Summary
Highlights
The video demonstrates a local AI agent that answers questions about a pizza restaurant using data from a CSV file containing reviews. The agent can retrieve relevant reviews and provide answers to specific questions, such as the quality of the pizza or the availability of vegan options.
The tutorial begins by outlining the setup process, which includes downloading a CSV file (provided in the video description), creating a new folder in VS Code, and creating a requirements.txt file. The video walks through creating a Python virtual environment and installing the necessary dependencies which include Langchain, Langchain-Ollama, and Langchain-Chroma.
The video highlights the use of Ollama to run models locally and not requiring external API keys. The video explains downloading and installing Ollama from ollama.com, and then pulling two specific models: llama3:2 and mxbai-embed-large. The llama3:2 model is used for answering questions, while the mxbai-embed-large model is for embedding documents for the vector store.
The video briefly mentions the use of GitHub Copilot for code auto-completion. The video shares stories of developers using Github Copilot to increase efficiency.
The coding part starts with importing necessary modules from Langchain, and defining the model to be used for the agent. A prompt template is created. The video showcases building a simple yet modular AI agent, by taking the various user inputs, injecting it into the Language Model prompt, and then printing out the result.
The video explains how to set up vector search using ChromaDB to improve the AI agent's ability to provide contextually relevant replies. It involves creating a new file called vector.py, importing necessary modules, loading the CSV file using pandas, defining the embeddings model, and initializing ChromaDB for local vector storage.
The video explains how to take the data from the CSV file, create documents from each row that can be consumed by the vector database, adding the content of each row as a document into the vector store (ChromaDB). It elaborates on creating a retriever from the vector store to look up relevant documents based on similarity search, set to grab the top five reviews.
The video describes the final crucial step of integrating all of the moving parts. Firstly retrieving the data in vector.py with a simple import retriever statement. The retriever will be invoked with the question, and then the reviews, along with the question, is passed as an argument to the Langchain Prompt Template to deliver the final AI agent.
Demonstrates the working AI agent, answering questions about restaurant reviews with speed. Highlights the flexibility of the setup, allowing customization with different CSV files or data sources. Concludes by encouraging viewers to like, subscribe, and check the video description for the code.