nara is a powerful Python library that provides a comprehensive suite of tools for AI-driven tasks, including real-time information retrieval, chatbot interactions, and code generation. It also offers a rich set of utility functions for tasks like temporary email creation, random data generation, caching, JSON manipulation, asynchronous task handling, and structured threading. nara simplifies complex tasks, boosts productivity, and streamlines your workflow.

Install nara using pip:

pip install nara

• Code Generation:

  • CreateFunc(): This decorator dynamically generates the implementation of a Python function based on a docstring or placeholder function definition. This feature leverages the power of large language models (LLMs) to automate code creation.

    from nara import CreateFunc
    
    @CreateFunc()
    def calculate_area(length: int, width: int) -> int:
        '''Calculate the area of a rectangle.'''
        # ... (This function body will be replaced by generated code)

  • CreateTemplate(): Generates entire Python templates (e.g., Flask apps, Machine Learning models) from high-level descriptions. This feature is ideal for quickly scaffolding projects and generating boilerplate code.

    from nara import CreateTemplate
    
    CreateTemplate("Create a Flask template with a route that returns 'Hello, World!'") 

• Large Language Model (LLM) Interaction:

  • nara.llm: This module provides a unified interface for interacting with various LLMs, including Groq, OpenAI, Cohere, Sambanova, Together, and Ollama. You can easily switch between different models and customize parameters like temperature and max tokens. Supports both synchronous and streaming responses.

    from nara.llm._groq import Groq, LLAMA_32_90B_TEXT_PREVIEW
    
    llm = Groq(LLAMA_32_90B_TEXT_PREVIEW)  # Initialize the LLM
    response = llm.run("Translate 'Hello, World!' to Spanish.")
    print(response) # Output: Hola, Mundo!

    For more advanced use, see the streaming example in the Usage section.

• Prompt Engineering:

  • nara.extra.prompt: Provides tools for constructing and managing prompts for LLMs, including support for different roles (system, user, assistant), text, images, and function calls.

    from nara.extra.prompt import Prompt, Role, Text
    
    prompt = Prompt(role=Role.user, template=[Text("What is the capital of France?")])
    print(prompt.prompt) # Output: What is the capital of France?

• Temporary Email:

  • mailOtp(), onlyMail(), mailUrl(): Create temporary email addresses for receiving OTPs or URLs, useful for testing and automation. (Powered by mailtm and temp-mail.io).

    from nara.extra.temp_mail import mailOtp
    
    email_generator = mailOtp(OtpLength=6, Printable=True, Timeout=60)
    email_address = next(email_generator)  # Get the temporary email address
    print(f"Your temporary email: {email_address}")
    
    otp = next(email_generator) # Wait for the OTP
    print(f"Received OTP: {otp}")  

• Random Data Generation:

  • RandomDataGenerator: Easily generate realistic fake data for various purposes, such as testing, populating databases, and creating mock data. Includes names, addresses, usernames, passwords, and more. (Powered by faker).

    from nara.extra.fake import RandomDataGenerator
    
    generator = RandomDataGenerator()
    name = generator.name()
    address = generator.address()
    print(f"Name: {name}, Address: {address}")

• Caching:

  • CacheManager: Simplifies caching function results to disk, improving performance by avoiding redundant computations.

    from nara.extra.extended_json import CacheManager
    
    cache_manager = CacheManager("my_cache.json")
    
    @cache_manager.cache
    def expensive_function(arg1, arg2):
        # ... complex and time-consuming computation ...
        return result
    
    # Subsequent calls with the same arguments will retrieve the cached result
    result = expensive_function(1, 2)  

• JSON Manipulation:

  • loadJson(FileName="Json.json", PickRandom=None): Loads JSON data from a file. If PickRandom is an integer, it returns a dictionary or list containing that many randomly selected items from the JSON data.

    from nara.extra.extended_json import loadJson
    
    data = loadJson("data.json")  # Load all data
    random_data = loadJson("data.json", PickRandom=2)  # Load 2 random items

    Error Handling: Raises FileNotFoundError if the file doesn't exist and ValueError for invalid JSON or if PickRandom exceeds the data length.

  • jsonList(FileName="JsonList.json", **kwargs): Appends key-value pairs (provided as keyword arguments) as a new dictionary entry to a JSON list. Creates a new list if the file doesn't exist or handles invalid data.

    from nara.extra.extended_json import jsonList
    
    jsonList(FileName="my_list.json", name="Alice", age=30)
    jsonList(FileName="my_list.json", name="Bob", age=25)

    Error Handling: Displays warnings for non-existent files, empty files, or files not containing a list. Raises ValueError if the JSON file is empty or does not contain a list.

  • jsonDict(Key, Value, FileName="JsonDict.json"): Updates or adds a single Key-Value pair to a JSON dictionary stored in a file. Creates a new dictionary if the file doesn't exist.

    from nara.extra.extended_json import jsonDict
    
    jsonDict("name", "Charlie", FileName="my_dict.json")
    jsonDict("age", 35, FileName="my_dict.json")

    Error Handling: Displays warnings for similar file errors as jsonList. Raises ValueError if the JSON file is empty or does not contain a dictionary.

• Asynchronous Task Handling:

  • asyncThreadedTask(thread_count=1, *threading_args, **threading_kwargs): Executes an asynchronous function in a separate thread. Useful for running async operations within synchronous code or managing blocking I/O. This example demonstrates the usage with and without parameters:

  import asyncio
  from nara import asyncThreadedTask
  
  @asyncThreadedTask
  async def async_function():
      print("Async function started")
      await asyncio.sleep(2)  # Simulate some async operation
      print("Async function finished")
  
  @asyncThreadedTask(2) # Use two threads
  async def async_function_2():
      print("Async function 2 started")
      await asyncio.sleep(2)
      print("Async function 2 finished")
  
  
  async def main():
      await async_function()
      await async_function_2()
  
  
  asyncio.run(main())

  • runMultipleTimes(count=1): Runs an asynchronous function multiple times concurrently using asyncio.gather.

    import asyncio
    from nara.extra.tools import runMultipleTimes
    
    @runMultipleTimes(5)  # Run the function 5 times concurrently
    async def my_async_task():
        print("Task started")
        await asyncio.sleep(1)
        print("Task finished")
    
    asyncio.run(my_async_task())

• Structured Threading:

  • retry(retries=3, delay=1): This decorator retries a function call if it raises an exception. It will retry up to retries times, pausing for delay seconds between attempts.

    from nara.extra.tools import retry
    import random
    
    @retry(retries=5, delay=2)
    def might_fail():
        if random.random() < 0.5:
            raise Exception("Function failed!")
        print("Function succeeded!")
    
    might_fail()

  • repeatForever(sleep_interval=None, end_after=None): Executes a function or coroutine repeatedly. sleep_interval specifies the pause between calls. end_after (a human-readable time string, e.g., "10 minutes") allows setting a time limit.

    from nara.extra.tools import repeatForever
    import asyncio
    import time
    
    @repeatForever(sleep_interval=1, end_after="5 seconds")  # Repeat every second for 5 seconds
    def print_hello():
        print("Hello")
    
    @repeatForever(sleep_interval=1, end_after="5 seconds")
    async def async_print_hello():
        print("Async Hello")
        await asyncio.sleep(0.5) # Simulate some work
    
    
    print_hello() # Normal function example
    
    async def main(): # Asyncio function example
        await async_print_hello()
    
    asyncio.run(main())

    Error Handling: repeatForever raises humanfriendly.InvalidTimespan if end_after is not a valid timespan string.

• File Management:

  • clearPycacheDirectories(directory, display_status=False): Recursively deletes all __pycache__ directories within the given directory. Set display_status=True to print which directories were removed.

    from nara.extra.file_manager import clearPycacheDirectories
    
    clearPycacheDirectories(".", display_status=True)  # Clear __pycache__ in the current directory and subdirectories

  • displayFileStructure(directory=os.getcwd(), tree=None, console=None): Prints a tree-like representation of the directory structure to the console.

    from nara.extra.file_manager import displayFileStructure
    
    displayFileStructure(".")  # Display the file structure of the current directory

• Date and Time Utilities:

  • CurrentDateTime: Retrieve current date and time information for various countries using external API. Uses this API.

    from nara.extra.date_time import CurrentDateTime
    
    dt = CurrentDateTime()
    country_codes = dt.countryCodes()  # Get available country codes
    indian_time = dt.time("IN", encode=True)  # Get current Indian time as datetime object
    us_date = dt.date("US") # Get current US date as string
    print(f"Country Codes: {country_codes}")
    print(f"Indian Time: {indian_time}")
    print(f"US Date: {us_date}")
    

• Testing Tools:

  • saveTestResults(OutputFile, **outerkwargs): This decorator saves test results to a JSON file. outerkwargs can include functions to calculate additional metrics (like mean or max) based on the test result.

    from nara.extra.extended_json.testing_tools import saveTestResults
    
    @saveTestResults("results.json", mean=lambda x: sum(x)/len(x), maximum=max)
    def test_function(a, b):
        return [a + b, a * b]
    
    test_function(2, 3)  # Save results and calculated metrics to results.json

  • LoadTestResults(inputFile): Loads and returns the test results saved by saveTestResults.

    from nara.extra.extended_json.testing_tools import LoadTestResults
    
    results = LoadTestResults("results.json")
    print(results)

• SQL Utilities: (Examples use a database file named mydatabase.db)

  • SqlQueue(db_file): Implements a queue using a SQLite database, enabling persistent queueing even if your application restarts.

    from nara.extra.extended_sql import SqlQueue
    
    q = SqlQueue("mydatabase.db")
    q.put(10)
    q.put("Hello")
    
    retrieved_item = q.get() # Returns 10 (FIFO)
    print(retrieved_item)

  • TextStore(dbName): Stores and manages text lines in a SQLite database, providing methods for adding, updating, deleting, and retrieving text lines by ID or slices.

    from nara.extra.extended_sql import TextStore
    
    ts = TextStore("mydatabase.db")
    ts.addRecord("This is a test line.")
    ts.addRecord("Another line of text.")
    
    all_records = ts.listRecords() # Retrieve all records
    print(all_records)

  • SqlList(DbName, TableName): Enables you to use a SQLite table as if it were a Python list. You can append, access by index, slice, extend, pop, remove, and perform other list-like operations. Elements are stored as JSON strings.

    from nara.extra.extended_sql import SqlList
    
    sl = SqlList("mydatabase.db", "my_list_table")
    sl.append({"name": "Alice", "age": 30})
    sl.append({"name": "Bob", "age": 25})
    
    print(sl[0])  # Access by index (returns a dictionary)
    print(len(sl)) # Get the length of the SqlList

  • SQLiteDict(db_name): Uses a SQLite database as a persistent dictionary. Supports dictionary operations like setting, getting, deleting items, checking membership (in), and iterating through keys or items. Stores all data types as strings.

    from nara.extra.extended_sql import SQLiteDict
    
    sd = SQLiteDict("mydatabase.db")
    sd['name'] = "Charlie"
    sd['age'] = 35
    
    print(sd['name'])  # Access by key
    print("name" in sd) # Check for key existence

    Error Handling: The get method will return None if the requested key is not found and the data in the "value" column cannot be evaluated as valid Python code, the SQLiteDict's get method will return None, instead of raising an exception. It will also print an error message indicating the failure to evaluate the value.

• Time Utilities:

  • timeIt(func): A decorator to measure and print the execution time of a function (both synchronous and asynchronous).

    import asyncio
    from nara.extra.tools import timeIt
    import time
    
    @timeIt
    def my_function():
        time.sleep(1) # Simulate a second of work
    
    @timeIt
    async def my_async_function():
        await asyncio.sleep(1)
    
    my_function()
    asyncio.run(my_async_function())

from nara.llm._groq import Groq, LLAMA_32_90B_TEXT_PREVIEW

llm = Groq(LLAMA_32_90B_TEXT_PREVIEW)  # Initialize the LLM

for chunk in llm.streamRun("Write a short story about a cat who goes on an adventure."):
    print(chunk, end="", flush=True) # Print the story as it's generated

from nara.extra.prompt import Prompt, Role, Text, Function

def get_current_time():
    from datetime import datetime
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")


prompt = Prompt(role=Role.user, template=[
    Text("The current time is: "),
    Function(get_current_time), 
    Text(".  Tell me a fun fact about cats.")
])

print(prompt.prompt)

We welcome contributions! See CONTRIBUTING.md for guidelines.

MIT License. See LICENSE.txt for details.

naravirtualai@gmail.com

We thank all contributors and the open-source community for their support.