Vector DB Integration

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

Related skills

rag-pipeline-setup, supabase-prisma-database-management