Rediscovering Apollo 11: Using Spring AI + Redis OM Spring to explore the mission to the moon!

What happens when you combine the Apollo program’s historical data with modern AI tools? You get a way to interact with one of humanity’s greatest adventures like never before!

In this session, I’ll show you how I used Redis OM Spring and Spring AI to explore Apollo mission data—aligning transcripts, telemetry, and images to uncover hidden connections and insights. We’ll dive into how Semantic Search powered by vector embeddings makes sense of unstructured text, how Redis as a vector database enables lightning-fast retrieval, and why these tools unlock new ways to explore complex datasets.

Don’t know what embeddings or vector databases are? No worries—I’ll break it all down and show you how it works.

Come for the Moon missions, stay for the AI magic, and leave ready to build your own intelligent search experiences!

Transcript

1. Raphael De Lio REDISCOVERING APOLLO 11 Using Java, Redis, and Vector Search
2. CONTEXT
3. FLIGHT PLAN
4. OBJECTIVE "That's one small step for man, one giant leap for Mankind." "That's one small step for a human, one big jump for humanity.” IS SIMILAR TO
5. VECTOR SIMILARITY SEARCH
6. How does it work? VECTOR SIMILARITY SEARCH "That's one small step for man, one giant leap for Mankind." "That's one small step for a human, one big jump for humanity.” [0.56, 0.76, 0.80, 0.54, 0.99, -0.87 … ] [0.58, 0.75, 0.75, 0.63, 0.9, -0.7 … ]
7. How does it work? Temperature Planet Size Temp Vector Mercury 0,33 167 (-1, 0.5) Venus 4,87 464 (-0.6, 1) Earth 5,97 15 (-0.5, 0.2) Mars 0,642 -65 (-0.7, -0.1) Jupiter 1888 -110 (1.0, -0.3) Saturn 568 -140 (0.3, -0.5) Uranus 86.8 -195 (0, -0.9) Neptune 102 -200 (-0.2, -1) VECTOR SIMILARITY SEARCH Size
8. Size Temperature Cosine Similarity VECTOR SIMILARITY SEARCH How do we search? Angle Based
9. Size Temperature VECTOR SIMILARITY SEARCH How do we search? Cosine Similarity Angle Based
10. Size Temperature VECTOR SIMILARITY SEARCH How do we search? Cosine Similarity Angle Based
11. Size Temperature VECTOR SIMILARITY SEARCH How do we search? Cosine Similarity Angle Based
12. Size Temperature Euclidean Distance VECTOR SIMILARITY SEARCH How do we search? Magnitude Based
13. How many dimensions? VECTOR SIMILARITY SEARCH *heads up de como escolher o embedding model
14. Where to store our vectors? VECTOR SIMILARITY SEARCH
15. How to interact with Redis? VECTOR SIMILARITY SEARCH Saturn V Stage 1: JVM Stage 2: Spring Boot Stage 3: Spring Data & Jedis Command Module & Service: Redis OM Spring Lunar Module: Spring AI & DJL Escape System: Kotlin
16. The Command Module VECTOR SIMILARITY SEARCH How to interact with Redis? JSON System Vector System Query Engines Probabilistic Data Structures Support Perform ance Boosters DS Enhancers Redis OM Spring
17. Mission Control VECTOR SIMILARITY SEARCH How to interact with Redis?
18. TAKING OFF
19. • Collect our data • Load our data into Redis • Query our data
20. COLLECTING THE DATA
21. LOADING THE DATA Annotation for de fi ning a HASH object Annotation for allowing e ffi cient querying based on that fi eld’s value in Redis. Creating the repository
22. VECTORIZING THE DATA Data to be vectorized Field where the vectorize data will be stored in
23. VECTORIZING THE DATA Di ff erent embedding model Number of dimensions must match the number of dimensions of the model
24. VECTORIZING THE DATA
25. QUERY THE DATA Vectorizing the query Searching for the 3 nearest neighbors This is going to generate the command to search and sort on Redis. This is done e ff i ciently by the Redis Query Engine
26. DEMO
27. HOUSTON, WE HAVE A PROBLEM
28. • Short text: “Apollo” → Doesn’t tell if it’s about a Greek god, a space mission, or a music album. • Long text: A full transcript of a mission → May contain too much irrelevant information for a speci fi c query. GRANULARITY PROBLEM *Explicar como isso afeta a utilização de vector search
29. TABLE OF CONTENTS
30. SUMMARIZATION
31. EXTRACTION
32. QUERYING Searching on the questions instead of texts
33. • Feed the LLM with the related u tt erances • Ask it to answer our question based only on the information we provide RETRIEVAL AUGMENTED GENERATION
36. DEMO
37. TAKEAWAYS
38. RAPHAEL DE LIO DEVELOPER ADVOCATE