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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

LangChain for Production-Ready LLM Applications

Prerequisites: Python & Basic LangChain

Level: Advanced

What You'll Learn

Advanced Architecture
Design and implement complex, multi-step chains and agents using advanced LangChain features.
Memory Management
Select and configure appropriate memory mechanisms for maintaining long-term conversational context.
Data Retrieval Optimization
Architect and optimize retrieval-augmented generation (RAG) pipelines for production scale.
Evaluation and Monitoring
Implement comprehensive evaluation, monitoring, and observability for LangChain applications using tools like LangSmith.
Optimization and Scaling
Apply performance optimization techniques and cost management strategies for LLM applications.
Deployment Strategies
Package, deploy, and scale LangChain applications using containerization and cloud-native technologies.
Security Implementation
Integrate security best practices throughout the LangChain application lifecycle.

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