> ## 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.

# graphrag index

> Build a knowledge graph index from your documents

The `index` command builds a knowledge graph index from your source documents by extracting entities, relationships, and communities.

## Usage

```bash theme={null}
graphrag index [OPTIONS]
```

## Options

<ParamField path="--root" type="string" default="current directory">
  The project root directory containing the `settings.yaml` configuration file.

  **Aliases**: `-r`
</ParamField>

<ParamField path="--method" type="string" default="standard">
  The indexing method to use.

  **Aliases**: `-m`

  Available methods:

  * `standard` - Traditional GraphRAG indexing with all graph construction and summarization performed by an LLM
  * `fast` - Fast indexing using NLP for graph construction and LLM for summarization
</ParamField>

<ParamField path="--verbose" type="boolean" default="false">
  Run the indexing pipeline with verbose logging to see detailed progress information.

  **Aliases**: `-v`
</ParamField>

<ParamField path="--dry-run" type="boolean" default="false">
  Run the indexing pipeline without executing any steps. Useful for inspecting and validating the configuration before running.
</ParamField>

<ParamField path="--cache" type="boolean" default="true">
  Use LLM response caching to avoid redundant API calls and reduce costs.

  Use `--no-cache` to disable caching.
</ParamField>

<ParamField path="--skip-validation" type="boolean" default="false">
  Skip any preflight validation checks. Useful when running indexing without LLM steps or in specialized configurations.
</ParamField>

## Examples

### Basic indexing

Run indexing with default settings:

```bash theme={null}
graphrag index
```

### Specify project directory

```bash theme={null}
graphrag index --root ./my-project
```

### Use fast indexing method

```bash theme={null}
graphrag index --method fast
```

Fast indexing uses NLP-based entity extraction instead of LLM-based extraction, which is faster and cheaper but may be less accurate.

### Verbose logging

```bash theme={null}
graphrag index --verbose
```

### Dry run to validate configuration

```bash theme={null}
graphrag index --dry-run
```

This will load and validate your configuration without actually running the indexing pipeline.

### Disable caching

```bash theme={null}
graphrag index --no-cache
```

### Skip validation

```bash theme={null}
graphrag index --skip-validation
```

## Output

The indexing pipeline creates several output files in the `output/` directory:

* **`entities.parquet`** - Extracted entities with descriptions and embeddings
* **`relationships.parquet`** - Relationships between entities
* **`communities.parquet`** - Detected community structure
* **`community_reports.parquet`** - Summarized reports for each community
* **`text_units.parquet`** - Chunked text units with embeddings
* **`covariates.parquet`** - Extracted claims (if claim extraction is enabled)

## Indexing process

The indexing pipeline performs the following steps:

1. **Document chunking** - Split documents into manageable text chunks
2. **Entity extraction** - Extract entities and relationships using LLM or NLP
3. **Entity resolution** - Merge duplicate entities and summarize descriptions
4. **Community detection** - Detect hierarchical communities using Leiden algorithm
5. **Community summarization** - Generate natural language summaries for each community
6. **Embedding generation** - Create vector embeddings for entities and text units

## Performance considerations

* **Standard method**: More accurate but slower and more expensive (uses LLM for all extractions)
* **Fast method**: Faster and cheaper but potentially less accurate (uses NLP for entity extraction)
* **Caching**: Keep caching enabled to avoid redundant API calls during re-runs
* **Concurrent requests**: Adjust `concurrent_requests` in `settings.yaml` to control API rate limits

## Error handling

The indexing command will exit with status code 1 if any errors are encountered during the pipeline. Check the logs for detailed error messages.

Common issues:

* **Missing API key**: Ensure `GRAPHRAG_API_KEY` is set in your `.env` file
* **Invalid configuration**: Run with `--dry-run` to validate your configuration
* **Rate limits**: Reduce `concurrent_requests` in `settings.yaml`
* **Out of memory**: Reduce `chunk_size` or process fewer documents

## Next steps

After building an index:

* [Query your knowledge graph](/cli/query)
* [Update the index with new data](/cli/update)
* [Configure indexing settings](/indexing/overview)