2026 Deep Agent - Multi Agent RAG with Gemini and Langchain

2026 Deep Agent - Multi Agent RAG with Gemini and Langchain - Langchain v1 AI Agents, Multi-Modal Deep Agents, Multi Agent Deep Advanced RAG, Google Gemini 3, Qdrant, Docker, Docling

Preview This Course - GET COUPON CODE

Description

This course is a complete, hands-on guide to building real-world AI agents and deep research (DeepAgent) systems using Google Gemini, LangChain v1, MCP, and modern RAG techniques.

You will start from the absolute basics of AI agents and slowly move towards building advanced autonomous multi-agent systems used for deep financial research. The course is designed in a progressive way so that beginners can follow along, while experienced developers will still learn advanced production-grade patterns.

The focus of this course is not only theory. You will build everything step by step using Python notebooks, real APIs, real documents, and real data pipelines.

What this course covers

You will first understand what an AI agent really is. You will learn different agent patterns, how agents reason, how they take actions, and how to choose the right agent design for a real project.

You will then set up Google Gemini AI Studio and LangSmith properly. This includes creating API keys, understanding pricing, rate limits, and tracing agent executions so you can debug and monitor your agents like a professional.

After that, you will go through a complete Gemini and LangChain bootcamp. You will learn how to use Gemini models in Python, how messages work internally, how streaming responses work, how multimodal inputs are handled, and how to use tools, function calling, reasoning mode, grounding, and context caching to reduce cost and improve performance.

Once the foundations are clear, you will move into LangChain agents. You will build agents with memory, state management, summarization middleware, fallback models, PII protection, planners, streaming responses, and structured outputs using Pydantic.

The course then introduces MCP through a finance use case. You will connect external MCP servers like Yahoo Finance, load them as LangChain tools, and build a complete stock research agent with structured prompts and planners.

Deep RAG and Multimodal Finance Systems

A large part of this course focuses on Deep RAG systems for finance.

You will learn why multimodal RAG is hard, what problems occur with PDFs, tables, images, and long documents, and how to design a reliable deep RAG pipeline.

You will extract data from financial PDFs using Docling. This includes converting PDFs to markdown, extracting tables with context, tracking page numbers, extracting images, and validating data integrity at scale.

You will then generate accurate image descriptions using multimodal Gemini models and store those descriptions in markdown so everything can be handled in a single text-based pipeline.

Next, you will ingest large amounts of multimodal data into Qdrant vector database. You will learn dense search, sparse search, hybrid search, metadata filtering, de-duplication using file hashes, and best practices for chunking and retrieval models.

On top of that, you will build advanced retrieval pipelines using hybrid search and cross-encoder re-ranking for better answer quality.

Building Real Multi-Agent Deep Research Systems

In the final sections, you will build full multi-agent deep research systems from scratch.

You will design autonomous agents that work like an expert research team with orchestrator, researcher, and editor agents. These agents will plan tasks, run deep research, synthesize results, and produce structured outputs.

You will learn how agent states are shared, how tools are injected at runtime, how files are managed by agents, and how prompts are designed differently for orchestrator, researcher, and editor roles.

You will also explore LangChain’s built-in deep agent architecture and build a complete deep finance research agent using sub-agents and a file backend.

Who this course is for

This course is for developers who want to go beyond basic chatbots and build serious AI systems.

It is ideal for:

AI engineers working with LLMs

Backend developers building RAG systems

Data scientists working with documents and research

Finance and analytics professionals interested in AI automation

Anyone who wants to understand how real multi-agent systems are built in production

Basic Python knowledge is recommended, but some prior agent or RAG experience is recommended.

By the end of this course, you will be able to design, build, and debug advanced AI agents, multimodal RAG pipelines, and autonomous multi-agent research systems using Gemini and LangChain.

You will not just understand concepts. You will have built complete, end-to-end systems that you can reuse in real projects, startups, or enterprise environments.

Who this course is for:

• AI engineers, backend developers, and data scientists who want to build Gemini-based agents, multimodal RAG systems, and deep research workflows using LangChain, Docling, Docker, and Qdrant.

Learn LangChain, Pinecone & OpenAI: Build Next-Gen LLM Apps

Learn LangChain, Pinecone & OpenAI: Build Next-Gen LLM Apps - Hands-On Applications with LangChain, Pinecone, and OpenAI. Build Web Apps with Streamlit. Join the AI Revolution Today!

Preview this Course

What you'll learn

• How to Use LangChain, Pinecone, and OpenAI to Build LLM-Powered Applications.
• Learn about LangChain components, including LLM wrappers, prompt templates, chains, and agents.
• Learn about the different types of chains available in LangChain, such as stuff, map_reduce, refine, and LangChain agents.
• Acquire a solid understanding of embeddings and vector data stores.
• Learn how to use embeddings and vector data stores to improve the performance of your LangChain applications.
• Deep Dive into Pinecone.
• Learn about Pinecone Indexes and Similarity Search.
• Project: Build an LLM-powered question-answering app with a modern web-based front-end for custom or private documents.
• Project: Build a summarization system for large documents using various methods and chains: stuff, map_reduce, refine, or LangChain Agents.
• This will be a Learning-by-Doing Experience. We'll Build Together, Step-by-Step, Line-by-Line, Real-World Applications (including front-ends using Streamlit).
• You'll learn how to create web interfaces (front-ends) for your LLM and generative AI apps using Streamlit.
• Streamlit: main concepts, widgets, session state, callbacks.
• Learn how to use Jupyter AI efficiently.

Master LangChain, Pinecone, and OpenAI. Build hands-on generative LLM-powered applications with LangChain.

Create powerful web-based front-ends for your generative apps using Streamlit.

The AI revolution is here and it will change the world! In a few years, the entire society will be reshaped by artificial intelligence.

By the end of this course, you will have a solid understanding of the fundamentals of LangChain, Pinecone, and OpenAI. You'll also be able to create modern front-ends using Streamlit in pure Python.

This LangChain course is the 2nd part of “OpenAI API with Python Bootcamp”. It is not recommended for complete beginners as it requires some essential Python programming experience.

Currently, the effort, knowledge, and money of major technology corporations worldwide are being invested in AI.

In this course, you'll learn how to build state-of-the-art LLM-powered applications with LangChain.

What is LangChain?

LangChain is an open-source framework that allows developers working with AI to combine large language models (LLMs) like GPT-4 with external sources of computation and data. It makes it easy to build and deploy AI applications that are both scalable and performant.

It also facilitates entry into the AI field for individuals from diverse backgrounds and enables the deployment of AI as a service.

In this course, we'll go over LangChain components, LLM wrappers, Chains, and Agents. We'll dive deep into embeddings and vector databases such as Pinecone.

This will be a learning-by-doing experience. We'll build together, step-by-step, line-by-line, real-world LLM applications with Python, LangChain, and OpenAI. The applications will be complete and we'll also contain a modern web app front-end using Streamlit.

We will develop an LLM-powered question-answering application using LangChain, Pinecone, and OpenAI for custom or private documents. This opens up an infinite number of practical use cases.

We will also build a summarization system, which is a valuable tool for anyone who needs to summarize large amounts of text. This includes students, researchers, and business professionals.

I will continue to add new projects that solve different problems. This course, and the technologies it covers, will always be under development and continuously updated.

The topics covered in this "LangChain, Pinecone and OpenAI" course are:

LangChain Fundamentals

Setting Up the Environment with Dotenv: LangChain, Pinecone, OpenAI

LLM Models (Wrappers): GPT-3

ChatModels: GPT-3.5-Turbo and GPT-4

LangChain Prompt Templates

Simple Chains

Sequential Chains

Introduction to LangChain Agents

LangChain Agents in Action

Vector Embeddings

Introduction to Vector Databases

Diving into Pinecone

Diving into Chroma

Splitting and Embedding Text Using LangChain

Inserting the Embeddings into a Pinecone Index

Asking Questions (Similarity Search) and Gettings Answers (GPT-4)

Proficient in using AI Coding Assistants (Jupyter AI)   

Creating front-ends for LLM and generative AI apps using Streamlit

Streamlit: main concepts, widgets, session state, callbacks

The skills you'll acquire will allow you to build and deploy real-world AI applications. I can't tell you how excited I am to teach you all these cutting-edge technologies.

Come on board now, so that you are not left behind.

I will see you in the course!

Who this course is for:

• Python programmers who want to build LLM-Powered Applications using LangChain, Pinecone and OpenAI.
• Any technical person interested in the most disruptive technology of this decade.
• Any programmer interested in AI.

LangChain MasterClass- #7 OpenAI LLM & Pinecone Python Apps

 LangChain MasterClass- #7 OpenAI LLM & Pinecone Python Apps - Experience Hands-On learning-Acquire LangChain Skills, Deploy AI Applications with LLM Models such as ChatGPT-Pinecone

Preview This Course - GET COUPON CODE

Description

Are you interested in harnessing the power of AI to create groundbreaking language-based applications?

Look no further than LangChain - a comprehensive course that will take you from a novice to an expert in no time.

By implementing AI applications powered with state-of-the-art LLM models like OpenAI and Hugging Face using Python, you will embark on an exciting project-based learning journey.

With LangChain, you will gain the skills and knowledge necessary to develop innovative LLM solutions for a wide range of problems.

Here are some of the projects we will work on:

Project 1: Construct a question-answering application powered by LLM using LangChain, OpenAI, and Hugging Face Spaces.

Project 2: Develop a conversational bot using LangChain and OpenAI.

Project 3: Build an AI-powered app for kids that helps them find similar classes of things.

Project 4: Create a marketing campaign app focused on increasing sales through well-crafted sales copy.

Project 5: Develop a ChatGPT clone with a summarization option, offering a useful chatbot experience.

Project #6 - MCQ Quiz Creator App - Helps you create MCQs for your students using langchain & pinecone

This course is designed to provide you with a complete understanding of LangChain, starting from the basics and progressing towards creating practical LLM-powered applications.

LangChain empowers programmers to fully utilize large language models, such as ChatGPT, pinecone and seamlessly integrate them with external data sources. This integration enhances the models' ability to comprehend and respond to human language.

Built with Python, LangChain offers a user-friendly interface tailored specifically for beginners, making it accessible to aspiring developers.

Course Overview:

Aspiring to build sophisticated language-based applications?

LangChain is the perfect library for you.

Move beyond basic techniques like keyword matching or rule-based systems and maximize your reach by langchain.

Leverage the power of LLMs, applications using LangChain and combine them with cognitive or information sources & pinecone.

Unlock tremendous potential and explore new possibilities with applications using LangChain and pinecone.

Course Contents:

LangChain

LLMs

Chat Models

Prompts

Indexes

Chains

Agents

Memory

But this isn't just a theory-based course; it's a hands-on experience. You will engage in practical activities and real-world projects, reinforcing your understanding of these concepts and techniques.

By the end of the course, you will be equipped with the skills to apply Langchain effectively, building robust, pinecone, powerful, and scalable LLM applications for various purposes.

Don't miss this opportunity to become a language model expert.

Enroll in the LangChain course and embark on a transformative journey that will elevate your AI app development skills. LangChain , OpenAI , ChatGPT , LLM, langchain pinecone ,  Python - these are the tools that will empower you to create cutting-edge AI applications that push the boundaries of what's possible.

Get ready to unlock your full potential and become a hero in the world of language-based AI development through langchain.

You will do practical activities and real-world projects throughout the applications using LangChain pinecone course to strengthen your understanding of the concepts and techniques.

You will be competent in applying Langchain pinecone to build strong, effective, and scalable LLM applications for a variety of uses by the end of the course.

OpenAI

ChatGPT

Pinecone

Langchain pinecone

Applications using LangChain

Who this course is for:

• Anyone who is excited to buil;d AI powered LLM apps using Langchain
• Someone who is planning to start his a career as an LangChiain Professional and would like to explore the tool.
• AI Enthusiast