Initial project setup for Telegram-to-Qdrant knowledge base.

Add unified n8n workflow, userbot ingestion script, and detailed README for running n8n with a separate Dockerized Telethon collector. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-23 20:20:56 +03:00
commit a8d634f422
13 changed files with 1768 additions and 0 deletions
--- a/(2).json
+++ b/(2).json
@@ -0,0 +1,178 @@
+{
+  "name": "Создание базы (TG Text)",
+  "nodes": [
+    {
+      "parameters": {
+        "mode": "insert",
+        "qdrantCollection": {
+          "__rl": true,
+          "value": "=11111",
+          "mode": "id"
+        },
+        "options": {}
+      },
+      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
+      "typeVersion": 1.3,
+      "position": [
+        112,
+        -208
+      ],
+      "id": "44a1fb40-5b1f-401a-9fea-ca61f111d2ca",
+      "name": "Qdrant Vector Store",
+      "credentials": {
+        "qdrantApi": {
+          "id": "gS1LJOMgnR7VJFRO",
+          "name": "QdrantApi account 2"
+        }
+      }
+    },
+    {
+      "parameters": {
+        "model": "qwen3-embedding:8b"
+      },
+      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
+      "typeVersion": 1,
+      "position": [
+        32,
+        0
+      ],
+      "id": "7448ed28-7971-4d95-b073-1251dfff242e",
+      "name": "Embeddings Ollama",
+      "credentials": {
+        "ollamaApi": {
+          "id": "XVSCsyxx8Z57lSRa",
+          "name": "Ollama account 3"
+        }
+      }
+    },
+    {
+      "parameters": {
+        "chunkSize": 500,
+        "chunkOverlap": 50
+      },
+      "type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter",
+      "typeVersion": 1,
+      "position": [
+        272,
+        192
+      ],
+      "id": "aed6ea3c-ad56-432b-8ef5-823a7778da27",
+      "name": "Character Text Splitter"
+    },
+    {
+      "parameters": {
+        "httpMethod": "POST",
+        "path": "tg-inbox",
+        "options": {}
+      },
+      "type": "n8n-nodes-base.webhook",
+      "typeVersion": 2.1,
+      "position": [
+        -448,
+        -208
+      ],
+      "id": "1770ab28-9cfa-4bc8-864a-fe793f1b95db",
+      "name": "Webhook",
+      "webhookId": "d857e864-ed25-447f-84ef-27c63190fb93",
+      "notesInFlow": false
+    },
+    {
+      "parameters": {
+        "textSplittingMode": "custom",
+        "options": {}
+      },
+      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
+      "typeVersion": 1.1,
+      "position": [
+        272,
+        0
+      ],
+      "id": "6c98f0d9-97c1-4d94-bdea-a1941fd7d3f8",
+      "name": "Default Data Loader"
+    },
+    {
+      "parameters": {
+        "jsCode": "// В n8n данные из POST запроса всегда лежат в объекте body\nconst msg = $input.item.json.body;\n\n// 1. Берем текст\nconst rawText = msg.text || \"\"; \n\n// 2. Достаем метаданные из объекта metadata, который прислал Python\nconst meta = msg.metadata || {};\n\n// 3. Извлекаем имя и ID из объекта metadata\nconst senderName = meta.sender_name || \"Unknown\";\nconst senderId = meta.sender_id || \"0\";\n\n// 4. Формируем результат для Qdrant\nreturn {\n  json: {\n    content: `Пользователь ${senderName} (ID: ${senderId}) написал: ${rawText}`,\n    metadata: {\n      ...meta, // Копируем всю метадату (date, message_id, group_id и т.д.)\n      sender_name: senderName,\n      sender_id: senderId,\n      original_text: rawText\n    }\n  }\n};\n"
+      },
+      "type": "n8n-nodes-base.code",
+      "typeVersion": 2,
+      "position": [
+        -192,
+        -208
+      ],
+      "id": "0ff56c0f-9208-4ae9-920c-4332c09f6df4",
+      "name": "Code in JavaScript"
+    }
+  ],
+  "pinData": {},
+  "connections": {
+    "Webhook": {
+      "main": [
+        [
+          {
+            "node": "Code in JavaScript",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Embeddings Ollama": {
+      "ai_embedding": [
+        [
+          {
+            "node": "Qdrant Vector Store",
+            "type": "ai_embedding",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Default Data Loader": {
+      "ai_document": [
+        [
+          {
+            "node": "Qdrant Vector Store",
+            "type": "ai_document",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Code in JavaScript": {
+      "main": [
+        [
+          {
+            "node": "Qdrant Vector Store",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Character Text Splitter": {
+      "ai_textSplitter": [
+        [
+          {
+            "node": "Default Data Loader",
+            "type": "ai_textSplitter",
+            "index": 0
+          }
+        ]
+      ]
+    }
+  },
+  "active": false,
+  "settings": {
+    "executionOrder": "v1",
+    "binaryMode": "separate",
+    "availableInMCP": false
+  },
+  "versionId": "f5079d8e-590c-4db5-988a-3fe709346a4a",
+  "meta": {
+    "templateCredsSetupCompleted": true,
+    "instanceId": "96706479c2e398d2a4e75bb05002310bb6030e8268be45294cab77c6640a0fe6"
+  },
+  "id": "QpXms3TNMSqlQD2ULX3WO",
+  "tags": []
+}