smartifyLLM 0.1.1

A package that enhances local LLMs with real-time web awareness and adaptive context retrieval, making them smarter with no retraining or fine-tuning.

SmartifyLLM

SmartifyLLM is a Python package designed to make local LLMs (Large Language Models) web-aware, context-aware, and dynamically updated without requiring model retraining, fine-tuning, or solely relying on RAG. It enables local LLMs to fetch, process, and rank online information in real time, making responses more relevant and up-to-date.

Features

• Web Awareness: Automatically fetches and processes relevant web content, ensuring responses from local LLM are accurate, well-informed, and up-to-date. (Making local LLM smarter)

• In-house RAG system: Implements an in-house, fast, and lightweight RAG (Retrieval Augmented Generation) approach for enhanced efficiency.

• Customizable Prompts: Supports both predefined and user-defined prompts tailored to specific models and tasks.

• Optimized Text Splitter: Supports PDF, DOCX, and plain text processing.

• Stock Market Data: Get real-time stock prices. (NEW UPDATE 🚩)

• Smart Search Ranking: Uses semantic similarity to rank sources.

• Efficient Caching: Avoids redundant downloads and improves performance.

Installation

pip install smartifyLLM

Quick Start Guide 🚀

1. Smartify: Making LLMs Web & Context Aware

from smartifyLLM import Smartify
from transformers import pipeline

# Load an LLM pipeline (Example using Phi-2)
llm = pipeline("text-generation", model="microsoft/phi-2")

# Initialize Smartify
smart_llm = Smartify(llm)

# Setting up prompt specific for phi2 model - NOTE ! {context} and {query) must be present in the prompt template. Refer to point 5 for more info.
question =  "What is the real name of ishowspeed?"
prompt_temp = """Instruct: {query) Answer based on this context: {context}
                 Output:		
              """
# Get an intelligent response
response = smart_llm.smart_response(query=question,custom_prompt=prompt_temp)
print(response)

Output Example:

Without Smartify (Phi-2):

"The real name of ishowspeed is Ishowspeed."

With Smartify:

"The real name of ishowspeed is Darren Jason Watkin Jr., source: Wikipedia"

Smartify.smart_response Parameters:

• query (str): The user query.

• custom_prompt (str, optional): A custom prompt format. Important: The custom prompt must include both {context} and {query} , exactly as shown (case-sensitive) for proper functionality. Any deviation will result in errors.

• custom_context (dict, optional): A dictionary containing predefined context to get context aware answers from model.

• max_context_tokens (int, default=4000): Max token limit for context passed into LLM.

• buffer (int, default=200): Buffer space to avoid overloading model.

• return_source (bool, default=False): Whether to return source URLs along with answer.

• advanced_stockmrkt - New Parameter 🚩

Parameter	Type	Default	Description
advanced_stockmrkt	bool	False	If True, detects stock-related queries and returns real-time price data.

Example usage:

question = "What is the price of Google stock today?"
response = smart_llm.smart_response(query=question, advanced_stockmrkt=True)
print(response)

Output:

The current price of Google stock (GOOG) is $147.74 (Source: Yahoo Finance).

2. Text Splitting (Handling Large Documents)

from smartifyLLM import TextSplitter

splitter = TextSplitter(chunk_size=300, chunk_overlap=50)
text_chunks = splitter.split_text("This is a long document that needs splitting...")
print(text_chunks)

TextSplitter Parameters:

• chunk_size (int, default=300): The number of words per chunk.

• chunk_overlap (int, default=50): Overlap size between chunks.

Supports:

• split_text(text: str) -> list[str]

• split_pdf(pdf_path: str) -> list[str]

• split_docx(docx_path: str) -> list[str]

3. Retrieval-Augmented Generation (RAG) with RAGnarok

from smartifyLLM import RAGnarok, TextSplitter

rag_model = RAGnarok()
splitter = TextSplitter()

# Split text before using vector database
document_text = "AI is transforming the world. Machine learning enables AI."
text_chunks = splitter.split_text(document_text)

rag_model.vectordb_from_document(text_chunks)
retrieved_docs = rag_model.retrieve("How is AI transforming society?", top_k=2)
print(retrieved_docs)

RAGnarok Methods:

• vectordb_from_document(chunks: List[str]): Creates an embedding database.

• saveDB_to_disk(path: str): Saves the vector database to disk.

• loadDB_from_disk(path: str): Loads a vector database from disk.

• retrieve(query: str, top_k: int) -> List[Dict]: Finds the most relevant documents.

• generate(model_pipeline, query: str, **kwargs) -> str: Generates an answer using RAG.

4. Web Search & Retrieval

from smartifyLLM import get_online_results

# Fetch web content related to a query
web_data = get_online_results("Latest advancements in AI", num_results=3)
print(web_data)

get_online_results Parameters:

• query (str): The search query.

• num_results (int, default=5): Number of search results to show.

• paragraphs (int, default=5): Number of paragraphs to extract per page.

• min_delay (int, default=2): Min delay between requests.

• max_delay (int, default=5): Max delay between requests.

• verify_ssl (bool, default=True): Whether to verify SSL certificates of websites.

• region (str, optional): Country-specific search filtering.

5. Custom Prompting

from smartifyLLM import DEFAULT_PROMPT

#or you can make a custom prompt of your own ; must have "{query}" and "{context}" inside the prompt
custom_prompt = """
Give me a summarized, Shakespearean answer based on this context:
{context}

Question: {query}
Answer:
"""

Note: The placeholders {context} and {query} must be present in the custom prompt with the exact same spelling and case, or else an error will occur. The smart_response method expects {context} and {query} to be provided indefinitely by the user in the custom prompt for proper functioning.

Functions & Methods (Detailed guide)

Here's a detailed breakdown of all available functions and methods in each module.

1. RAGnarok (Retrieval Augmented Generation)

from smartifyLLM import RAGnarok

Provides tools for creating vector embedding from documents, retrieving relevant information, and generating responses.

• vectordb_from_document(chunks: List[str]) -> None

  • Converts a list of text chunks into a searchable vector database.

• _update_vectordb(texts: List[str]) -> None

  • Updates the vector database with new embeddings.

• saveDB_to_disk(path: str) -> None

  • Saves the vector database to disk as a .pt file.
  • Parameters: path (must end with .pt)

• loadDB_from_disk(path: str) -> None

  • Loads a previously saved vector database from disk.
  • Parameters: path (must be an existing .pt file)

• retrieve(query: str, top_k: int = 3) -> List[Dict]

  • Retrieves the most relevant documents based on the input query.
  • Parameters: query (search term), top_k (number of results to return)
  • Returns: List of dictionaries containing content, similarity score, and ranking.

• generate(model_pipeline, query: str, **kwargs) -> str

  • Generates a response based on retrieved documents.
  • Parameters: model_pipeline (language model function), query (input query)
  • Returns: A generated response.

2. Smartify

from smartifyLLM import Smartify

Enhances responses by incorporating online search results and structured context.

• Base argument: (model_pipeline)

  • args:
    • Accepts the language model pipeline to be made smart.

• smart_response(query: str, custom_prompt: str = None, custom_context: Optional[Dict[str, str]] = None, max_context_tokens: int = 4000, buffer: int = 200, return_source: bool = False) -> Union[str, Tuple[str, List[str]]]

  • Generates a smart response by retrieving the best available context (either online or from a given custom context).
  • Parameters:
    • query: The input question.
    • custom_prompt: Custom formatting for the response.
    • custom_context: A dictionary of possible sources.
    • max_context_tokens: Maximum tokens for context (default: 4000).
    • buffer: Safety buffer for token overflow (default: 200).
    • return_source: If True, returns the source alongside the response.
  • Returns: The generated response or a tuple (response, sources).

3. TextSplitter (Document Processing & Chunking)

from smartifyLLM import TextSplitter

This module splits text documents into manageable chunks for better retrieval and processing.

• Base argument: (chunk_size: int = 300, chunk_overlap: int = 50)

  • args:
    • chunk_size: The number of words per chunk.
    • chunk_overlap: The number of overlapping words between chunks.

• split_text(text: str) -> List[str]

  • Splits a given raw text into chunks.
  • Parameters: text (string input to split)
  • Returns: A list of text chunks.

• split_pdf(pdf_path: str) -> List[str]

  • Extracts text from a PDF file and splits it into chunks.
  • Parameters: pdf_path (path to a .pdf file)
  • Returns: A list of text chunks.

• split_docx(docx_path: str) -> List[str]

  • Extracts text from a Word document and splits it into chunks.
  • Parameters: docx_path (path to a .docx file)
  • Returns: A list of text chunks.

4. Web Tools (Search & Scraping)

Functions

These functions facilitate web searches, content scraping, and result ranking.

• get_random_headers() -> Dict[str, str]

  • Returns randomized headers for web requests to reduce detection.

• search_urls(query: str, num_results: int = 5, region: Optional[str] = None) -> List[str]

  • Fetches a list of relevant URLs based on a search query.
  • Parameters:
    • query: Search term.
    • num_results: Number of URLs to retrieve.
    • region: Optional region for search.
  • Returns: A list of URLs.

• scrape_website(url: str, paragraphs: int = 5, min_delay: int = 2, max_delay: int = 5, verify_ssl: bool = True) -> str

  • Scrapes a webpage for textual content.
  • Parameters:
    • url: Website URL.
    • paragraphs: Number of paragraphs to extract.
    • min_delay, max_delay: Randomized delays to prevent blocking.
    • verify_ssl: Whether to verify SSL certificates.
  • Returns: Extracted content as a string.

• get_online_results(query: str, num_results: int = 5, paragraphs: int = 5, min_delay: int = 2, max_delay: int = 5, verify_ssl: bool = True, region: Optional[str] = None) -> Dict[str, str]

  • Performs an online search and retrieves content from relevant sources.
  • Returns: A dictionary mapping URLs to extracted text.

• rank_best_answer(query: str, answers_dict: Dict[str, str]) -> Tuple[Optional[str], Optional[str]]

  • Identifies the most relevant answer from a dictionary of responses.
  • Returns: A tuple containing the best source and content.

5. Utility Functions

These functions provide additional support for managing models and resources.

• clear_model_cache() -> None
  • Clears all cached models to free up memory.

6. Real-Time Stock Market Info (NEW FEATURE 🚨)

SmartifyLLM now includes built-in support for real-time stock price retrieval.
Just set advanced_stockmrkt=True(default = False) in smart_response.

Use Case:

from smartifyLLM import Smartify
from transformers import pipeline

llm = pipeline("text-generation", model="gpt2")  # or any local model
smart_llm = Smartify(llm)

question = "What is the price of Google stock today?"
response = smart_llm.smart_response(query=question, advanced_stockmrkt=True)
print(response)

---

Output Example:

The current price of Google stock (GOOG) is $147.74 (Source: Yahoo Finance).

Output Without Smartify: (Standard Local LLM Output)

Local LLMs without access to real-time data may hallucinate or return outdated/incorrect values:

"What is the price of tesla stock today?"

LLM output:

"The price of tesla stock today is $1,895.00."

With Smartify and advanced_stockmrkt=True, you get real-time, accurate results.

License

SmartifyLLM is licensed under the MIT License.