LangChain For Generative AI: Using OpenAI LLMs in Python

Learn to work with large language models in LangChain using OpenAI LLMs and Hugging Face, manage tokens and memory, test with T5, and compare base models to GPT.

Explore prompting best practices to improve model outputs, including clear, specific prompts; delimiter formatting to prevent injections; system messages; and few-shot and chain-of-thought techniques for robust reasoning.

Explore output parsers in LangChain to automate format instructions and parse json outputs using the response schema module and structured output parser within prompt templates.

Revisit vector stores with OpenAI embeddings and Chroma, loading and splitting documents to index chunks for similarity search. Retrieve relevant text as context for answering questions with a transformer-based encoder–decoder.

Load and split documents, store them in a vector store, and query chunks with LangChain and a GPT-3.5 turbo chat model to answer and return source documents.

Hook your chatbot to Wikipedia using LangChain and OpenAI by loading Wikipedia documents, querying related topics, and building a retrieval chain with a vector store.

Learn function calling in LangChain with OpenAI LLMs by converting a math function into a tool, describing json arguments, and using a model with tools via the expression language.

Build agents with tools using LangChain to connect arithmetic tools to a GPT model, enabling automated problem solving with context via prompts and an agent scratchpad.

Build an economics chatbot with LangChain by connecting an LM to Bureau of Labor Statistics data, using tools to fetch CPI, PPI, and unemployment rate series.

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