Master Generative AI with Python for SQL & Databases

Explore building a LangChain-powered bot in Python to interact with a Postgres database and its tables for demonstration, using any database of your choice.

Connect to a Postgres database using psycopg2, define credentials (db name, user, password, host, port), and establish the connection on the local machine.

Learn to test database queries in Python by executing a SQL query to select first name, last name, and salary from employees, limiting to five random records.

Discover how LangChain, an open source framework, enables building AI apps with LLMs and agents that query databases and convert language to SQL safely.

Explore LMS chains, tools, agents, and memory as core components of long chain large language models, and how they transform inputs into answers, SQL queries, or summaries.

Access the grok API to leverage ultra fast AI interface hardware, the DSP Tensor Streaming Processor, delivering real-time, low-latency responses for chatbots and SQL assistants.

Navigate to grok.com, start building, and create an account; then go to API keys, create API key, name it ai sql, submit, and the API is created.

Set up a grok-compatible LangChain OpenAI wrapper, import chat OpenAI and human message schema, configure an OpenAI LLM with zero temperature, and specify the API key and base URL.

Explore how LangChain enables AI models to learn from and interact with each other, forming a chain that processes and generates human-like language for your prompt in a response pipeline.

Wrap PostgreSQL with LangChain by using the LangChain SQL database chain to import a SQL database, connect via a database URI, and retrieve table info for employees and departments.

Build a LangChain-powered bot by creating a sql database chain, pass a language model, and execute a natural language prompt to return the highest salary employee, Stephen King, salary 24,000.

Restrict the LLM to predefined, safe queries to secure AI-to-database interactions, prevent SQL injection and destructive queries, ensure consistent schemas, optimize performance, and empower non-technical users.

Build and execute predefined queries to fetch highest salary, average salary, and five newly hired employees from SQL databases, using intent, connection, and cursor handling.

Learn how intent classification translates natural language queries into predefined keys that reflect the user's goal to fetch relevant results for a LangChain-powered bot.

Memory lets applications remember past interactions, making them context aware, enabling chatbots to recall previous answers and summarize conversations with memory and entity memory that tracks usernames.

Import a conversation buffer memory from Lightchain memory, define a chat history key, save context, and load memory variables to preserve input and results across conversations in LangChain.

Dynamically load input and output variables into memory to create a reusable context for a LangChain bot, leveraging conventional buffer memory to persist chat history and AI messages.

Discover how agents orchestrate tools to answer natural language queries, choosing databases, APIs, or chains, and executing planning then execution with zero-shot or conversational styles.

Define tools for database querying in a LangChain-powered bot using a PostgreSQL db tool to fetch the three most recently hired employees.

Explore how prompt engineering guides LLMs to generate SQL from natural language. Ensure queries reference actual tables and columns, adapt to schema variations, and are syntactically valid and semantically relevant.

Develop a safe query execution mechanism in Python. The safe_execute function inspects SQL for drop, delete, alter, or truncate commands and blocks unsafe queries.

Learn to debug generated SQL in a Python-driven LangChain bot by printing the SQL, catching runtime errors, and using a debug function to explain the query plan.

Build a Streamlit app for a LangChain-powered chatbot UI that connects to PostgreSQL, generates SQL via an llm, and safely displays queries and results.