LangChain for Production-Ready LLM Applications
Chapter 1: Advanced LangChain Architecture and Customization
LangChain Expression Language (LCEL) Internals
Asynchronous Operations and Concurrency
Customizing Core Components: LLMs, Prompts, Parsers
Advanced Output Parsing Strategies
Managing State in Complex Chains
Debugging LangChain Execution Flow
Hands-on Practical: Building a Custom Chain Component
Chapter 2: Building Sophisticated Agents and Tools
Agent Architectures: ReAct, Self-Ask, Plan-and-Execute
Developing Custom Tools for Agents
Handling Tool Errors and Agent Recovery
Multi-Agent Systems and Collaboration Patterns
Structured Tool Calling and Function Integration
Agent Execution Tracing and Analysis
Practice: Creating an Agent with Custom API Tools
Chapter 3: Advanced Memory Management Techniques
Comparing Advanced Memory Types
Implementing Persistent Memory Stores
Context Window Management Strategies
Custom Memory Module Development
Integrating Memory with Agents and Chains
Handling Memory in Asynchronous Applications
Hands-on Practical: Implementing Vector Store Memory
Chapter 4: Production-Grade Data Integration and Retrieval
Advanced Document Loading and Transformation
Vector Store Selection and Optimization at Scale
Advanced Indexing Strategies
Hybrid Search Implementation
Re-ranking and Query Transformation
Managing Data Updates and Synchronization
Practice: Building an Optimized RAG Pipeline
Chapter 5: Evaluation, Monitoring, and Observability
Introduction to LangSmith for Production
Defining Custom Evaluation Metrics
Automated Evaluation Pipelines
Using LangSmith for Debugging and Root Cause Analysis
Monitoring Application Performance and Cost
Integrating with Third-Party Observability Platforms
Human-in-the-Loop Feedback and Annotation
Practice: Evaluating an Agent with LangSmith
Chapter 6: Optimizing and Scaling LangChain Applications
Identifying Performance Bottlenecks
LLM Call Optimization Techniques
Cost Management and Token Usage Tracking
Scaling Data Retrieval Systems
Handling High Concurrency and Throughput
Batch Processing for Offline Tasks
Practice: Performance Tuning a LangChain Chain
Chapter 7: Deployment Strategies for Production
Structuring LangChain Projects for Deployment
Containerizing LangChain Applications with Docker
Deployment Options: Servers, Kubernetes, Serverless
Serverless Deployment Patterns for LangChain
Managing Environment Variables and Secrets
Setting Up CI/CD Pipelines
Blue/Green and Canary Deployment Strategies
Hands-on Practical: Deploying a LangChain App via Docker
Chapter 8: Security Considerations in LangChain Applications
Understanding Attack Vectors in LLM Applications
Input Validation and Sanitization
Mitigating Prompt Injection Risks
Secure Output Handling and Parsing
Securing Custom Tools and API Interactions
Data Privacy and Handling Sensitive Information
Dependency Security Management
Practice: Implementing Input Validation
Data Privacy and Handling Sensitive Information
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- Information Privacy Engineering and Privacy by Design, Marit Hansen, Ann Cavoukian, Florian K./Tschohl, Sebastian O. K./B./Petersen, Rainer Rehagel, Robert S./R./S./T./V./W., 2020 (Springer) DOI: 10.1007/978-3-030-41002-1 - A guide to implementing privacy by design principles and privacy engineering techniques, directly relevant to building secure systems from the ground up.
- Privacy-preserving natural language processing: A survey, Jing He, Hongling Ma, 2022 Computer Science Review, Vol. 44 (Elsevier) DOI: 10.1016/j.cosrev.2022.100516 - Reviews techniques for anonymization, pseudonymization, and other privacy-enhancing methods in the context of natural language processing, which is key for handling user inputs and generated content in LLM applications.
- SoK: Security and Privacy in Large Language Models, Tong Zhang, Ruixiang Wang, Zimu Zhou, Kai Chen, 2023 Proceedings of the 32nd USENIX Security Symposium (USENIX Association) DOI: https://doi.org/10.5555/3593014.3593026 - A comprehensive systematic overview of security and privacy challenges specific to Large Language Models, including data leakage, prompt injection, and model misuse.