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GNN-RAG Graph Neural Retrieval for Large Language Model Reasoning in Python

5 min read

Introduction to GNN-RAG

GNN-RAG (Graph Neural Network for Reasoning and Access to Graphs) is a framework that combines the power of large language models with graph neural networks and information retrieval. It enables language models to reason over structured knowledge graphs, providing more accurate and contextual responses.

import torch
from transformers import RagTokenizer, RagModel

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagModel.from_pretrained("facebook/rag-token-nq")

Architecture Overview

GNN-RAG consists of three main components: a language model, a graph neural network, and an information retrieval module. The language model generates initial predictions, the graph neural network updates these predictions using structured knowledge, and the information retrieval module retrieves relevant information from a corpus.

import torch
from transformers import RagTokenizer, RagModel, RagSequenceForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")

input_ids = tokenizer.encode("What is the capital of France?", return_tensors="pt")
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Language Model Component

The language model component is typically a pre-trained transformer-based model, such as BERT or RoBERTa. It generates initial predictions based on the input text.

from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")

input_ids = tokenizer.encode("What is the capital of France?", return_tensors="pt")
output = model(input_ids)[0]

Graph Neural Network Component

The graph neural network component updates the language model's predictions using structured knowledge from a knowledge graph. It learns to reason over the graph structure and incorporate relevant information.

import torch
from torch_geometric.nn import GATConv

class GNNModule(torch.nn.Module):
    def __init__(self):
        super(GNNModule, self).__init__()
        self.conv1 = GATConv(in_channels, out_channels)
        self.conv2 = GATConv(out_channels, out_channels)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.conv2(x, edge_index)
        return x

Information Retrieval Component

The information retrieval component retrieves relevant information from a corpus (e.g., Wikipedia) based on the input text and the knowledge graph. This information can be used to enhance the language model's predictions.

from transformers import DPRContextEncoder, DPRQuestionEncoder

question_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
context_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")

question = "What is the capital of France?"
question_input_ids = question_encoder.encode(question, return_tensors="pt")

scores = torch.matmul(question_input_ids, context_input_ids.t())
top_ids = torch.topk(scores, k=5).indices

GNN-RAG Training

GNN-RAG is trained end-to-end using a combination of supervised and reinforcement learning techniques. The goal is to optimize the model's ability to generate accurate and informative responses.

from transformers import RagSequenceForGeneration
from transformers import AdamW

model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
optimizer = AdamW(model.parameters(), lr=1e-5)

for epoch in range(num_epochs):
    for batch in dataloader:
        input_ids, labels = batch
        output = model(input_ids, labels=labels)
        loss = output.loss
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

GNN-RAG Inference

During inference, the GNN-RAG model takes an input text and generates a response by combining the language model's predictions with the graph neural network's reasoning and the retrieved information.

from transformers import RagTokenizer, RagSequenceForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")

input_text = "What is the capital of France?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

output = model.generate(input_ids)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Knowledge Graph Construction

GNN-RAG requires a structured knowledge graph as input. This graph can be constructed from various sources, such as Wikipedia or domain-specific knowledge bases.

from rdflib import Graph, Literal, BNode, Namespace, RDF, URIRef

g = Graph()
EXAMPLE = Namespace("http://example.org/")

g.add((EXAMPLE.France, RDF.type, EXAMPLE.Country))
g.add((EXAMPLE.France, EXAMPLE.capital, EXAMPLE.Paris))
g.add((EXAMPLE.Paris, RDF.type, EXAMPLE.City))

print(g.serialize(format="turtle"))

Knowledge Graph Embedding

To use the knowledge graph in the GNN component, it needs to be embedded into a low-dimensional vector space. This can be done using techniques like TransE or RotatE.

from ampligraph.latent_features import TransE

model = TransE(batches_count=64, seed=555, epochs=200, k=100)
X = np.array([sp.csr_matrix(spm.load_npz("data/kg.npz"))])
model.fit(X)

embeddings = model.get_embeddings(X)

Retrieval Corpus Preprocessing

The information retrieval component requires a preprocessed corpus to retrieve relevant information. This typically involves tokenization, indexing, and other preprocessing steps.

from transformers import DPRContextEncoder
import faiss

context_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
corpus = ["This is a sample corpus.", "It contains multiple documents."]

corpus_embeddings = context_encoder.embed_documents(corpus)
index = faiss.IndexFlatIP(corpus_embeddings.shape[1])
index.add(corpus_embeddings)

GNN-RAG Evaluation

Evaluating the performance of GNN-RAG involves various metrics, such as exact match accuracy, F1 score, and ROUGE scores. The evaluation should be done on a held-out test set.

from transformers import RagTokenizer, RagSequenceForGeneration
from datasets import load_metric

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
metric = load_metric("rouge")

for input_text, target_text in test_data:
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output = model.generate(input_ids)
    prediction = tokenizer.decode(output[0], skip_special_tokens=True)
    metric.add_batch(predictions=[prediction], references=[target_text])

print(metric.compute())

GNN-RAG Applications

GNN-RAG has been successfully applied to various natural language processing tasks, including question answering, open-domain dialogue, and knowledge-grounded generation. Its ability to reason over structured knowledge graphs makes it particularly useful in scenarios where contextual information and reasoning are required.

from transformers import RagTokenizer, RagSequenceForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")

# Open-domain question answering
question = "What is the capital of France?"
input_ids = tokenizer.encode(question, return_tensors="pt")
output = model.generate(input_ids)
answer = tokenizer.decode(output[0], skip_special_tokens=True)
print(answer)

# Knowledge-grounded dialogue
context = "Paris is the capital of France."
query = "What is the largest city in France?"
input_ids = tokenizer.encode(context + tokenizer.sep_token + query, return_tensors="pt")
output = model.generate(input_ids)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)

Limitations and Future Directions

While GNN-RAG has shown promising results, it still faces several limitations, such as the quality and completeness of the knowledge graph, the efficiency of the retrieval process, and the potential for hallucinated or inconsistent responses. Future research directions include improving the reasoning capabilities, incorporating multi-modal information, and enhancing the scalability and efficiency of the framework.

# Pseudocode for potential future enhancements

# Incorporate multi-modal information (e.g., images, videos)
multimodal_input = concatenate(text_input, image_embeddings, video_embeddings)
output = model(multimodal_input)

# Improve reasoning capabilities with advanced graph neural networks
advanced_gnn = GraphTransformer(num_layers=12, num_heads=16)
reasoned_output = advanced_gnn(input_embeddings, knowledge_graph)

# Enhance scalability with distributed training and inference
distributed_model = DistributedRagModel(num_gpus=8)
distributed_output = distributed_model.generate(input_ids)

Additional Resources

For further reading and exploration of GNN-RAG and related topics, consider the following resources:

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