localrag.py

import torch
from sentence_transformers import SentenceTransformer, util
import os
from openai import OpenAI
from PyPDF2 import PdfReader

prompt = "Describe this document in detail."

# ANSI escape codes for colors
PINK = '\033[95m'
CYAN = '\033[96m'
YELLOW = '\033[93m'
NEON_GREEN = '\033[92m'
RESET_COLOR = '\033[0m'

# Configuration for the Ollama API client
client = OpenAI(
    base_url='http://localhost:11434/v1',
    api_key='llama3'
)

# Function to open a file and return its contents as a string
def open_file(filepath):
    with open(filepath, 'r', encoding='utf-8') as infile:
        return infile.read()

# Function to get relevant context from the vault based on user input
def get_relevant_context(user_input, vault_embeddings, vault_content, model, top_k=3):
    if vault_embeddings.nelement() == 0:  # Check if the tensor has any elements
        return []
    # Encode the user input
    input_embedding = model.encode([user_input])
    # Compute cosine similarity between the input and vault embeddings
    cos_scores = util.cos_sim(input_embedding, vault_embeddings)[0]
    # Adjust top_k if it's greater than the number of available scores
    top_k = min(top_k, len(cos_scores))
    # Sort the scores and get the top-k indices
    top_indices = torch.topk(cos_scores, k=top_k)[1].tolist()
    # Get the corresponding context from the vault
    relevant_context = [vault_content[idx].strip() for idx in top_indices]
    return relevant_context

def extract_text_from_pdf(file_path):
    with open(file_path, "rb") as file:
        pdf = PdfReader(file)
        text = ""
        for page in range(len(pdf.pages)):
            text += pdf.pages[page].extract_text()
        return text

# Function to interact with the Ollama model
def ollama_chat(user_input, system_message, vault_embeddings, vault_content, model):
    # Get relevant context from the vault
    relevant_context = get_relevant_context(user_input, vault_embeddings, vault_content, model)
    if relevant_context:
        # Convert list to a single string with newlines between items
        context_str = "\n".join(relevant_context)
    else:
        print(CYAN + "No relevant context found." + RESET_COLOR)
    
    # Prepare the user's input by concatenating it with the relevant context
    user_input_with_context = user_input
    if relevant_context:
        user_input_with_context = context_str + "\n\n" + user_input

    # Create a message history including the system message and the user's input with context
    messages = [
        {"role": "system", "content": system_message},
        {"role": "user", "content": user_input_with_context}
    ]
    # Send the completion request to the Ollama model
    response = client.chat.completions.create(
        model="llama3",
        messages=messages
    )
    # Return the content of the response from the model
    return response.choices[0].message.content


# How to use:
# Load the model and vault content
model = SentenceTransformer("all-MiniLM-L6-v2")
vault_content = []
if os.path.exists("Job_Description_Governmental_Sales _Customer_Sales_Representative.pdf"):
    vault_content = extract_text_from_pdf("Job_Description_Governmental_Sales _Customer_Sales_Representative.pdf").splitlines()

vault_embeddings = model.encode(vault_content) if vault_content else []

# Convert to tensor and print embeddings
vault_embeddings_tensor = torch.tensor(vault_embeddings) 

# Example usage
system_message = "You are a helpful assistat that is an expert at extracting the most useful information from a given text. Provide as much detail as you can to get the best response."
response = ollama_chat(prompt, system_message, vault_embeddings_tensor, vault_content, model)
print(NEON_GREEN + "Llama3 Response: \n\n" + response + RESET_COLOR)