Course Content

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Module 1: Introduction to LangFlow

• What is LangFlow?

• LangChain vs LangFlow

• Why Visual Flow-Based Development?

• Real-world Use Cases

  • Chatbots

  • RAG (Retrieval-Augmented Generation)

  • Automation Agents

  • Data Extraction & Summarization

  • API-based AI workflows

• LangFlow Interface Walkthrough

Module 2: LangFlow Building Blocks

• Components Overview

  • Prompts

  • Models (OpenAI, Groq, Gemini, Local LLMs)

  • Tools & Utilities

  • Chains

  • Memory

• Inputs & Outputs

• Nodes, Connections & Parameters

• Debugging Nodes and Flow Logs

Module 3: Prompt Engineering in LangFlow

• Types of Prompts:

  • System, User, Template

• Variables inside prompts

• Building dynamic prompts

• Good vs Bad prompts

• Prompt optimization techniques

Module 4: Working with Tools & APIs

• Built-in Tools:

  • Search

  • Calculator

  • Document Loaders

  • Web Request

  • Code Execution

• Adding Custom Tools

• Connecting to external APIs (Weather, Finance, CRM)

Module 5: Document Processing in LangFlow

• Document Loaders (PDF, Docs, Text, URL)

• Embeddings (OpenAI, HuggingFace, Local)

• Vector Stores

  • Chroma

  • FAISS

  • Pinecone

  • Weaviate

• RAG Architecture Overview

• Using Indexes & Retrievers

Module 6: Memory & Context Handling

• Types of Memory:

  • ConversationBufferMemory

  • ConversationSummaryMemory

  • VectorStoreRetrieverMemory

• When to use which memory?

• Improving conversational agents using memory

Module 7: Advanced LangFlow Chains

• Sequential Chains

• Conditional/Branching Chains

• Parallel Chains

• Custom Logic Nodes

• Error Handling

• Handling large data pipelines