"
application:
resources:
OpenAI Azure configuration:
id: null
name: "OpenAI Azure configuration"
type: "open-ai-configuration"
configuration:
access-key: "${ secrets.open-ai.access-key }"
provider: "openai"
modules:
- id: "default"
pipelines:
- id: "chatbot"
module: "default"
name: null
resources:
parallelism: 1
size: 1
errors:
retries: 0
on-failure: "fail"
agents:
- id: "chatbot-ai-chat-completions-1"
name: "ai-chat-completions"
type: "ai-chat-completions"
input:
connectionType: "TOPIC"
definition: "input-topic"
enableDeadletterQueue: false
output:
connectionType: "TOPIC"
definition: "output-topic"
enableDeadletterQueue: false
configuration:
completion-field: "value"
messages:
- content: "What can you tell me about {{ value}} ?"
role: "user"
model: "gpt-35-turbo"
resources:
parallelism: 1
size: 1
errors:
retries: 0
on-failure: "fail"
topics:
- name: "output-topic"
config: null
options: null
keySchema: null
valueSchema: null
partitions: 0
implicit: false
creation-mode: "create-if-not-exists"
deletion-mode: "none"
- name: "input-topic"
config: null
options: null
keySchema: null
valueSchema: null
partitions: 0
implicit: false
creation-mode: "create-if-not-exists"
deletion-mode: "none"
gateways:
gateways:
- id: "produce-input"
type: "produce"
topic: "input-topic"
authentication: null
parameters:
- "sessionId"
produceOptions:
headers:
- key: "langstream-client-session-id"
value: null
valueFromParameters: "sessionId"
valueFromAuthentication: null
consumeOptions: null
chat-options: null
events-topic: null
- id: "consume-output"
type: "consume"
topic: "output-topic"
authentication: null
parameters:
- "sessionId"
produceOptions: null
consumeOptions:
filters:
headers:
- key: "langstream-client-session-id"
value: null
valueFromParameters: "sessionId"
valueFromAuthentication: null
chat-options: null
events-topic: null
instance:
streamingCluster:
type: "kafka"
configuration:
admin:
bootstrap.servers: "localhost:9092"
computeCluster:
type: "kubernetes"
configuration: {}
globals: null
status:
status:
status: "DEPLOYED"
reason: null
executors:
- id: "chatbot-ai-chat-completions-1"
status:
status: "DEPLOYED"
reason: null
replicas:
- id: "docker"
status: "RUNNING"
agents:
- agent-id: "topic-source"
agent-type: "topic-source"
component-type: "SOURCE"
info:
topic: "input-topic"
- agent-id: "chatbot-ai-chat-completions-1"
agent-type: "ai-chat-completions"
component-type: "PROCESSOR"
- agent-id: "topic-sink"
agent-type: "topic-sink"
component-type: "SINK"
info:
topic: "output-topic"
​
  1. 7.
    Send a query to OpenAI about "Italian pizza":
langstream gateway chat sample-app -cg consume-output -pg produce-input -p sessionId=$(uuidgen)
At the prompt ask about "Italian pizza" and see the results.