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Comprehensive ベクタ検索 Tools for Every Need

Get access to ベクタ検索 solutions that address multiple requirements. One-stop resources for streamlined workflows.

ベクタ検索

  • Connery SDK

    Connery SDK enables developers to build, test, and deploy memory-enabled AI agents with tool integrations.
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    What is Connery SDK?
    Connery SDK is a comprehensive framework that simplifies the creation of AI agents. It provides client libraries for Node.js, Python, Deno, and the browser, enabling developers to define agent behaviors, integrate external tools and data sources, manage long-term memory, and connect to multiple LLMs. With built-in telemetry and deployment utilities, Connery SDK accelerates the entire agent lifecycle from development to production.
    Connery SDK Core Features
    • Multi-language client libraries (Node.js, Python, Deno, Browser)
    • Persistent memory stores for context retention
    • Multi-model and vector search integrations
    • Plugin and tool invocation framework
    • Built-in telemetry and logging
    • Deployment utilities for cloud and on-premises
    Connery SDK Pro & Cons

    The Cons

    Documentation indicates last update was 7 months ago, which may imply slower recent development.
    Lack of explicit pricing or enterprise support details might limit adoption for larger businesses.
    No mention of ready-made end-user applications or demo apps available.

    The Pros

    Open-source, facilitating transparency and community contributions.
    Combines SDK and CLI to streamline AI plugin and action development.
    Standardized REST API simplifies app integration and interaction consistency.
    Handles authorization, input validation, and logging automatically.
    Flexible plugin architecture enabling interaction with external services.
    Connery SDK Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://docs.connery.io/sdk

  • Llama-Index-Go

    An open-source Go library providing vector-based document indexing, semantic search, and RAG capabilities for LLM-powered applications.
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    What is Llama-Index-Go?
    Serving as a robust Go implementation of the popular LlamaIndex framework, Llama-Index-Go offers end-to-end capabilities for constructing and querying vector-based indexes from textual data. Users can load documents via built-in or custom loaders, generate embeddings using OpenAI or other providers, and store vectors in memory or external vector databases. The library exposes a QueryEngine API that supports keyword and semantic search, boolean filters, and retrieval-augmented generation with LLMs. Developers can extend parsers for markdown, JSON, or HTML, and plug in alternative embedding models. Designed with modular components and clear interfaces, it provides high performance, easy debugging, and flexible integration in microservices, CLI tools, or web applications, enabling rapid prototyping of AI-powered search and chat solutions.
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