🛡️RAG vector database API

reflection - Vector Data Blockchain Library Documentation

Introduction

The Vector Data Blockchain Library is an innovative technology that utilizes vectorization techniques to convert various types of blockchain data into a format easily understood and processed by machines. This transformation process allows data to be efficiently stored and organized, creating a vector blockchain library. This database has unique advantages in handling and analyzing large amounts of unstructured data, which is challenging for traditional database systems.

Installation

To install the reflection project, use the following command:

pip install reflection-vdb

Basic Concepts

  • Vectorization: The process of converting data into vector representations, enabling more efficient machine processing and analysis.

  • Vector Data Blockchain Library: A database that stores and organizes blockchain data in vectorized form, allowing for fast data retrieval and analysis.

  • Vector Database: A specialized database system for storing and retrieving vector data, utilizing advanced indexing structures and algorithms.

System Architecture

Vector Data Blockchain Library

The Vector Data Blockchain Library combines blockchain technology with vector databases, bringing new possibilities to the blockchain field. This system improves the usability and scalability of blockchain data, better supporting complex smart contracts and decentralized applications. It also enables broader application of blockchain data in machine learning and artificial intelligence, opening new paths for innovation and application of blockchain technology.

Working Principle

  1. Vector Space Theory: The system stores blockchain data in a three-dimensional vector space.

  2. Vector Operations: Data storage and queries are performed using vector addition, subtraction, multiplication, etc.

  3. Entity Attribute Set: Each vector represents a set of entity attributes and can contain any on-chain data.

  4. Efficient Indexing and Similarity Calculation: High-speed indexing and similarity algorithms enable rapid querying and analysis of on-chain data.

API Reference

VectorDataBlockchain

class reflection.VectorDataBlockchain

Manages the global state and configuration for vector data blockchain operations.

  • Methods:

    • __init__(config: dict): Initialize with the given configuration.

    • store_data(data: dict): Store data in the vector blockchain.

    • query_data(query: dict) -> dict: Query data from the vector blockchain.

    • dispose(): Dispose of the context and free resources.

VectorDatabase

class reflection.VectorDatabase

Specialized for storing and retrieving vector data.

  • Methods:

    • __init__(config: dict): Initialize with the given configuration.

    • add_vector(vector: list): Add a vector to the database.

    • find_similar(vector: list, top_n: int) -> list: Find the top N similar vectors.

    • dispose(): Dispose of the context and free resources.

DataTransformation

class reflection.DataTransformation

Converts blockchain data into vector representations.

  • Methods:

    • __init__(context: VectorDataBlockchain): Initialize with the given context.

    • transform(data: dict) -> list: Transform data into vector representation.

    • inverse_transform(vector: list) -> dict: Convert vector back to data.

BlockchainRetrievalEnhancedRAG

class reflection.BlockchainRetrievalEnhancedRAG

Enhances blockchain data retrieval using advanced techniques.

  • Methods:

    • __init__(context: VectorDataBlockchain): Initialize with the given context.

    • process_text(text: str) -> dict: Process text and convert to blockchain index model.

    • set_parameter(name: str, value: any): Set a parameter.

    • get_parameter(name: str) -> any: Get a parameter value.

Key Components

  1. Hallucination Handling:

    • Ensures model output accuracy and transparency using blockchain's immutable properties.

    • Implements a monitoring mechanism based on smart contracts for real-time detection and correction of hallucinations.

  2. Dynamic Training Data Update:

    • Stores model parameters on the blockchain, supporting dynamic dataset updates and model retraining.

    • Uses decentralized storage for secure sharing and updating of training information.

  3. Domain Knowledge Expansion:

    • Integrates different domain knowledge graphs using cross-chain technology.

    • Uses smart contracts to automatically update and integrate domain knowledge, enhancing model performance in specific fields.

  4. Secure Training Data Interaction:

    • Protects sensitive training data with blockchain encryption algorithms.

    • Establishes smart contract-based access control for secure transmission and storage of training data.

Examples

Creating a Vector Data Blockchain Context and Storing Data

from reflection import VectorDataBlockchain

# Create a new context with configuration
config = {"resolution": "1080p", "frame_rate": 30}
context = VectorDataBlockchain(config)

# Store data in the vector blockchain
data = {"transaction": "0x1234", "amount": 100, "timestamp": "2023-01-01T12:00:00Z"}
context.store_data(data)

# Query data from the vector blockchain
query = {"transaction": "0x1234"}
result = context.query_data(query)

# Dispose of the context
context.dispose()

Converting Data to Vector Representation

from reflection import VectorDataBlockchain, DataTransformation

# Create a new context with configuration
config = {"resolution": "720p", "frame_rate": 24}
context = VectorDataBlockchain(config)

# Create a data transformation processor
transformer = DataTransformation(context)

# Transform data to vector
data = {"transaction": "0x1234", "amount": 100, "timestamp": "2023-01-01T12:00:00Z"}
vector = transformer.transform(data)

# Inverse transform vector to data
inverse_data = transformer.inverse_transform(vector)

# Dispose of the context
context.dispose()

Enhancing Blockchain Data Retrieval

from reflection import VectorDataBlockchain, BlockchainRetrievalEnhancedRAG

# Create a new context with configuration
config = {"resolution": "1080p", "frame_rate": 30}
context = VectorDataBlockchain(config)

# Create a retrieval enhanced RAG processor
rag_processor = BlockchainRetrievalEnhancedRAG(context)

# Process text input and get blockchain index model
text = "Show me the transactions for account 0x1234"
blockchain_model = rag_processor.process_text(text)

# Set and get parameters
rag_processor.set_parameter("similarity_threshold", 0.9)
threshold = rag_processor.get_parameter("similarity_threshold")

# Dispose of the context
context.dispose()

Contributing

Contributions to the reflection project are welcome. Please follow the standard GitHub workflow for contributing:

  1. Fork the repository.

  2. Create a new branch for your feature or bugfix.

  3. Commit your changes and push them to your branch.

  4. Create a pull request.

Ensure your code follows the project's coding standards and includes appropriate tests.

License

The reflection project is licensed under the MIT License. See the LICENSE file for more details.

This documentation provides an overview of the reflection API for vector data blockchain operations. For more detailed information and advanced usage, please refer to the source code and additional documentation in the project's repository.

Previousvideo model API

Last updated