LangChain Training Bangalore
Learn to build practical LLM applications using LangChain, prompt templates, RAG pipelines, vector databases, tools, agents, LangGraph and production-ready AI workflows with Technovids from Bengaluru.
LangChain Training Bangalore — Quick Facts
| Item | Details |
|---|---|
| Training | LangChain Training Bangalore — LLM apps, RAG, agents and LangGraph |
| Location served | Bangalore (Bengaluru), Karnataka, India |
| Office support | HSR Layout, Bengaluru — counselling and consultation |
| Delivery mode | Live online instructor-led · Blended learning |
| Best suited for | Software developers, Python developers, AI engineers, data scientists, data engineers, full stack developers, corporate AI teams |
| Key topics | LangChain fundamentals, prompt templates, chains, RAG pipelines, embeddings, vector stores, retrievers, tools, agents, LangGraph |
| Tools covered | LangChain, LangGraph, Chroma, FAISS, Pinecone, OpenAI/Anthropic APIs, Python |
| Projects / exercises | PDF Q&A assistant, RAG knowledge assistant, prompt template library, tool-using assistant, LangGraph workflow |
| Corporate option | Yes — customised corporate LangChain training for Bangalore teams |
| Contact | corporate@technovids.com · +91 86183 46384 · WhatsApp available |
Why Learn LangChain in Bangalore?
Bangalore developers are moving from GenAI experiments to LLM application development
Teams across Bangalore have experimented with ChatGPT prompts and basic API calls. The next step is building structured, maintainable LLM applications — RAG pipelines, prompt template libraries, tool-using agents — which requires a framework like LangChain and structured training.
LangChain brings structure to what was previously ad-hoc LLM code
Without a framework, LLM applications become tangled chains of prompt strings and API calls. LangChain provides reusable components — chains, retrievers, tools, memory — that make LLM applications maintainable, testable, and composable in a way that raw API code cannot.
RAG is the most important LangChain use case for enterprise teams
Enterprise teams in Bangalore need AI assistants that answer from internal documents — product docs, SOPs, knowledge bases. RAG is the architectural pattern that enables this. LangChain is the most common framework for building RAG pipelines in Python, making LangChain training directly applicable to real project work.
LangChain connects naturally to LangGraph, MCP, RAG and production AI engineering
LangChain is not isolated — it integrates with LangGraph for agent workflows, with vector databases for RAG, with MCP for tool integrations, and with standard Python deployment patterns. Learning LangChain is the practical foundation for the broader AI engineering stack.
What You Will Learn
A practical, code-first LangChain curriculum — from fundamentals through production-ready RAG pipelines, agents, and LangGraph workflows.
LangChain Fundamentals
What LangChain is, how it structures LLM applications, and why it has become the standard framework for building AI-powered systems.
LLM Application Structure
Design LangChain applications with clear separation of concerns — prompts, models, parsers, memory, and chains.
Prompt Templates
Build reusable, dynamic prompt templates using PromptTemplate, ChatPromptTemplate, and template composition patterns.
Chains and Runnable Workflows
Compose LLM, prompt, and output-parser steps into chains using LCEL (LangChain Expression Language) for readable, composable pipelines.
Document Loaders
Load content from PDFs, Word docs, web pages, CSVs, and databases into LangChain pipelines using the built-in loader ecosystem.
Embeddings
Convert text to vector embeddings using OpenAI, Cohere, or sentence-transformer models within the LangChain embeddings interface.
Vector Stores
Store and search embeddings with Chroma, FAISS, and Pinecone — index documents, run similarity search, and retrieve relevant passages.
Retrievers
Build retrieval components that fetch relevant context for LLM answers — similarity, MMR, and metadata-filtered retrieval strategies.
RAG Pipelines
Build complete Retrieval-Augmented Generation pipelines in LangChain — ingestion, retrieval, augmentation, and grounded answer generation.
Tools and Tool Calling
Define custom tools and connect agents to external APIs, calculators, search engines, and databases using the LangChain tools interface.
Agents Basics
Build LangChain agents that reason over tools, decide which to call, parse outputs, and loop until a task is complete.
LangGraph Introduction
Extend LangChain with LangGraph for stateful, graph-based agent workflows — nodes, edges, state objects, and multi-step loops.
Production Considerations
Structure LangChain applications for production — error handling, retries, streaming responses, and API integration patterns.
Monitoring, Evaluation and Cost Awareness
Trace LangChain runs, evaluate chain outputs, monitor token usage, and manage LLM API costs in development and production.
Practical LangChain Projects and Exercises
Every module is reinforced with a working LangChain project you can extend and adapt for your own use case.
PDF Q&A Assistant
Build a LangChain application that loads a PDF, splits it into chunks, embeds it into a vector store, and answers questions with retrieved context.
Learn more →RAG Knowledge Assistant
Create a RAG-powered knowledge assistant using LangChain retrievers that answers from a multi-document corpus with source citations.
Learn more →LangChain Retriever Workflow
Design a multi-retriever workflow using LCEL — combine similarity search and metadata filtering to retrieve precise context passages.
Learn more →Prompt Template Library
Build a reusable library of ChatPromptTemplates for common use cases — summarisation, extraction, classification, and structured output generation.
Tool-Using Assistant
Build a LangChain agent with custom tools — connect it to a search API, a calculator, and a data lookup tool using the LangChain tools interface.
LangGraph Workflow Introduction
Build a basic stateful agent workflow using LangGraph — define nodes, manage state, add conditional edges, and run a multi-step agent loop.
Learn more →Deployed LLM API Workflow
Wrap a LangChain RAG chain in a simple API endpoint and deploy it — understand production structure, streaming, and basic observability.
Who Should Join in Bangalore?
Designed for technical learners and teams — basic Python knowledge is recommended for the practical projects.
Software Developers
Add LangChain to your developer toolkit — build LLM applications, RAG systems, and agent tools from structured Python code.
Python Developers
Apply your Python skills directly — LangChain is Python-native and the training is practical from day one.
AI Engineers
Deepen LangChain expertise — LCEL, advanced retrieval, tool orchestration, LangGraph workflows, and production patterns.
Data Scientists
Extend data science workflows with LangChain — build LLM-powered analysis assistants and RAG pipelines over structured data.
Data Engineers
Build document ingestion pipelines, vector store management workflows, and LLM-powered data processing systems with LangChain.
Full Stack Developers
Add LLM capabilities to your full stack applications — connect LangChain backends to React or Next.js frontends via API.
Technical Managers
Understand LangChain architecture and RAG pipelines to architect AI product features and evaluate development timelines.
Corporate AI Teams
Train your Bangalore engineering team to build LangChain-powered internal tools, knowledge assistants, and AI workflows.
Startup Founders (Technical)
Build the LLM application backbone of your AI product — RAG knowledge bases, prompt-powered workflows, and agent tools.
LangChain Training Bangalore vs AI Engineering Course Bangalore
Focused LangChain depth vs the full AI engineering curriculum — choose based on your immediate goal.
| Criteria | LangChain Training Bangalore | AI Engineering Course Bangalore |
|---|---|---|
| Audience | Developers, data engineers, Python learners, AI teams | Developers, engineers, Python learners |
| Scope | LangChain-focused deep dive | Full AI engineering curriculum |
| LangChain depth | Complete — all LangChain modules + RAG + LangGraph intro | LangChain as one of several core modules |
| Other AI topics | None — LangChain, RAG and LangGraph only | Prompt engineering, agents, MCP, FastAPI, deployment, cloud |
| Tools | LangChain, LangGraph, vector stores, embeddings | LangChain, LangGraph, MCP, RAG, FastAPI, cloud |
| Project complexity | LLM apps, RAG pipelines, agent tools, LangGraph workflows | 5 production AI systems including LangChain RAG |
| Best suited for | Teams building LangChain LLM apps and RAG systems now | Developers wanting the full AI engineering stack |
| Natural next step | AI Engineering Course Bangalore for broader AI skills | Production AI Engineering programme |
Want the full AI engineering curriculum? Explore the AI Engineering Course Bangalore →
LangChain Training Bangalore vs LangChain Training India
Same training content — the difference is local Bangalore support and in-person access.
| Criteria | LangChain Training Bangalore | LangChain Training India |
|---|---|---|
| Geography | Bangalore-specific — Bengaluru, Karnataka | India-wide — all cities |
| Office support | HSR Layout, Bengaluru — in-person consultation | Online only |
| Delivery | Live online + Bangalore office support | Live online nationally |
| Corporate option | Bangalore on-site team training available | India-wide corporate delivery |
| Local batches | Bangalore-timezone cohorts with local scheduling | National cohorts |
| Who it is for | Bangalore-based learners and companies | Learners across India |
Outside Bangalore? Explore LangChain Training India →
LangChain vs LangGraph — Understanding Both
LangChain and LangGraph are complementary, not competing. Understanding how they relate helps you build better AI systems.
LangChain
- →Building blocks for LLM applications
- →Prompt templates, chains, retrievers, tools
- →RAG pipelines, document loading, vector stores
- →LCEL for composable, readable pipelines
LangGraph
- →Stateful, graph-based agent workflows
- →Nodes, edges, shared state, loops
- →Multi-step agent execution with memory
- →Human-in-the-loop checkpoints
Recommended learning path: Learn LangChain first — chains, RAG, tools and the LCEL pattern. Then add LangGraph for stateful agent workflows when you need multi-step reasoning and memory. Both are covered in this training. Compare LangGraph and CrewAI →
Book Your Free LangChain Training Consultation
Tell us your background, current tech stack, and what you want to build with LangChain. We will get back within one business day to schedule your free 30-minute consultation.
- Free 30-minute consultation — no obligation
- Weekend cohorts for Bangalore working professionals
- Live instructor-led sessions with session recordings
- Local office support at HSR Layout, Bengaluru
- Corporate LangChain training available for Bangalore teams
Local Learning Options for Bangalore
Flexible LangChain training delivery for individuals, teams, and corporate clients.
Live Online Cohort
Weekend instructor-led sessions. Recordings within 24 hours. Open to all Bangalore learners.
HSR Layout Office Support
Visit our Bengaluru office for in-person consultation and course guidance before or during training.
Blended Learning
Live online sessions with optional in-person consultation at HSR Layout for Bangalore-based participants.
Corporate LangChain Training Bangalore
Customised on-site or online LangChain training for Bangalore engineering and AI teams. Scoped to your stack and use cases.
Learn more →1:1 AI Engineering Mentorship
Personalised project guidance, LangChain architecture review, and AI engineering mentorship for advanced learners.
Learn more →National LangChain Training
Also available as a national programme for learners across India outside Bangalore.
Learn more →Find Technovids in Bangalore
Office
Technovids Consulting Pvt. Ltd.
2nd Floor, Chandrodaya Complex
19/19 24th Main Rd, 1st Sector
HSR Layout, Bengaluru
Karnataka 560102, India
Hours
Monday – Friday, 9 AM – 6 PM IST
Weekend cohorts available for working professionals
Related Courses and Learning Resources
Deepen your LangChain and AI engineering knowledge with these Technovids resources.
All Technovids AI courses for Bangalore learners
Full AI engineering curriculum for Bangalore developers
Custom AI training for Bangalore corporate teams
National LangChain training programme — India-wide
How LangChain works and what it powers — free guide
LangGraph for stateful, graph-based agent workflows
Which agentic framework to choose for your project
RAG training — embeddings, vector databases and retrieval
How retrieval-augmented generation works — full guide
All Technovids AI guides and references
Frequently Asked Questions — LangChain Training Bangalore
Common questions from Bangalore developers, data professionals and corporate clients.
Do you offer LangChain Training in Bangalore?+
Where is Technovids located in Bangalore?+
Is LangChain training suitable for software developers?+
Does the LangChain training cover RAG pipelines?+
Does the LangChain training include LangGraph?+
What is the difference between LangChain Training Bangalore and AI Engineering Course Bangalore?+
What is the difference between LangChain Training Bangalore and LangChain Training India?+
Do you offer corporate LangChain training in Bangalore?+
Can I visit the HSR Layout office before enrolling?+
How do I contact Technovids for LangChain Training Bangalore?+
Start Learning LangChain from Bangalore
Build practical LLM applications, RAG pipelines, tool-using agents and LangGraph workflows with structured, hands-on LangChain training.
Exploring all AI courses in Bangalore? Browse all AI courses in Bangalore — GenAI, AI Engineering, RAG, Agentic AI and more.
Want the broader AI engineering curriculum? Explore the AI Engineering Course Bangalore — LangChain, RAG, agents, MCP, deployment and more.