LangChain — Brand Summaries
AI-powered summaries of 44 videos about LangChain.
44 summaries
What is Agentic AI? Important For GEN AI In 2025
Agentic AI is positioned as the next step beyond generative AI: instead of producing text as the end goal, autonomous AI agents pursue a defined...
GPT-4 Tutorial: How to Chat With Multiple PDF Files (~1000 pages of Tesla's 10-K Annual Reports)
Answering questions across multiple massive PDF files—like several years of Tesla 10-K annual reports—becomes practical when each document is...
Build Anything with AI Agents, Here's How
AI agents are positioned as the practical route to the next wave of general-purpose intelligence—because they can do work toward a goal instead of...
GPT-4 & LangChain Tutorial: How to Chat With A 56-Page PDF Document (w/Pinecone)
A practical architecture for turning a long PDF into a chat-ready assistant hinges on two phases: ingest the document into a vector database, then...
Opal - Google Labs Killer NEW App
Google Labs’ Opal is a no-code workflow builder aimed at turning natural-language requests into working LLM “mini apps,” with built-in steps for web...
Google Launches an Agent SDK - Agent Development Kit
Google has launched an “Agent Development Kit” (Agent SDK) aimed at building deployable AI agents in the cloud, with built-in support for evaluation,...
Hybrid Search RAG With Langchain And Pinecone Vector DB
Hybrid search for RAG is built on a simple but powerful idea: retrieve relevant chunks using both semantic similarity (dense vector search) and...
Testing Gemini 1.5 and a 1 Million Token Window
Gemini 1.5 Pro marks a major step up for long-context AI: it pairs a newly updated model with a dramatically expanded context window—up to 1,048,576...
LangSmith Crash Course | LangSmith Tutorial for Beginners | Observability in GenAI | CampusX
LangSmith is positioned as the missing “white-box” layer for LLM applications—turning opaque, non-deterministic behavior into traceable,...
Creating an AI Agent with LangGraph Llama 3 & Groq
LangGraph is positioned as the “middle layer” for building AI agents that need structure, state, and controllable decision points—without handing...
Function Calling with Local Models & LangChain - Ollama, Llama3 & Phi-3
Running function calling and structured JSON outputs locally is practical with smaller open models—especially Llama 3 8B on Ollama—and it enables...
8-Building Gen AI Powered App Using Langchain And Huggingface And Mistral
A practical end-to-end recipe for building an open-source RAG (retrieval-augmented generation) Q&A app comes together by chaining LangChain document...
LangChain Beginner's Tutorial for Typescript/Javascript
LangChain is positioned as a practical framework for building JavaScript/TypeScript applications on top of large language models—especially when...
Mistral Small 3 - The NEW Mini Model Killer
Mistral has released “Mistral Small 3,” a new 24B-parameter open-weight model positioned as a fast, capable “workhorse” for everyday tasks—aimed at...
LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website
A practical blueprint for turning a website into a ChatGPT-style chatbot hinges on one move: retrieve the most relevant chunks of your site’s text...
What Are Deep Agents? Shallow Agents Vs Deep Agents
Deep agents are built for complex, multi-step work that shallow agent loops struggle to handle—by adding explicit planning, task decomposition into...
Advanced RAG with Llama 3 in Langchain | Chat with PDF using Free Embeddings, Reranker & LlamaParse
Building a high-quality “chat with your PDF” system hinges less on the language model and more on the pipeline around it: parsing complex documents...
Mastering LLM Chatbots And RAG Evaluation Crash Course
LLM chatbot and RAG quality can be measured systematically by combining three ingredients: curated test data (inputs plus ground-truth outputs),...
Observability in LangGraph | LangSmith Integration with LangGraph
Observability for LangGraph agents becomes practical once every user turn is captured as an end-to-end trace in LangSmith—complete with timing, token...
Build Anything with CrewAI, Here’s How
CrewAI is presented as a fast way to build multi-step AI “agents” that can generate lead-targeting search queries, search the web, scrape relevant...
AI News! HUGE Chatbot Research, Viral AI Songs, Text to Video & More!
GPT-4’s 32,000-token “long context” access is emerging as a practical unlock for developer workflows: it can ingest far more text and code at...
Mistral Agents API - The NEW Agent System
Mistral has launched an “agents API” designed to let developers build agentic systems that run against Mistral models through a cloud-based...
Bard can now code and put that code in Colab for you.
Google’s Bard has gained a practical new capability: it can generate Python code and export that code directly into Google Colab, turning prompts...
Prompting Your AI Agents Just Got 5X Easier...
Anthropic has added an “experimental prompt generator” that turns a plain task description into a high-quality, ready-to-use prompt built from...
Cohere's Command-R a Strong New Model for RAG
Cohere’s Command-R arrives as a purpose-built model for retrieval-augmented generation (RAG) and tool/function calling, not as a bid to replace top...
LangChain Demo + Q&A with Harrison Chase
LangChain’s core value is turning large language models from “text-in, text-out” into usable applications by providing the missing framework:...
Analyze Custom CSV Data with GPT-4 using Langchain
A LangChain “CSV agent” can turn a custom Bitcoin price spreadsheet into a question-answering system that writes and runs pandas code on the fly—then...
Camel + LangChain for Synthetic Data & Market Research
Camel—an “autonomous GPT” approach built around two agents talking to each other—gets positioned as a practical engine for synthetic data and market...
GPT-4 Vision: How to use LangChain with Multimodal AI to Analyze Images in Financial Reports
Financial reports often hide the real answers inside tables, charts, and other images—not in the surrounding text. The core takeaway is a practical...
Advanced Q&A Chatbot Using Ragstack With vector-enabled Astra DB Serverless database And Huggingface
A practical RAG (retrieval-augmented generation) chatbot setup ties together Ragstack, a vector-enabled Astra DB Serverless database, and Hugging...
Intro to LLM Security - OWASP Top 10 for Large Language Models (LLMs)
LLM security hinges on treating every prompt-and-response cycle as potentially hostile—then building monitoring and guardrails that catch failures...
Microsoft Loves SLM (Small Language Models) - Phi-2 / Ocra 2
Microsoft is pushing open-source small language models (SLMs) into practical, pay-as-you-go deployment—an approach that could make high-quality...
LLM JSON Output - Get Valid JSON with Pydantic and LangChain Output Parsers
Getting reliable JSON from large language models—especially ones that don’t natively support structured outputs—requires more than “please output...
Build Anything With HYBRID AI AGENTS: Here`s How
Hybrid AI agents are emerging as a practical workaround for data sources that don’t offer APIs: combine a browser automation agent to extract...
You're Building AI Agents on Layers That Won't Exist in 18 Months. (What this Means for You)
Agent infrastructure is shifting from “human-first tools” to “agent-first primitives,” and the biggest near-term advantage will go to builders who...
100% Local CAG with Qwen3, Ollama and LangChain - AI Chatbot for Your Private Documents
Cache-augmented generation (CAG) is presented as a simpler alternative to retrieval-augmented generation (RAG) for private-document chat: instead of...
Loaders, Indexes & Vectorstores in LangChain: Question Answering on PDF files with ChatGPT
A practical LangChain pipeline for turning PDFs, YouTube transcripts, and plain text into question-answering over embeddings is the core takeaway—and...
Intro to LLM Security - OWASP Top 10 for Large Language Models (LLMs)
Large language model security is increasingly about catching risky behavior before it reaches users—and doing it continuously once models go live. A...
Build Local Long-Running AI Agent (Stop Your Agents from Getting Lost) | LangChain, Ollama, Pydantic
Long-running AI agents often lose their footing as tasks stretch across multiple context windows—hallucinations creep in, code can be rewritten or...
Is RAG Dead in 2026? | Build Local RAG from First Principles
Retrieval-Augmented Generation (RAG) is still considered necessary in 2026—not because large language models can’t answer, but because they often...
How RAG Finds Answers in Millions of Documents | Embeddings, Vector Databases, LangChain & Supabase
Retrieval in RAG hinges on one practical step: turning a user question into a vector and then finding the most semantically similar document chunks...
LangChain Tutorial: The Core Building Blocks | LLMs, JSON output, RAGs, Tools and Observability
LangChain’s practical value comes from a small set of reusable building blocks: a unified way to call different LLM providers, structured outputs...
Top AI Agent Frameworks You Should Know | LangGraph, IBM Bee, CrewAI, AutoGen, AutoGPT
Five agent frameworks are positioned as practical building blocks for autonomous AI systems—each optimized for a different kind of complexity, from...
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
This paper addresses a conceptual and practical problem in the generative AI era: the field often uses the terms “AI Agents” and “Agentic AI”...