Sample Code in Python for Solid Search Engine

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize

# Sample data

library_catalog = {

    "001.2": "Introduction to Computer Science",

    "123.4": "Advanced Mathematics",

    "456.7": "History of Ancient Civilizations",

    # Add more entries as needed

}

# NLP setup

nltk.download('stopwords')

nltk.download('punkt')

stop_words = set(stopwords.words('english'))

# Function to tokenize and preprocess user query

def preprocess_query(query):

    tokens = word_tokenize(query)

    tokens = [token.lower() for token in tokens if token.isalnum() and token.lower() not in stop_words]

    return tokens

# Function to search the library catalog based on Dewey Decimal System

def search_library(query):

    tokens = preprocess_query(query)

   

    # Implement your search logic here

    results = [catalog_entry for dewey_code, catalog_entry in library_catalog.items() if any(token in dewey_code.lower() or token in catalog_entry.lower() for token in tokens)]

   

    return results

# Sample language skill function

def greet_user():

    return "Hello! How can I assist you today?"

# Sample usage

user_query = "Tell me about computer science books"

print(greet_user())

results = search_library(user_query)

print("Search Results:")

for result in results:

    print(result)

Creating a fully working Dewey Decimal based search engine with language skills is quite complex, and providing 30 functions along with the entire code in a single response is not feasible due to space constraints. However, I can give you a simplified example with a few functions to get you started.

Before that, it's important to note that building a robust search engine involves handling various aspects like data storage, retrieval, parsing user queries, and natural language processing (NLP). This example will be quite basic and serve as a starting point. It assumes you have a pre-existing dataset with Dewey Decimal System classifications.

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python
import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize  # Sample data library_catalog = {     "001.2": "Introduction to Computer Science",     "123.4": "Advanced Mathematics",     "456.7": "History of Ancient Civilizations",     # Add more entries as needed }  # NLP setup nltk.download('stopwords') nltk.download('punkt') stop_words = set(stopwords.words('english'))  # Function to tokenize and preprocess user querydef preprocess_query(query):     tokens = word_tokenize(query)     tokens = [token.lower() for token in tokens if token.isalnum() and token.lower() not in stop_words]     return tokens  # Function to search the library catalog based on Dewey Decimal Systemdef search_library(query):     tokens = preprocess_query(query)          # Implement your search logic here     results = [catalog_entry for dewey_code, catalog_entry in library_catalog.items() if any(token in dewey_code.lower() or token in catalog_entry.lower() for token in tokens)]          return results  # Sample language skill functiondef greet_user():     return "Hello! How can I assist you today?"# Sample usage user_query = "Tell me about computer science books"print(greet_user()) results = search_library(user_query) print("Search Results:") for result in results:     print(result) 

This example provides a basic framework for a Dewey Decimal based search engine. You'd need to enhance it with more sophisticated functions, error handling, and possibly integrate a proper NLP library for better query understanding. Additionally, you might want to consider a database for storing and retrieving a larger catalog of books.

Building a full-featured search engine requires careful consideration of many factors and typically involves a dedicated backend, database, and frontend components. This example is meant to give you a starting point, and you should adapt and expand it based on your specific requirements and constraints.

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