vector database with python sdk

If you want to use a vector database with a Python SDK, here are the most common options and how to get started quickly.

Popular vector databases with Python SDKs

• Pinecone — fully managed, easy to use
• Weaviate — open-source and managed options
• Qdrant — open-source and managed
• Milvus — open-source, scalable
• Chroma — simple local/vector store for prototyping
• FAISS — library for vector search, not a database

Example: Qdrant with Python SDK

Install

pip install qdrant-client

Basic usage

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

client = QdrantClient(host="localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="my_collection",
    vectors_config=VectorParams(size=4, distance=Distance.COSINE),
)

# Insert vectors
client.upsert(
    collection_name="my_collection",
    points=[
        PointStruct(id=1, vector=[0.1, 0.2, 0.3, 0.4], payload={"text": "hello"}),
        PointStruct(id=2, vector=[0.2, 0.1, 0.4, 0.3], payload={"text": "world"}),
    ],
)

# Search
results = client.search(
    collection_name="my_collection",
    query_vector=[0.1, 0.2, 0.3, 0.4],
    limit=2,
)

for r in results:
    print(r.id, r.score, r.payload)

Example: Pinecone with Python SDK

Install

pip install pinecone

Basic usage

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("my-index")

index.upsert([
    ("id1", [0.1, 0.2, 0.3], {"text": "hello"}),
    ("id2", [0.3, 0.2, 0.1], {"text": "world"}),
])

res = index.query(vector=[0.1, 0.2, 0.3], top_k=2, include_metadata=True)
print(res)

Example: Chroma

Install

pip install chromadb

Basic usage

import chromadb

client = chromadb.Client()
collection = client.create_collection("my_collection")

collection.add(
    ids=["1", "2"],
    embeddings=[[0.1, 0.2, 0.3], [0.3, 0.2, 0.1]],
    metadatas=[{"text": "hello"}, {"text": "world"}],
)

results = collection.query(
    query_embeddings=[[0.1, 0.2, 0.3]],
    n_results=2
)
print(results)

Which should you choose?

• Quick prototype / local app: Chroma
• Production with managed service: Pinecone
• Open-source production option: Qdrant or Weaviate
• Large-scale self-hosted: Milvus
• Pure similarity search library: FAISS

If you want, I can also give you:

1. a full Python example with embeddings + vector DB
2. a comparison table of Pinecone vs Qdrant vs Weaviate vs Chroma
3. a Docker setup for local vector DB testing