Vector DB Integration
Quick setup patterns for the three most common vector databases in Claude Code projects.
Chroma (local dev)
Best for: prototyping, local development, no server needed
pip install chromadb
import chromadb
client = chromadb.Client() # in-memory
# OR: chromadb.PersistentClient(path="./chroma_data")
collection = client.create_collection("knowledge")
# Add documents
collection.add(
documents=["doc text here", "another doc"],
ids=["id1", "id2"]
)
# Query
results = collection.query(query_texts=["search query"], n_results=3)
print(results["documents"])
Qdrant (production)
Best for: high-performance production, filtering, payload storage
pip install qdrant-client
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
client = QdrantClient(":memory:") # or QdrantClient(url="http://localhost:6333")
client.create_collection(
collection_name="docs",
vectors_config=VectorParams(size=384, distance=Distance.COSINE)
)
# Upsert vectors
client.upsert(
collection_name="docs",
points=[PointStruct(id=1, vector=[0.1]*384, payload={"text": "doc content"})]
)
# Search
results = client.search(collection_name="docs", query_vector=[0.1]*384, limit=3)
Weaviate (hybrid search)
Best for: combining keyword + semantic search, GraphQL queries
pip install weaviate-client
import weaviate
client = weaviate.connect_to_local()
collection = client.collections.create(
name="Document",
vectorizer_config=weaviate.classes.config.Configure.Vectorizer.text2vec_openai()
)
# Hybrid search
results = collection.query.hybrid(
query="search text",
limit=3
)
Choosing a vector DB
| Need | Recommended |
|---|---|
| Local dev / quick prototype | Chroma |
| Production, high throughput | Qdrant |
| Hybrid keyword+vector search | Weaviate |
| Already on Supabase | pgvector via supabase-prisma-database-management skill |
| Maximum scale | Milvus |
Related skills
rag-pipeline-setup, supabase-prisma-database-management
Related Skills
qdrant— Qdrant integrationpinecone— Pinecone integrationembedding-pipeline— embedding generation
GitNexus Index
This skill is indexed by GitNexus for knowledge graph traversal. Index path: /Users/localuser/.claude/skills/vector-db-integration/.gitnexus Last indexed: 2026-05-23