Free Download 2026 Deep Agent - Multi Agent RAG with Gemini and Langchain
Published 12/2025
Created by KGP Talkie | Laxmi Kant
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English + subtitle | Duration: 166 Lectures ( 18h 24m ) | Size: 15.2 GB
Langchain v1 AI Agents, Deep Agents, Multi Agent RAG, Deep RAG, Advanced RAG, Gemini AI, Google Gemini 3, Qdrant, Docker
What you'll learn
Build production-ready AI agents using Google Gemini, LangChain v1, MCP, and modern agent design patterns.
Design and implement multimodal RAG pipelines using Docling, Gemini, Qdrant vector database, and hybrid search.
Process PDFs, tables, and images at scale using Docling, Docker, and structured data extraction techniques.
Implement hybrid search, re-ranking, memory, MCP tools, and cost-optimized context caching in real AI systems
Create autonomous multi-agent research systems with orchestrator, researcher, and editor agents for finance use cases.
Requirements
Basic Python knowledge is required. Familiarity with APIs, Docker, or RAG concepts is helpful but not mandatory.
Description
This course is a complete, hands-on guide to building real-world AI agents and deep research 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 coversYou 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 SystemsA 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 SystemsIn 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 forThis course is for developers who want to go beyond basic chatbots and build serious AI systems.It is ideal for:AI engineers working with LLMsBackend developers building RAG systemsData scientists working with documents and researchFinance and analytics professionals interested in AI automationAnyone who wants to understand how real multi-agent systems are built in productionBasic 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.
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