vecmindb 1.0.0

Python SDK for VecminDB - High-performance vector database HTTP client

VecminDB SDK

The official SDK for VecminDB — The Sovereign Memory OS for AI Agents.

Stop letting your AI Agents hallucinate from memory rot. VecminDB naturally decays outdated memories, distills knowledge via PCA, and provides 100% Air-Gapped cryptographic data sovereignty.

⚠️ License Note: VecminDB is a commercial Cognitive Vector Database. The Free Tier supports up to 5 agents and 100K vectors/agent forever. For enterprise scale-out or clusters, please visit our official website to register and obtain a license key: https://lingxinmind.com.

Deployment & Installation

VecminDB can be run via Docker or as optimized, standalone pre-compiled native binary packages. No local compilers, dependencies, or Python runtimes are needed.

Method A: Docker Deployment (All Platforms - Windows, macOS, Linux)

The fastest way to spin up VecminDB with automatic in-database bilingual embedding support.

# For Global / Overseas users:
docker run -d --name vecmindb-trial -p 5520:5520 ghcr.io/lingxinmind/vecmindb:latest

# For Domestic users (China Aliyun Mirror):
# docker run -d --name vecmindb-trial -p 5520:5520 crpi-ngtfnt7d3tsnwk7l.cn-shanghai.personal.cr.aliyuncs.com/vecmindb/vecmindb:latest

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Method B: Pre-Compiled Native Binary Bundles (Zero-Docker / Zero-Python)

Ideal for high-performance, air-gapped on-premise or private cloud servers. Download the appropriate package from our official website Downloads portal:

• Windows (AMD64): Download vecmindb-1.0.0-beta-x86_64-pc-windows-msvc.zip. Extract the ZIP archive, open Command Prompt or PowerShell in the directory, and run:

  .\vecmindb-server.exe
  

• macOS (Apple Silicon M1/M2/M3): Download vecmindb-1.0.0-beta-aarch64-apple-darwin.tar.gz. Open Terminal, extract and run:

  tar -xzf vecmindb-1.0.0-beta-aarch64-apple-darwin.tar.gz
  cd vecmindb-1.0.0-beta-aarch64-apple-darwin
  ./vecmindb-server
  

• Linux (AMD64): Download vecmindb-offline-linux-amd64.tar.gz. Extract and run:

  tar -xzf vecmindb-offline-linux-amd64.tar.gz
  cd vecmindb-offline-linux-amd64
  ./vecmindb-server
  

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SDK Quickstart

First, install the target client SDK:

# Install core client
pip install vecmindb

# Install with LangChain integration
pip install vecmindb[langchain]

# Install with CrewAI integration
pip install vecmindb[crewai]

Using with LangChain

from vecmindb.memory_plugin import VecminDBMemoryPlugin
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain

# Initialize Sovereign Agent Memory
memory = VecminDBMemoryPlugin.for_langchain(agent_id="support_agent_01", base_url="http://localhost:5520")

llm = ChatOpenAI(temperature=0)
conversation = ConversationChain(llm=llm, memory=memory)

conversation.predict(input="Hi, I need help with my billing.")

Using with CrewAI

from vecmindb.memory_plugin import VecminDBMemoryPlugin
from crewai import Agent, Crew

# Initialize Sovereign Agent Memory
memory_storage = VecminDBMemoryPlugin.for_crewai(agent_id="finance_agent_01", base_url="http://localhost:5520")

agent = Agent(
    role='Financial Analyst',
    goal='Analyze billing data',
    backstory='An expert in financial data.',
    memory=True,
    memory_config={"storage": memory_storage} # Inject VecminDB memory
)

Why VecminDB?

Unlike generic vector databases that act as static drives, VecminDB acts as a cognitive memory operating system with native lifecycle management and cryptographic isolation:

• Biological Forgetting (LTSM): Episodic memories decay dynamically following $W(t) = \exp(-\lambda \times \Delta t)$ with automatic 90-day semantic pruning (let semantic_prune_threshold_secs = 90 * 86400; on disk). Frequently accessed memories persist; transient noise is permanently retired.
• Welford & PCA Memory Distillation: Fuses decaying memory clusters into stable Abstract Centroids using real-time Welford online variance and DP-Federated PCA. Storage converges and scales logarithmically, locking in long-term TCO budgets.
• 3-Sigma Sentinel Guard: Performs real-time anomaly detection and adversarial injection pruning. Evaluates cosine outlier distance with dynamic cutoffs: $\text{Threshold} = \max(\text{Mean}_s - 3 \times \text{Std}_s, 0.7)$.
• Sovereign Federation: Shares collective intelligence across multiple agent domains or VPCs without raw data leak. Fuses PCA Candidate Centroids with differential privacy and a 10% principal bias: $\vec{v}{\text{centroid}} = \text{Mean}{\text{global}} + P_0 \times 0.1$.
• Raft Consensus & 1024-Bucket Anti-Entropy: Combines strong consensus replication with self-healing topology. Employs monotonic lock validation (pub fencing_token: u64) and an adaptive sync cap: (resolution * 2).min(1024).
• 100% Air-Gapped Single-Binary: Built-in BGE-M3 ONNX runtime. No Python, PyTorch, or external embedding API keys needed. Bounded tightly to machine-level HAI hardware fingerprints.

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Enterprise Licensing: For multi-node SOC-2 compliant deployments, please purchase subscriptions or contact us at [e-mail].