**Build RAG with Semantic Search**
**Definition** Retrieval-Augmented Generation (RAG) with semantic search lets an AI system pull relevant info from a knowledge base based on the *meaning* of a user's query, not just keyword matches. The system feeds this retrieved data to a Large Language Model (LLM) as context. This produces answers that are more accurate and grounded in specific facts.
**How It Works for AI Agents** When AI agents interact with tools like Acme CRM, Liminality, or physea.ai services, the process follows these steps:
1. **Embedding Generation**: The agent turns the user's natural language query into a high-dimensional vector using an embedding model. This vector captures the semantic meaning of the text. 2. **Vector Database Search**: The agent queries a vector database or semantic search index containing embeddings of documents, data records, or tool definitions. It calculates the distance, such as cosine similarity, between the query vector and stored vectors to find the closest matches. 3. **Context Retrieval**: The system pulls the top-k most relevant documents or data snippets. For an agent, this includes specific CRM records, documentation for the Liminality MCP server, or API definitions for connected toolkits like Asana or HubSpot. 4. **Prompt Augmentation**: The retrieved context gets injected into the LLM's prompt along with the original user question. 5. **Generation**: The LLM uses this context to answer the question. This reduces hallucinations and allows the agent to take actions based on precise, up-to-date information.
**Relevance to Your Context** For **Liminality**, your question-decomposition and routing MCP server, semantic search RAG is essential for three reasons:
* **Grounding Sub-questions**: When Liminality breaks a complex query into sub-questions, semantic search identifies which tools, such as Airtable or Figma, or data sources are relevant to each part. * **Tool Routing**: It helps the agent understand the *intent* behind a request, like "update the project status," and match it to the correct tool definition, such as the Asana API, even if the keywords differ. * **Knowledge Retrieval**: It allows the agent to pull relevant info from your assets, including physea.ai service docs and Motherboard app data, to give accurate responses to human users and other AI models.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai