LangChain — Topic Summaries
AI-powered summaries of 26 videos about LangChain.
26 summaries
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...
Structured Output in LangChain | Generative AI using LangChain | Video 5 | CampusX
Structured output in LangChain is the practical bridge that lets large language models return data in a predictable format—so databases, APIs, and...
LangChain - Using Hugging Face Models locally (code walkthrough)
Running Hugging Face models locally inside LangChain is the practical workaround when Hugging Face Hub access fails—especially for conversational...
AI vs ML vs DL vs Generative Ai
Generative AI sits at the top of a ladder that starts with AI and narrows through machine learning and deep learning—then expands again into models...
Ollama meets LangChain
Running Ollama models locally turns LangChain into an on-device workflow: Python code can call a local LLaMA-2 instance through an API, generate...
LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!
Getting rid of OpenAI entirely for Retrieval QA with LangChain is feasible, but the quality hinges on the local LLM’s context limits, prompt format...
7-End To End Advanced RAG Project using Open Source LLM Models And Groq Inferencing engine
The core takeaway is an end-to-end RAG (retrieval-augmented generation) app built with open-source LLMs, where web content is scraped, chunked,...
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...
Using LangChain Output Parsers to get what you want out of LLMs
LLM apps fail most often when they accept whatever text a model happens to generate instead of forcing that output into a structure the application...
Private GPT4All : Chat with PDF with Local & Free LLM using GPT4All, LangChain & HuggingFace
Running a local, privacy-friendly “chat with your PDF” pipeline is practical with GPT4All—provided the workflow is built around retrieval (embeddings...
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 & 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...
Advanced RAG 06 - RAG Fusion
RAG Fusion aims to narrow the gap between what users type and what they actually mean by turning one user query into several targeted search queries,...
Gemma 2 - Local RAG with Ollama and LangChain
Running a fully local RAG pipeline with Gemma 2 is practical—and the fastest path starts with a clean indexing step, local embeddings, and a...
Getting Started With Nvidia NIM-Building RAG Document Q&A With Nvidia NIM And Langchain
NVIDIA NIM is positioned as a fast, scalable way to deploy generative AI through inference microservices, letting developers call multiple model...
Information Extraction with LangChain & Kor
Turning messy text into structured data is the bottleneck for many NLP workflows—especially when there’s no labeled dataset to train a named-entity...
Comparing LLMs with LangChain
Choosing a “good for production” large language model isn’t about picking the biggest name—it’s about matching model behavior to the task. A...
HOW to Make Conversational Form with LangChain | LangChain TUTORIAL
Conversational forms don’t have to feel like web-page data entry. By extracting structured fields from free-form chat and then asking only what’s...
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...
Easy RAG Setup - Load Anything into Context - Mistral 7B / ChromaDB / LangChain
A practical RAG (retrieval-augmented generation) pipeline can be built in roughly 90 lines of code by pairing LangChain with ChromaDB for vector...
Local Gemma 4 with OpenCode & llama.cpp | Build a Local RAG with LangChain | 🔴 Live
A local RAG app built around Gemma 4 can work surprisingly well on a single machine—but getting reliable retrieval depends less on the chat model and...
Analyzing Cryptocurrency Sentiment on Twitter with LangChain and ChatGPT | CryptoGPT
CryptoGPT’s sentiment pipeline turns an author’s Twitter activity into daily sentiment scores by combining LangChain with ChatGPT and forcing...
Build Private Chatbot wtih LangChain, Ollama and Qwen 2.5 | Local AI App with Private LLM
A fully local “private chatbot” workflow can be built by combining LangChain’s message orchestration (via LangGraph), Ollama for on-device model...
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...
Build Private AI Assistant That Actually Remembers | Chatbot Memory with Ollama, LangChain & SQLite
A fully local chatbot can keep “memory” across restarts by writing each conversation turn into a local SQL database and re-injecting that history...
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...