langchain-enterprise-rbac

# LangChain Enterprise RBAC

## Overview
Implement role-based access control (RBAC) for LangChain applications with multi-tenant support and fine-grained permissions.

## Prerequisites
- LangChain application with user authentication
- Identity provider (Auth0, Okta, Azure AD)
- Understanding of RBAC concepts

## Instructions

### Step 1: Define Permission Model
```python
from enum import Enum
from typing import Set, Optional
from pydantic import BaseModel
from datetime import datetime

class Permission(str, Enum):
    # Chain permissions
    CHAIN_READ = "chain:read"
    CHAIN_EXECUTE = "chain:execute"
    CHAIN_CREATE = "chain:create"
    CHAIN_DELETE = "chain:delete"

    # Model permissions
    MODEL_GPT4 = "model:gpt-4"
    MODEL_GPT4_MINI = "model:gpt-4o-mini"
    MODEL_CLAUDE = "model:claude"

    # Feature permissions
    FEATURE_STREAMING = "feature:streaming"
    FEATURE_TOOLS = "feature:tools"
    FEATURE_RAG = "feature:rag"

    # Admin permissions
    ADMIN_USERS = "admin:users"
    ADMIN_BILLING = "admin:billing"
    ADMIN_AUDIT = "admin:audit"

class Role(BaseModel):
    name: str
    permissions: Set[Permission]
    description: str = ""

# Predefined roles
ROLES = {
    "viewer": Role(
        name="viewer",
        permissions={Permission.CHAIN_READ},
        description="Read-only access to chains"
    ),
    "user": Role(
        name="user",
        permissions={
            Permission.CHAIN_READ,
            Permission.CHAIN_EXECUTE,
            Permission.MODEL_GPT4_MINI,
        },
        description="Standard user with execution rights"
    ),
    "power_user": Role(
        name="power_user",
        permissions={
            Permission.CHAIN_READ,
            Permission.CHAIN_EXECUTE,
            Permission.CHAIN_CREATE,
            Permission.MODEL_GPT4_MINI,
            Permission.MODEL_GPT4,
            Permission.FEATURE_STREAMING,
            Permission.FEATURE_TOOLS,
        },
        description="Power user with advanced features"