graphrag（graphrag 搜索）

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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

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

2. Text Chunking

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

3. Entity Extraction

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

4. Graph Construction

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

5. Community Detection

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

6. Community Summarization

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

7. Embedding Generation

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

8. Output Storage

# 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:

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

Example:

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:

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:

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

Example:

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:

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:

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

Example:

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

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

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

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

# 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

# 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

# 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

# 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

# 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

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

llm:
  model: gpt-4o  # Multilingual model

# Customize prompts in target language

4. Azure OpenAI Integration

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)

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

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

2. Limit Entity Extraction Passes

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

3. Use Smaller Models

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

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

4. Process Subset First

# 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

cache:
  type: file
  base_dir: cache

Cost Estimation

# 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

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

2. Monitor Indexing Progress

# 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

# 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

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

# 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"

# 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"

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

parallelization:
  num_threads: 2      # Less parallelism

"Poor quality entity extraction"

# 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"

# 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"

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

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

Performance Optimization

Indexing Performance

# 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

# 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

# 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

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

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

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

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

Documentation

• Official Docs: https://microsoft.github.io/graphrag/
• GitHub: https://github.com/microsoft/graphrag
• Research Paper: https://arxiv.org/abs/2404.16130

Community

• GitHub Discussions: https://github.com/microsoft/graphrag/discussions
• Issues: https://github.com/microsoft/graphrag/issues

Examples

• Notebooks: https://github.com/microsoft/graphrag/tree/main/examples
• Sample Configs: https://github.com/microsoft/graphrag/tree/main/examples/configs

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.