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

# LlamaIndex Integration (OSS)

> Use AuditedReader to load privacy-safe local documents into LlamaIndex.

<Note>
  This guide covers the **zero-dependency OSS library** (`flexorch-audit`) for loading local files. To load FlexOrch dataset chunks directly using `FlexOrchReader`, see [LlamaIndex — Platform SDK](/guides/llamaindex).
</Note>

## Overview

`flexorch-audit` ships a LlamaIndex-compatible reader that audits documents and optionally masks PII before indexing.

```bash theme={null}
pip install flexorch-audit llama-index
```

***

## AuditedReader

```python theme={null}
from flexorch_audit.integrations.llamaindex import AuditedReader

reader = AuditedReader(
    min_grade="B",
    mask_pii=True,
    locales=["universal", "tr"],
)

documents = reader.load_data(
    file_paths=["contracts/agreement.pdf", "reports/q1.docx"]
)

for doc in documents:
    print(doc.metadata["quality_grade"])
    print(doc.metadata["pii_findings_count"])
    print(doc.text[:200])  # PII masked
```

***

## Parameters

| Parameter   | Type       | Default | Description                      |
| ----------- | ---------- | ------- | -------------------------------- |
| `min_grade` | str        | `"D"`   | Minimum quality grade to include |
| `mask_pii`  | bool       | `True`  | Mask PII before indexing         |
| `locales`   | list\[str] | all     | PII detection scope              |

***

## In an index pipeline

```python theme={null}
from llama_index.core import VectorStoreIndex
from flexorch_audit.integrations.llamaindex import AuditedReader

reader = AuditedReader(min_grade="B", mask_pii=True)
documents = reader.load_data(file_paths=["data/"])

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

response = query_engine.query("What are the payment terms in the contracts?")
print(response)
```

Low-quality or excluded documents are available in `reader.skipped` with their grade and reason.