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‎AI-Agents/langgraph/langchain-academy/using_gemini/module-6/01_creating.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xDtrRfNmaIzV"
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},
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"source": [
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"# Creating a deployment\n",
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"\n",
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"Let's create a deployment of the `task_maistro` app that we created in module 5.\n",
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"\n",
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"## Code structure\n",
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"\n",
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"[The following information should be provided](https://langchain-ai.github.io/langgraph/concepts/application_structure/) to create a LangGraph Platform deployment:\n",
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"\n",
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"* A [LangGraph API Configuration file](https://langchain-ai.github.io/langgraph/concepts/application_structure/#configuration-file) - `langgraph.json`\n",
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"* The graphs that implement the logic of the application - e.g., `task_maistro.py`\n",
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"* A file that specifies dependencies required to run the application - `requirements.txt`\n",
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"* Supply environment variables needed for the application to run - `.env` or `docker-compose.yml`\n",
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"\n",
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"We have this already in the `module-6/deployment` directory!\n",
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"\n",
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"## CLI\n",
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"\n",
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"The [LangGraph CLI](https://langchain-ai.github.io/langgraph/concepts/langgraph_cli/) is a command-line interface for creating a LangGraph Platform deployment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "dt8qCZr_aIzg"
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},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"%pip install -U langgraph-cli"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nKLJCLY9aIzk"
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},
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"source": [
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"To create a [self-hosted deployment](https://langchain-ai.github.io/langgraph/how-tos/deploy-self-hosted/#how-to-do-a-self-hosted-deployment-of-langgraph), we'll follow a few steps.\n",
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"\n",
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"### Build Docker Image for LangGraph Server\n",
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"\n",
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"We first use the langgraph CLI to create a Docker image for the [LangGraph Server](https://docs.google.com/presentation/d/18MwIaNR2m4Oba6roK_2VQcBE_8Jq_SI7VHTXJdl7raU/edit#slide=id.g313fb160676_0_32).\n",
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"\n",
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"This will package our graph and dependencies into a Docker image.\n",
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"\n",
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"A Docker image is a template for a Docker container that contains the code and dependencies required to run the application.\n",
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"\n",
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"Ensure that [Docker](https://docs.docker.com/engine/install/) is installed and then run the following command to create the Docker image, `my-image`:\n",
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"\n",
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"```\n",
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"$ cd module-6/deployment\n",
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"$ langgraph build -t my-image\n",
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"```\n",
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"\n",
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"### Set Up Redis and PostgreSQL\n",
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"\n",
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"If you already have Redis and PostgreSQL running (e.g., locally or on other servers), then create and run the LangGraph Server container [by itself](https://langchain-ai.github.io/langgraph/how-tos/deploy-self-hosted/#running-the-application-locally) with the URIs for Redis and PostgreSQL:\n",
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"\n",
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"```\n",
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"docker run \\\n",
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" --env-file .env \\\n",
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" -p 8123:8000 \\\n",
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" -e REDIS_URI=\"foo\" \\\n",
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" -e DATABASE_URI=\"bar\" \\\n",
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" -e LANGSMITH_API_KEY=\"baz\" \\\n",
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" my-image\n",
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"```\n",
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"\n",
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"Alternatively, you can use the provided `docker-compose.yml` file to create three separate containers based on the services defined:\n",
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"\n",
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"* `langgraph-redis`: Creates a new container using the official Redis image.\n",
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"* `langgraph-postgres`: Creates a new container using the official Postgres image.\n",
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"* `langgraph-api`: Creates a new container using your pre-built image.\n",
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"\n",
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"Simply copy the `docker-compose-example.yml` and add the following environment variables to run the deployed `task_maistro` app:\n",
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"\n",
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"* `IMAGE_NAME` (e.g., `my-image`)\n",
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"* `LANGCHAIN_API_KEY`\n",
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"* `OPENAI_API_KEY`\n",
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"\n",
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"Then, [launch the deployment](https://langchain-ai.github.io/langgraph/how-tos/deploy-self-hosted/#using-docker-compose):\n",
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"\n",
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"```\n",
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"$ cd module-6/deployment\n",
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"$ docker compose up\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SsGtrOMVaIzn"
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},
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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},
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"colab": {
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"provenance": []
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎AI-Agents/langgraph/langchain-academy/using_gemini/module-6/deployment/configuration.py‎

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import os
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from dataclasses import dataclass, field, fields
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from typing import Any, Optional
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from langchain_core.runnables import RunnableConfig
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from typing_extensions import Annotated
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from dataclasses import dataclass
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@dataclass(kw_only=True)
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class Configuration:
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"""The configurable fields for the chatbot."""
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user_id: str = "default-user"
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todo_category: str = "general"
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task_maistro_role: str = (
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"You are a helpful task management assistant. You help you create, organize, and manage the user's ToDo list."
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)
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@classmethod
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def from_runnable_config(
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cls, config: Optional[RunnableConfig] = None
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) -> "Configuration":
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"""Create a Configuration instance from a RunnableConfig."""
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configurable = (
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config["configurable"] if config and "configurable" in config else {}
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)
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values: dict[str, Any] = {
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f.name: os.environ.get(f.name.upper(), configurable.get(f.name))
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for f in fields(cls)
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if f.init
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}
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return cls(**{k: v for k, v in values.items() if v})

‎AI-Agents/langgraph/langchain-academy/using_gemini/module-6/deployment/docker-compose-example.yml‎

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volumes:
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langgraph-data:
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driver: local
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services:
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langgraph-redis:
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image: redis:6
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healthcheck:
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test: redis-cli ping
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interval: 5s
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timeout: 1s
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retries: 5
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langgraph-postgres:
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image: postgres:16
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ports:
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- "5433:5432"
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environment:
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POSTGRES_DB: postgres
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POSTGRES_USER: postgres
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POSTGRES_PASSWORD: postgres
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volumes:
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- langgraph-data:/var/lib/postgresql/data
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healthcheck:
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test: pg_isready -U postgres
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start_period: 10s
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timeout: 1s
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retries: 5
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interval: 5s
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langgraph-api:
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image: "my-image"
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ports:
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- "8123:8000"
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depends_on:
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langgraph-redis:
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condition: service_healthy
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langgraph-postgres:
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condition: service_healthy
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environment:
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REDIS_URI: redis://langgraph-redis:6379
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OPENAI_API_KEY: "xxx"
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LANGSMITH_API_KEY: "xxx"
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POSTGRES_URI: postgres://postgres:postgres@langgraph-postgres:5432/postgres?sslmode=disable

‎AI-Agents/langgraph/langchain-academy/using_gemini/module-6/deployment/langgraph.json‎

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{
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"dockerfile_lines": [],
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"graphs": {
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"task_maistro": "./task_maistro.py:graph"
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},
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"python_version": "3.11",
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"dependencies": [
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"."
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]
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}

‎AI-Agents/langgraph/langchain-academy/using_gemini/module-6/deployment/requirements.txt‎

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langgraph
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langchain-core
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langchain-community
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langchain-openai
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trustcall

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