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https://github.com/yswa-var/langchain-bs
experiments and learning
https://github.com/yswa-var/langchain-bs
Last synced: 7 days ago
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experiments and learning
- Host: GitHub
- URL: https://github.com/yswa-var/langchain-bs
- Owner: yswa-var
- Created: 2025-01-09T17:55:48.000Z (17 days ago)
- Default Branch: main
- Last Pushed: 2025-01-13T10:24:04.000Z (13 days ago)
- Last Synced: 2025-01-13T11:35:07.293Z (13 days ago)
- Language: Jupyter Notebook
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# langchain-bs
# LangGraph Learning Path: A Project-Based Journey
## Foundation Level Projects
### Project 1: Simple Q&A Assistant
**Learning Goals:**
- Basic StateGraph setup
- Simple state management
- Basic LLM integration
```python
# Example core structure
from langgraph.graph import StateGraph
def process_question(state):
# Process user question
return {"response": "processed_question"}
graph = StateGraph()
graph.add_node("process", process_question)
```
**Implementation Steps:**
1. Create a basic question-answering system
2. Implement single-turn conversations
3. Add basic error handling
4. Learn state management basics
### Project 2: Memory-Enhanced Chatbot
**Learning Goals:**
- Conversation history management
- Basic context tracking
- Multi-turn dialogues
```python
class ChatState(TypedDict):
history: List[str]
context: Dict
current_input: str
```
**Key Features:**
1. Store conversation history
2. Track basic user preferences
3. Implement simple context management
4. Handle basic follow-up questions
## Intermediate Level Projects
### Project 3: Multi-Agent Task Manager
**Learning Goals:**
- Multiple agent coordination
- Complex state management
- Task routing
**Implementation Features:**
1. Create specialized agents (Researcher, Writer, Editor)
2. Implement task delegation logic
3. Manage shared state between agents
4. Add result aggregation
```python
def route_task(state):
task_type = analyze_task(state['input'])
if task_type == "research":
return "researcher_agent"
elif task_type == "writing":
return "writer_agent"
return "default_agent"
```
### Project 4: Context-Aware Document Assistant
**Learning Goals:**
- Document processing
- Advanced context management
- Information retrieval
**Key Components:**
1. Document chunking and indexing
2. Relevant context retrieval
3. Answer generation with citations
4. Context window management
## Advanced Level Projects
### Project 5: Workflow Automation System
**Learning Goals:**
- Complex workflow management
- Integration with external systems
- Advanced state machines
**System Components:**
1. Workflow definition engine
2. Task scheduling system
3. Progress monitoring
4. Error recovery mechanisms
```python
class WorkflowState(TypedDict):
status: str
current_step: str
progress: float
error_count: int
recovery_attempts: int
```
### Project 6: Enterprise Knowledge Management System
**Learning Goals:**
- Large-scale state management
- Advanced caching strategies
- System integration
**Advanced Features:**
1. Knowledge graph integration
2. Multi-user state management
3. Version control for conversations
4. Advanced security features
## Expert Level Projects
### Project 7: Autonomous Business Process Manager
**Learning Goals:**
- Complex business logic
- Advanced decision making
- System orchestration
**Core Capabilities:**
1. Process mining and optimization
2. Automated decision making
3. Performance monitoring
4. Advanced error prediction
### Project 8: AI Operations Platform
**Learning Goals:**
- System scaling
- Performance optimization
- Advanced monitoring
**Platform Features:**
1. Load balancing
2. State persistence
3. Real-time monitoring
4. Advanced debugging tools
## Best Practices Throughout
1. **Code Organization:**
```python
project_root/
│
├── src/
│ ├── agents/
│ ├── states/
│ ├── workflows/
│ └── utils/
│
├── tests/
│ ├── unit/
│ └── integration/
│
└── configs/
```
2. **Testing Strategy:**
- Unit tests for each component
- Integration tests for workflows
- State transition testing
- Error scenario coverage
3. **Documentation:**
- Architecture diagrams
- State flow documentation
- API documentation
- Usage examples
4. **Monitoring:**
- Performance metrics
- Error tracking
- State transition logging
- User interaction analytics
## Learning Resources
1. **Core Knowledge:**
- LangGraph documentation
- State machine principles
- LLM fundamentals
- Python best practices
2. **Advanced Topics:**
- Distributed systems
- Cache optimization
- Security patterns
- Performance tuning
3. **Tools:**
- Debugging tools
- Monitoring systems
- Testing frameworks
- Development environments
## Career Development Path
1. **Entry Level:**
- Focus on Projects 1-2
- Learn basic concepts
- Build simple applications
2. **Mid Level:**
- Implement Projects 3-4
- Understand complex workflows
- Handle real-world use cases
3. **Senior Level:**
- Develop Projects 5-6
- Design system architecture
- Lead technical decisions
4. **Expert Level:**
- Create Projects 7-8
- Innovate new solutions
- Mentor others