waffledb 0.1.0

Python client for WaffleDB vector database

WaffleDB Python SDK - Dead Simple, Fully Featured

Vector search with 2 lines of code. Auto-creates everything. Handles every use case.

Installation

\\ash pip install waffledb \\

5-Minute Start

1. Start Server

\\ash docker run -p 8080:8080 waffledb \\

2. Add & Search

\\python from waffledb import client

Add vectors (collection auto-creates!)

client.add("docs", ids=["doc1", "doc2"], embeddings=[[0.1]*384, [0.2]*384], metadata=[{"title": "A"}, {"title": "B"}] )

Search

results = client.search("docs", [0.15]*384) for r in results: print(f"{r.id}: {r.score:.4f}") \\

Done! No setup, no config, everything auto-created.

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Core API

Method	Purpose
\client.add(collection, ids, embeddings, metadata)\	Add/insert vectors
\client.search(collection, embedding, limit)\	Find similar
\client.delete(collection, ids)\	Remove vectors
\client.get(collection, id)\	Get one vector
\client.update(collection, id, embedding)\	Update embedding
\client.update_metadata(collection, id, metadata)\	Update metadata
\client.batch_search(collection, queries)\	Multi-query
\client.list()\	List collections
\client.info(collection)\	Collection stats
\client.drop(collection)\	Delete collection
\client.snapshot(collection, name)\	Backup
\client.health()\	Server health

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Real World Examples

RAG / Semantic Search

\\python from waffledb import client

docs = load_documents() client.add("kb", ids=[d["id"] for d in docs], embeddings=[d["emb"] for d in docs], metadata=[{"text": d["text"]} for d in docs])

results = client.search("kb", embed("What is Python?"), limit=5) context = "\n".join(r.metadata["text"] for r in results) answer = llm.ask(f"Based on: {context}") \\

Recommendations

\\python from waffledb import client

users = load_users() client.add("users", ids=[u["id"] for u in users], embeddings=[u["emb"] for u in users], metadata=[{"name": u["name"]} for u in users])

similar = client.search("users", user_embedding, limit=10) print([r.metadata["name"] for r in similar]) \\

Product Search

\\python from waffledb import client

products = load_products() client.add("products", ids=[p["id"] for p in products], embeddings=[p["emb"] for p in products], metadata=[{"name": p["name"], "price": p["price"]} for p in products])

results = client.search("products", embed("blue running shoes under 100"), limit=20) for r in results: if r.metadata["price"] < 100: print(f"{r.metadata['name']}: ") \\

Image Search

\\python from waffledb import client

images = load_images() client.add("images", ids=[img["id"] for img in images], embeddings=[img["emb"] for img in images], metadata=[{"url": img["url"]} for img in images])

results = client.search("images", image_embedding, limit=20) for r in results: print(r.metadata["url"]) \\

Duplicate Detection

\\python from waffledb import client

docs = load_docs() client.add("documents", ids=[d["id"] for d in docs], embeddings=[d["emb"] for d in docs], metadata=[{"text": d["text"]} for d in docs])

for doc in docs: similar = client.search("documents", doc["emb"], limit=5) duplicates = [r for r in similar[1:] if r.score > 0.95] if duplicates: print(f"Doc {doc['id']} duplicated: {[r.id for r in duplicates]}") \\

Time Series Patterns

\\python from waffledb import client

windows = extract_time_windows(data) client.add("patterns", ids=[w["id"] for w in windows], embeddings=[w["emb"] for w in windows], metadata=[{"ts": w["ts"]} for w in windows])

current = extract_window(latest_data) similar = client.search("patterns", current["emb"], limit=10) if similar[0].score < 0.8: print("Anomaly detected!") \\

Multi-Tenant

\\python from waffledb import client

for tenant in tenants: docs = load_tenant_docs(tenant.id) client.add(f"tenant_{tenant.id}", ids=[d["id"] for d in docs], embeddings=[d["emb"] for d in docs])

results = client.search(f"tenant_{tenant_id}", query_emb) \\

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Configuration

\\python from waffledb import WaffleClient

client = WaffleClient("http://server:8080", timeout=60) \\

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Dead simple. Fully featured. 49.5K vectors/sec.