🚀 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: qdrant

Qdrant

Qdrant as the vector store for an EDDI knowledge base, a purpose-built vector database with room to grow.

ConfigurationDocs ↗

What you get

  • A dedicated vector database behind your knowledge base
  • Optional API key and TLS
  • The same agent configuration as every other store

How it works in EDDI

  1. Run a Qdrant server and note its host and gRPC port (the EDDI default is 6334).
  2. Create a RagConfiguration with storeType: "qdrant" and a collectionName.
  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)Qdrant takes a host, a port and a collection name. apiKey and useTls are optional.
{
  "name": "product-docs",
  "embeddingProvider": "openai",
  "embeddingParameters": {
    "model": "text-embedding-3-small",
    "apiKey": "${vault:openai-key}"
  },
  "storeType": "qdrant",
  "storeParameters": {
    "host": "localhost",
    "port": 6334,
    "collectionName": "product-docs",
    "useTls": false
  },
  "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

  • If Qdrant needs an API key, reference it from the vault.
  • Embedding calls send document text to the embedding provider you configure.
  • An open vector store is a copy of your corpus. Keep it off public interfaces.

Good to know

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

Related integrations

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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.