alita-sdk 0.1.3

SDK for building langchain agents using resouces from Alita

Alita SDK

Alita SDK, built on top of Langchain, enables the creation of intelligent agents within the Alita Platform using project-specific prompts and data sources. This SDK is designed for developers looking to integrate advanced AI capabilities into their projects with ease.

Prerequisites

Before you begin, ensure you have the following requirements met:

• Python 3.10
• An active deployment of Project Alita
• Access to personal project

Installation

First, you need to install the Langchain library. Alita SDK depends on Langchain for its core functionalities. You can install Langchain using pip:

pip install langchain

Next, clone the Alita SDK repository (assuming it's available on GitHub or another source):

git clone https://github.com/ProjectAlita/alita-sdk.git
cd alita-sdk

Install the SDK along with its dependencies:

pip install -r requirements.txt

Environment Setup

Before running your Alita agents, set up your environment variables. Create a .env file in the root directory of your project and include your Project Alita credentials:

AUTH_TOKEN=<your_api_token>
PROJECT_ID=<your_project_id> 
INTEGRATION_UID=<your_integration_uid>

Ensure you load these variables in your application:

from dotenv import load_dotenv 
load_dotenv('.env')

Basic Usage

The Alita SDK allows you to create and execute agents with ease. Here's a simple example to get you started:

import logging 
from src.alita_sdk.llms.alita import AlitaChatModel 
from langchain_core.messages import HumanMessage  
# Set up 
logging logging.basicConfig(level=logging.INFO) 
logger = logging.getLogger(__name__)  

# Load environment variables 
from os import environ 
load_dotenv('.env')  

# Configuration settings for AlitaChatModel 
settings = {
    "deployment": "https://eye.projectalita.ai",
    "model": "gpt-4-1106-preview",
    "api_key": environ.get("AUTH_TOKEN"),
    "project_id": environ.get("PROJECT_ID"),
    "integration_uid": environ.get("INTEGRATION_UID"),
    "max_tokens": 2048,
    "stream": True 
}  
# Instantiate AlitaChatModel 
llm = AlitaChatModel(**settings)  

# Define tools for agent operations 
from demo.demo_react_agent.tools import getRepoTreeTool, getRawFileTool, storeSpecFile  t
ools = [getRepoTreeTool(), getRawFileTool, storeSpecFile]  

# Construct the agent with mixed capabilities 

from src.alita_sdk.agents import create_mixed_agent 
from langchain.agents import AgentExecutor 

prompt = llm.client.prompt(prompt_id=<prompt_id>, prompt_version_id=<prompt_version_id>) 
agent = create_mixed_agent(llm, tools, prompt)  

# Execute the agent 
agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent, tools=tools, verbose=True, handle_parsing_errors=True, max_execution_time=None, return_intermediate_steps=True)  

input_data = {"input": """Use repository spring-petclinic/spring-framework-petclinic with branch main. 
It is a Java Spring application, please create a swagger spec. 
Deployment URL is https://petclinic.example.com"""}  

agent_executor.invoke(input_data, include_run_info=True)