LangChain Mastery: Build GenAI Apps with LangChain &Pinecone

Build dynamic chat prompts with system and human messages using chat prompt templates. Learn to replace placeholders, format messages, and leverage templates for reusable, scalable, and accurate conversations.

Explore why large language models need agents to perform tasks like calculations, web search, and SQL queries, and see how long chain agents enable tools beyond text generation.

Demonstrate using LangChain agents to generate and execute Python code in a Python Repl, leveraging tools, prompt templates, and agent executors to solve problems and verify results.

Learn how react blends reasoning and acting to generate reasoning traces. Build a react agent that selects the best tool for each query.

Explore how text embeddings convert words into numeric representations for NLP and ML, measure relatedness and similarity with cosine similarity or Euclidean distance, and enable classification, clustering, and question answering.

Authenticate to pinecone, the vector database for lm applications, by creating and securely storing an API key in a dot env file and loading it with the pinecone client library.

Explore how namespaces partition a pinecone index into default and named scopes, enabling namespace-scoped upserts, fetches, deletes, and statistics.

Learn to use Gemini Pro with LangChain by setting up the model and API key, tweaking temperature, and generating a tweet via a prompt template and LM chain.

Learn how to use a system prompt to guide AI behavior and enable streaming, delivering Gemini 1.5 flash responses piece by piece to reduce latency.

Install and set up Jupyter Lab and Jupyter AI extension across Python environments, using a virtual environment and pip installs for OpenAI and Long Chain.

Install the required Jupyter packages and authenticate with an OpenAI API key in Jupyter Notebook. Load Jupyter magics extension and use double percent cell magic to list providers and models.

Save chat sessions in a json file for cross-session persistence, load history on startup, and maintain context with a conversation buffer memory in a LangChain GenAI app.

Discover how to load private pdf documents into LangChain using transform loaders and the pi pdf loader, with authentication setup for OpenAI and Pinecone, and modular code.

Use Streamlit callbacks to clear session history when a new document loads or when chunk size or key changes, ensuring fresh chat state.

Learn the basics of one-shot summarization with a basic prompt, inserting text into the prompt to generate a concise summary in a language model call, noting the 4096 token limit.

Master summarizing using prompt templates with LangChain, leveraging dynamic prompts and token awareness to create concise summaries that can be translated into multiple languages.

Apply the stuffing method, loading all text into the prompt as context via the StuffDocumentsChain, enabling a single language model call for small documents while noting context-length limits.

Explore summarizing long documents with the refine chain, building progressive summaries from chunked content, and compare it to the MapReduce method, including practical coding steps for loading and splitting pdfs.

Learn to summarize long documents beyond token limits with a refine chain and custom prompts, using initial and refine templates to iteratively update summaries.

Test a custom chat app built with LangChain and Streamlit, exploring chat history, system and user message panels, and multilingual responses. Enhance security, user experience, and flexibility.