AI Applications Made Easy: Dive into LangChain & GPT

Explore the two model types in long chain models—large language models (LMS) and chat models—and understand their features and use cases.

Explore large language models that turn data into reports and offer medical guidance, integrated with LangChain and providers like OpenAI, Cohere, and HuggingFace.

Explore hands-on setup with LangChain and OpenAI in a Jupyter notebook, initialize the OpenAI LM, run simple prompts, and execute batch queries with the generate method to handle multiple prompts.

Discover chat models as a message-based variant of language models, using a series of chat messages as input to deliver a chat message output for interactive applications like chat bots.

Learn to work with chat models in LangChain via a live Jupyter demo, using system and human messages, batch calls, and token checks to summarize Cinderella and Snow White.

Explore prompt templates in LangChain to craft dynamic, context-aware prompts. Learn how predefined recipes with instructions, examples, and content create model-agnostic prompt values.

Follow a live demo to build a simple prompt template in LangChain, import modules in a Jupyter notebook, and generate a dynamic, context aware prompt ready for language models.

Explore example selectors to dynamically choose prompt examples based on user input, moving beyond hard-coded templates to personalize and contextually tune prompts for more flexible language models.

Demonstrate example selectors in a Jupyter notebook, importing dependencies, defining and instantiating an example selector, and testing weather based examples to enrich the interactions with language models in long chain.

Explore fused prompt templates and few-shot prompt templates to blend templates with few-shot learning, focusing on self-park with search and formatting examples with a prompt template object for Lang Chain.

Learn few shot learning with OpenAI and LangChain by setting up libraries, authenticating with an API key, and crafting example-based prompts to guide GPT responses; test and iterate.

Explore few-shot learning with OpenAI and LangChain, using example selectors and chroma for semantic similarity search to craft context-rich prompts and accurate responses.

Build a joke generator with OpenAI and Langton's output parser by importing libraries, authenticating with an API key, crafting a prompt template, and parsing model output with a pedantic parser.

Import the LangChain and OpenAI libraries in a notebook, authenticate with an API key, set up a Pydantic model and prompt template, and build a pedantic output parser for jokes.

Introduction to chains, the cornerstone of long chain mastery, showing how sequences of actions transform input, generate prompts, and fetch answers with large language models.

A chain acts as your personal query guide, taking your question, packaging it neatly, and sending it to the language model to return a neatly formatted answer.

Import libraries in a Jupyter notebook, build and manage an LLM chain, set the prompt, and run a query with chat OpenAI to generate a creative company name.

Explore advanced long chain sequential chains and learn how to pass the output of one chain as input to another, in AI applications made easy with LangChain and GPT.

Harness OpenAI's GPT and the Langson library to compose lyrics on a topic, then verify their appropriateness via a sequential chain.

Explore how memory and context power conversational systems by remembering previous conversations to drive future responses in large language models. See why systems that forget disrupt dialogue, and how memory underpins coherent interactions in AI applications.

Store and retrieve past interactions with Lang Tune's memory module, which can be standalone or embedded in a Lang chain, reads before logic and writes after, including chat message history.

Dive into memory management for AI with LangChain and OpenAI, importing libraries, authenticating with an API key, and using chat history and entity memory to capture and recall interactions.

Explore memory integrated chat bots using the Langson library and OpenAI, as memory and conversation chains create context-aware interactions with the GPT model.

Discover how indexes fetch up-to-date information for language models by retrieving relevant documents via LangChain, using document loaders and text splitters to boost retrieval performance.

Explore document loaders that translate raw, unstructured data into clean documents with metadata for LangChain. Use the load method for one-shot imports or lazy load for large datasets.

Import the text file loader from the Lang change document loaders package, load hello.txt, and print the long chain Langton document object with text and metadata.

Explore text splitters in lang chain to break long documents into chunks that preserve context. Compare splitter strategies and measurements, with the recursive character splitter as the default.

Import the recursive character text splitter from LangChain, initialize it with chunk size and overlap, and use create_documents to split a 300-page paper into bite-sized, context-preserving chunks for analysis.

Explore embeddings that convert text and categorical data into numerical representations, learned from data to map items into a lower dimensional space and reveal meaningful relationships.

Explore vector stores as a Google-like index for your document collection, enabling rapid retrieval of the most relevant papers to a query. Apply to climate change research and customer support.

Set up a chroma vector store in a Jupyter notebook, embed and store document chunks, then search papers on carbon pricing to demonstrate practical retrieval.

Discover how LangChain agents use a language model to make dynamic decisions, employ a toolkit of tools, and balance real-time actions with predefined workflows.

Explore AI agents powered by LangChain and GPT that use tools like Wikipedia and calculator, authenticate with API keys, and convert queries into actions and answers.

Discover Streamlit, an open source Python library for building interactive web apps for machine learning and data science with minimal code, and learn its basics for integrating with Lang Chain.

Learn to turn Python code into interactive web apps with streamlit by setting up a fresh anaconda environment, installing streamlit, and running app.py with text input and a pdf uploader.

Explore retrieval-augmented generation using rag to fetch and embed external cricket data for real-time insights into the World Test Championship Final 2023, delivering accurate, updated answers.

Explore a Jupyter walkthrough of text retrieval and question answering with LangChain and GPT, from data loading and embeddings to semantic search with a multi query retriever and source citations.

Explore a business chatbot that delivers instant, accurate responses and detailed product explanations to boost user experience. Maintain chat histories for seamless, referable conversations across complex interactions.

Explore a Streamlit chat bot code walkthrough built with OpenAI's GPT and the Langson library, demonstrating memory, a prompt system, real-time interaction, and key handling via constants file.