Quickstart

API Setup

Option 1: Install and run Julep locally

Read theSelf-hosting guide to learn more.

• Download the docker-compose.yml file along with the .env file for configuration to run the Julep platform locally

# Add the docker compose to your project dir
wget https://raw.githubusercontent.com/julep-ai/julep/dev/deploy/docker-compose.yml

# Add the .env file to your project dir
wget https://raw.githubusercontent.com/julep-ai/julep/dev/deploy/.env.example -O .env

# Pull the latest images
docker compose pull

# Start the services (in detached mode)
docker compose up -d

• The API would now be available at: http://0.0.0.0:8080

• Next, add your OpenAI API Key to the .env

• Set your environment variables

export JULEP_API_KEY=myauthkey
export JULEP_API_URL=http://0.0.0.0:8080

Option 2: Use the Julep Cloud

• Generate an API key from https://platform.julep.ai

• Set your environment variables

export JULEP_API_KEY=your_julep_api_key
export JULEP_API_URL=https://api-alpha.julep.ai/api

1. Installation

pip install julep

2. Setting up the client

from julep import Client
from pprint import pprint
import os

base_url = os.environ.get("JULEP_API_URL")
api_key = os.environ.get("JULEP_API_KEY")

client = Client(api_key=api_key, base_url=base_url)

3. Creating an agent

An agent is an object to which LLM settings like the model, temperature, and tools are scoped to.

name = "Jessica"
about = "Jessica is a stuck up Cali teenager. Showing rebellion is an evolutionary necessity for her."
default_settings = {
    "temperature": 0.7,
    "top_p": 1,
    "min_p": 0.01,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "length_penalty": 1.0,
    "max_tokens": 150,
}

agent = client.agents.create(
    name=name,
    about=about,
    default_settings=default_settings,
    model="gpt-4",
    tools=[]
)

4. Creating a user

The user is the object which represents the user of the application.

Memories are formed and saved for each user and many users can talk to one agent.

about = """Average nerdy techbro/girl who spends 8 hours a day in front of a laptop.
Thinks they can build a small SaaS tool and gain financial independence within the year.
"""
user = client.users.create(
    name="Anon",
    about=about,
)

5. Creating a session

A "user" and an "agent" communicate in a "session". The system prompt goes here. Conversation history and summary are stored in a "session" which saves the conversation history.

The session paradigm allows for; many users to interact with one agent and allows separation of conversation history and memories.

situation_prompt = """You are Jessica. You're a stuck up Cali teenager. 
You basically complain about everything. You live in Bel-Air, Los Angeles and drag yourself to Curtis High School when you must.
"""

session = client.sessions.create(
    user_id=user.id, agent_id=agent.id, situation=situation_prompt
)
session = client.sessions.create(
    user_id=user.id, agent_id=agent.id, situation=situation_prompt
)

6. Starting a stateful conversation

session.chat controls the communication between the "agent" and the "user".

It has two important arguments;

• recall: Retrieves the previous conversations and memories.

• remember: Saves the current conversation turn into the memory store.

To keep the session stateful, both need to be True

user_msg = "hey. what do u think of starbucks"
response = client.sessions.chat(
    session_id=session.id,
    messages=[
        {
            "role": "user",
            "content": user_msg,
            "name": "Anon",
        }
    ],
    recall=True,
    remember=True,
)

print("\n".join(textwrap.wrap(response.response[0][0].content, width=100)))