Vector Stores

Choose and configure the right vector database for your KnowledgeBase

Overview

Upsonic supports 8 vector database providers out of the box. Each provider shares a common configuration interface (BaseVectorDBConfig) while exposing provider-specific options for advanced tuning.

All providers support dense vector search, full-text search, and hybrid search through a unified API — so you can switch providers without changing your application logic.

Provider Comparison

Provider	Deployment	Index Types	Hybrid Search	Best For
Chroma	Embedded, Local, Cloud	HNSW, Flat	Yes	Quick prototyping, embedded apps
FAISS	Local file	HNSW, IVF, Flat	Yes	High-performance local search, large datasets
Qdrant	Embedded, Local, Cloud	HNSW, Flat	Yes	Production workloads, advanced filtering
Milvus	Embedded (Lite), Local, Cloud	HNSW, IVF, Flat	Yes	Scalable production, distributed search
Pinecone	Cloud only	Managed	Yes	Fully managed, zero-ops production
Weaviate	Embedded, Local, Cloud	HNSW, Flat	Yes	Schema-based collections, AI modules
PGVector	PostgreSQL	HNSW, IVF	Yes	Existing PostgreSQL infrastructure
SuperMemory	Cloud (managed)	Managed	Yes	Zero-config RAG (no embedding provider needed)

Quick Start

Every provider follows the same pattern — swap the provider and config to switch vector databases:

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# 1. Create embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# 2. Configure and create vector database
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./db")
))

# 3. Create knowledge base with documents
kb = KnowledgeBase(
    sources=["document.pdf"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# 4. Query with Agent and Task
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the key points in the document?",
    context=[kb]
)

result = agent.do(task)
print(result)

Connection Modes

Most providers support multiple connection modes via ConnectionConfig:

Mode	Description	Use Case
Mode.IN_MEMORY	Ephemeral, no persistence	Testing, prototyping
Mode.EMBEDDED	Local file storage	Development, single-node apps
Mode.LOCAL	Connect to local server	Self-hosted deployments
Mode.CLOUD	Connect to cloud service	Production, managed services

Not all providers use ConnectionConfig. FAISS uses db_path directly, Pinecone uses api_key + environment, and PGVector uses connection_string. SuperMemory only requires an api_key. See each provider’s page for details.

Shared Configuration

All providers inherit these base parameters from BaseVectorDBConfig:

Parameter	Type	Default	Description
collection_name	str	"default_collection"	Name of the collection
vector_size	int	(required)	Dimension of vectors (must match your embedding model)
distance_metric	DistanceMetric	COSINE	Similarity metric: COSINE, EUCLIDEAN, or DOT_PRODUCT
recreate_if_exists	bool	False	Recreate collection if it already exists
default_top_k	int	10	Default number of results returned
default_similarity_threshold	float | None	None	Minimum similarity score (0.0-1.0)
dense_search_enabled	bool	True	Enable dense vector search
full_text_search_enabled	bool	True	Enable full-text search
hybrid_search_enabled	bool	True	Enable hybrid search
default_hybrid_alpha	float	0.5	Alpha for hybrid search blending (0.0 = full-text, 1.0 = dense)
default_fusion_method	str	'weighted'	Fusion method: 'rrf' or 'weighted'

Choosing a Provider

For prototyping and development:

• Chroma (embedded mode) or FAISS — zero infrastructure, fast iteration

For production with managed infrastructure:

• Pinecone — fully managed, auto-scaling, zero ops
• SuperMemory — zero-config (handles embeddings too)

For production with self-hosted infrastructure:

• Qdrant or Milvus — feature-rich, scalable, battle-tested
• Weaviate — if you need schema-based collections and AI modules

For existing PostgreSQL setups:

• PGVector — adds vector search to your existing database