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Toolbox helps you build Gen AI tools that let your agents access data in your database. Toolbox provides: Simplified development: Integrate tools to your agent in less than 10 lines of code, reuse tools between multiple agents or frameworks, and deploy new versions of tools more easily. Better performance: Best practices such as connection pooling, authentication, and more. Enhanced security: Integrated auth for more secure access to your data End-to-end observability: Out of the box metrics and tracing with built-in support for OpenTelemetry. ⚡ Supercharge Your Workflow with an AI Database Assistant ⚡ GitHub:- https://lnkd.in/gs-jT-6V

WSO2 Integrator now features AI-driven Intelligent Document Processing. This is a powerful utility for developers who need to integrate data stuck inside unstructured documents. Using the VS Code extension, the process becomes engineering-focused: •Schema Generation: The AI suggests the optimal data schema instantly, based on your document structure (like lab reports or invoices). •Easy Customization: You can fine-tune the extracted schema using natural language or the JSON editor. •Automated Flow: Critical data is extracted correctly and immediately flowed into the target database. Watch this demo video to see the tool in action.

Prompt to build AI agent workflows in seconds. Apache 2.0 alternative to n8n. Sim is redefining how teams build and deploy AI agents — all in one open-source platform. From visual workflow design to Copilot-powered iteration, it turns complex agentic systems into something anyone can create in minutes. Built for speed, collaboration, and innovation, it’s a glimpse into the future of AI development. Ref. https://lnkd.in/gnNchPxz #AI #OpenSource #Innovation #Automation #Agents #n8n

A New Solution for Managed RAG: Gemini API Launches the File Search Tool This new tool almost fully manages the complex Retrieval-Augmented Generation (RAG) process, allowing developers to focus on their business applications. RAG acts like an external knowledge base for AI, enabling it to consult documents (e.g., PDFs) before responding to reduce "hallucinations." Previously, self-hosting a RAG system was complex, requiring manual management of data chunking and vector databases. Key Features of File Search: 1️⃣ Extremely Low Cost: File storage and query embeddings are free. There is only a one-time fee of $0.15 per 1 million tokens for initial file indexing. 2️⃣ Fully Automated: It automatically handles file storage, intelligent chunking, embedding generation (based on a top-ranked MTEB model), and context injection. 3️⃣ Traceable Results: The API automatically generates citations, ensuring answers are well-supported by evidence. 🔹 The free tier offers 1GB of storage, while Tier 1 provides up to 10GB. 🔹 It supports chunking_config for custom chunking strategies. This tool is ideal for rapidly building applications like enterprise knowledge bases and customer service Q&A systems. Official Documentation: https://lnkd.in/gGd52abe #GeminiAPI #RAG #AIDevelopment #AIApplications #GoogleAI

Everyone’s talking about MCP Let’s get to what it actually is: 👉 It’s just JSON schema with agreed-upon endpoints. Anthropic basically said: “What if every AI tool spoke the same JSON language when connecting to apps?” And the industry said, “Finally.” Before MCP: • Every LLM integration was custom. • You needed M × N connections for M apps and N tools. • Engineers were duct-taping APIs with no shared spec. After MCP: • You build one MCP server for your tool. • It now works with any MCP-compatible AI assistant. • That’s M + N integrations instead of M × N. If you can read or write JSON, you already know MCP. The difference is that now, your schema actually scales. Why it matters: • Build once, integrate everywhere. • No more adapter soup for every platform. • Your product instantly works with Claude, GPT, and whatever comes next. What used to take weeks of integration work now takes hours. Connect your database, API, or internal tool through a single clean protocol. Instead of reading 50 different integration docs, you read one spec. Instead of maintaining endless adapters, you maintain one server. MCP isn’t a revolution. It’s alignment. And alignment scales faster than innovation. Have you started experimenting with MCP yet? What use case are you connecting first?

🚀 Just implemented an MCP Server to massively boost efficiency in one of my applications! Super excited to share this — I recently integrated an MCP (Model Context Protocol) server into one of my apps, and the impact has been immediate. For anyone hearing about MCP for the first time: It allows AI to do more than just “chat.” It enables AI models to interact directly with your backend, trigger workflows, and use tools safely — almost like adding a smart operator inside your system. 🔧 What I did (high-level steps): 1️⃣ Added an MCP server inside our FastAPI backend This created a dedicated, secure endpoint where AI models can call structured tools. 2️⃣ Defined tools for core operations Things like: Data lookups Creating records Updating entities Triggering internal services Integrations (calendar, meetings, tasks, etc.) 3️⃣ Connected the MCP server to our AI layer So the AI can automatically decide when to use tools instead of giving generic text-based instructions. 4️⃣ Tested real workflows end-to-end AI now performs tasks that previously required multiple manual steps — instantly, reliably, and with context. 5️⃣ Optimized performance & permissions Ensured everything runs safely, fast, and only within the allowed scope. ✨ The Results ✔ Faster internal operations ✔ Less manual work ✔ Better automation flows ✔ AI that can take action — not just respond ✔ A smarter, more connected backend This is exactly the direction modern AI-powered systems are headed: From conversational → to operational. #genai #mcp #rag #python #ai #developer #fastapi

🔗 MODEL CONTEXT PROTOCOL (MCP) "the new USB-C" for AGENTS & TOOLS The current AI agents are evolving beyond single-model limited to tasks or chatbots, they’re are designed as multi-tool, multi-agent systems capable of reasoning, retrieval with real-world results. But to build these a common standard with features supporting interoperability, ease of use & modularity MCP is introduced by Anthropic in late 2024 as a universal connector. MCP primarily provides a common interface for tool execution. Instead of each AI agent reimplementing custom tool schemas or APIs, MCP defines a universal structure that both sides can understand. It has two main primitives: 🖥️ Servers – These wrap collections of tools. A “tool” could be anything from a database query, API call, web browser action, or file operation. Servers expose these tools via a standard HTTP interface. 🤖 Clients – These are typically agents, models, or orchestrators. Clients discover available tools from the server, send execution requests (with inputs), & receive structured outputs. Frameworks such as Mastra have built-in abstractions for creating both MCP servers & clients in TypeScript but despite this momentum its still maturing: Discovery: There’s no single, standardized registry for MCP tools yet though Anthropic is working on a “meta-registry”. Quality: The ecosystem lacks formal scoring or verification like npm’s reputation system. Configuration: Providers interpret the spec slightly differently, which can cause compatibility issues. But the direction is clear with MCP as the standard for its interoperability, reusability & modularity. 💬 Why It Matters Agents are no longer isolated silos, they are built as composable modules. MCP changes the way we think about AI systems where tools can be used portably & reusable across frameworks which compounds Innovation. Once agents can connect, share tools through MCP, the next frontier is coordination. How the agents think & work together is where Graph-Based Workflows come in which is a topic for a later post. 👉 Follow me for more insights & reflections on LLMs, agent design, & data-driven intelligence. #ModelContextProtocol #LLMs #MCP #Interoperability #Modularity #Reusability #Mastra #Anthropic #OpenAI #AIEngineering #AgentFrameworks

Struggling to choose the right RAG framework? Here's your ultimate guide to the top open-source libraries [https://lnkd.in/gFSNrKum]: • LangChain - The complete toolkit for building sophisticated LLM applications • Haystack - Enterprise-grade composable pipelines for production RAG • LlamaIndex - Data framework specialist for superior indexing & querying • RAGFlow - Deep document understanding with verifiable citations • txtai - All-in-one embeddable engine for local semantic search • LLMWare - Secure RAG optimized for smaller models & edge deployment • Cognita - Collaborative platform for scaling from experiment to production Each framework has unique strengths - the key is matching them to your specific use case, whether you're building chatbots, document QA systems, or enterprise AI agents. Find all details here: https://lnkd.in/gFSNrKum Author: Vipin Vashisth #RAG #AIEngineering #OpenSource #LangChain #LlamaIndex #AIAgents #MachineLearning #DeveloperTools #AIDevelopment #TechEducation

Building a robust Retrieval-Augmented Generation (RAG) pipeline is becoming one of the most critical challenges in modern AI system design. As large language models continue to evolve, they still struggle with factual grounding and access to real-time or domain-specific information. That’s where RAG comes in by combining information retrieval and generation, it enables systems to produce accurate, explainable, and context-aware responses based on trusted data sources. Frameworks like LangChain, LlamaIndex, Haystack, RAGFlow, txtai, and LLMWare have made it easier to build modular RAG architectures, offering components for document loading, text splitting, embeddings, retrieval, and generation. However, the process of building an efficient RAG pipeline is far from simple. It involves careful consideration of data preprocessing and chunking, choosing the right retrieval strategy (dense, sparse, or hybrid), selecting the most suitable embedding models, and balancing latency with accuracy. Beyond that, maintaining data freshness, ensuring security and compliance in sensitive industries, and optimizing the pipeline for scalability are ongoing challenges. As new techniques like GraphRAG, HyDE, and other experimental approaches emerge, they continue to push the boundaries of retrieval efficiency and knowledge grounding. Ultimately, mastering RAG isn’t just about connecting a retriever and a generator,it’s about engineering an intelligent, trustworthy system that can learn, adapt, and reason over ever-changing data landscapes.

💡 The RAG → GraphRAG Journey: From Retrieval to Reasoning In the GenAI world, Retrieval-Augmented Generation (RAG) has become the backbone for building intelligent assistants and knowledge bots. But as enterprise data grows more interconnected and complex, the industry is moving to the next evolution — GraphRAG. Here’s how the journey unfolds 👇 🔹 RAG (Retrieval-Augmented Generation) RAG enhances LLMs by retrieving relevant context from vector databases before generating responses. 🧩 Flow: Query → Embed → Retrieve (via vector DB) → Generate (via LLM) ✅ Quick to build ✅ Ideal for document chatbots ⚠️ But limited understanding of relationships between entities 🔹 Why GraphRAG? Enterprises deal with deeply connected data — people, processes, contracts, assets, and workflows. RAG finds similar text, but can’t reason over connections. For example: “Which vendor’s delayed delivery impacted project launch?” A standard RAG model can’t trace vendor → contract → asset → project. That’s where GraphRAG shines. 🔹 GraphRAG: Connecting Knowledge GraphRAG combines vector search + knowledge graphs, enabling LLMs to reason over entities and relationships. 🧠 Flow: Query → Graph traversal → Context retrieval → LLM reasoning 💼 Benefits: Understands context and relationships Reduces hallucination Enables multi-hop and explainable QA 🔹 RAG vs GraphRAG in Practice Use CaseRAGGraphRAGSimple Q&A✅❌Contract, asset, or CRM reasoning⚠️✅Compliance or lineage tracing⚠️✅Real-time retrieval✅⚠️ 🔹 Infra & Cost View AspectRAGGraphRAGInfra Cost💰 Low💰💰 Medium–HighAccuracy⚠️ Limited context✅ Relational reasoningComplexity⭐ Easy⭐⭐⭐ Complex 🔹 What’s Next The future is HybridRAG — combining the speed of vector retrieval with the reasoning power of knowledge graphs. This evolution is redefining how enterprises build context-aware, explainable AI systems. 💬 Curious to know how RAG or GraphRAG could fit into your product architecture (e.g., post-call analytics, contract insights, or asset tracking)? Happy to share some patterns we’re implementing at SN Cloud Tech and Servixo. #GenAI #RAG #GraphRAG #KnowledgeGraph #LLM #EnterpriseAI #AIArchitecture #AWSBedrock #Claude #Qdrant #Neo4j #AIInnovation