GPT4All: Run AI Chat Models Locally

from gpt4all import GPT4All
def quick_chat():    # Initialize the model (will automatically download on first use)    model = GPT4All("ggml-gpt4all-j-v1.3-groovy")        # Start chatting    print("🤖 AI assistant is ready, let's chat!")    print("(Type 'exit' to end the conversation)")        while True:        # Get user input        user_input = input("You: ")        if user_input.lower() == 'exit':            break                    # Generate response        response = model.generate(            user_input,            max_tokens=200,            temp=0.7,  # Temperature parameter, controls creativity            top_k=40,  # Keep the top 40 most likely words            top_p=0.9  # Control the diversity of output        )                print(f"AI: {response}\n")        print("👋 Talk to you next time!")

def custom_assistant():    model = GPT4All("ggml-gpt4all-j-v1.3-groovy")        # Define role and behavior    system_prompt = """    You are a humorous and witty AI assistant, who likes to explain problems with vivid metaphors and interesting examples.    Your answers should:    1. Always remain positive and optimistic    2. Use emojis appropriately    3. Use simple and understandable language    4. Make harmless jokes at appropriate times    """        def get_response(prompt):        # Combine full prompt        full_prompt = f"{system_prompt}\n\nUser: {prompt}\nAI Assistant:"                response = model.generate(            full_prompt,            max_tokens=300,            temp=0.8,            repeat_penalty=1.2  # Avoid repetition        )                return response        # Test dialog    questions = [        "Explain what a quantum computer is?",        "Why is the sky blue?",        "How to manage time well?"    ]        for q in questions:        print(f"\nQuestion: {q}")        print(f"Answer: {get_response(q)}\n")

from gpt4all import GPT4All
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
def expert_ai():    # Load knowledge base    knowledge_base = pd.read_csv('knowledge.csv')        # Vectorize text    vectorizer = TfidfVectorizer()    knowledge_vectors = vectorizer.fit_transform(        knowledge_base['content']    )        def find_relevant_info(query, top_k=3):        # Find relevant knowledge        query_vector = vectorizer.transform([query])        similarities = cosine_similarity(            query_vector,             knowledge_vectors        )[0]                # Get the most relevant documents        top_indices = similarities.argsort()[-top_k:][::-1]        relevant_docs = knowledge_base.iloc[top_indices]                return "\n".join(relevant_docs['content'])        # Initialize model    model = GPT4All("ggml-gpt4all-j-v1.3-groovy")        def expert_response(query):        # Find relevant knowledge        context = find_relevant_info(query)                # Construct prompt        prompt = f"""        Answer the question based on the following information:        {context}                Question: {query}        Professional Answer:        """                return model.generate(prompt, max_tokens=500)        # Test professional Q&A    print(expert_response("What is overfitting in machine learning?"))

class AIEnsemble:    def __init__(self):        # Load multiple models        self.models = {            'general': GPT4All("ggml-gpt4all-j-v1.3-groovy"),            'code': GPT4All("ggml-gpt4all-j-v1.3-groovy"),  # Can use different models            'creative': GPT4All("ggml-gpt4all-j-v1.3-groovy")        }                # Define model specialties        self.specialties = {            'general': ['Common sense', 'Encyclopedia', 'Life'],            'code': ['Programming', 'Algorithms', 'Debugging'],            'creative': ['Creativity', 'Stories', 'Art']        }        def choose_model(self, query):        # Simple model selection logic        if any(word in query for word in ['code', 'program', 'bug']):            return 'code'        elif any(word in query for word in ['create', 'story', 'design']):            return 'creative'        else:            return 'general'        def get_response(self, query):        # Select the appropriate model        model_type = self.choose_model(query)        model = self.models[model_type]                print(f"🤖 Using {model_type} model to answer...")                # Generate response        response = model.generate(            query,            max_tokens=400,            temp=0.7        )                return response# Example usage
aI_team = AIEnsemble()print(ai_team.get_response("Write a Python bubble sort"))print(ai_team.get_response("Tell an interesting story"))

pip install gpt4all