Qdrant Vector Database Embedding Pipeline | Operasyon için n8n Otomasyon İş Akışı Şablonu

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        "mode": "insert",
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        "qdrantCollection": {
          "__rl": true,
          "mode": "list",
          "value": "sv_lang_data",
          "cachedResultName": "sv_lang_data"
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        "embeddingBatchSize": 100
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        "width": 260,
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        "content": "### Fetch JSON File List\n**Node:** FTP (all files)\n**Operation:** List\n**Path:** \n\nRecursively lists all .json files prepared for embedding."
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        "content": "### Iterate Over Files\n**Node:** Loop Over Items\n\nBatches each file path individually for processing."
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        "content": "### Download Each File\n**Node:** FTP (1 file download)\n\nDownloads the current file in binary form using:\n```\nPath = file_path/{{ $json.name }}\n```"
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        "content": "### Parse JSON Document (Default Data Loader)\n**Node:** Default Data Loader\n**Loader Type**: binary\n- Converts JSON structure into a document format compatible with embedding.\n\n\n### Split into Smaller Chunks\n**Node:** Character Text Splitter\n**Split by:** \"chunk_id\" or custom logic based on chunk formatting\n\nOptional node if chunk size normalization is required before embedding."
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        "content": "### Store in Vector DB\n**Node:** Qdrant Vector Store\n**Batch Size:** 100\n\n**Collection:** \nSends cleaned text chunks to OpenAI to get embeddings (1536 dim for text-embedding-ada-002)\n\n#### collection settings in Qdrant cluster\n```\nPUT /collections/{collection_name}\n{\n    \"vectors\": {\n      \"size\": 1536,\n      \"distance\": \"Cosine\"\n    }\n}\n```\nEmbed Chunks\n**Node:** Embeddings OpenAI\nPushes the embedded chunks (with metadata) into Qdrant for semantic retrieval."
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