GraphRAG using NVIDIA NIM for Medical Documents QA Chain

GraphRAG (Graph-based Retrieval Augmented Generation) is a technique that combines graph-based data structures with Retrieval Augmented Generation (RAG), a popular method used in large language models (LLMs). This combination aims to improve the performance of models by enhancing the retrieval process with structured knowledge representations like graphs.

Key Concepts Behind GraphRAG:

1. Retrieval Augmented Generation (RAG):

• RAG combines retrieval and generation. A language model retrieves relevant documents or information from an external knowledge base, then uses the retrieved information to generate responses.
• It allows models to incorporate up-to-date, specific information that may not be present in the base model’s training data, which is critical for tasks requiring external knowledge (e.g., question answering).

2. Graph-based Knowledge Representation:

• A graph consists of nodes (representing entities, concepts, or data points) and edges (representing relationships between nodes).
• Graphs are excellent for representing structured knowledge, as they can capture complex relationships between entities.
• In GraphRAG, these graphs serve as an enhanced retrieval mechanism where relationships between data points are leveraged to retrieve more contextually relevant information.

Why GraphRAG is Important:

1. Enhanced Contextual Understanding:

• Graphs allow a model to understand and retrieve not just isolated facts, but also the relationships between those facts. This is particularly useful in complex reasoning tasks where understanding dependencies or connections between concepts is crucial.

2. Structured Knowledge Integration:

• Traditional retrieval methods like dense vector retrieval rely on similarity between text embeddings, which may miss out on the inherent structure between data points. With GraphRAG, structured knowledge (e.g., from knowledge graphs) can be integrated into the retrieval process, enabling models to generate responses that are more coherent and contextually accurate.

3. Better for Complex Queries:

• When a query requires navigating through a large amount of interconnected data (e.g., legal documents, research papers), graph-based retrieval can be more effective than traditional methods. GraphRAG can help the model retrieve information along multiple related paths, providing more holistic responses.

4. Improved Generalization:

• By leveraging a graph’s structure, GraphRAG can help models generalize better across tasks that involve relational or contextual information, such as recommendation systems, knowledge-based QA, or even conversational agents that need deep, context-aware responses.

5. Real-time and Dynamic Knowledge Use:

• Since RAG-based models retrieve external information in real-time, GraphRAG can dynamically update its responses by using the latest information. This is useful in domains like medicine, finance, and law, where new data constantly emerges.

Applications of GraphRAG:

• Question Answering (QA): It can enhance complex QA tasks where the answer lies not in a single fact but in understanding relationships between various data points.
• Recommendation Systems: By using user behavior graphs or item relationship graphs, recommendations can be made more accurate and context-aware.
• Scientific Research: Researchers can navigate through interconnected studies, citations, and concepts more effectively using graph-based retrieval.
• Legal Applications: For systems like legal assistants, GraphRAG can be highly valuable in understanding the relationships between legal precedents, cases, and statutes.

In summary, GraphRAG enhances traditional retrieval-augmented generation by incorporating graph-based knowledge, making it more powerful for tasks requiring relational understanding and structured data.

We will leverage langchain and use the nvidia endpoints using langchain

!pip install langchain langchain_community langchain_nvidia_ai_endpoints

import os
import time
from langchain.chains import GraphQAChain
from langchain_community.graphs.networkx_graph import NetworkxEntityGraph
import pandas as pd

Ref — NIM for Developers | NVIDIA Developer

from langchain_nvidia_ai_endpoints import ChatNVIDIA

client = ChatNVIDIA(
  model="meta/llama-3.1-405b-instruct",
  api_key="your_api_key",
  temperature=0.2,
  top_p=0.7,
  max_tokens=1024,
)

Meta’s LLaMA 3.1 405B is a powerful large language model with 405 billion parameters, designed to push the boundaries of natural language understanding and generation. It focuses on improving efficiency, accuracy, and contextual understanding, making it highly effective for complex tasks like question answering, content generation, and reasoning. LLaMA 3.1 builds on the advancements of earlier models, providing state-of-the-art performance across various NLP applications.

We will use the input data for medical Question Answers. The file contains relationships between medical conditions, with key columns as follows:

• Source: Lists the primary condition (e.g., “Cardiovascular Disease History”).
• Target: Lists associated conditions (e.g., “Hypertension”, “Type 2 Diabetes”).
• Description: Provides details on the connection between the source and target conditions (e.g., “Hypertension is a significant part of the Cardiovascular Disease History”).

Each row establishes a relationship between a source and target condition with additional metadata like description, weight, and ranking information.

df = pd.read_parquet('/content/create_final_relationships.parquet')
df.head()

We will create a graph using the NetworkxEntityGraph() class.

1. Adding nodes: The first loop iterates through each row of the dataframe (df) and adds both the source and target values as nodes in the graph.
2. Adding edges: The second loop adds edges (connections) between the nodes based on the source and target, with an additional edge attribute called relation, which stores the description of the relationship between the nodes.

graph = NetworkxEntityGraph()

#Add nodes
for id,row in df.iterrows():
  graph.add_node(row['source'])
  graph.add_node(row['target'])

#Add edges to the graph
for id,row in df.iterrows():
  graph._graph.add_edge(row['source'],
                        row['target'],
                        relation=row['description']
                        )

chain = GraphQAChain.from_llm(
    llm=client,
    graph=graph,
    verbose=True
)

We will create a chain that uses the GraphQAChain class to create a question-answering (QA) chain based on a graph, leveraging a language model (LLM) to interpret the graph's information.

1. Chain creation: The GraphQAChain.from_llm initializes a question-answering system that uses the provided llm (language model client) and graph (a NetworkxEntityGraph).
2. Question: The variable question holds the query ("Does Type 2 Diabetes play a role in CVD?").
3. Running the chain: chain.run(question) processes the question using the graph and the LLM to extract an answer by traversing relationships and entities in the graph. The verbose=True parameter ensures detailed logging of the process.

question = "Does Type 2 Diabetes play a role in CVD?"
chain.run(question)

Conclusion

NVIDIA NIM integrates seamlessly with LangChain, providing robust support for large-scale language models and enabling advanced NLP pipelines. Here’s how smooth the integration can be:

1. LLM Model Access: NVIDIA Nim provides pretrained models optimized for high performance. When integrated with LangChain, NIM’s models can be leveraged for various tasks such as summarization, question answering, and text generation, offering versatility and scale.
2. Efficiency and Customization: NIM is built for highly efficient inference on NVIDIA hardware. LangChain’s infrastructure allows easy customization and chaining of different modules like retrievers, vector stores, and transformers, which complements Nim’s capabilities.
3. End-to-End Workflows: LangChain supports end-to-end workflows combining NIM’s high-performance models with external data sources (e.g., document search, graph embeddings). This integration allows users to build pipelines that are fast and adaptable to various use cases.
4. Deployment Flexibility: NIM’s models are optimized for deployment on both cloud and on-premise environments, allowing LangChain workflows to scale effortlessly in different production setups.

In summary, NIM and LangChain together offer a powerful combination, with smooth integration supporting efficient, large-scale AI solutions.

References:

1. Deploy Generative AI with NVIDIA NIM | NVIDIA
2. langchain.chains.graph_qa.base.GraphQAChain — 🦜🔗 LangChain 0.1.18
3. https://www.ema.europa.eu/en/documents/assessment-report/vazkepa-epar-public-assessment-report_en.pdf