27 lines
653 B
Markdown
27 lines
653 B
Markdown
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---
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title: "Vector Search with ChromaDB"
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date: 2026-04-02
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draft: false
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tags: ['chromadb', 'python', 'llm', 'tools']
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---
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ChromaDB is an embedding database for building search and retrieval systems.
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## How I use it
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I chunk documentation (VyOS, Hugo) into paragraphs, embed them with
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`nomic-embed-text` via Ollama, and store the vectors in ChromaDB for
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semantic search.
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## Stack
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```
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Documents → Chunker → Ollama embeddings → ChromaDB → Query API
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```
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## Key concepts
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- **Collection**: a named group of embeddings (like a table).
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- **Document**: the raw text stored alongside the vector.
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- **Metadata**: key-value pairs for filtering results.
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