⚡ FAISS Integration¶

🔐 Licensed Component - Contact: bajpaikrishna715@gmail.com for licensing

FAISS Quantum Integration¶

graph TB
    subgraph "FAISS Core"
        A[Vector Indexing]
        B[Fast Similarity Search]
        C[GPU Acceleration]
        D[Compression Algorithms]
    end

    subgraph "Quantum Enhancement"
        E[Quantum Indexing]
        F[Grover Search]
        G[Quantum Compression]
        H[Amplitude Amplification]
    end

    subgraph "QuantumLangChain Integration"
        I[Hybrid Indexing]
        J[Quantum-Fast Search]
        K[Optimized Performance]
        L[Scalable Architecture]
    end

    A --> E
    B --> F
    C --> G
    D --> H

    E --> I
    F --> J
    G --> K
    H --> L

🌟 Core Features¶

Quantum-Enhanced FAISS¶

from quantumlangchain.vectorstores import QuantumFAISS

# Initialize quantum FAISS
vectorstore = QuantumFAISS(
    index_type="IVF",
    quantum_indexing=True,
    gpu_acceleration=True,
    quantum_compression=True
)

# Build quantum-enhanced index
await vectorstore.build_quantum_index(
    embeddings=embeddings,
    quantum_optimization=True
)

# Ultra-fast quantum search
results = await vectorstore.quantum_search(
    query_vector=query_embedding,
    k=10,
    grover_acceleration=True
)

🔐 License Requirements¶

• Basic FAISS: Basic license tier (standard integration)
• Professional FAISS: Professional license tier (quantum indexing)
• Enterprise FAISS: Enterprise license tier (GPU + quantum)
• Research FAISS: Research license tier (experimental algorithms)

Contact bajpaikrishna715@gmail.com for licensing.