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import torch
from transformers import AutoTokenizer, AutoModel
from sentence_transformers import SentenceTransformer
import networkx as nx
import matplotlib.pyplot as plt
# Load pre-trained model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Function to get embeddings
def get_embeddings(texts):
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
return outputs.last_hidden_state[:, 0, :].numpy()
# Sample data (replace with your own data import)
documents = [
"The quick brown fox jumps over the lazy dog.",
"A journey of a thousand miles begins with a single step.",
"To be or not to be, that is the question.",
"All that glitters is not gold.",
]
# Get embeddings for documents
embeddings = get_embeddings(documents)
# Create graph
G = nx.Graph()
# Add nodes and edges based on cosine similarity
threshold = 0.5 # Adjust this threshold as needed
for i in range(len(documents)):
G.add_node(i, text=documents[i])
for j in range(i+1, len(documents)):
similarity = torch.cosine_similarity(torch.tensor(embeddings[i]), torch.tensor(embeddings[j]), dim=0)
if similarity > threshold:
G.add_edge(i, j, weight=similarity.item())
# Visualize the graph
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=500, font_size=8, font_weight='bold')
edge_labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
plt.title("Document Similarity Graph")
plt.show()
# Example of querying the graph
query = "What is the meaning of life?"
query_embedding = get_embeddings([query])[0]
# Find most similar document
similarities = [torch.cosine_similarity(torch.tensor(query_embedding), torch.tensor(emb), dim=0) for emb in embeddings]
most_similar_idx = max(range(len(similarities)), key=similarities.__getitem__)
print(f"Most similar document to the query: {documents[most_similar_idx]}")
# You can extend this to implement more complex graph-based retrieval algorithms |