graphrag

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Translated

Comprehensive skill for Microsoft GraphRAG - modular graph-based RAG system for reasoning over private datasets

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Added on2026-02-21

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SKILL.md Content

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Microsoft GraphRAG Skill

Expert assistance for using Microsoft GraphRAG, a modular graph-based Retrieval-Augmented Generation system that extracts structured knowledge from unstructured text to enhance LLM reasoning over private data.

When to Use This Skill

This skill should be used when:

Building RAG systems that need to "connect the dots" across dispersed information
Querying large document collections holistically
Extracting structured knowledge graphs from unstructured text
Implementing graph-based retrieval for LLM applications
Processing private datasets with enhanced reasoning capabilities
Working with narrative, unstructured documents
Building question-answering systems over document corpora
Extracting entities, relationships, and claims from text
Creating hierarchical knowledge summaries
Implementing multi-hop reasoning over documents
Comparing GraphRAG with traditional vector-based RAG
Tuning prompts for domain-specific datasets
Configuring indexing pipelines for knowledge extraction

Overview

What is GraphRAG?

Microsoft GraphRAG is a data pipeline and transformation system that:

Extracts meaningful, structured data from unstructured text using LLMs
Builds knowledge graph memory structures
Enhances LLM outputs through graph-based retrieval
Supports private data processing without external exposure

Core Innovation:

"GraphRAG addresses fundamental limitations of baseline RAG: connecting the dots across disparate information pieces and holistically understanding summarized concepts over large collections."

Key Differentiators from Baseline RAG

Traditional vector-based RAG has limitations:

❌ Struggles to connect information across multiple documents
❌ Limited holistic understanding of document collections
❌ Misses relationships between dispersed facts
❌ Poor performance on "summarize the corpus" queries

GraphRAG solves these with:

✅ Knowledge graph extraction from text
✅ Hierarchical community detection
✅ Multi-level summarization
✅ Graph-based reasoning and traversal
✅ Better performance on complex queries

Core Concepts

1. Knowledge Graph Extraction

GraphRAG extracts three primary elements:

Entities: Objects, people, places, concepts

Examples:
- "Microsoft" (Organization)
- "Seattle" (Location)
- "Cloud Computing" (Concept)
- "Satya Nadella" (Person)

Relationships: Connections between entities

Examples:
- Microsoft → headquartered_in → Seattle
- Satya Nadella → is_CEO_of → Microsoft
- Microsoft → provides → Cloud Computing

Claims: Factual statements with supporting evidence

Examples:
- "Microsoft is the largest software company" [Source: Document X, Page 5]
- "Azure revenue grew 30% in Q4" [Source: Earnings Report]

2. Hierarchical Community Detection

GraphRAG uses the Leiden algorithm to:

Cluster related entities into communities
Create hierarchical levels of organization
Generate summaries at each level
Enable bottom-up reasoning

Example Hierarchy:

Level 0 (Detailed):
  Community 1: Azure services (Compute, Storage, Networking)
  Community 2: Office products (Word, Excel, PowerPoint)

Level 1 (Mid-level):
  Community A: Cloud services (includes Community 1)
  Community B: Productivity tools (includes Community 2)

Level 2 (High-level):
  Community X: Microsoft product ecosystem (includes A & B)

3. TextUnits

Documents are segmented into TextUnits:

Manageable chunks for analysis
Sized based on token limits
Overlapping to preserve context
Form the basis of entity extraction

4. Query Modes

GraphRAG offers multiple search strategies:

Global Search: Holistic corpus reasoning

Best for: "Summarize the main themes"
Uses: Community summaries at all levels
Method: Bottom-up aggregation

Local Search: Entity-specific reasoning

Best for: "Tell me about Entity X"
Uses: Entity neighborhoods in graph
Method: Traversal from seed entities

DRIFT Search: Entity reasoning with community context

Best for: "How does X relate to broader themes?"
Uses: Entities + community summaries
Method: Hybrid approach

Basic Search: Traditional vector similarity

Best for: Simple semantic matching
Uses: Embedding similarity
Method: Baseline RAG fallback

Installation

Prerequisites

bash

# Python 3.10 or higher required
python --version

# Install GraphRAG
pip install graphrag

# Or install from source
git clone https://github.com/microsoft/graphrag.git
cd graphrag
pip install -e .

Environment Setup

bash

# Create environment file
cat > .env << EOF
# LLM Configuration (OpenAI)
GRAPHRAG_LLM_API_KEY=your-openai-api-key
GRAPHRAG_LLM_TYPE=openai_chat
GRAPHRAG_LLM_MODEL=gpt-4o

# Embedding Configuration
GRAPHRAG_EMBEDDING_API_KEY=your-openai-api-key
GRAPHRAG_EMBEDDING_TYPE=openai_embedding
GRAPHRAG_EMBEDDING_MODEL=text-embedding-3-small

# Optional: Azure OpenAI
# GRAPHRAG_LLM_API_BASE=https://your-resource.openai.azure.com
# GRAPHRAG_LLM_API_VERSION=2024-02-15-preview
# GRAPHRAG_LLM_DEPLOYMENT_NAME=gpt-4

# Optional: Local models
# GRAPHRAG_LLM_TYPE=ollama
# GRAPHRAG_LLM_API_BASE=http://localhost:11434
EOF

Quick Start

1. Initialize Project

bash

# Create new GraphRAG project
mkdir my-graphrag-project
cd my-graphrag-project

# Initialize configuration
graphrag init --root .

# This creates:
# - settings.yaml (configuration)
# - .env (environment variables)
# - prompts/ (customizable prompts)

2. Prepare Your Data

bash

# Create input directory
mkdir -p input

# Add your documents
cp /path/to/documents/*.txt input/

# Supported formats: .txt, .pdf, .docx, .md
# Each file will be processed independently

3. Run Indexing Pipeline

bash

# Index your data (this can take time and cost money!)
graphrag index --root .

# The indexing process will:
# 1. Load and chunk documents
# 2. Extract entities, relationships, claims
# 3. Build knowledge graph
# 4. Detect communities (Leiden algorithm)
# 5. Generate community summaries
# 6. Create embeddings
# 7. Store results in output/

# Monitor progress
graphrag index --root . --verbose

4. Query Your Data

bash

# Global Search (holistic queries)
graphrag query --root . \
  --method global \
  --query "What are the main themes in this dataset?"

# Local Search (entity-specific queries)
graphrag query --root . \
  --method local \
  --query "Tell me about Microsoft's cloud strategy"

# DRIFT Search (entity + community context)
graphrag query --root . \
  --method drift \
  --query "How does Azure relate to the broader Microsoft ecosystem?"

Configuration

settings.yaml Structure

yaml

# Core Configuration
llm:
  api_key: ${GRAPHRAG_LLM_API_KEY}
  type: openai_chat  # or azure_openai_chat, ollama
  model: gpt-4o
  max_tokens: 4000
  temperature: 0
  top_p: 1

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

# Chunking Configuration
chunks:
  size: 1200          # Token size per chunk
  overlap: 100        # Overlap between chunks
  group_by_columns: [id]

# Entity Extraction
entity_extraction:
  prompt: "prompts/entity_extraction.txt"
  max_gleanings: 1    # Re-extraction passes
  entity_types: [organization, person, location, event]

# Community Detection
community_reports:
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_input_length: 8000

# Claim Extraction
claim_extraction:
  enabled: true
  prompt: "prompts/claim_extraction.txt"
  max_gleanings: 1

# Embeddings
embed_graph:
  enabled: true
  strategy: node2vec  # or deepwalk

# Storage
storage:
  type: file          # or blob, cosmosdb
  base_dir: output

# Reporting
reporting:
  type: file
  base_dir: output/reports

Advanced Configuration Options

yaml

# Custom LLM Configuration
llm:
  type: azure_openai_chat
  api_base: https://your-resource.openai.azure.com
  api_version: "2024-02-15-preview"
  deployment_name: gpt-4
  api_key: ${AZURE_OPENAI_API_KEY}
  request_timeout: 180
  max_retries: 10
  max_retry_wait: 10

# Parallelization
parallelization:
  stagger: 0.3        # Delay between requests
  num_threads: 4      # Concurrent workers

# Cache Configuration
cache:
  type: file
  base_dir: cache

# Input Configuration
input:
  type: file
  file_type: text     # or csv, parquet
  base_dir: input
  encoding: utf-8
  file_pattern: ".*\\.txt$"

Prompt Tuning

Why Tune Prompts?

"Using GraphRAG with your data out of the box may not yield the best possible results."

Domain-specific datasets require custom prompts for:

Relevant entity types
Appropriate relationship types
Domain-specific language
Expected output format

Auto-Tuning Process

bash

# Generate domain-adapted prompts
graphrag prompt-tune --root . \
  --config settings.yaml \
  --output prompts/

# This will:
# 1. Analyze your input documents
# 2. Identify domain-specific patterns
# 3. Generate custom entity extraction prompts
# 4. Generate custom summarization prompts
# 5. Save to prompts/ directory

Manual Prompt Customization

bash

# Edit generated prompts
nano prompts/entity_extraction.txt

Example Entity Extraction Prompt:

-Target activity-
You are an AI assistant helping to identify entities in documents about {DOMAIN}.

-Goal-
Extract all entities and relationships from the text below.

Entity Types:
{ENTITY_TYPES}

Relationship Types:
{RELATIONSHIP_TYPES}

Format your response as JSON:
{{
  "entities": [
    {{"name": "Entity Name", "type": "ENTITY_TYPE", "description": "..."}}
  ],
  "relationships": [
    {{"source": "Entity 1", "target": "Entity 2", "type": "RELATIONSHIP_TYPE", "description": "..."}}
  ]
}}

Text to analyze:
{INPUT_TEXT}

Indexing Pipeline Deep Dive

Step-by-Step Process

1. Document Loading

python

# Input documents are loaded from input/ directory
# Supported formats: .txt, .pdf, .docx, .md

2. Text Chunking

python

# Documents split into TextUnits
# Default: 1200 tokens with 100 token overlap
# Preserves context across chunk boundaries

3. Entity Extraction

python

# For each TextUnit:
#   - Extract entities (with types and descriptions)
#   - Extract relationships (with types and weights)
#   - Extract claims (with sources and confidence)

4. Graph Construction

python

# Build knowledge graph:
#   - Nodes = Entities
#   - Edges = Relationships
#   - Properties = Attributes and metadata

5. Community Detection

python

# Leiden algorithm for hierarchical clustering:
#   - Level 0: Fine-grained communities
#   - Level 1: Mid-level aggregations
#   - Level 2+: High-level themes

6. Community Summarization

python

# For each community at each level:
#   - Aggregate entity and relationship info
#   - Generate natural language summary
#   - Store for query-time retrieval

7. Embedding Generation

python

# Create vector embeddings for:
#   - TextUnits (for similarity search)
#   - Entities (for semantic matching)
#   - Community summaries (for global search)

8. Output Storage

python

# Results saved to output/:
#   - create_final_entities.parquet
#   - create_final_relationships.parquet
#   - create_final_communities.parquet
#   - create_final_community_reports.parquet
#   - create_final_text_units.parquet

Query Modes in Detail

Global Search

Best For:

"What are the main themes?"
"Summarize the entire dataset"
"What are the key trends?"

How It Works:

Query is matched against community summaries
Relevant communities selected at all hierarchy levels
Summaries aggregated bottom-up
Final answer synthesized from multiple levels

Example:

bash

graphrag query --root . \
  --method global \
  --query "What are the major technology trends discussed in these documents?"

# Behind the scenes:
# 1. Match query to relevant communities
# 2. Retrieve summaries from levels 0, 1, 2
# 3. Aggregate: AI/ML, Cloud, Cybersecurity communities
# 4. Synthesize comprehensive answer

Python API:

python

from graphrag.query import GlobalSearch

searcher = GlobalSearch(
    llm=llm,
    context_builder=context_builder,
    map_system_prompt=map_prompt,
    reduce_system_prompt=reduce_prompt
)

result = await searcher.asearch(
    query="What are the major themes?",
    conversation_history=[]
)
print(result.response)

Local Search

Best For:

"Tell me about [specific entity]"
"What is the relationship between X and Y?"
"Find information about [topic]"

How It Works:

Identify entities mentioned in query
Traverse graph from those entities
Collect neighborhood information (N-hop)
Retrieve associated TextUnits
Synthesize answer from local context

Example:

bash

graphrag query --root . \
  --method local \
  --query "What is Microsoft's strategy for artificial intelligence?"

# Behind the scenes:
# 1. Identify: "Microsoft", "artificial intelligence" entities
# 2. Traverse: Find related entities (Azure AI, OpenAI partnership, etc.)
# 3. Collect: Relationships, claims, TextUnits
# 4. Synthesize: Answer from local graph neighborhood

Python API:

python

from graphrag.query import LocalSearch

searcher = LocalSearch(
    llm=llm,
    context_builder=context_builder,
    system_prompt=system_prompt
)

result = await searcher.asearch(
    query="Tell me about Microsoft's AI strategy",
    conversation_history=[]
)
print(result.response)

DRIFT Search

Best For:

"How does [entity] fit into [broader context]?"
"What is the significance of [topic]?"
Hybrid queries needing both local and global context

How It Works:

Identify query entities (like Local Search)
Find relevant communities (like Global Search)
Combine entity neighborhoods with community summaries
Synthesize answer from both perspectives

Example:

bash

graphrag query --root . \
  --method drift \
  --query "How does Azure AI relate to Microsoft's overall cloud strategy?"

# Behind the scenes:
# 1. Local: Find "Azure AI" entity and neighborhood
# 2. Global: Find "cloud strategy" community summaries
# 3. Combine: Entity details + strategic context
# 4. Synthesize: Comprehensive answer

Python API Usage

Basic Setup

python

import asyncio
from graphrag.query import LocalSearch, GlobalSearch
from graphrag.llm import create_openai_chat_llm
from graphrag.config import GraphRagConfig

# Load configuration
config = GraphRagConfig.from_file("settings.yaml")

# Create LLM
llm = create_openai_chat_llm(
    api_key=config.llm.api_key,
    model=config.llm.model,
    temperature=0.0
)

Custom Indexing

python

from graphrag.index import run_pipeline_with_config

# Run indexing programmatically
await run_pipeline_with_config(
    config_path="settings.yaml",
    verbose=True
)

Advanced Query Customization

python

from graphrag.query.context_builder import LocalContextBuilder

# Build custom context
context_builder = LocalContextBuilder(
    entities=entities_df,
    relationships=relationships_df,
    text_units=text_units_df,
    embeddings=embeddings
)

# Custom search with parameters
result = await searcher.asearch(
    query="Your question here",
    conversation_history=[
        {"role": "user", "content": "Previous question"},
        {"role": "assistant", "content": "Previous answer"}
    ],
    top_k=10,              # Number of results
    temperature=0.5,       # LLM creativity
    max_tokens=2000        # Response length
)

# Access detailed results
print("Response:", result.response)
print("Context used:", result.context_data)
print("Sources:", result.sources)

Use Cases and Examples

1. Research Paper Analysis

bash

# Index academic papers
mkdir -p input/papers
cp research_papers/*.pdf input/papers/

graphrag index --root .

# Global query
graphrag query --method global \
  --query "What are the main research themes across these papers?"

# Local query
graphrag query --method local \
  --query "What methodologies does the Smith et al. paper use?"

2. Legal Document Processing

bash

# Index legal contracts
mkdir -p input/contracts
cp contracts/*.docx input/contracts/

# Tune prompts for legal domain
graphrag prompt-tune --root . --domain "legal contracts"

# Index with legal-specific entities
graphrag index --root .

# Query
graphrag query --method local \
  --query "What are the termination clauses in the Microsoft contracts?"

3. Customer Feedback Analysis

bash

# Index customer feedback
mkdir -p input/feedback
cp feedback_*.txt input/feedback/

# Global themes
graphrag query --method global \
  --query "What are the main customer pain points?"

# Specific product feedback
graphrag query --method local \
  --query "What feedback relates to product X features?"

4. News Article Summarization

bash

# Index news articles
mkdir -p input/news
cp articles/*.txt input/news/

graphrag index --root .

# Get comprehensive summary
graphrag query --method global \
  --query "Summarize the key events and trends from these news articles"

# Entity-specific news
graphrag query --method local \
  --query "What news relates to climate change initiatives?"

Advanced Features

1. Incremental Indexing

bash

# Initial indexing
graphrag index --root .

# Add new documents
cp new_documents/*.txt input/

# Re-index only new content
graphrag index --root . --incremental

# Note: Full graph may need periodic rebuilding

2. Custom Entity Types

Edit

prompts/entity_extraction.txt

Entity Types:
- PRODUCT: Software products, services
- FEATURE: Product features and capabilities
- TECHNOLOGY: Technologies and frameworks
- METRIC: Performance metrics, KPIs
- INITIATIVE: Projects and strategic initiatives
- COMPETITOR: Competing products or companies

3. Multi-Language Support

yaml

# settings.yaml
input:
  encoding: utf-8
  language: es  # Spanish

llm:
  model: gpt-4o  # Multilingual model

# Customize prompts in target language

4. Azure OpenAI Integration

yaml

llm:
  type: azure_openai_chat
  api_base: https://your-resource.openai.azure.com
  api_version: "2024-02-15-preview"
  deployment_name: gpt-4
  api_key: ${AZURE_OPENAI_API_KEY}

embeddings:
  type: azure_openai_embedding
  api_base: https://your-resource.openai.azure.com
  api_version: "2024-02-15-preview"
  deployment_name: text-embedding-3-small
  api_key: ${AZURE_OPENAI_API_KEY}

5. Local LLM Support (Ollama)

yaml

llm:
  type: ollama
  api_base: http://localhost:11434
  model: llama3:70b
  temperature: 0

embeddings:
  type: ollama
  api_base: http://localhost:11434
  model: nomic-embed-text

Cost Management

Understanding Costs

GraphRAG uses LLM APIs which incur costs:

Indexing Phase (most expensive):

Entity extraction: Multiple LLM calls per TextUnit
Relationship extraction: Additional calls
Community summarization: Calls per community
Embedding generation: Per entity/TextUnit

Query Phase (less expensive):

Context retrieval: Minimal LLM use
Answer synthesis: Single LLM call per query

Cost Optimization Strategies

1. Reduce Chunk Size

yaml

chunks:
  size: 600  # Smaller chunks = fewer tokens
  overlap: 50

2. Limit Entity Extraction Passes

yaml

entity_extraction:
  max_gleanings: 0  # 0 = single pass, 1 = two passes

3. Use Smaller Models

yaml

llm:
  model: gpt-4o-mini  # Cheaper than gpt-4o

embeddings:
  model: text-embedding-3-small  # Cheaper than large

4. Process Subset First

bash

# Test on small sample
mkdir input/sample
cp input/full/*.txt input/sample/ | head -5
graphrag index --root . --input-dir input/sample

5. Cache Aggressively

yaml

cache:
  type: file
  base_dir: cache

Cost Estimation

python

# Estimate before indexing
from graphrag.index import estimate_index_cost

cost_estimate = estimate_index_cost(
    input_dir="input/",
    config_path="settings.yaml"
)

print(f"Estimated cost: ${cost_estimate.total_cost}")
print(f"Total tokens: {cost_estimate.total_tokens}")
print(f"Estimated time: {cost_estimate.estimated_hours} hours")

Best Practices

1. Start Small

bash

# Test with 5-10 documents first
# Validate outputs before scaling
# Tune prompts on small sample
# Then scale to full dataset

2. Monitor Indexing Progress

bash

# Use verbose mode
graphrag index --root . --verbose

# Check output files periodically
ls -lh output/*.parquet

# Monitor logs
tail -f output/reports/indexing.log

3. Version Control Configuration

bash

# Track changes
git add settings.yaml prompts/
git commit -m "Update entity types for domain X"

# Tag successful configurations
git tag -a v1.0-config -m "Working config for dataset X"

4. Validate Outputs

python

import pandas as pd

# Check extracted entities
entities = pd.read_parquet("output/create_final_entities.parquet")
print(f"Total entities: {len(entities)}")
print(f"Entity types: {entities['type'].value_counts()}")

# Check relationships
relationships = pd.read_parquet("output/create_final_relationships.parquet")
print(f"Total relationships: {len(relationships)}")
print(f"Relationship types: {relationships['type'].value_counts()}")

# Check communities
communities = pd.read_parquet("output/create_final_communities.parquet")
print(f"Total communities: {len(communities)}")
print(f"Hierarchy levels: {communities['level'].value_counts()}")

5. Iterate on Prompts

bash

# Run initial index
graphrag index --root .

# Evaluate quality
graphrag query --method global --query "Test query"

# If quality is poor:
# 1. Adjust entity types in prompts
# 2. Modify extraction instructions
# 3. Re-run indexing
# 4. Validate improvements

Troubleshooting

Common Issues

"API rate limit exceeded"

yaml

# Add delays between requests
parallelization:
  stagger: 1.0        # Increase delay
  num_threads: 2      # Reduce concurrency

llm:
  max_retries: 20     # More retries
  max_retry_wait: 60  # Longer backoff

"Out of memory during indexing"

yaml

# Reduce batch sizes
chunks:
  size: 600           # Smaller chunks

parallelization:
  num_threads: 2      # Less parallelism

"Poor quality entity extraction"

bash

# Run prompt tuning
graphrag prompt-tune --root . --domain "your domain"

# Manually refine prompts
nano prompts/entity_extraction.txt

# Add domain-specific examples
# Specify expected entity types clearly

"Queries return irrelevant results"

bash

# Check if indexing completed successfully
ls -lh output/*.parquet

# Validate extracted entities
python -c "import pandas as pd; print(pd.read_parquet('output/create_final_entities.parquet').head())"

# Try different query methods
graphrag query --method local --query "Your query"
graphrag query --method global --query "Your query"

"Version incompatibility after update"

bash

# Reinitialize configuration
graphrag init --root . --force

# This updates settings.yaml to new schema
# Review and merge your customizations

Performance Optimization

Indexing Performance

yaml

# Optimize for speed
parallelization:
  num_threads: 8            # Max concurrent workers
  stagger: 0.1              # Minimal delay

chunks:
  size: 1500               # Larger chunks (fewer API calls)

entity_extraction:
  max_gleanings: 0         # Single pass only

Query Performance

python

# Cache query results
from functools import lru_cache

@lru_cache(maxsize=100)
def cached_query(query_text):
    return searcher.search(query_text)

# Pre-load data structures
entities_df = pd.read_parquet("output/create_final_entities.parquet")
relationships_df = pd.read_parquet("output/create_final_relationships.parquet")

# Keep in memory for fast access

Storage Optimization

yaml

# Use compressed storage
storage:
  type: file
  compression: gzip         # Or snappy, lz4

# Or use database storage
storage:
  type: cosmosdb
  connection_string: ${COSMOS_CONNECTION_STRING}

Integration Examples

LangChain Integration

python

from langchain.retrievers import GraphRAGRetriever
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

# Create GraphRAG retriever
retriever = GraphRAGRetriever(
    index_path="output/",
    search_method="local"
)

# Build QA chain
llm = ChatOpenAI(model="gpt-4o")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True
)

# Query
result = qa_chain("What are the main themes?")
print(result["answer"])

FastAPI Service

python

from fastapi import FastAPI
from graphrag.query import LocalSearch, GlobalSearch

app = FastAPI()

# Initialize searchers
local_searcher = LocalSearch(...)
global_searcher = GlobalSearch(...)

@app.post("/query/local")
async def query_local(query: str):
    result = await local_searcher.asearch(query)
    return {"response": result.response, "sources": result.sources}

@app.post("/query/global")
async def query_global(query: str):
    result = await global_searcher.asearch(query)
    return {"response": result.response}

# Run: uvicorn main:app --reload

Streamlit UI

python

import streamlit as st
from graphrag.query import GlobalSearch

st.title("GraphRAG Query Interface")

# Query input
query = st.text_input("Enter your question:")
method = st.selectbox("Search method:", ["global", "local", "drift"])

if st.button("Search"):
    with st.spinner("Searching..."):
        # Run query
        result = await searcher.asearch(query)

        # Display results
        st.write("### Answer")
        st.write(result.response)

        st.write("### Sources")
        st.write(result.sources)

Comparison with Other Approaches

GraphRAG vs. Vector RAG

Feature	Vector RAG	GraphRAG
Structure	Flat embeddings	Knowledge graph
Relationships	Implicit (similarity)	Explicit (edges)
Multi-hop	Poor	Excellent
Summarization	Difficult	Natural (communities)
Setup Cost	Low	High (indexing)
Query Cost	Low	Medium
Best For	Simple lookups	Complex reasoning

When to Use GraphRAG

✅ Use GraphRAG when:

Queries require connecting multiple pieces of information
Need holistic understanding of document corpus
Relationships between entities matter
Multi-hop reasoning is important
Domain has rich entity/relationship structure

❌ Use Vector RAG when:

Simple semantic search is sufficient
Low setup cost is priority
Documents are independent
Queries are straightforward lookups
Budget is constrained

Resources

Important Notes

⚠️ Not an Official Microsoft Product

"This codebase is a demonstration of graph-based RAG and not an officially supported Microsoft offering."

💰 Cost Considerations

Indexing can be expensive (especially with GPT-4)
Test on small samples first
Monitor API costs closely

🔄 Version Management

Configuration schemas change between versions
Run
```
graphrag init --force
```
after updates
Review migration guides for breaking changes

🎯 Prompt Tuning is Critical

Out-of-box results may be suboptimal
Domain-specific tuning significantly improves quality
Invest time in prompt customization

License

Microsoft GraphRAG is released under the MIT License.

Note: This skill provides comprehensive guidance for using Microsoft GraphRAG. Always test on small datasets first, monitor costs, and tune prompts for your specific domain.

graphrag

NPX Install

Tags

SKILL.md Content

Microsoft GraphRAG Skill

When to Use This Skill

Overview

What is GraphRAG?

Key Differentiators from Baseline RAG

Core Concepts

1. Knowledge Graph Extraction

2. Hierarchical Community Detection

3. TextUnits

4. Query Modes

Installation

Prerequisites

Environment Setup

Quick Start

1. Initialize Project

2. Prepare Your Data

3. Run Indexing Pipeline

4. Query Your Data

Configuration

settings.yaml Structure

Advanced Configuration Options

Prompt Tuning

Why Tune Prompts?

Auto-Tuning Process

Manual Prompt Customization

Indexing Pipeline Deep Dive

Step-by-Step Process

Query Modes in Detail

Global Search

Local Search

DRIFT Search

Python API Usage

Basic Setup

Custom Indexing

Advanced Query Customization

Use Cases and Examples

1. Research Paper Analysis

2. Legal Document Processing

3. Customer Feedback Analysis

4. News Article Summarization

Advanced Features

1. Incremental Indexing

2. Custom Entity Types

3. Multi-Language Support

4. Azure OpenAI Integration

5. Local LLM Support (Ollama)

Cost Management

Understanding Costs

Cost Optimization Strategies

Cost Estimation

Best Practices

1. Start Small

2. Monitor Indexing Progress

3. Version Control Configuration

4. Validate Outputs

5. Iterate on Prompts

Troubleshooting

Common Issues

"API rate limit exceeded"

"Out of memory during indexing"

"Poor quality entity extraction"

"Queries return irrelevant results"

"Version incompatibility after update"

Performance Optimization

Indexing Performance

Query Performance

Storage Optimization

Integration Examples

LangChain Integration

FastAPI Service

Streamlit UI

Comparison with Other Approaches

GraphRAG vs. Vector RAG

When to Use GraphRAG

Resources

Documentation