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Supports Elasticsearch 7.5 and 8.11, using the built-in Elasticsearch credential and Embedding node input.\n\n## Features\n\n- Upsert documents with embeddings into Elasticsearch\n- Similarity search using kNN (ES 8.11) or script_score fallback (ES 7.5)\n- Embedding node input (ai_embedding)\n- Optional index creation with mapping\n- Deterministic hash IDs based on text+metadata\n\n## Compatibility\n\n- Elasticsearch 7.5 and 8.11\n- n8n self-hosted (including Kubernetes)\n\n## Credentials\n\n- **Elasticsearch API**: Uses the same fields as n8n built-in Elasticsearch credential (basic auth, base URL, SSL ignore)\n\n## Node Parameters\n\n- **Connection**: Elasticsearch credentials, Index Name\n- **Embeddings**: Embedding node input, vector dimensions, batch size\n- **Upsert**: text/id/metadata fields, auto-generate ID, ensure index\n- **Search**: search mode (AUTO/KNN/SCRIPT_SCORE), topK, minScore\n\n## Build & Run\n\n```bash\nnpm install\nnpm run build\nnpm pack\n```\n\n## Install in n8n\n\n### Option A: Community package installation (UI)\n\n1. Enable community packages in n8n (Settings → Community Nodes).\n2. Install `n8n-nodes-elasticsearch-vectorstore`.\n\n### Option B: Bake into a custom Docker image (K8s friendly)\n\n```bash\nFROM n8nio/n8n:latest\nUSER root\nRUN npm install /path/to/n8n-nodes-elasticsearch-vectorstore.tgz\nUSER node\n```\n\nRestart the deployment after updating the image.\n\n### Option C: Mount as custom extension\n\nBuild and copy the package to the custom extensions directory:\n\n```bash\nexport N8N_CUSTOM_EXTENSIONS=/home/node/.n8n/custom\nmkdir -p /home/node/.n8n/custom\ncp -R /path/to/n8n-nodes-elasticsearch-vectorstore /home/node/.n8n/custom\n```\n\nRestart n8n.\n\n## How to test locally\n\n```bash\nnpm install\nnpm run dev\n```\n\nOpen http://localhost:5678, create a workflow, and add the **Elasticsearch Vector Store Tool** node.\n\n### Using an Embedding Node Input\n\nSet **Embedding Source** to **Use Embedding Node** and connect your Embeddings node to the **Embeddings** input of the Elasticsearch Vector Store Tool node.\n\n### Use as AI Agent Tool\n\nSet **Operation** to **Similarity Search (As Tool)** and connect the **Elasticsearch Vector Store Tool** node to the AI Agent **Tool** port. Connect an Embeddings node to the **Embeddings** input.\n\n## Example workflow JSON (Upsert)\n\n```json\n{\n  \"nodes\": [\n    {\n      \"parameters\": {\n        \"operation\": \"upsert\",\n        \"indexName\": \"documents\",\n        \"ensureIndex\": true,\n        \"vectorDimensions\": 1536,\n        \"batchSize\": 10,\n        \"textField\": \"text\",\n        \"idField\": \"id\",\n        \"metadataField\": \"metadata\",\n        \"autoId\": \"hash\"\n      },\n      \"name\": \"Elasticsearch Vector Store Tool\",\n      \"type\": \"n8n-nodes-elasticsearch-vectorstore.elasticsearchVectorStoreTool\",\n      \"typeVersion\": 1,\n      \"position\": [520, 300],\n      \"credentials\": {\n        \"elasticsearchApi\": {\n          \"id\": \"1\",\n          \"name\": \"ES Cluster\"\n        }\n      }\n    }\n  ],\n  \"connections\": {}\n}\n```\n\n## Example workflow JSON (Similarity Search)\n\n```json\n{\n  \"nodes\": [\n    {\n      \"parameters\": {\n        \"operation\": \"search\",\n        \"indexName\": \"documents\",\n        \"ensureIndex\": false,\n        \"vectorDimensions\": 1536,\n        \"batchSize\": 5,\n        \"query\": \"Find related documents to prompt engineering\",\n        \"searchMode\": \"auto\",\n        \"topK\": 5,\n        \"minScore\": 0\n      },\n      \"name\": \"Elasticsearch Vector Store Tool\",\n      \"type\": \"n8n-nodes-elasticsearch-vectorstore.elasticsearchVectorStoreTool\",\n      \"typeVersion\": 1,\n      \"position\": [520, 300],\n      \"credentials\": {\n        \"elasticsearchApi\": {\n          \"id\": \"1\",\n          \"name\": \"ES Cluster\"\n        }\n      }\n    }\n  ],\n  \"connections\": {}\n}\n```\n","readmeFilename":"README.md"}