> ## 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.

# LangChain Integration (OSS)

> Use AuditedLoader to load local documents with automatic PII detection and quality filtering.

<Note>
  This guide covers the **zero-dependency OSS library** (`flexorch-audit`) for loading local files. To query FlexOrch datasets directly from LangChain using `FlexOrchRetriever`, see [LangChain — Platform SDK](/guides/langchain).
</Note>

## Overview

`flexorch-audit` ships a LangChain-compatible document loader that audits each document before it enters your chain.

```bash theme={null}
pip install flexorch-audit langchain-community
```

***

## AuditedLoader

```python theme={null}
from flexorch_audit.integrations.langchain import AuditedLoader

loader = AuditedLoader(
    file_paths=["contracts/agreement.pdf", "invoices/inv_001.pdf"],
    min_grade="B",          # Skip documents graded C or D
    mask_pii=True,          # Replace PII before loading
    locales=["universal", "tr", "de"],
)

docs = loader.load()

for doc in docs:
    print(doc.metadata["quality_grade"])      # "A"
    print(doc.metadata["pii_findings_count"]) # 2
    print(doc.page_content[:200])             # PII already masked
```

***

## Parameters

| Parameter    | Type       | Default  | Description                                           |
| ------------ | ---------- | -------- | ----------------------------------------------------- |
| `file_paths` | list\[str] | required | Paths to documents                                    |
| `min_grade`  | str        | `"D"`    | Minimum grade to include (`"A"`, `"B"`, `"C"`, `"D"`) |
| `mask_pii`   | bool       | `True`   | Replace PII with `[MASKED_...]` placeholders          |
| `locales`    | list\[str] | all      | Restrict PII detection to specific jurisdictions      |

***

## In a RAG pipeline

```python theme={null}
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from flexorch_audit.integrations.langchain import AuditedLoader

loader = AuditedLoader(file_paths=["docs/"], min_grade="B", mask_pii=True)
docs = loader.load()

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)

retriever = vectorstore.as_retriever()
```

Documents with grades below `min_grade` are silently skipped. Check `loader.skipped` for a list of excluded files and their grades.

***

## As a pre-processing step

```python theme={null}
from langchain_core.runnables import RunnableLambda
from flexorch_audit import redact_for_llm

# Use redact_for_llm() inline without a file loader
safe_chain = RunnableLambda(lambda text: redact_for_llm(text)[0]) | your_chain
```