PostgreSQL for AI Apps with pgvector is a hands-on workshop in building semantic search and retrieval-augmented generation on PostgreSQL with pgvector, from creating embeddings and choosing HNSW or IVFFlat indexes to hybrid search and measuring retrieval quality. It suits backend and AI developers already using PostgreSQL.
Semantic search and RAG have become features many organizations want in their applications, but adding a separate vector database means keeping data in sync across two systems, managing access control twice and taking on more operational work. For teams already running PostgreSQL, the pgvector extension keeps vectors next to business data in the same database, lets you query them with familiar SQL, and reuses the transactions, backups and permissions you already have.
This hands-on course starts with running PostgreSQL 18 and pgvector in Docker, creating embeddings from documents in Python, and choosing the right data types and indexes for your data size. Learners tune HNSW and IVFFlat parameters, build hybrid search that combines full-text search with vectors, isolate each customer's data with Row-Level Security, and build a RAG pipeline that cites its sources. The day closes with measuring retrieval quality, running the system in production, and a document Q&A workshop. (1 day, 6 hours, Intermediate level.)
Course Details
A 1-day, 6-hour hands-on course (09:00-16:00) at Intermediate level for people who can write SQL and Python, using PostgreSQL 18 and pgvector 0.8.x on Docker. Learners bring an API key for an embeddings/LLM provider or use a local model running on their own machine. Learners take home the course handbook, a Docker Compose file, SQL scripts, Python code for chunking, embeddings, hybrid search and the RAG pipeline, a test question set with a Recall@k script, and a pre-production checklist.
Day 1: Building Semantic Search and RAG on PostgreSQL
What embeddings are and why text with similar meaning ends up close together in vector space
Similarity measures: cosine distance, L2 distance and inner product, and which to choose
Vectors in PostgreSQL versus a dedicated vector database: data consistency, permissions and operations
Limits to know: data size, index memory and query throughput
Run PostgreSQL 18 with pgvector using Docker Compose and enable it with CREATE EXTENSION vector
The vector, halfvec and sparsevec types, and choosing one for your data size and dimensions
Binary quantization with the bit type and two-stage search to cut memory use
Lab: create documents and chunks tables and query them with all three distance operators
Call an embeddings API or a local open-source model from Python
Chunking: by size, by heading, and setting overlap between chunks
Store the metadata you need, such as source, page, date and model version
Lab: batch-load documents quickly with psycopg 3 and COPY
Exact search with a sequential scan versus approximate nearest neighbor search
HNSW: the m, ef_construction and hnsw.ef_search parameters and their effect on accuracy and speed
IVFFlat: setting lists and ivfflat.probes, and why the table needs data before you build the index
Iterative index scans in pgvector 0.8 for queries with WHERE filters
Lab: build both index types and compare timing and results against exact search
Where vector search struggles: proper names, product codes and abbreviations
Full-text search with tsvector, tsquery and a GIN index
Combine both result sets with Reciprocal Rank Fusion in a single SQL statement
Lab: compare vector, keyword and hybrid results for the same set of questions
Design metadata and JSONB columns for filtering by type, date and permission
Partial indexes and partitioning when groups of data differ greatly in size
Row-Level Security so each tenant's searches only see their own data
Lab: verify that users in different tenants cannot see each other's documents
The RAG flow: take a question, embed it, retrieve relevant chunks and assemble the context
Prompt the LLM to answer only from the context and to say when the answer is not found
Attach the sources for each answer, such as document name and page
Workshop: build a small Q&A API that retrieves from PostgreSQL
Build a test question set with correct answers taken from real documents
Measure Recall@k and compare the effect of changes to chunking, indexes and hybrid search
Read EXPLAIN ANALYZE to see whether a query uses the index and where it is slow
Lab: tune ef_search and probes and record accuracy against query time
Index build time and memory, maintenance_work_mem and parallel builds
Updating documents, removing old data and re-embedding when you change models
Backup, restore and replication for tables that hold vectors
Managed services that support pgvector, such as Supabase, Neon and Amazon RDS
Optional: exposing the database to an AI agent over MCP safely with a read-only role
Workshop: take the sample document set or your own documents from chunking through to indexing
Test real questions, check the cited sources and fix what search misses
Wrap up with how to apply this to your own systems and a pre-production checklist
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
Built for Backend and AI developers building semantic search or RAG features · Teams already on PostgreSQL who do not want to add a separate vector database · Data engineers who maintain data pipelines and prepare data for AI systems Background you should have: Able to write SQL including SELECT, JOIN, CREATE TABLE and CREATE INDEX · Basic Python: functions, installing packages and calling APIs Not sure the fit is right? Talk to our team on LINE @itgenius or call 02-570-8449.
THB 3,900 (currently THB 3,510 on promotion). The course runs 6 hours. The price excludes 7% VAT (for payment in a company's name). Pay by bank transfer to the company account, confirm it on our payment page, and we can issue the receipt or tax invoice in your company's name.
Yes. Everyone who completes the course receives a Certificate of Completion from IT Genius Institute. Each certificate carries its own number, and anyone holding that number can verify it online on our certificate page, so you can add it to your portfolio or pass it to HR as evidence of training.
You can attend onsite at IT Genius Institute or arrange to join online, and we also run it as a private in-house session for your team. Ask about dates and venues on LINE @itgenius or call 02-570-8449.
Yes. You may retake the same course free of charge in a later round, under the institute's conditions. Tell our team which course and round you attended, and we will check it and offer you the rounds that still have seats. Ask us on LINE @itgenius or call 02-570-8449.
Enrol online with the registration form on this page. You can register several attendees at once and enter your tax ID and billing address for the tax invoice. Or request a company quotation straight from the quote button. For anything else call 02-570-8449 or reach us on LINE @itgenius.
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