02-vector-search

Vector Search

2.1 Getting Started with Vector Search and Qdrant

Links:

• Notebook: sematic_search.ipynb

Install qdrant and fastembed:

pip install -q "qdrant-client[fastembed]>=1.14.2"

Run in Docker:

docker pull qdrant/qdrant

docker run -p 6333:6333 -p 6334:6334 \
   -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
   qdrant/qdrant

2.2 Embedding Text Data

2.3 Indexing

2.4 Search

2.5 RAG with Vector Search

Links:

• Starter notebook: rag-starter.ipynb
• Finished notebook: rag.ipynb

2.6 Hybrid Search

Links:

• Notebook: hybrid_search.ipynb

Homework

See here

Notes

• Notes from 2024 edition
• Cohort 2025| Vector Search using Qdrant study guide & FAQ by Nitin Gupta
• Cohort 2025| Cognee and dlt workshop study guide & FAQ by Nitin Gupta
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