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.github/workflows/deploy-docs.yml

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push:
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branches:
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- main
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paths:
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- 'website/**'
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workflow_dispatch:
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permissions:

README.md

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@@ -42,14 +42,6 @@ Bizy consists of several key components:
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- **FastMCP**: High-performance MCP implementations and tool coordination
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- **Zep AI**: Memory systems and temporal knowledge graphs
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This project is organized into sprints focusing on ecosystem contribution:
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- **Sprint 1**: Cross-Framework Foundation (Days 1-10)
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- **Sprint 2**: Advanced Multi-Framework Patterns (Days 11-20)
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- **Sprint 3**: Ecosystem Leadership Establishment (Days 21-30)
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See `CLAUDE.md` for detailed implementation tasks.
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## Contributing
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We welcome contributions that enhance cross-framework business logic coordination. Please see our contribution guidelines in `/docs/community/contributing.md`.

website/README.md

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# Bizy Documentation Website
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This directory contains the Docusaurus-based documentation for Bizy.
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## Local Development
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```bash
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cd website
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npm install
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npm start
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```
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## Build
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```bash
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npm run build
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```
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The build artifacts will be stored in the `build/` directory.
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## Deployment
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The site is automatically deployed to GitHub Pages when changes are pushed to the main branch.
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---
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sidebar_position: 3
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---
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# Your First Workflow
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Build a complete customer service workflow using multiple AI frameworks.
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## Overview
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We'll create a workflow that:
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1. Analyzes customer sentiment (LangChain)
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2. Checks customer history (Zep Memory)
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3. Creates support ticket (MCP Tools)
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4. Starts resolution workflow (Temporal)
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## Step 1: Define the Business Rules
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Create `rules/customer_service.yaml`:
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```yaml
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rule_set: customer_service
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rules:
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- rule: analyze_sentiment
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description: Analyze customer message sentiment
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priority: high
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conditions:
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- message.text != null
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actions:
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- framework: langchain
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action: sentiment_analysis
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output: sentiment_score
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- rule: check_escalation
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description: Determine if escalation needed
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priority: high
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conditions:
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- sentiment_score < 0.3
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- customer.tier == "premium"
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actions:
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- framework: zep
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action: get_customer_history
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- framework: temporal
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action: start_escalation_workflow
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params:
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priority: high
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- rule: create_ticket
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description: Create support ticket
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priority: medium
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conditions:
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- sentiment_score < 0.7
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actions:
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- framework: mcp
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action: create_ticket
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params:
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type: support
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auto_assign: true
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```
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## Step 2: Initialize the Frameworks
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```python
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from bizy.core import MetaOrchestrator
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from bizy.adapters import (
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LangChainAdapter,
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TemporalAdapter,
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MCPAdapter,
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ZepAdapter
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)
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async def setup_orchestrator():
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orchestrator = MetaOrchestrator()
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# Configure adapters
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adapters = {
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"langchain": LangChainAdapter({"api_key": "..."}),
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"temporal": TemporalAdapter({"host": "localhost"}),
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"mcp": MCPAdapter({"server_url": "http://localhost:8080"}),
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"zep": ZepAdapter({"api_url": "http://localhost:8000"})
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}
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# Register all adapters
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for name, adapter in adapters.items():
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await adapter.connect()
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orchestrator.register_adapter(name, adapter)
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# Load rules
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orchestrator.load_rules("rules/customer_service.yaml")
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return orchestrator
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```
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## Step 3: Create the Workflow Handler
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```python
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class CustomerServiceHandler:
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def __init__(self, orchestrator):
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self.orchestrator = orchestrator
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async def handle_customer_message(self, customer_id: str, message: str):
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# Prepare context
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context = {
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"customer": await self.get_customer_info(customer_id),
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"message": {"text": message}
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}
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# Execute rules
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result = await self.orchestrator.execute(
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rule_set="customer_service",
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context=context
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)
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return {
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"ticket_id": result.get("ticket_id"),
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"workflow_id": result.get("workflow_id"),
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"sentiment": result.get("sentiment_score"),
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"actions_taken": result.get("executed_actions")
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}
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```
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## Step 4: Run the Workflow
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```python
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async def main():
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# Setup
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orchestrator = await setup_orchestrator()
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handler = CustomerServiceHandler(orchestrator)
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# Handle a customer message
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result = await handler.handle_customer_message(
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customer_id="cust_123",
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message="I'm very frustrated with the service!"
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)
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print(f"Ticket created: {result['ticket_id']}")
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print(f"Sentiment score: {result['sentiment']}")
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print(f"Actions taken: {result['actions_taken']}")
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# Run
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import asyncio
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asyncio.run(main())
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```
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## Expected Output
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```
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Ticket created: TICK-2024-001
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Sentiment score: 0.2
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Actions taken: [
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"langchain.sentiment_analysis",
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"zep.get_customer_history",
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"temporal.start_escalation_workflow",
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"mcp.create_ticket"
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]
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```
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## Next Steps
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- Add error handling and retries
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- Implement custom actions
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- Create workflow monitoring
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- Scale with multiple workers
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## Learn More
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- [Business Rule Patterns](../rules/best-practices)
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- [Error Handling](../tutorials/error-handling)
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- [Production Deployment](../deployment/requirements)
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---
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sidebar_position: 1
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---
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# Installation
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Get Bizy up and running in your environment.
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## Prerequisites
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- Python 3.12 or higher
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- Poetry (for dependency management)
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- Redis (for event bus)
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- Git
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## Install with Poetry
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```bash
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# Clone the repository
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git clone https://github.com/getfounded/bizy.git
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cd bizy
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# Install dependencies
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poetry install
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# Activate virtual environment
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poetry shell
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```
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## Install with pip
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```bash
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pip install bizy
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```
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## Verify Installation
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```python
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import bizy
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print(bizy.__version__)
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# Output: 0.1.0
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```
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## Configure Environment
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Copy the example environment file:
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```bash
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cp .env.example .env
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```
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Edit `.env` with your configuration:
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```env
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# LangChain Configuration
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LANGCHAIN_API_KEY=your-api-key
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LANGCHAIN_MODEL=gpt-4
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# Temporal Configuration
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TEMPORAL_HOST=localhost
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TEMPORAL_PORT=7233
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# Redis Configuration
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REDIS_HOST=localhost
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REDIS_PORT=6379
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```
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## Next Steps
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- Follow the [Quick Start](./quick-start) guide
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- Create your [First Workflow](./first-workflow)
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---
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sidebar_position: 2
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---
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# Quick Start
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Build your first cross-framework workflow in 5 minutes.
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## Basic Example
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### 1. Define a Business Rule
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Create a YAML file `rules/hello_world.yaml`:
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```yaml
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rule: hello_world
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description: Simple greeting rule
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priority: high
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conditions:
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- input.name != null
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actions:
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- framework: langchain
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action: generate_greeting
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params:
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template: "Hello, {name}!"
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```
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### 2. Create the Orchestrator
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```python
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from bizy.core import MetaOrchestrator
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from bizy.adapters import LangChainAdapter
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# Initialize orchestrator
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orchestrator = MetaOrchestrator()
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# Register LangChain adapter
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langchain = LangChainAdapter({
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"api_key": "your-api-key"
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})
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orchestrator.register_adapter("langchain", langchain)
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# Load business rules
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orchestrator.load_rules("rules/hello_world.yaml")
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```
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### 3. Execute the Rule
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```python
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# Execute business rule
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result = await orchestrator.execute(
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rule_name="hello_world",
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context={"name": "Alice"}
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)
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print(result)
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# Output: {"greeting": "Hello, Alice!"}
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```
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## Multi-Framework Example
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Coordinate between LangChain and Temporal:
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```python
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from bizy.adapters import TemporalAdapter
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# Add Temporal adapter
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temporal = TemporalAdapter({
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"host": "localhost",
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"port": 7233
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})
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orchestrator.register_adapter("temporal", temporal)
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# Define multi-framework rule
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rule = """
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rule: process_document
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conditions:
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- document.type == "invoice"
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actions:
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- framework: langchain
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action: extract_data
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- framework: temporal
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action: start_approval_workflow
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params:
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workflow: "invoice_approval"
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"""
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# Execute
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result = await orchestrator.execute(
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rule_name="process_document",
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context={"document": {"type": "invoice", "content": "..."}}
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)
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```
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## What's Next?
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- Learn about [Business Rules](../rules/rule-syntax)
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- Explore [Framework Adapters](../adapters/langchain)
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- Build complex [Multi-Framework Scenarios](../tutorials/multi-framework-scenarios)

website/static/CNAME

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docs.bizy.work

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