🚀 EDDI 6.5.0

19 LLM providers, knowledge bases that crawl your site and read your files, and conversations that follow compatible agent versionsWhat's new

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Data and RAGstoreType: elasticsearch

Elasticsearch

Elasticsearch as the vector store for an EDDI knowledge base, useful when you already run it and want keyword and vector search in one place.

ConfigurationDocs ↗

What you get

  • A knowledge base in a cluster you already operate
  • API key or user name and password authentication
  • The same agent configuration as every other store

How it works in EDDI

  1. Make sure an Elasticsearch cluster is reachable and pick an index name.
  2. Create a RagConfiguration with storeType: "elasticsearch", the serverUrl and the indexName.
  3. Bind it with an eddi://ai.labs.rag workflow step.
  4. Select it by name in the LLM task and ingest documents.

Configuration

Taken from the EDDI documentation. Placeholders such as your-agent-id are yours to replace.

RagConfiguration (POST /ragstore/rags)The default serverUrl is localhost:9200. Add an apiKey, or a userName and password, for a secured cluster.
{
  "name": "product-docs",
  "embeddingProvider": "openai",
  "embeddingParameters": {
    "model": "text-embedding-3-small",
    "apiKey": "${vault:openai-key}"
  },
  "storeType": "elasticsearch",
  "storeParameters": {
    "serverUrl": "localhost:9200",
    "indexName": "product-docs"
  },
  "chunkStrategy": "recursive",
  "chunkSize": 512,
  "chunkOverlap": 64,
  "maxResults": 5,
  "minScore": 0.6
}
workflow step + langchain.json taskBinding the knowledge base to the agent and using it in a task.
// 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." }
} ] }

Governance

  • Reference the API key or password from the vault.
  • Embedding calls send document text to the embedding provider you configure.
  • Your existing cluster access control applies to the vectors.

Good to know

  • Replacing a document's chunks is expected to work with Elasticsearch but is not covered by tests. Without it, re-ingested documents can leave stale chunks.
Get startedDocs ↗

Related integrations

Data and RAGpgvectorPostgreSQL with pgvector as the vector store for an EDDI knowledge base. The recommended persistent choice when you already run PostgreSQL.Data and RAGQdrantQdrant as the vector store for an EDDI knowledge base, a purpose-built vector database with room to grow.Data and RAGMongoDB Atlas Vector SearchMongoDB Atlas Vector Search as the vector store for an EDDI knowledge base, a natural fit if you already run on Atlas.

Configuration and behavior on this page follow the EDDI documentation, checked on 2026-10-14. Field names and defaults can change between releases, so confirm them in the docs for your version.