Vector Database Toolkit#

Operations toolkit for vector databases used in AI and RAG systems.

Supported Backends#

• Qdrant - Open-source vector DB (REST/gRPC API)
• Chroma - Lightweight embedded vector DB
• InMemory - Pure Python fallback for small datasets

Quick Start#

Qdrant#

python

from scripts.qdrant_client import VectorClient

client = VectorClient(backend="qdrant", url="http://localhost:6333")
client.create_collection("docs", dimension=768)
client.upsert("docs", ids=["a", "b"], vectors=[[0.1, ...], [0.2, ...]], payloads=[{"title": "A"}, {"title": "B"}])
results = client.search("docs", vector=[0.1, ...], top_k=5)

Chroma#

python

client = VectorClient(backend="chroma", path="/tmp/chroma")
client.create_collection("docs")
client.upsert("docs", ids=["a"], vectors=[[0.1, ...]], payloads=[{"title": "A"}])
results = client.search("docs", vector=[0.1, ...], top_k=5)

Scripts#

• scripts/vector_client.py - Unified client for all backends
• scripts/qdrant_client.py - Qdrant-specific operations
• scripts/chroma_client.py - Chroma-specific operations
• scripts/embedding_utils.py - Text-to-embedding helpers (optional OpenAI, sentence-transformers)

References#

• references/qdrant_api.md - Qdrant API patterns
• references/chroma_api.md - Chroma API patterns