14. Vector Databases - Semantic Search
Vector databases enable semantic search and RAG - give AI "memory" of your docs, code, or data.
(Spoiler: "We need a vector database!" is 2025's "We need blockchain!" Most of you need SQLite with pgvector. Don't @ me. 😏)
Top Vector Databases
- Pinecone: Managed, scalable. Best for production apps. (€53/mo+)
- Weaviate: Open-source, self-hosted or cloud. Powerful hybrid search.
- Qdrant: Rust-based, fast. Good for embedded use cases.
- Chroma: Python-native, simple. Great for prototypes.
- pgvector: Postgres extension. If you already use Postgres.
RAG Pattern (Retrieval Augmented Generation)
- 1. Embed your documents (OpenAI embeddings API)
- 2. Store vectors in vector DB
- 3. Query: User asks question → embed query → find similar vectors
- 4. Augment: Inject retrieved docs into LLM prompt
- 5. Generate: LLM answers using your data
Use Cases
- • Chatbots with company knowledge
- • Code search semantic (find by meaning, not keywords)
- • Recommendation systems
- • Documentation Q&A