Ragwalla Memory and Knowledge Graphs are now generally available!

Ragwalla Blog

AI agents, RAG, and enterprise AI implementations.

Vector-Grounded Graph Retrieval: The Pattern Behind Modern GraphRAG

Knowledge graphs encode structured facts that LLMs can reason over, but they require precise entry points — while user queries are fuzzy natural language. Vector-grounded graph retrieval solves this by using embedding similarity to discover seed entities, then walking the graph outward to surface connected relationships. This retrieve-and-traverse pattern has become the standard approach in production GraphRAG systems, combining the semantic flexibility of vector search with the structural reasoning of graph traversal.

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What's Next After Agentic AI

Beyond Agentic AI: The Next Wave of Intelligent Systems The current wave of agentic AI represents a significant leap from generative models, but it's just one step in a...

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MCP Enterprise Adoption Report 2025: Challenges, Best Practices & ROI Analysis

The Model Context Protocol (MCP) is rapidly becoming the enterprise standard for AI-tool integration, with organizations reporting 30% reductions in development overhead and 50-75% time savings on common tasks. However, successful enterprise adoption requires navigating complex technical, security, and operational challenges. This comprehensive report analyzes real-world MCP implementations, identifies key roadblocks, and provides actionable strategies for enterprise success.

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MCP (Model Context Protocol) Developer Guide

MCP (Model Context Protocol) Developer Guide The Model Context Protocol (MCP) enables Ragwalla agents to connect with external tools and services through a standardized interface. This guide covers everything...

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Ragwalla Agents Developer Guide

Ragwalla Agents Developer Guide Ragwalla Agents provide a powerful framework for creating AI-powered assistants that can execute tools, maintain conversation state, and integrate with external services through the Model...

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Key Differences Between the OpenAI Responses API and the Assistants API

​The OpenAI Responses API simplifies conversation state management by handling it server-side. Developers can maintain context across interactions by including the `previous_response_id` parameter in their requests, referencing the last response's ID. This approach eliminates the need to manually track conversation history, as the API retrieves and incorporates the entire conversation chain automatically. However, it's important to note that all previous input tokens in the conversation chain are billed as input tokens.

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Hybrid Search With The OpenAI Assistants API

The OpenAI Assistants API does not natively support hybrid search or metadata indexing, but Ragwalla's implementation does. Hybrid search allows developers to build smarter, faster, and more precise data retrieval systems. Whether manually setting keywords or automating the process with LLM Auto mode, this approach enhances the effectiveness and efficiency of vector-based applications.

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Real Use Cases for Cosine Similarity, Dot Product, and Euclidean Distance

Understanding cosine similarity, dot product, and Euclidean distance can be much easier with real-world analogies. These measures each capture “similarity” or “distance” in different ways — direction vs. magnitude vs. straight-line distance . We explore two narrative-style scenarios for each measure, showing when to use them and why.

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