{ "id": "uYYw1E8JAYRYC7sE", "name": "My workflow", "tags": [], "nodes": [ { "id": "c46b2f23-abb5-4ae8-8f16-0ecfd39533d7", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [ 1344, 464 ], "parameters": { "public": true, "options": {} }, "typeVersion": 1.1 }, { "id": "22f89ea8-71d0-40bf-bad1-24a803ddbdcb", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ 1232, 352 ], "parameters": { "color": 5, "width": 1232, "height": 624, "content": "## Database Query Agent\n" }, "typeVersion": 1 }, { "id": "ec22405a-53d0-47ee-b667-2132ecce2a1a", "name": "Set Table Name", "type": "n8n-nodes-base.set", "position": [ 1600, 464 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "25d58d22-7bf2-45eb-900b-5e35ced2d6d3", "name": "table_name", "type": "string", "value": "product_inventory" } ] } }, "typeVersion": 3.4 }, { "id": "addd05ca-9db5-436c-93e8-9c08ce40bc14", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 1616, 768 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4o-mini", "cachedResultName": "gpt-4o-mini" }, "options": {} }, "typeVersion": 1.2 }, { "id": "ed57c796-1d8c-4b68-aa95-ee5a65f27b38", "name": "Simple Memory", "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow", "position": [ 1808, 768 ], "parameters": { "sessionKey": "={{ $('When chat message received').item.json.sessionId }}", "sessionIdType": "customKey" }, "typeVersion": 1.3 }, { "id": "8ac733ea-97de-4bd3-b669-6235c4ec55c6", "name": "Execute SQL Query", "type": "n8n-nodes-base.postgresTool", "position": [ 2000, 768 ], "parameters": { "query": "{{ $fromAI(\"sql_query\", \"SQL Query\") }}", "options": {}, "operation": "executeQuery", "descriptionType": "manual", "toolDescription": "Fetch all the data from Postgres, ensuring that table names are always written with their appropriate schema prefix. Each table in the database belongs to a specific schema, so make sure to reference every table using its full schema-qualified name." }, "typeVersion": 2.5 }, { "id": "e9e87bc9-048b-47fa-b52c-937563c74c1c", "name": "Get Table Definition", "type": "n8n-nodes-base.postgresTool", "position": [ 2224, 768 ], "parameters": { "query": "select\n c.column_name,\n c.data_type,\n c.is_nullable,\n c.column_default,\n tc.constraint_type,\n ccu.table_name AS referenced_table,\n ccu.column_name AS referenced_column\nfrom\n information_schema.columns c\nLEFT join\n information_schema.key_column_usage kcu\n ON c.table_name = kcu.table_name\n AND c.column_name = kcu.column_name\nLEFT join\n information_schema.table_constraints tc\n ON kcu.constraint_name = tc.constraint_name\n AND tc.constraint_type = 'FOREIGN KEY'\nLEFT join\n information_schema.constraint_column_usage ccu\n ON tc.constraint_name = ccu.constraint_name\nwhere\n c.table_name = '{{ $json.table_name }}'\n AND c.table_schema = 'public'\norder by\n c.ordinal_position", "options": {}, "operation": "executeQuery", "descriptionType": "manual", "toolDescription": "Get table schema definition to find all columns and its types" }, "executeOnce": true, "typeVersion": 2.5 }, { "id": "1246e3ac-9a70-4bd7-a736-bb8e0d82d757", "name": "Database Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 1856, 464 ], "parameters": { "text": "={{ $('When chat message received').item.json.chatInput }}", "options": { "systemMessage": "=# AI Agent Database Query System - Dynamic Column Mapping\n\n# Role\nYou are an expert SQL assistant that can answer natural-language questions by querying a **single table**. \nThe active table schema will always be provided via the **Get Table Definition** tool. \nYour job is to understand the user\u2019s request, map it to the actual columns/values in that table, generate a safe SQL query, run it with **Execute SQL Query**, and return only the matching rows. \nThis must work for any type of table (education, product inventory, HR, finance, etc.).\n\n# Workflow (internal \u2013 do not show to the user)\n1. Call **Get Table Definition** first to get the schema (`table` name and `columns`). \n2. Identify which user terms map to which columns by inspecting column names + data types. \n - Examples: \n - \u201cbachelor/master/PhD\u201d \u2192 `course_level` / `level` \n - \u201cprice under 500\u201d \u2192 numeric/price/cost/fee column \n - \u201cin stock/available\u201d \u2192 quantity/stock/active/available columns \n - \u201cSydney/Melbourne\u201d \u2192 location/campus/city/state columns \n - Free-text terms \u2192 name/title/description columns \n3. For each relevant column, run a **discovery query** to fetch distinct values (e.g. `SELECT DISTINCT col FROM {{ $json.table_name }} LIMIT 200`) and use fuzzy/ILIKE matching to align user terms with actual values. \n4. Build a safe SQL query: \n - **FROM**: always the `{{ $json.table_name }}` from schema. \n - **SELECT**: `*` unless the user asks for specific columns (then only select existing ones). \n - **WHERE**: \n - String: `col ILIKE '%term%'` \n - Exact match if distinct value matches exactly \n - For multi-value/CSV/JSON text fields: `col::text ILIKE '%term%'` \n - Numeric filters: parse numbers from text; use `<`, `>`, `BETWEEN` with safe casting \n - Date filters: cast text to date if needed; support \u201cbefore/after/between/latest\u201d \n - Boolean filters: map \u201cyes/true/available/on\u201d \u2192 `true`; \u201cno/false/off\u201d \u2192 `false` \n - **ORDER BY**: \n - Prefer `updated_at` or `last_updated` desc if present and user asks for \u201clatest/recent\u201d \n - Or order by `created_at` if that exists \n - **LIMIT**: 50 by default; adjust only if user explicitly requests a number. \n5. Run the query with **Execute SQL Query**. \n6. Return only the resulting rows to the user. \n7. If no results, reply: *\u201cNo records found matching your criteria.\u201d*\n\n# Output Rules\n- Do **not** show SQL, tool calls, or internal reasoning. \n- Always return just the clean data results (table or JSON) to the user.\n\n# Example usage\n- User: \u201cShow bachelor IT courses in Sydney under 35,000\u201d \n \u2192 Return rows where course_level matches \u201cUndergraduate\u201d, course_name contains \u201cIT\u201d/\u201cInformation Technology\u201d, campus_location contains \u201cSydney\u201d, fee < 35000.\n\n- User: \u201cList in-stock laptops under $800 in Melbourne\u201d \n \u2192 Return rows where category/name contains \u201claptop\u201d, stock > 0, price < 800, location contains \u201cMelbourne\u201d.\n" }, "promptType": "define" }, "typeVersion": 2 }, { "id": "f13f2521-0c8a-41da-aaad-7c3e82fff652", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 2496, 352 ], "parameters": { "color": 4, "width": 320, "height": 624, "content": "\n### Step 1: Prerequisites\n- n8n instance (cloud/self-hosted)\n- PostgreSQL database with read access\n- OpenAI API key/You can use other LLM as well\n\n### Step 2: Import & Configure\n1. Import this workflow template into n8n\n2. **Add Credentials**:\n - OpenAI API: Add your API key\n - PostgreSQL: Configure database connection\n3. **Set Table Name**: Edit \"Set Table Name\" node \u2192 Replace `\"table_name\"` with your actual table\n4. **Test Connection**: Ensure your database user has SELECT permissions\n\n### Step 3: Deploy & Use\n1. Start the workflow\n2. Open the chat interface\n3. Ask questions like:\n - *\"Show all active users\"*\n - *\"Find orders from last month over $100\"*\n - *\"List products with low inventory\"*" }, "typeVersion": 1 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "4043d2be-67cd-491d-854a-cec912f59ec0", "connections": { "Simple Memory": { "ai_memory": [ [ { "node": "Database Agent", "type": "ai_memory", "index": 0 } ] ] }, "Set Table Name": { "main": [ [ { "node": "Database Agent", "type": "main", "index": 0 } ] ] }, "Execute SQL Query": { "ai_tool": [ [ { "node": "Database Agent", "type": "ai_tool", "index": 0 } ] ] }, "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "Database Agent", "type": "ai_languageModel", "index": 0 } ] ] }, "Get Table Definition": { "ai_tool": [ [ { "node": "Database Agent", "type": "ai_tool", "index": 0 } ] ] }, "When chat message received": { "main": [ [ { "node": "Set Table Name", "type": "main", "index": 0 } ] ] } } }