> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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](/concepts/knowledgebase/vector-stores/chroma)           | Embedded, Local, Cloud        | HNSW, Flat      | Yes           | Quick prototyping, embedded apps               |
| [FAISS](/concepts/knowledgebase/vector-stores/faiss)             | Local file                    | HNSW, IVF, Flat | Yes           | High-performance local search, large datasets  |
| [Qdrant](/concepts/knowledgebase/vector-stores/qdrant)           | Embedded, Local, Cloud        | HNSW, Flat      | Yes           | Production workloads, advanced filtering       |
| [Milvus](/concepts/knowledgebase/vector-stores/milvus)           | Embedded (Lite), Local, Cloud | HNSW, IVF, Flat | Yes           | Scalable production, distributed search        |
| [Pinecone](/concepts/knowledgebase/vector-stores/pinecone)       | Cloud only                    | Managed         | Yes           | Fully managed, zero-ops production             |
| [Weaviate](/concepts/knowledgebase/vector-stores/weaviate)       | Embedded, Local, Cloud        | HNSW, Flat      | Yes           | Schema-based collections, AI modules           |
| [PGVector](/concepts/knowledgebase/vector-stores/pgvector)       | PostgreSQL                    | HNSW, IVF       | Yes           | Existing PostgreSQL infrastructure             |
| [SuperMemory](/concepts/knowledgebase/vector-stores/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:

```python theme={null}
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  |

<Note>
  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.
</Note>

## 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