Building a Simple Data Analyst AI Agent with Llama and Flask

Install Python 3.11, add it to your path, and verify with Python and pip. Select a compatible release like 3.11.9 and recognize that pip3 may be needed.

Leverage Awem Studio to build prompt engineering and simulate an OpenAI API for scalable, local development with Llama, choosing models by hardware, and deploying via Docker.

Install PyCharm, the Python IDE used in this course, and download the free community edition instead of the paid professional version; VS Code is also an option.

Install Docker and Docker Desktop, enable WSL on Windows, or use Mac, and run containers and images to set up databases and SQL inside containers.

Learn to use DBeaver to query multiple databases, install the cross-platform tool, and prepare for Docker-based connections in the next lecture.

Learn to set up a Postgres database with Docker by pulling the official image and running a container. Expose port 5432, connect with DBeaver, and test the connection for access.

Load and populate an empty database by running a guided script that creates customers, accounts, transactions, employees, and loans tables, inserts sample data, and builds an ERD for SQL queries.

Learn prompt engineering for a data analyst by loading llama and crafting SQL prompts in a local AI chat, generating queries from a Postgres schema while preserving data privacy.

Explore prompt engineering with one-shot and multi-shot prompting, using in-context learning to guide AI responses, improve accuracy, and adapt to tasks across sentiment analysis, content creation, and coding tools.

Explore chain of thought, tree of thought, and self-refinement prompting techniques to enhance AI reasoning, generate high-quality outputs, and support data analyst tasks with Llama and Flask.

Explore persona-based prompting, interview-style prompts, and creative prompts with logical reasoning to shape ai responses, delivering tailored, structured insights and enhanced domain accuracy.

Identify weak prompts, debug responses, and refine prompts with structured formats, examples, and stepwise tasks to improve accuracy; test variations, iterate, and craft goal-oriented prompts.

Deploy an open source llama model with Ollama and Docker to create a local API endpoint, test multi-model prompts, and compare eight-bit versus larger variants.

Set up the architecture for a data analyst ai agent by initializing a project, configuring Postgres and container services, and enriching prompts with the live schema before querying the database.

Retrieve the postgres database schema by querying information_schema.columns and format the results as a markdown table using a cursor, handling connection details and errors.

Learn how to send a prompt to a llama 3.1 model via a generate endpoint in a Flask setup, including constructing the payload, post request, and testing with logs.

Generate sql using a Llama-powered prompt to create and test a sql function against a Postgres schema, then run the sql via an orm loop to analyze results.

Improve prompt optimization to extract only sql, debug sql generation and analysis in python, and present results in a markdown table while validating with cursor schema and loan count.

Process the analyze response to produce a human readable answer using a large language model prompt and llama 3.1, with results formatted in markdown.

Build a Flask app with a /generate POST endpoint that accepts JSON with prompt and optional model, defaults to llama 3.1, and returns the generated response.

Finalize the first version of the flask app by adding and testing generate, get schema, and analyze endpoints, using request args and json responses.