Relancer prospects et clients demande réactivité et personnalisation. L’automatisation planifie les relances, adapte les messages, analyse les taux de retour et centralise les réponses. Ce dispositif dynamise la performance commerciale et valorise la gestion du portefeuille client.
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"systemMessage": "=You are an AI-powered SQL assistant specialized in supply chain analytics. nYour role is to execute SQL queries on BigQuery and return only the results in a structured format.nnToday we are May 31, 2021.nn### **Behavior & Rules**n1ufe0fu20e3 **Query Execution:**n - Your only task is to process user requests and return **direct results** from BigQuery.n - Do **not** display the SQL query.n - Only return structured **data** as output.nn2ufe0fu20e3 **Data Presentation:**n - Format the results as a **table** whenever possible.n - If results are numerical (counts, percentages, aggregates), return them **clearly and concisely**.n - If results contain multiple rows, return **only the first 10** for preview, unless the user specifies otherwise.nn3ufe0fu20e3 **Handling Large Datasets:**n - If the user asks for many rows, show the first **100 rows max** unless specified.n - Provide a **summary** when dealing with large data instead of showing everything.nn4ufe0fu20e3 **Response Format:**n - u2705 **For counts & metrics:** n `"There were 5,432 delayed shipments in the last 21 days."`n - u2705 **For tables:** n | ShipmentID | City | Store | Order Date | Delivery Date | On Time? |n |-----------|-------|--------|------------|--------------|----------|n | 12345 | NYC | ST1 | 2024-03-10 | 2024-03-15 | No |n | 67890 | Paris | ST4 | 2024-03-11 | 2024-03-16 | Yes |nn5ufe0fu20e3 **Clarifying Unclear Requests:**n - If the user request is **too broad**, ask for clarification instead of running an expensive query.nn---nn### Schema AwarenessnAll SQL queries must use the BigQuery table: n`transport.shipments` nnThis table includes fields such as:n- `Shipment ID`, `City`, `Store`, `Order Date`, `Delivery Date`, `On Time Delivery`n- As well as operational timestamps: `Transmission`, `Loading`, `Airport Arrival`, etc.n- And status flags: `Transmission OnTime`, `Loading OnTime`, `Airport OnTime`, `Store Open`nnUse these fields appropriately when analyzing shipment performance.nn---nn### Tool Usage Instruction (for "bigquery_tool")nnWhenever you need to run a SQL query, use the tool called `bigquery_tool`.nnYou must provide the query in the following format:n```jsonn{n "query": "SELECT COUNT(*) FROM `transport.shipments` WHERE `On Time Delivery` = FALSE"n}n"
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"content": "### 1. Workflow Trigger with ChatnThis workflow uses a simple chat window as a trigger. You can replace it with Telegram, Slack, Teams or a webhook trigger linked to your chat.nn#### How to setup?n*Nothing to do.*n"
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"content": "### 2. AI Agent equipped with the query toolnIn order to have more control on the input of the BigQuery node, we don't use the BigQuery tool. Instead we have a set of nodes to retrieve the SQL query, clean it and send it to a BigQuery Node.nn#### How to setup?n- **AI Agent with the Chat Model**:n 1. Add a **chat model** with the required credentials *(Example: Open AI 4o-mini)*n 2. Adapt the **name of your BigQuery table** in the system prompt *(Example: transports.shipments)*n 3. Adapt the **tables fields explanation** in the system promptn [Learn more about the AI Agent Node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)n- Copy and past the **nodes in the yellow sticker** in another workflow. Point the query tool to this workflow.n[Learn more about the Custom n8n Workflow Tool node](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolworkflow)"
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{
"id": "c5967f58-00e8-4f03-9110-913547f7ab9c",
"name": "Call Query Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
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"parameters": {
"name": "bigquery_tool",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "4Os7DoxHjFuTwWio",
"cachedResultName": "ud83dudd28 Big Query Tool"
},
"description": "=Use this tool to run an SQL query and fetch the result from the BigQuery database.nnThe tool expects input in the following format:n{n "query": "SELECT COUNT(*) FROM `transport.shipments` WHERE `On Time Delivery` = FALSE"n}nnOnly provide the SQL query as a string inside the 'query' key. Do not include code formatting (like ```sql), comments, or explanations. The tool will return only the raw result from the database.n",
"workflowInputs": {
"value": {
"query": "={{ $fromAI("query", "SQL query to run") }}"
},
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"id": "query",
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"display": true,
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"content": "### 3. Big Query WorkflownExecute the SQL query generated by the AI agent in Big Query. Retrieve the results and send them back to the AI Agent.nn### How to set up?n- Paste these nodes in a separate workflow so you can use it with multiple agents.n- **Google BigQuery API**:n 1. Add your Google Translate API credentialsn 2. The project in which your table is locatedn [Learn more about the Google BigQuery Node](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.googlebigquery)n"
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{
"id": "bede0624-8923-4af0-8adc-8be22d556066",
"name": "Query Database",
"type": "n8n-nodes-base.googleBigQuery",
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"parameters": {
"options": {},
"sqlQuery": "={{ $json.query }}",
"projectId": {
"__rl": true,
"mode": "list",
"value": "=",
"cachedResultUrl": "=",
"cachedResultName": "="
}
},
"notesInFlow": true,
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"name": "Sanitising the Query",
"type": "n8n-nodes-base.code",
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"parameters": {
"jsCode": "return [n {n json: {n query: $input.first().json.query.replace(/```sql|```/g, "").trim()n }n }n];n"
},
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{
"id": "7c86fda0-116c-47ad-aaf5-8b83d2c083c6",
"name": "Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
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"type": "@n8n/n8n-nodes-langchain.chatTrigger",
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"webhookId": "ee7c418b-d7d6-41f9-8e87-0f71b8ae1cf9",
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"parameters": {
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"content": "### 4. Do you need more details?nFind a step-by-step guide in this tutorialnn[ud83cudfa5 Watch My Tutorial](https://www.loom.com/share/50271f9d50214d7184830985497a75ec?sid=d0c410dc-29f1-488f-b89a-4011de0ded07)"
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