Best Graph RAG Framework for POC: Sensemaking and Broader Context Questions

For your POC focused on sensemaking and broader context questions, here's a comprehensive evaluation of the best Graph RAG frameworks:

1. LlamaIndex with Knowledge Graph Module

Recommendation Level: HIGHEST

LlamaIndex emerges as the most convenient and effective option for your POC requirements based on several key advantages:

Strengths for POC Development:

• Quick Implementation: Provides ready-to-use Knowledge Graph RAG components
• Simplified API: Requires minimal code to get a working system
• Excellent Documentation: Comprehensive tutorials and examples
• Structured Data Support: Strong capabilities for handling structured relationships

According to Towards AI: "LlamaIndex easily deals with long context and requires no ML expertise to implement RAG."

Specific Features for Sensemaking:

• KnowledgeGraphIndex: Purpose-built for relationship-based queries
• Built-in Graph Store: Simplified graph storage without external dependencies
• Subgraph Retrieval: Can retrieve relevant subgraphs for context-rich answers
• Hybrid Retrieval: Combines graph traversal with semantic search

Implementation Example:

from llama_index.indices.knowledge_graph import KnowledgeGraphIndex
from llama_index.storage.storage_context import StorageContext
from llama_index.graph_stores import SimpleGraphStore

# Create graph store
graph_store = SimpleGraphStore()
storage_context = StorageContext.from_defaults(graph_store=graph_store)

# Build index
kg_index = KnowledgeGraphIndex.from_documents(
    documents,
    storage_context=storage_context,
    max_triplets_per_chunk=10
)

# Create query engine
query_engine = kg_index.as_query_engine()
response = query_engine.query(
    "What are the relationships between different entities in my team?"
)

POC Development Time: 1-3 days

2. LangChain with Neo4j Integration