GraphRAG supports multiple storage backends for different parts of the pipeline. You can use local files, Azure Blob Storage, or Azure Cosmos DB depending on your requirements.
Storage types GraphRAG uses storage in four main areas:
Input storage Where source documents are read from
Output storage Where processed artifacts are written
Cache storage Where LLM responses are cached
Vector storage Where embeddings are stored for search
Configure where GraphRAG reads source documents:
File storage (default) input_storage : type : file base_dir : "input"
Place your documents in the input/ directory:
project/ ├── input/ │ ├── document1.txt │ ├── document2.txt │ └── data.csv ├── settings.yaml └── .env
Azure Blob Storage input_storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-input account_url : https://myaccount.blob.core.windows.net/
Azure Cosmos DB input_storage : type : cosmosdb connection_string : ${COSMOS_CONNECTION_STRING} container_name : graphrag-input database_name : graphrag account_url : https://myaccount.documents.azure.com:443/
Output storage Configure where GraphRAG writes processed artifacts:
File storage (default) output_storage : type : file base_dir : "output"
Outputs are organized by artifact type:
project/ └── output/ ├── create_base_text_units.parquet ├── create_final_entities.parquet ├── create_final_relationships.parquet ├── create_final_communities.parquet ├── create_final_community_reports.parquet └── lancedb/ # Vector store
Azure Blob Storage output_storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-output account_url : https://myaccount.blob.core.windows.net/
Azure Cosmos DB output_storage : type : cosmosdb connection_string : ${COSMOS_CONNECTION_STRING} container_name : graphrag-output database_name : graphrag account_url : https://myaccount.documents.azure.com:443/
Update output storage For incremental indexing, specify a separate output location:
update_output_storage : type : file base_dir : "update_output"
This preserves original outputs when re-indexing with updated data.
Cache storage Caching stores LLM responses to avoid redundant API calls:
JSON cache (default) cache : type : json storage : type : file base_dir : "cache"
The cache directory structure:
project/ └── cache/ ├── extract_graph/ │ └── responses.json ├── summarize_descriptions/ │ └── responses.json └── community_reporting/ └── responses.json
Memory cache For temporary caching (not persisted):
Memory cache is lost when the process ends. Only use for testing.
Disable caching Azure Blob cache cache : type : json storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-cache account_url : https://myaccount.blob.core.windows.net/
Azure Cosmos DB cache cache : type : json storage : type : cosmosdb connection_string : ${COSMOS_CONNECTION_STRING} container_name : graphrag-cache database_name : graphrag account_url : https://myaccount.documents.azure.com:443/
Vector storage Configure where embeddings are stored for similarity search:
LanceDB (default) vector_store : type : lancedb db_uri : output/lancedb
LanceDB provides fast vector search with automatic indexing and works well for most use cases.
Azure AI Search vector_store : type : azure_ai_search url : https://my-search-service.search.windows.net api_key : ${AZURE_SEARCH_API_KEY} audience : https://search.azure.com/.default # for managed identity
Azure Cosmos DB vector_store : type : cosmosdb url : https://myaccount.documents.azure.com:443/ connection_string : ${COSMOS_CONNECTION_STRING} database_name : graphrag
Custom index schema Customize field names and vector sizes:
vector_store : type : lancedb db_uri : output/lancedb index_schema : text_unit_text : index_name : "text-unit-embeddings" id_field : "id_custom" vector_field : "vector_custom" vector_size : 3072 entity_description : index_name : "entity-embeddings" id_field : "id" vector_field : "vector" vector_size : 3072 community_full_content : index_name : "community-embeddings" vector_size : 3072
Name for the embedding index/table
Field name for document IDs
Field name for embedding vectors
Dimension of embedding vectors (must match model)
Reporting storage Configure where pipeline logs and reports are written:
File reporting (default) reporting : type : file base_dir : "logs"
Azure Blob reporting reporting : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-logs storage_account_blob_url : https://myaccount.blob.core.windows.net/
Storage parameters Common parameters Storage backend: file, blob, memory, or cosmosdb
Character encoding for file operations
Base directory for file storage (relative to project root)
Azure Blob parameters Azure Storage connection string (use environment variable)
Name of the blob container
Azure Cosmos DB parameters Cosmos DB connection string (use environment variable)
Name of the Cosmos container
Name of the Cosmos database
Example: Full Azure configuration Using Azure services for all storage:
# Environment variables in .env # AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;... # COSMOS_CONNECTION_STRING=AccountEndpoint=https://...;AccountKey=...; # AZURE_SEARCH_API_KEY=your-search-api-key input_storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-input account_url : https://mystorage.blob.core.windows.net/ output_storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-output account_url : https://mystorage.blob.core.windows.net/ update_output_storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-update-output account_url : https://mystorage.blob.core.windows.net/ cache : type : json storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-cache account_url : https://mystorage.blob.core.windows.net/ vector_store : type : azure_ai_search url : https://my-search.search.windows.net api_key : ${AZURE_SEARCH_API_KEY} reporting : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-logs storage_account_blob_url : https://mystorage.blob.core.windows.net/
Example: Hybrid configuration Using local files for development with cloud caching:
input_storage : type : file base_dir : "input" output_storage : type : file base_dir : "output" cache : type : json storage : type : blob connection_string : ${AZURE_STORAGE_CONNECTION_STRING} container_name : graphrag-cache account_url : https://mystorage.blob.core.windows.net/ vector_store : type : lancedb db_uri : output/lancedb reporting : type : file base_dir : "logs"
Best practices
Use environment variables for credentials
Never commit connection strings or API keys to version control. Store them in .env files and reference with ${VAR_NAME} syntax.
Enable caching for development
Use JSON file cache during development to avoid redundant API calls. Consider blob cache for team sharing.
Separate production and test storage
Use different containers/directories for production and testing to prevent data mixing.
Azure Blob and Cosmos DB incur storage and transaction costs. Monitor usage and consider file storage for development.
Back up cache and outputs
Cache files can save significant API costs. Back them up before clearing or re-indexing.
Next steps
Caching Learn more about cache configuration and optimization
Settings reference Complete configuration options
LLM models Configure language models
Start indexing Begin processing documents