MongoDB Atlas Vector Search

{
  "name": "product-docs",
  "embeddingProvider": "openai",
  "embeddingParameters": {
    "model": "text-embedding-3-small",
    "apiKey": "${vault:openai-key}"
  },
  "storeType": "mongodb-atlas",
  "storeParameters": {
    "connectionString": "${vault:atlas-connection-string}",
    "databaseName": "eddi",
    "collectionName": "product_docs",
    "indexName": "vector_index"
  },
  "chunkStrategy": "recursive",
  "chunkSize": 512,
  "chunkOverlap": 64,
  "maxResults": 5,
  "minScore": 0.6
}

// 1. workflow step: binds the knowledge base to the agent
{ "type": "eddi://ai.labs.rag",
  "extensions": {},
  "config": { "uri": "eddi://ai.labs.rag/ragstore/rags/{ragId}?version=1" } }

// 2. LLM task: selects it by the "name" of the RagConfiguration
{ "tasks": [{
    "actions": ["*"],
    "type": "openai",
    "knowledgeBases": [{ "name": "product-docs", "maxResults": 5, "minScore": 0.7 }],
    "parameters": { "systemMessage": "You are a helpful assistant." }
} ] }