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import gradio as gr
import faiss
import numpy as np
import openai
from sentence_transformers import SentenceTransformer
from nltk.tokenize import sent_tokenize
# Load the Ubuntu manual from a .txt file
with open("ubuntu_manual.txt", "r", encoding="utf-8") as file:
full_text = file.read()
# Function to chunk the text into smaller pieces
def chunk_text(text, chunk_size=500): # Larger chunks
sentences = sent_tokenize(text)
chunks = []
current_chunk = []
for sentence in sentences:
if len(current_chunk) + len(sentence.split()) <= chunk_size:
current_chunk.append(sentence)
else:
chunks.append(" ".join(current_chunk))
current_chunk = [sentence]
if current_chunk:
chunks.append(" ".join(current_chunk))
return chunks
# Apply chunking to the entire text
manual_chunks = chunk_text(full_text, chunk_size=500)
# Load your FAISS index
index = faiss.read_index("manual_chunked_faiss_index_500.bin")
# Load your embedding model
embedding_model = SentenceTransformer('FridayMaster/fine_tune_embedding')
# OpenAI API key
openai.api_key = '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'
# Function to create embeddings
def embed_text(text_list):
return np.array(embedding_model.encode(text_list), dtype=np.float32)
# Function to retrieve relevant chunks for a user query
def retrieve_chunks(query, k=5):
query_embedding = embed_text([query])
# Search the FAISS index
distances, indices = index.search(query_embedding, k=k)
# Debugging: Print out the distances and indices
print("Distances:", distances)
print("Indices:", indices)
# Check if indices are valid
if len(indices[0]) == 0:
return []
# Ensure indices are within bounds
valid_indices = [i for i in indices[0] if i < len(manual_chunks)]
if not valid_indices:
return []
# Retrieve relevant chunks
relevant_chunks = [manual_chunks[i] for i in valid_indices]
return relevant_chunks
# Function to truncate long inputs
def truncate_input(text, max_length=512):
tokens = generator_tokenizer.encode(text, truncation=True, max_length=max_length, return_tensors="pt")
return tokens
# Function to perform RAG: Retrieve chunks and generate a response
def rag_response(query, k=5, max_new_tokens=150):
# Step 1: Retrieve relevant chunks
relevant_chunks = retrieve_chunks(query, k=k)
if not relevant_chunks:
return "Sorry, I couldn't find relevant information."
# Step 2: Combine the query with retrieved chunks
augmented_input = query + "\n" + "\n".join(relevant_chunks)
# Truncate and encode the input
inputs = truncate_input(augmented_input)
# Generate response
outputs = generator_model.generate(inputs, max_new_tokens=max_new_tokens)
generated_text = generator_tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
# Gradio Interface
iface = gr.Interface(
fn=rag_response,
inputs="text",
outputs="text",
title="RAG Chatbot with FAISS and GPT-3.5",
description="Ask me anything!"
)
if __name__ == "__main__":
iface.launch()
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