Programming

LangChain Framework

Master the LangChain Framework from beginner to advanced. Build production-grade LLM applications, conversational systems, intelligent agents, and RAG pipelines. Learn core components including Chat Models, Prompt Templates, Output Parsers, LCEL (LangChain Expression Language), Document Processing, Text Splitters, Embeddings, Retrievers, Custom …

6 Months Duration
15 Modules
Paid Access

Course fee ~~₹20000.00~~ ₹10000.00

Login to enroll and access modules, quizzes, and AI tutoring.

Login to Enroll

Learning path

Course Modules

Work through each module and pass quizzes to unlock the next step in your journey.

Module 1 Locked

Introduction to LangChain

Get started with the LangChain framework. Understand what LangChain is, why it is essential for modern LLM-powered application development, its layered architecture, and the broader ecosystem. Set up your development environment and build your very …

Enroll in this course to access modules.

Learning objectives

• Understand what LangChain is and the problems it solves
• Explore why developers use LangChain instead of direct API calls
• Discover core features and capabilities of the framework
• Understand the layered architecture of LangChain
• Navigate the LangChain ecosystem (langchain-core, community, partner packages)
• Install LangChain and configure your Python development environment
• Build and execute your first end-to-end LangChain application

Module 2 Locked

Language Models

Deep dive into language models and chat models in LangChain. Understand the distinction between traditional LLMs and modern Chat Models, establish model connections, tune critical parameters like temperature and max tokens, enable real-time streaming responses, …

Enroll in this course to access modules.

Learning objectives

• Understand the architectural difference between LLMs (text-in/text-out) and Chat Models (messages-in/messages-out)
• Connect and authenticate various language model providers
• Configure model hyperparameters including temperature and max tokens
• Implement streaming responses for real-time user experiences
• Handle API failures, timeouts, and rate limits gracefully with fallbacks and retry logic

Module 3 Locked

Prompt Templates

Master prompt engineering through reusable, parameter-driven prompt templates. Learn PromptTemplate and ChatPromptTemplate, construct multi-role message sequences (System, Human, AI), inject dynamic conversational context using MessagePlaceholders, use partial variables, compose modular prompts, and follow industry best …

Enroll in this course to access modules.

Learning objectives

• Understand the role and benefits of Prompt Templates over raw f-strings
• Construct string PromptTemplates and structured ChatPromptTemplates
• Understand role-based messages: SystemMessage, HumanMessage, and AIMessage
• Inject dynamic conversation history using MessagesPlaceholder
• Bind static or computed partial variables to prompts
• Compose multiple modular prompt templates together cleanly
• Implement prompt engineering best practices for consistency and precision

Module 4 Locked

Output Parsers

Transform raw unstructured language model outputs into clean, reliable, and typed data structures. Explore String, JSON, and Pydantic Output Parsers, learn structured outputs with native tool calling, build custom parsers, and implement schema validation with …

Enroll in this course to access modules.

Learning objectives

• Understand the role and importance of Output Parsers in LLM architectures
• Use StrOutputParser to stream and extract plain text from AIMessages
• Parse model responses into JSON data structures
• Leverage PydanticOutputParser for strict schema enforcement and type validation
• Implement model.with_structured_output() for native JSON schemas
• Create custom output parsers by subclassing BaseOutputParser
• Validate outputs and remediate parsing failures automatically

Module 5 Locked

LCEL (LangChain Expression Language)

Master LangChain Expression Language (LCEL), the declarative standard for composing production-ready LLM workflows. Understand Runnable objects, utilize RunnableSequence, RunnableParallel, and RunnableLambda, master the pipe (|) operator, implement conditional branching, debug pipelines, and adopt best practices.

Enroll in this course to access modules.

Learning objectives

• Understand the design philosophy and capabilities of LCEL
• Master the standard Runnable protocol (invoke, stream, batch, ainvoke, astream)
• Chain components together seamlessly using the pipe (|) operator
• Execute tasks concurrently with RunnableParallel
• Integrate custom Python logic into chains using RunnableLambda
• Implement conditional routing and branching in pipelines
• Debug LCEL chains with inspection tools and follow best practices

Module 6 Locked

Chains

Build sophisticated, multi-step workflows by composing LangChain chains. Learn sequential, parallel, and conditional chains, combine multiple chains into nested architectures, handle dynamic execution flows, debug chain execution, optimize performance, and build complete enterprise workflows.

Enroll in this course to access modules.

Learning objectives

• Understand chain architecture and composition patterns
• Build linear sequential chains passing data from step to step
• Execute parallel chains for simultaneous extraction or multi-perspective analysis
• Build dynamic and conditional chains based on runtime evaluation
• Design modular nested chains for reusable workflow components
• Profile, debug, and optimize chain latency and token consumption
• Construct complete end-to-end applications combining prompts, models, and tools

Module 7 Locked

Memory

Enable contextual continuity in conversational AI systems. Learn core memory concepts, compare ConversationBufferMemory, ConversationBufferWindowMemory, ConversationSummaryMemory, and EntityMemory. Manage chat histories efficiently, avoid context window overflow, and persist memory in production databases.

Enroll in this course to access modules.

Learning objectives

• Understand why memory is critical for stateful conversational LLM applications
• Implement ConversationBufferMemory for exact transcript preservation
• Use Window Memory (k-turns) to control token usage and cost
• Apply Summary Memory to condense long chat histories using an LLM
• Track specific entities across interactions using Entity Memory
• Integrate persistent storage backends (Redis, PostgreSQL, DynamoDB) for chat histories
• Use RunnableWithMessageHistory in modern LCEL applications

Module 8 Locked

Messages

Master the message abstraction that underpins modern chat models. Understand HumanMessage, AIMessage, SystemMessage, ToolMessage, and FunctionMessage. Learn message serialization, historical sequencing, session persistence, and best practices for multi-turn conversations.

Enroll in this course to access modules.

Learning objectives

• Understand the standard message hierarchy in langchain_core.messages
• Distinguish the roles of HumanMessage, AIMessage, and SystemMessage
• Work with ToolMessage and FunctionMessage during agent tool execution
• Inspect tool_calls and token usage metadata stored in AIMessage
• Serialize messages to JSON/dictionaries and deserialize back to objects
• Manage conversational sequences effectively across multi-turn sessions

Module 9 Locked

Document Processing

Learn to ingest and process unstructured data from diverse sources into standardized Document objects. Work with Document Loaders for Text, PDF, CSV, Word, and HTML files, manage document metadata for downstream filtering, and create custom …

Enroll in this course to access modules.

Learning objectives

• Understand the structure and attributes of the LangChain Document object (page_content, metadata)
• Load data from text files, PDF documents, CSV spreadsheets, and DOCX files
• Scrape and clean web content using HTML and web document loaders
• Enrich and preserve metadata (source, author, date, page) for precise retrieval
• Create custom Document Loaders by subclassing BaseLoader

Module 10 Locked

Text Splitters

Break large documents into semantically coherent chunks optimized for embedding models and vector search. Master CharacterTextSplitter and RecursiveCharacterTextSplitter, explore token-based, Markdown, and HTML splitters, tune chunk size and chunk overlap, and implement best practices to …

Enroll in this course to access modules.

Learning objectives

• Understand why text splitting is essential for embedding and retrieval
• Compare CharacterTextSplitter with RecursiveCharacterTextSplitter
• Split documents based on token limits using TokenTextSplitter
• Preserve document structure using Markdown and HTML splitters
• Tune chunk size and chunk overlap for optimal retrieval balance
• Avoid semantic truncation and context loss across chunk boundaries

Module 11 Locked

Embeddings

Understand how language models represent text semantics as dense numerical vectors. Learn embedding models from leading providers, generate embeddings for queries and documents, compute similarity using cosine similarity and dot product, optimize embedding pipelines, and …

Enroll in this course to access modules.

Learning objectives

• Understand the mathematical concept of semantic vector embeddings
• Explore embedding model providers (OpenAI, HuggingFace, Ollama, Cohere)
• Distinguish embed_documents() from embed_query()
• Measure vector distances using Cosine Similarity and Euclidean distance
• Optimize embedding throughput, batching, and cost
• Diagnose and troubleshoot common issues like semantic drift and dimensionality mismatches

Module 12 Locked

Retrievers

Build advanced retrieval strategies for Retrieval-Augmented Generation (RAG). Move beyond simple VectorStoreRetriever to MultiQueryRetriever, ContextualCompressionRetriever, ParentDocumentRetriever, and EnsembleRetriever. Learn retriever configuration, scoring thresholds, and custom retriever development.

Enroll in this course to access modules.

Learning objectives

• Understand the standard Retriever interface in LangChain (invoke returning List[Document])
• Configure VectorStoreRetriever with similarity, MMR, and score threshold search types
• Use MultiQueryRetriever to overcome query formulation limitations
• Apply ContextualCompressionRetriever to strip out irrelevant text before passing to the LLM
• Implement ParentDocumentRetriever for small chunk search with large chunk context
• Combine sparse (BM25) and dense (vector) retrieval using EnsembleRetriever
• Build custom retrievers for unique data backends

Module 13 Locked

Tools

Empower language models to interact with external systems, APIs, and computation engines. Explore built-in tools, create custom tools using the @tool decorator and BaseTool class, define strict parameter schemas with Pydantic, execute tools safely, implement …

Enroll in this course to access modules.

Learning objectives

• Understand the role of Tools in enabling LLM agency and external interaction
• Use LangChain built-in tools (search, calculation, file system, python REPL)
• Create custom tools effortlessly using the @tool decorator
• Build class-based tools subclassing BaseTool with Pydantic input schemas
• Document tools with clear names and descriptions to guide model tool selection
• Handle tool execution errors gracefully to allow LLM self-correction
• Apply best practices for tool security, sandboxing, and performance

Module 14 Locked

Agents

Build intelligent, autonomous AI agents capable of multi-step reasoning, dynamic tool invocation, and goal execution. Understand agent architectures (ReAct, Plan-and-Solve), implement tool calling agents, manage AgentExecutors, handle iteration limits and error recovery, and construct production …

Enroll in this course to access modules.

Learning objectives

• Understand how Agents differ from deterministic Chains
• Explore agent architectures (Reasoning + Acting: ReAct, Tool Calling agents)
• Create tool-calling agents using create_tool_calling_agent
• Run and configure AgentExecutor with max_iterations and execution timeouts
• Manage intermediate steps, thoughts, and observation loops
• Implement robust error recovery for tool failures during reasoning
• Build multi-step goal-oriented AI agents from scratch

Module 15 Locked

Callbacks & Observability

Gain complete visibility and observability into your LangChain applications. Learn callback handlers, log chain and agent executions, track token consumption and API expenditures, trace execution graphs with LangSmith, diagnose errors, and perform deep performance and …

Enroll in this course to access modules.

Learning objectives

• Understand the callback system and lifecycle hooks in LangChain
• Implement custom callback handlers subclassing BaseCallbackHandler
• Hook into on_llm_start, on_llm_end, on_chain_start, and on_tool_start events
• Monitor agent thought processes, action dispatches, and tool observations
• Track token usage and estimate API costs using context managers
• Integrate LangSmith for tracing, debugging, and prompt evaluation
• Diagnose latency bottlenecks and optimize production performance

Techietact AI Assistant

Ask me about courses and features

Hello! 👋 I'm your Techietact AI assistant. I'd love to help you! To get started, could you please share your name, email, and contact number?