LangChain Crash Course For Beginners 2025 | LangChain V0.3!

Explore how chat models in LangChain provide a unified interface to communicate with diverse lm APIs, enabling consistent workflows, easy llm switching, and context-rich pipelines, with a focus on OpenAI.

Install and import the long chain OpenAI chat model, initialize with GPT-4, and invoke the API; set up API key via dot env, handle balance, and extract content from responses.

Learn how to pass past chat history to a language model using LangChain by organizing system, human, and AI messages and invoking LLMs for concise, context-aware responses.

Switch between OpenAI, Google Gemini, and Anthropic chat models with LangChain, using long chain to abstract APIs and make switching consistent, tailored to task speed, cost, or accuracy.

Implement a real-time chat with a chat model in a terminal, running locally, and manage chat history with system and human messages to preserve conversation context.

Explore prompt templates in LangChain, learn to create templates with placeholders, convert them for the model, and customize system and human messages to generate tailored emails and other prompts.

Learn how chains connect tasks to form a unified workflow, with sequential, parallel, and conditional chaining, illustrated by examples like prompt templates, language translation to French, and sending emails.

Learn to build prompts using chains, combining system and human messages with prompt templates, passing placeholders to the OpenAI chat model, and extracting content with a string output parser.

Demonstrates the inner workings of LangChain chains by building three runnables (runnable lambdas), including prompt formatting, model invocation, and content extraction, and compares runnable sequence with the pipe-based approach.

Learn sequential chaining in LangChain by extending chains with runnables, translation prompts, and output handling, then post generated content to Twitter.

Trigger one task to start multiple chains that run in parallel, then combine their results. Use a movie critique with plot and characters analysis as a practical example.

Explore conditional chaining in LangChain by classifying user feedback into positive, negative, neutral, or escalate branches, and routing responses through dedicated chains based on the classification.

Explore how RAGs (retrieval augmented generation) combine LLMs with a retrieval system to access embeddings, vector databases, and external documents and knowledge bases while managing context limits.

Dissect the RAGs workflow by explaining document chunking, vector stores, and embeddings, then show how a retriever feeds the LLM to answer user questions.

Discover how vector embeddings convert words, sentences, or images into high-dimensional numbers, and how vector databases store and retrieve meaning rather than exact matches.

Split a large document into chunks, convert each to vector embeddings with the OpenAI embedding API, store both plain text and embeddings in a vector store for querying relevant chunks.

Explore the second part of the rag workflow: convert questions to embeddings, retrieve top five relevant chunks from a vector store, and use them with the lm to answer users.

Demonstrates a rag workflow by loading a book, chunking text, creating embeddings with text embedding 3 small, and storing them in a local chroma db vector store.

Demonstrates pulling the relevant chunks from a vector database using the text embedding three small model, similarity thresholds, and top-k retrieval to answer user prompts, with Gandalf and Frodo examples.

Learn how to attach metadata to chunks, track source provenance, and store embedded documents in a vector database for precise retrieval with embeddings.

Use rags to answer questions only from provided documents by constructing a document-backed prompt and joining top chunks. Build context-aware retrieval AI that connects to private data and tools.

Explore how AI agents act as autonomous decision makers that select tools to solve problems, using the react pattern (think, act, observe) and loop through steps to reach answers.

Build an ai agent from scratch using tools and a react prompt to overcome llm real-time data limits, and explore a step-by-step reasoning, action, and observation workflow.