index.md

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---

$ whoami

• Kumar Shivendu

• Software Engineer @ Qdrant

• I ❤️ information retrieval, performance, and data mining.

• First talk!

• Qdrant: Future of search and beyond

---

Traditional ways to build search

• Approaches:

  • Keyword match, Regex, Boolean operators
  • Extracting metadata using NLP and CV
  • Knowledge graphs, Vocabs

• Challenges:

  • Effort to maintain the knowledge
  • Growth of unstructured data
  • Multimodal search remains hard: Text, Image, Audio, Video

---

Vectors

• Points in an N-dimensional space
• Anything -> Vector
• Generated from:
  • ML models
  • Metric learning
    • CLIP

---

Vector search

• Nearest points
• Example: Google Lens
• But this is expensive and not easy to scale
• Solution: Indexing and approximation

---

What is Qdrant

• Vector Search Engine (aka Vector DB)

• 15k+ stars on Github

• Written in Rust 🦀

• SDKs for Python, JS, Go, Java, etc

• Twitter, Canva, Meesho, Flipkart

---

The HNSW Index

• Skip Lists + Graphs
• Approximate and Tunable
• Filter during search
• Quantization

---

Running search:

POST /collections/rentals/points/search
{
  "query": [0.2, 0.3, 0.4, 0.5], // vector generated from image/text/video
  "filter": { "must": [{"key": "locality", "match": {"value": "Indiranagar"}}] },
  "limit": 10
}

• [
    {"id": 4, "score": 0.56, "payload": {...}},
    {"id": 2, "score": 0.40, "payload": {...}},
    {"id": 5, "score": 0.23, "payload": {...}},
  ]

---

Beyond search: Recommendations

• Realtime addition of points is possible.
• average_vector and best_score

• POST /collections/rentals/points/recommend
  {
    "positive": [100, 231], // vector ids
    "negative": [718, [0.2, 0.3, 0.4, 0.5]], // vector id and vector
    "filter": { "must": [{"key": "locality", "match": {"value": "Indiranagar"}}] },
    "strategy": "best_score",
  }

• DailyMotion (Qdrant), Spotify (Annoy)

---

How to find this thing on the internet?

• No reverse image search
• No known name

---

Strategy One

• Describe the thing
  • "Combination of human, dragon and chicken"
  • "Mythology creature of human and dragon"

---

Strategy Two

• Search for similar images
  • Similarity bubble

---

Beyond search: Discovery API

• Unique iterative search by Qdrant

• Combine multi-modal vectors in single query

• POST collections/my-collection/points/discover
  {
    "target": [0.63, 0.10, 0.91, 0.55],
    "context": [
      {
        "positive": 7125, // <-- ID of the example
        "negative": 122   // <-- This can also be a vector
      }
    ],
  }

---

Summary

• Anything => vector

• Vectors >> similarity search

• Thousands of use-cases with Qdrant

• Find me at
  • kshivendu.dev/bio
  • kshivendu.dev/twitter