LangChain Agents Deep Dive with GPT 3.5 — LangChain #7
Large Language Models (LLMs) are incredibly powerful, yet they lack particular abilities that the "dumbest" computer programs can handle with ease. Logic, calculation, and search are examples of where computers typically excel, but LLMs struggle. With these weaknesses in today's generation of LLMs, we must find solutions to these problems. One "suite" of potential solutions comes in the form of "agents". These agents don't just solve the problems mentioned above but many others. In fact, adding agents has an almost unlimited upside in their LLM-enhancing abilities. In this video, we'll talk about agents. We'll learn what they are, how they work, and how to use them within the LangChain library to superpower our LLMs. 🌲 Pinecone article: https://pinecone.io/learn/langchain-agents 🙋🏽♂️ Francisco: https://twitter.com/fpingham 📌 LangChain Handbook Code: https://github.com/pinecone-io/examples/tree/master/learn/generation/langchain/handbook 📌 Notebook 1: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/xx-quick-agents-intro.ipynb 📌 Notebook 2: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/06-langchain-agents.ipynb 👋🏼 NLP + LLM Consulting: https://aurelio.ai 🎙️ Support me on Patreon: https://patreon.com/JamesBriggs 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Why LLMs need tools 02:35 What are agents? 03:33 LangChain agents in Python 04:25 Initializing a calculator tool 05:57 Initializing a LangChain agent 08:01 Asking our agent some questions 12:39 Adding more tools to agents 14:29 Custom and prebuilt tools 16:40 Francisco's definition of agents 17:52 Creating a SQL DB tool 19:49 Zero shot ReAct agents in LangChain 24:18 Conversational ReAct agent in LangChain 26:57 ReAct docstore agent in LangChain 28:31 Self-ask with search agent 30:33 Final thoughts on LangChain agents #artificialintelligence #nlp #openai #langchain
