Agent de prévision pour le CFO

• •Clé API Stripe
• •URL et Clé API Supabase
• •Informations d'identification Google Sheets API
• •Clé API OpenAI
• •Clé API Pinecone

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        "jsCode": "const charges = items.map(item => item.json);\nconst summary = charges.reduce((acc, charge) => {\n  const date = new Date(charge.created * 1000).toISOString().split(\"T\")[0];\n  acc[date] = (acc[date] || 0) + charge.amount / 100;\n  return acc;\n}, {});\nreturn [{ json: { summary } }];\n"
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        "text": "=You are a CFO AI Agent. Based on the following Stripe sales data:\n\n{{ $json.summary }}\n\nAnalyze the trends, identify any patterns (growth, decline, seasonality), and forecast expected daily or weekly revenue for the next 3 months.",
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            "forecast": "={{ $json.output.forecast }}",
            "insights": "={{ $json.output.insights }}",
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        "content": "## 1️⃣ **🔁 Data Retrieval & Preprocessing**\n\n**Nodes:**\n\n* 🕒 `Run Daily Forecast`\n* 🟦 `Fetch Stripe Charges`\n* 🧩 `Summarize Daily Sales`\n* ✏️ `Prepare Forecast Prompt`\n\n---\n\n### 🕒 `Run Daily Forecast`\n\n**Type:** Cron Trigger\n**Purpose:**\nAutomatically runs the workflow every day to keep forecasts updated with the latest sales data.\n\n🔧 **Configuration:**\n\n* Schedule: Daily at 6 AM UTC (or as needed)\n\n---\n\n### 🟦 `Fetch Stripe Charges`\n\n**Type:** Stripe Node\n**Purpose:**\nRetrieves all successful transactions from Stripe in a defined timeframe.\n\n📥 **Details:**\n\n* Resource: `Charges`\n* Operation: `Get Many`\n* Filters:\n\n  * `created[gte]` (e.g. last 30 days)\n  * `status: succeeded`\n* Expansion (optional): `data.customer` for customer context\n\n✅ **Output:** Raw Stripe sales data with timestamps and amounts\n\n---\n\n### 🧩 `Summarize Daily Sales`\n\n**Type:** Code Node\n**Purpose:**\nProcesses Stripe charges and summarizes revenue per day.\n\n🧠 **Logic:**\n\n* Converts Unix timestamps to `YYYY-MM-DD`\n* Aggregates total revenue per day\n* Converts cents to dollars\n\n📦 **Output Sample:**\n\n```json\n{\n  \"2025-05-01\": 1245.50,\n  \"2025-05-02\": 980.00\n}\n```\n\n---\n\n### ✏️ `Prepare Forecast Prompt`\n\n**Type:** Edit Fields / Function\n**Purpose:**\nFormats the summary into a natural language prompt for OpenAI.\n\n🧠 **Example Prompt:**\n\n```txt\nGiven the following sales data:\n{ \"2025-05-01\": 1245.50, ... }\n\nPredict trends and forecast sales for the next 3 months.\n```\n\n🧾 **Output:** `prompt` (String) → sent to the AI Agent\n"
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        "content": "## 2️⃣ **🤖 AI Agent Forecasting**\n\n**Nodes:**\n\n* 🤖 `Forecast with OpenAI Agent`\n* 🧠 `OpenAI GPT-4 Model`\n* 📄 `Extract Forecast Output`\n* 🌲 `Store Context in Pinecone` *(Optional)*\n* 🧬 `Generate Embeddings` *(Optional)*\n\n---\n\n### 🤖 `Forecast with OpenAI Agent`\n\n**Type:** Tools Agent\n**Purpose:**\nActs as an intelligent agent that reads the sales summary and responds with forecasts and reasoning.\n\n🧠 **Prompt Input:**\nPassed from `Prepare Forecast Prompt`\n\n💬 **Uses:**\n\n* Model: `GPT-4`\n* Output Parser: Structured JSON format\n\n📈 **Forecast Intent:**\nPredicts next 3 months, identifies trends, and gives a confidence level\n\n---\n\n### 🧠 `OpenAI GPT-4 Model`\n\n**Type:** OpenAI Node\n**Purpose:**\nHandles the natural language generation based on the supplied prompt.\n\n🧾 **Configuration:**\n\n* Model: `gpt-4` or `gpt-4-turbo`\n* Temperature: `0.2` (more deterministic)\n* Max Tokens: `1000`\n\n---\n\n### 📄 `Extract Forecast Output`\n\n**Type:** Structured Output Parser\n**Purpose:**\nParses the GPT response into usable JSON format.\n\n📦 **Expected Output:**\n\n```json\n{\n  \"forecast\": {\n    \"June\": \"$15,000.00\",\n    \"July\": \"$16,500.00\",\n    \"August\": \"$17,200.00\"\n  },\n  \"trend\": \"Increasing\",\n  \"confidence\": \"High\",\n  \"insights\": \"Sales show strong momentum...\"\n}\n```\n\n---\n\n### 🌲 `Store Context in Pinecone` *(optional)*\n\n**Type:** Vector Store\n**Purpose:**\nIndexes past data for retrieval-based prompting (RAG). Useful for long-term memory.\n\n---\n\n### 🧬 `Generate Embeddings` *(optional)*\n\n**Type:** Embeddings Node\n**Purpose:**\nConverts text into vector format before inserting into Pinecone."
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        "content": "## 3️⃣ **📦 Storage & Reporting**\n\n**Nodes:**\n\n* 🟩 `Save Forecast to Supabase`\n* 📊 `Log Forecast in Google Sheets`\n\n---\n\n### 🟩 `Save Forecast to Supabase`\n\n**Type:** Supabase Node\n**Purpose:**\nStores all forecast results for analytics, versioning, or historical comparisons.\n\n🛢️ **Table:** `forecasts`\n🧾 **Columns Example:**\n\n| timestamp  | raw\\_data | forecast\\_data |\n| ---------- | --------- | -------------- |\n| 2025-05-29 | {...}     | {...}          |\n\n---\n\n### 📊 `Log Forecast in Google Sheets`\n\n**Type:** Google Sheets Node\n**Purpose:**\nPushes structured data into a visual format for reporting dashboards or human review.\n\n📋 **Column Format:**\n\n| Date       | Forecast (USD) | Trend      | Confidence | Insights                   |\n| ---------- | -------------- | ---------- | ---------- | -------------------------- |\n| 2025-05-29 | \\$15,000.00    | Increasing | High       | Sales rising at 10% weekly |\n\n---\n\n## ✅ Summary Flow\n\n```txt\n🔁 Sales Data (Stripe) \n → 🧠 Forecast Agent (OpenAI) \n → 📦 Stored in Supabase \n → 📊 Reported in Google Sheets"
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        "content": "=======================================\n            WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n    Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n   - YouTube: https://www.youtube.com/@YaronBeen/videos\n   - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
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        "content": "# 📊 CFO Forecasting Agent – Workflow Documentation\n\n---\n\n## 1️⃣ **🔁 Data Retrieval & Preprocessing**\n\n**Nodes:**\n\n* 🕒 `Run Daily Forecast`\n* 🟦 `Fetch Stripe Charges`\n* 🧩 `Summarize Daily Sales`\n* ✏️ `Prepare Forecast Prompt`\n\n---\n\n### 🕒 `Run Daily Forecast`\n\n**Type:** Cron Trigger\n**Purpose:**\nAutomatically runs the workflow every day to keep forecasts updated with the latest sales data.\n\n🔧 **Configuration:**\n\n* Schedule: Daily at 6 AM UTC (or as needed)\n\n---\n\n### 🟦 `Fetch Stripe Charges`\n\n**Type:** Stripe Node\n**Purpose:**\nRetrieves all successful transactions from Stripe in a defined timeframe.\n\n📥 **Details:**\n\n* Resource: `Charges`\n* Operation: `Get Many`\n* Filters:\n\n  * `created[gte]` (e.g. last 30 days)\n  * `status: succeeded`\n* Expansion (optional): `data.customer` for customer context\n\n✅ **Output:** Raw Stripe sales data with timestamps and amounts\n\n---\n\n### 🧩 `Summarize Daily Sales`\n\n**Type:** Code Node\n**Purpose:**\nProcesses Stripe charges and summarizes revenue per day.\n\n🧠 **Logic:**\n\n* Converts Unix timestamps to `YYYY-MM-DD`\n* Aggregates total revenue per day\n* Converts cents to dollars\n\n📦 **Output Sample:**\n\n```json\n{\n  \"2025-05-01\": 1245.50,\n  \"2025-05-02\": 980.00\n}\n```\n\n---\n\n### ✏️ `Prepare Forecast Prompt`\n\n**Type:** Edit Fields / Function\n**Purpose:**\nFormats the summary into a natural language prompt for OpenAI.\n\n🧠 **Example Prompt:**\n\n```txt\nGiven the following sales data:\n{ \"2025-05-01\": 1245.50, ... }\n\nPredict trends and forecast sales for the next 3 months.\n```\n\n🧾 **Output:** `prompt` (String) → sent to the AI Agent\n\n---\n\n## 2️⃣ **🤖 AI Agent Forecasting**\n\n**Nodes:**\n\n* 🤖 `Forecast with OpenAI Agent`\n* 🧠 `OpenAI GPT-4 Model`\n* 📄 `Extract Forecast Output`\n* 🌲 `Store Context in Pinecone` *(Optional)*\n* 🧬 `Generate Embeddings` *(Optional)*\n\n---\n\n### 🤖 `Forecast with OpenAI Agent`\n\n**Type:** Tools Agent\n**Purpose:**\nActs as an intelligent agent that reads the sales summary and responds with forecasts and reasoning.\n\n🧠 **Prompt Input:**\nPassed from `Prepare Forecast Prompt`\n\n💬 **Uses:**\n\n* Model: `GPT-4`\n* Output Parser: Structured JSON format\n\n📈 **Forecast Intent:**\nPredicts next 3 months, identifies trends, and gives a confidence level\n\n---\n\n### 🧠 `OpenAI GPT-4 Model`\n\n**Type:** OpenAI Node\n**Purpose:**\nHandles the natural language generation based on the supplied prompt.\n\n🧾 **Configuration:**\n\n* Model: `gpt-4` or `gpt-4-turbo`\n* Temperature: `0.2` (more deterministic)\n* Max Tokens: `1000`\n\n---\n\n### 📄 `Extract Forecast Output`\n\n**Type:** Structured Output Parser\n**Purpose:**\nParses the GPT response into usable JSON format.\n\n📦 **Expected Output:**\n\n```json\n{\n  \"forecast\": {\n    \"June\": \"$15,000.00\",\n    \"July\": \"$16,500.00\",\n    \"August\": \"$17,200.00\"\n  },\n  \"trend\": \"Increasing\",\n  \"confidence\": \"High\",\n  \"insights\": \"Sales show strong momentum...\"\n}\n```\n\n---\n\n### 🌲 `Store Context in Pinecone` *(optional)*\n\n**Type:** Vector Store\n**Purpose:**\nIndexes past data for retrieval-based prompting (RAG). Useful for long-term memory.\n\n---\n\n### 🧬 `Generate Embeddings` *(optional)*\n\n**Type:** Embeddings Node\n**Purpose:**\nConverts text into vector format before inserting into Pinecone.\n\n---\n\n## 3️⃣ **📦 Storage & Reporting**\n\n**Nodes:**\n\n* 🟩 `Save Forecast to Supabase`\n* 📊 `Log Forecast in Google Sheets`\n\n---\n\n### 🟩 `Save Forecast to Supabase`\n\n**Type:** Supabase Node\n**Purpose:**\nStores all forecast results for analytics, versioning, or historical comparisons.\n\n🛢️ **Table:** `forecasts`\n🧾 **Columns Example:**\n\n| timestamp  | raw\\_data | forecast\\_data |\n| ---------- | --------- | -------------- |\n| 2025-05-29 | {...}     | {...}          |\n\n---\n\n### 📊 `Log Forecast in Google Sheets`\n\n**Type:** Google Sheets Node\n**Purpose:**\nPushes structured data into a visual format for reporting dashboards or human review.\n\n📋 **Column Format:**\n\n| Date       | Forecast (USD) | Trend      | Confidence | Insights                   |\n| ---------- | -------------- | ---------- | ---------- | -------------------------- |\n| 2025-05-29 | \\$15,000.00    | Increasing | High       | Sales rising at 10% weekly |\n\n---\n\n## ✅ Summary Flow\n\n```txt\n🔁 Sales Data (Stripe) \n → 🧠 Forecast Agent (OpenAI) \n → 📦 Stored in Supabase \n → 📊 Reported in Google Sheets\n```\n"
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            "name": "Jane Doe",
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          "customer": {
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