> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/microsoft/graphrag/llms.txt
> Use this file to discover all available pages before exploring further.

# Configuration enums

> Enumeration types used in GraphRAG configuration

GraphRAG uses several enumeration types to constrain configuration values to valid options.

## ReportingType

The reporting configuration type for the pipeline.

**Module:** `graphrag.config.enums`

**Base class:** `str, Enum`

### Values

<ParamField path="file" type="str" default="file">
  The file reporting configuration type. Writes reporting output to local files.
</ParamField>

<ParamField path="blob" type="str" default="blob">
  The blob reporting configuration type. Writes reporting output to Azure Blob Storage.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import ReportingType

# Use in configuration
reporting_type = ReportingType.file
print(reporting_type)  # "file"
```

## AsyncType

Enum for the type of async to use for concurrent operations.

**Module:** `graphrag.config.enums`

**Base class:** `str, Enum`

### Values

<ParamField path="AsyncIO" type="str" default="asyncio">
  Use Python's asyncio for asynchronous operations. Provides true async/await concurrency.
</ParamField>

<ParamField path="Threaded" type="str" default="threaded">
  Use thread-based concurrency for asynchronous operations. Default mode for most operations.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import AsyncType

# Use in configuration
async_mode = AsyncType.Threaded
```

## SearchMethod

The type of search to run.

**Module:** `graphrag.config.enums`

**Base class:** `Enum`

### Values

<ParamField path="LOCAL" type="str" default="local">
  Local search method. Searches within a local context using entities and relationships.
</ParamField>

<ParamField path="GLOBAL" type="str" default="global">
  Global search method. Performs map-reduce search across community reports.
</ParamField>

<ParamField path="DRIFT" type="str" default="drift">
  DRIFT search method. Dynamic reasoning and inference with followup trajectories.
</ParamField>

<ParamField path="BASIC" type="str" default="basic">
  Basic search method. Simple vector similarity search.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import SearchMethod

# Use string representation
search_type = SearchMethod.LOCAL
print(str(search_type))  # "local"
```

## IndexingMethod

Enum for the type of indexing to perform.

**Module:** `graphrag.config.enums`

**Base class:** `str, Enum`

### Values

<ParamField path="Standard" type="str" default="standard">
  Traditional GraphRAG indexing, with all graph construction and summarization performed by a language model. Provides highest quality but slower performance.
</ParamField>

<ParamField path="Fast" type="str" default="fast">
  Fast indexing, using NLP for graph construction and language model for summarization. Significantly faster than standard indexing.
</ParamField>

<ParamField path="StandardUpdate" type="str" default="standard-update">
  Incremental update with standard indexing. Updates an existing index with new documents using LLM-based extraction.
</ParamField>

<ParamField path="FastUpdate" type="str" default="fast-update">
  Incremental update with fast indexing. Updates an existing index with new documents using NLP-based extraction.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import IndexingMethod

# Use in indexing configuration
method = IndexingMethod.Fast
```

## NounPhraseExtractorType

Enum for the noun phrase extractor options used in NLP-based graph extraction.

**Module:** `graphrag.config.enums`

**Base class:** `str, Enum`

### Values

<ParamField path="RegexEnglish" type="str" default="regex_english">
  Standard extractor using regex. Fastest option, but limited to English language text.
</ParamField>

<ParamField path="Syntactic" type="str" default="syntactic_parser">
  Noun phrase extractor based on dependency parsing and named entity recognition (NER) using SpaCy. More accurate than regex but slower.
</ParamField>

<ParamField path="CFG" type="str" default="cfg">
  Noun phrase extractor combining context-free grammar (CFG) based noun-chunk extraction and NER. Balanced approach between speed and accuracy.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import NounPhraseExtractorType

# Use in NLP extraction configuration
extractor = NounPhraseExtractorType.RegexEnglish
```

## ModularityMetric

Enum for the modularity metric to use in graph clustering.

**Module:** `graphrag.config.enums`

**Base class:** `str, Enum`

### Values

<ParamField path="Graph" type="str" default="graph">
  Graph modularity metric. Standard modularity calculation for community detection.
</ParamField>

<ParamField path="LCC" type="str" default="lcc">
  Largest connected component modularity metric.
</ParamField>

<ParamField path="WeightedComponents" type="str" default="weighted_components">
  Weighted components modularity metric. Takes edge weights into account when calculating modularity.
</ParamField>

### Example

```python theme={null}
from graphrag.config.enums import ModularityMetric

# Use in clustering configuration
metric = ModularityMetric.Graph
```

## Usage in configuration

Enums are used throughout the GraphRAG configuration to provide type safety and validation:

```yaml theme={null}
# settings.yaml example
reporting:
  type: file  # Uses ReportingType.file
  base_dir: logs

async_mode: threaded  # Uses AsyncType.Threaded

extract_graph_nlp:
  text_analyzer:
    extractor_type: regex_english  # Uses NounPhraseExtractorType.RegexEnglish
```

When loading configuration, string values are automatically converted to the appropriate enum types.