HelixDB From Zero

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

HelixDB From Zero

Coursera · Intermediate ·🔍 RAG & Vector Search ·1mo ago

Key Takeaways

Builds a HelixDB project using Rust and HelixQL schema

Original Description

Stop stitching three databases together. HelixDB is a Rust-native graph plus vector engine that holds your nodes, your embeddings, your typed edges, and your key-value documents in one process — no separate Postgres, no separate Qdrant, no separate Neo4j. This course walks a Rust-fluent engineer from helix init through a typed HelixQL schema, the helix check and helix compile pre-deploy gates, side-by-side graph traversal and vector search in the same query language, and a typed Rust client that calls a live HelixDB instance with four runtime contracts. Every primitive you meet is wired into a working .hx schema and a running engine you can install in one command. Module 4 puts graph traversal verbs (Out, In, WHERE, ORDER, RANGE) and vector top-k search side by side in the same query language, and adds ShortestPathDijkstras with composable weight expressions for cost-aware routing. You finish with the canonical hybrid-RAG pattern — SearchV returns top-k embeddings, then a typed edge climbs back to the source documents — shipped as a typed Rust client (helix-rs plus serde) with four runtime assertion contracts. The course closes with an honest read on where HelixDB is the wrong choice: columnar OLAP, Spark execution semantics, multi-tenant analytical warehouses.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
AnswerSurvivalRAG: What Happens When RAG Finds the Answer, Then Drops It?
Learn how RAG systems can fail even when they find the correct answer, and why it matters for reliable AI performance
Medium · Machine Learning
📰
A RAG evaluator that admits what it can't judge
Learn how to build a reliable RAG evaluator that acknowledges its limitations, a crucial aspect of AI safety and robustness
Dev.to · Melissa D. Ellison
📰
RAG on Google Cloud in Regulated Environments: A Lifecycle Playbook from Inception to…
Learn to implement RAG on Google Cloud in regulated environments with a lifecycle playbook
Medium · Machine Learning
📰
Solving One of the Hardest Problems in Code RAG: Context Retrieval
Learn to solve context retrieval in code RAG systems, a crucial challenge in automation code generation, and improve your skills in RAG and code analysis.
Medium · RAG
Up next
This FREE Tool Turns ANY PDF into Perfect Markdown (MinerU Live Test)
Prompt Engineer
Watch →