About the course
Build robust and accurate AI-driven workflows with this instructor-led, hands-on training course designed for developers, data engineers, and AI practitioners. This course provides a comprehensive deep dive into integrating vector databases, large language models (LLMs), and JSON data within a Postgres-based architecture.
You'll gain a strong foundational understanding of vector stores, embeddings, and similarity search, learning why Postgres (with pgvector) is a powerful choice compared to other market tools. Through interactive labs, you'll set up and configure Postgres, generate and store embeddings, perform efficient vector searches, and integrate retrieved data with LLMs for advanced AI applications.
Additionally, you'll explore Postgres' JSON capabilities, mastering how to store, query, and index semi-structured data alongside relational and vector data. By the end of the course, you'll build a fully functional AI-powered pipeline, combining vector retrieval, LLM responses, and hybrid queries to support real-world AI use cases.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the globe.
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- Grasp AI Workflow Architecture: Comprehend the architecture of AI workflows, including LLMs, vector stores, and JSON data roles.
- Define Core AI Concepts: Explain fundamental AI workflow concepts like embeddings, similarity search, and contextual prompting.
- Set Up a Vector Database: Install and configure PostgreSQL with pgvector for robust vector storage.
- Manage Embeddings: Generate, store, and effectively organise vector embeddings from various sources.
- Perform Vector Searches: Execute and optimise vector similarity searches using indexing techniques.
- Build Contextual LLM Apps: Design and implement pipelines integrating vector data with LLMs for context-aware applications.
- Handle JSON Data in Postgres: Utilise and query JSON/JSONB data in PostgreSQL, applying performance indexing.
- Create Hybrid Data Queries: Develop queries combining vector, relational, and JSON data for advanced AI workflows.
- Construct AI Application Pipelines: Build a complete, working full-stack AI application pipeline, from data retrieval to LLM results.
- Optimise AI Workflows: Apply debugging and optimisation techniques to ensure efficient and reliable AI workflows.
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This course is aimed at Data Scientists, Analysts, Database Developers, Data Warehouse Managers, Business Intelligence Specialists, Software Developers and Architects.
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You should be comfortable with the basics of SQL, ideally with experience of using Postgres (PostgreSQL) database. We can help you get up to speed with a primer session as part of a custom workshop - get in touch to find out more.
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This Postgres Vector Database course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Overview of AI Workflows
Look at high-level architecture - LLMs, vector stores and JSON
Look at key vocabulary and concepts (embeddings, )
Why Postgres as a vector store
What is a vector store? Key concepts and use cases
Why Postgres and how does it compare with other market tools
Setting up Postgres with vector capabilities (pgvector)
Lab: Install and configure Postgres using Docker
Storing and managing vectors
Generating embeddings: Overview of tools and workflows (e.g. OpenAI, Hugging Face)
Storing and organizing embeddings in Postgres
Lab: Generate embeddings for a dataset and store them
Querying the vector store
Techniques for similarity search: k-NN, cosine similarity
Using indexes to optimize vector queries
Lab: Query stored vectors to retrieve similar items (document/image search)
Querying LLMs with retrieved data
Recap on querying LLMs vis APIs
Best practices for combining vector retrieval with LLM prompts
Lab: Build a pipeline where vector store results enhance LLM responses (context-aware Q&A, etc)
NoSQL with JSON in Postgres
Overview of JSON/JSONB support in Postgres
Querying JSON data with SQ:
Indexing JSON data for performance
Lab: Design a schema mixing vector, relational and JSON data for a sample project
Integrating Vector, Relational and JSON Data
Building hybrid queries to power advanced workflows
Case study: Combining embeddings, metadata (relational) and configurations (JSON)
Lab: Implement a hybrid query to support a sample AI use case
Putting it all together
Full stack pipeline demo: Retrieve data, query the LLM and return results
Debugging and optimising the workflow
Lab: Build a working application combining all elements
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https://www.postgresql.org/ - Get PostgreSQL
https://github.com/pgvector/pgvector - vector similarity search for Postgres
https://www.pgadmin.org/ - popular GUI for Postgres
https://dbeaver.io/ - another popular GUI (Open Source)
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