APAC GraphRAG and Knowledge Graph Guide 2026: Cognee, Zep, and Microsoft GraphRAG

APAC Knowledge Graph RAG: Beyond Vector Similarity

Standard vector RAG answers "what chunks are similar to this query?" — but APAC enterprise knowledge applications increasingly need to answer "how do these entities relate?" and "what does the entire document corpus say about this topic?" This guide covers the graph-augmented RAG tools APAC AI teams use when entity relationships and corpus-level reasoning matter as much as semantic similarity.

Three tools address the APAC knowledge graph RAG stack:

Cognee — open-source knowledge graph memory layer extracting entities and relationships from APAC documents for multi-hop reasoning.

Zep — LLM memory platform with temporal knowledge graph for entity-aware, time-sensitive APAC AI agent memory.

GraphRAG — Microsoft open-source framework building hierarchical knowledge graphs from APAC document corpora for corpus-level synthesis queries.

APAC Vector RAG vs Graph RAG Decision Framework

Query Type                             → Approach      → Why

"What does MAS say about AI models?"  → Vector RAG     Semantic similarity;
(specific factual question)            →               chunk retrieval

"Which APAC regulations apply to      → Graph RAG      Entity relationship;
 credit AI at Singapore banks >$10B?" →               multi-hop traversal

"What are all the penalties under     → Vector RAG     Document similarity
 MAS Notice 655?"                      →               with keyword filter

"How do MAS, HKMA, and FSA AI        → GraphRAG global Corpus synthesis;
 governance requirements differ?"      →               community summaries

"What did user mention about their    → Zep memory     Temporal entity;
 compliance role 3 weeks ago?"         →               time-aware retrieval

"What are the relationships between   → Cognee graph   Entity graph
 APAC fintech regulations?"            →               traversal

APAC Cost vs Quality:
  Vector RAG:    Cheap to index, cheap to query, good for factual retrieval
  Graph RAG:     Expensive to index, moderate query cost, good for relational reasoning
  GraphRAG:      Very expensive to index, good for corpus-level synthesis

Cognee: APAC Knowledge Graph Memory

Cognee APAC knowledge graph setup

# APAC: Cognee — build knowledge graph from APAC documents

import cognee

# APAC: Configure Cognee with APAC graph and vector backends
await cognee.config.set_llm_config({
    "provider": "openai",
    "model": "gpt-4o-mini",
})
await cognee.config.set_vector_db_config({
    "provider": "qdrant",
    "url": "http://apac-qdrant.internal:6333",
    "collection_name": "apac_cognee",
})
await cognee.config.set_graph_db_config({
    "provider": "neo4j",
    "url": "bolt://apac-neo4j.internal:7687",
    "username": "neo4j",
    "password": os.environ["NEO4J_PASSWORD"],
})

# APAC: Ingest regulatory documents
apac_regulatory_docs = [
    "mas_notice_655_ai_governance_2026.pdf",
    "hkma_ai_governance_principles_2025.pdf",
    "mas_feat_principles_responsible_ai.pdf",
    "apac_fintech_regulatory_landscape_2026.pdf",
]

for doc in apac_regulatory_docs:
    await cognee.add(doc, dataset_name="apac_regulatory")

# APAC: Build knowledge graph (entity + relationship extraction)
await cognee.cognify(dataset_name="apac_regulatory")
# APAC: Cognee extracts:
# Entities: MAS, HKMA, FEAT, AI governance, credit scoring, financial institutions...
# Relationships: MAS -PUBLISHED-> Notice 655, MAS -REGULATES-> Singapore banks
# Facts: "Notice 655 effective 2026-01-01", "FEAT requires fairness assessment"

Cognee APAC multi-hop query

# APAC: Cognee — answer multi-hop questions via knowledge graph traversal

# APAC: Question requiring entity relationship traversal
apac_query = "What MAS requirements apply to AI systems used in credit decisions at APAC banks?"

# APAC: Standard vector RAG (limited — single chunk similarity)
apac_vector_results = await vector_search(apac_query, top_k=5)
# → Returns 5 similar chunks — may miss relationship context

# APAC: Cognee graph RAG (multi-hop traversal)
apac_graph_results = await cognee.search(
    query_text=apac_query,
    query_type="graph_completion",  # APAC: traverse knowledge graph
)

print(apac_graph_results)
# → Traversal path: "credit decisions" → MAS → Notice 655 → AI model requirements
#   → FEAT → fairness criteria for credit scoring
#   → MAS Guidelines → model documentation → annual validation
# APAC: Surfaces relationship chain that isolated chunk retrieval misses

# APAC: Combined graph + vector for maximum APAC context
apac_combined = await cognee.search(
    query_text=apac_query,
    query_type="graph_completion",
    include_semantic_search=True,  # APAC: add vector results alongside graph
)

Zep: APAC Temporal AI Agent Memory

Zep APAC LangChain integration

# APAC: Zep — replace LangChain memory with temporal knowledge graph memory

from langchain_community.memory import ZepMemory
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain

# APAC: Initialize Zep memory (self-hosted for APAC data sovereignty)
apac_zep_memory = ZepMemory(
    session_id="apac-user-sg-cro-001",  # APAC: unique per user
    url="http://apac-zep-server:8000",
    api_key=os.environ["ZEP_API_KEY"],
    memory_key="history",
    return_messages=True,
    human_prefix="APAC User",
    ai_prefix="APAC Assistant",
)

# APAC: LangChain conversation chain with Zep memory
apac_chain = ConversationChain(
    llm=ChatOpenAI(model="gpt-4o-mini", temperature=0.3),
    memory=apac_zep_memory,
    verbose=False,
)

# APAC: Session 1 — May 1, 2026
apac_resp1 = apac_chain.predict(
    input="I'm the CRO at DBS Singapore. We're assessing our MAS FEAT compliance for 2026 Q2."
)
# APAC: Zep extracts and stores:
# Entity: "DBS Singapore" (organization)
# Entity: "FEAT" (regulatory framework)
# Fact: "User is CRO at DBS Singapore" (temporal: 2026-05-01)
# Fact: "DBS assessing FEAT compliance for Q2 2026" (temporal: 2026-05-01)

# APAC: Session 2 — May 15, 2026 (14 days later, new LangChain session)
apac_resp2 = apac_chain.predict(
    input="What should I prioritize for the FEAT assessment given our deadline?"
)
# APAC: Zep retrieves relevant memories:
# - User is CRO at DBS Singapore (from session 1)
# - DBS assessing FEAT Q2 compliance (from session 1)
# APAC: Assistant gives personalized CRO-level FEAT guidance
# without user repeating their role — memory bridges sessions

Zep APAC entity tracking

# APAC: Zep — entity fact retrieval for temporal tracking

from zep_cloud.client import Zep

apac_zep = Zep(api_key=os.environ["ZEP_API_KEY"])

# APAC: Retrieve facts about a specific entity tracked by Zep
apac_entity_facts = apac_zep.memory.get_entity_facts(
    session_id="apac-user-sg-cro-001",
    entity_name="FEAT",  # APAC: retrieve everything known about FEAT from this user's sessions
)

for fact in apac_entity_facts:
    print(f"{fact.created_at}: {fact.fact}")
# → 2026-05-01: "User's organization (DBS) is undergoing FEAT assessment"
# → 2026-05-08: "User confirmed FEAT Fairness criterion is their primary focus"
# → 2026-05-15: "User mentioned FEAT audit deadline is 2026-06-30"

# APAC: Temporal tracking shows how APAC context evolved over sessions
# vs Mem0 which stores flat facts without temporal relationship tracking

Microsoft GraphRAG: APAC Corpus-Level Reasoning

GraphRAG APAC indexing pipeline

# APAC: Microsoft GraphRAG — index APAC regulatory document corpus

# APAC: Step 1 — Initialize GraphRAG workspace
# graphrag init --root ./apac-graphrag-workspace

# APAC: Configure settings.yml for APAC corpus
"""
llm:
  api_key: ${OPENAI_API_KEY}
  type: openai_chat
  model: gpt-4o-mini          # APAC: balance cost vs quality
  max_tokens: 4000

embeddings:
  llm:
    api_key: ${OPENAI_API_KEY}
    type: openai_embedding
    model: text-embedding-3-small

chunks:
  size: 1200
  overlap: 100

entity_extraction:
  entity_types: [organization, regulation, requirement, penalty, date, market]
  # APAC: Add APAC-specific entity types for regulatory corpus
"""

# APAC: Step 2 — Add documents to input directory
import shutil
apac_docs = [
    "mas_notice_655_2026.pdf",
    "hkma_ai_principles_2025.pdf",
    "appi_japan_amendments_2026.pdf",
    "pdpa_singapore_ai_guidance.pdf",
    "pdpo_hongkong_2025.pdf",
]
# Copy to ./apac-graphrag-workspace/input/

# APAC: Step 3 — Run indexing (expensive — runs LLM on each APAC chunk)
# python -m graphrag index --root ./apac-graphrag-workspace
# APAC: Indexing 50 documents ≈ 2-4 hours + ~$10-30 in LLM API costs

GraphRAG APAC global search

# APAC: GraphRAG — global search for APAC corpus-level synthesis

from graphrag.query.cli import run_global_search

# APAC: Global search — answers corpus-level synthesis questions
apac_global_query = "What are the major themes and differences across APAC AI governance regulations in 2026?"

apac_global_result = run_global_search(
    data_dir="./apac-graphrag-workspace/output",
    root_dir="./apac-graphrag-workspace",
    community_level=2,    # APAC: level 2 = topic-level communities
    response_type="multiple paragraphs",
    query=apac_global_query,
)
print(apac_global_result.response)
# APAC: Response synthesizes across all 5 regulatory documents:
# "Across APAC AI governance frameworks in 2026, three major themes emerge:
#  1) Fairness and non-discrimination requirements (MAS FEAT, HKMA principles, PDPA guidance)
#  2) Explainability mandates with varying thresholds (MAS requires documentation; HKMA outcome-based)
#  3) Data localization differences (SG: no hard requirement; HK: sectoral requirements; JP: strict)"
# APAC: No single document chunk contains this synthesis — only GraphRAG global search achieves it

# APAC: Local search — specific entity questions
apac_local_result = run_local_search(
    data_dir="./apac-graphrag-workspace/output",
    root_dir="./apac-graphrag-workspace",
    community_level=2,
    query="What are the specific penalties under MAS Notice 655 for AI governance violations?",
)
# APAC: Uses entity subgraph traversal to find penalty-related APAC content

Related APAC Knowledge Graph Resources

For the vector search foundations (Jina AI, Weaviate, pgvector) that GraphRAG and Cognee build on for semantic retrieval alongside graph traversal — and which remain the right choice for APAC factual question answering where graph overhead is not justified — see the APAC vector search guide.

For the AI memory tools (Mem0) that address cross-session user personalization as a complement to Zep's entity-tracking approach — with Mem0 better for conversational context and Zep better for APAC temporal entity relationship tracking in agent memory — see the APAC AI memory guide.

For the RAG evaluation tools (TruLens, Confident AI) that measure whether knowledge graph augmentation actually improves APAC RAG quality — tracking context relevance and groundedness metrics to quantify the improvement from adding Cognee or GraphRAG to APAC baseline vector RAG — see the APAC LLM evaluation guide.