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  • Knowledge - Agno
    Knowledge is domain-specific information that the Agent can search at runtime to make better decisions (dynamic few-shot learning) and provide accurate responses (agentic RAG) Knowledge is stored in a vector db and this searching on demand pattern is called Agentic RAG
  • Integrating Agentic RAG with MCP Servers: Technical . . .
    At a high level, the system will include the following components: an Agent (LLM) with planning logic, one or more MCP servers providing access to knowledge sources (and possibly tools), a Vector database or knowledge store (sitting behind an MCP server) for long-term information, and the MCP client interface that connects the agent to those
  • The agent does not check the vector database and just uses . . .
    In your instructions you are telling it to “use the provided embeddings”, which is somewhat ambiguous to the AI n8n uses Langchain’s tool calling feature for vector store retrieval, therefore the AI needs to understand that the “embeddings” it needs to access is the tool it has to call
  • The Role of Vector Databases in Agentic Process Automation . . .
    Vector databases play a crucial role in long-term memory for AI models in APA Instead of reprocessing vast datasets, APA can retrieve past embeddings, significantly reducing processing time and improving AI response accuracy
  • Boost AI Agent Performance with Vector Databases
    After integrating a vector database with your AI agent, the next crucial phase involves testing and optimizing the performance of your intelligent system This step is vital to ensure that your AI agent operates at its peak efficiency and accuracy levels
  • Announcing General Availability of Native Vector Type . . .
    Uses the CustomerId from the CRM agent to fetch claims from dbo claims table; Payments Agent IT fetches payment history, outstanding balances, and reimbursement timelines Retrieves payment history from the dbo payments table: Each agent has an expert-level view of its domain and knows how to query its respective tables
  • Hands-on Practical: Integrating Vector DB Memory - apxml. com
    Theory provides the foundation, but practical implementation solidifies understanding This section guides you through building and integrating a persistent long-term memory module for an agent using a vector database


















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