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filip-michalsky · Mar 25, 2024 · 0b74c16 · 0b74c16
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diff --git a/.env.example b/.env.example
@@ -1,8 +1,9 @@
 OPENAI_API_KEY="xx"
 OTHER_API_KEY="yy"
-MINDWARE_URL="xx"
-MINDWARE_API_KEY="zz"
+PAYMENT_GATEWAY_URL=https://agent-payments-gateway.vercel.app/payment
 CONFIG_PATH=examples/example_agent_setup.json
 PRODUCT_CATALOG=examples/sample_product_catalog.txt
+PRODUCT_PRICE_MAPPING=examples/example_product_price_id_mapping.json
 GPT_MODEL=gpt-3.5-turbo-0613
-USE_TOOLS_IN_API=True
+USE_TOOLS_IN_API=True
+
diff --git a/README.md b/README.md
@@ -35,11 +35,13 @@ We are building SalesGPT to power your best AI Sales Agents. Hence, we would lov
 
 # Demos and Use Cases
 
-<i>Unload AI Sales Agent Demo - Powered by SalesGPT:</i> *A New Way to Sell?* 🤔
+<i>Unload AI Sales Agent Demos - Powered by SalesGPT:</i> *Our new virtual workforce?* 🤔
 
-**Demo #1: Rachel - Mattress Sales Field Representative Outbound Sales**
+**Demo #1: Sarah - Patient Coordinator at South Orange Pediatrics**
 
-[![Rachel - Mattress Sales Field Representative](https://cdn.loom.com/sessions/thumbnails/f0fac42954904471b266980e4948b07d-with-play.gif)](https://www.loom.com/share/f0fac42954904471b266980e4948b07d)
+- 100X Your Healthcare Admin with our Virtual Workforce 
+
+[![Sarah - South Orange Pediatrics Patient Coordinator](https://cdn.loom.com/sessions/thumbnails/314eb0562fda41ea94e25b267acda6f9-with-play.gif)](https://www.loom.com/share/314eb0562fda41ea94e25b267acda6f9)
 
 **Demo #2: Ted - Autonomously create payment links and collect customer payments**
 

diff --git a/examples/example_product_price_id_mapping.json b/examples/example_product_price_id_mapping.json
@@ -0,0 +1,7 @@
+{
+    "ai-consulting-services": "price_1Ow8ofB795AYY8p1goWGZi6m",
+    "Luxury Cloud-Comfort Memory Foam Mattress": "price_1Owv99B795AYY8p1mjtbKyxP",
+    "Classic Harmony Spring Mattress": "price_1Owv9qB795AYY8p1tPcxCM6T",
+    "EcoGreen Hybrid Latex Mattress": "price_1OwvLDB795AYY8p1YBAMBcbi",
+    "Plush Serenity Bamboo Mattress": "price_1OwvMQB795AYY8p1hJN2uS3S"
+}
diff --git a/examples/sales_agent_with_context.ipynb b/examples/sales_agent_with_context.ipynb
@@ -67,38 +67,6 @@
     "from typing import Union\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optional - create a free [Mindware](https://www.mindware.co/) account\n",
-    "\n",
-    "This enables your AI sales agents to actually sell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "I want to sell\n"
-     ]
-    }
-   ],
-   "source": [
-    "I_WANT_TO_SELL=True\n",
-    "if I_WANT_TO_SELL:\n",
-    "    if \"MINDWARE_API_KEY\" not in os.environ:\n",
-    "        raise ValueError(\"You cannot sell as you did not set up a Mindware account\")\n",
-    "    print(\"I want to sell\")\n",
-    "\n",
-    "MINDWARE_URL = os.getenv(\"MINDWARE_URL\", \"https://agent-payments-gateway.vercel.app/payment\")"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -471,6 +439,63 @@
     "product_catalog='sample_product_catalog.txt'"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from litellm import completion\n",
+    "import json\n",
+    "\n",
+    "def get_product_id_from_query(query, product_price_id_mapping_path):\n",
+    "    # Load product_price_id_mapping from a JSON file\n",
+    "    with open(product_price_id_mapping_path, 'r') as f:\n",
+    "        product_price_id_mapping = json.load(f)\n",
+    "    \n",
+    "    # Serialize the product_price_id_mapping to a JSON string for inclusion in the prompt\n",
+    "    product_price_id_mapping_json_str = json.dumps(product_price_id_mapping)\n",
+    "    \n",
+    "    # Dynamically create the enum list from product_price_id_mapping keys\n",
+    "    enum_list = list(product_price_id_mapping.values()) + [\"No relevant product id found\"]\n",
+    "    enum_list_str = json.dumps(enum_list)\n",
+    "    \n",
+    "    prompt = f\"\"\"\n",
+    "    You are an expert data scientist and you are working on a project to recommend products to customers based on their needs.\n",
+    "    Given the following query:\n",
+    "    {query}\n",
+    "    and the following product price id mapping:\n",
+    "    {product_price_id_mapping_json_str}\n",
+    "    return the product id that is most relevant to the query.\n",
+    "    ONLY return the product id, no other text. If no relevant product id is found, return 'No relevant product id found'.\n",
+    "    FORMAT OUTPUT AS the following schema:\n",
+    "    {{\n",
+    "    \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
+    "    \"title\": \"Product ID Response\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {{\n",
+    "        \"product_id\": {{\n",
+    "        \"type\": \"string\",\n",
+    "        \"enum\": {enum_list_str}\n",
+    "        }}\n",
+    "    }},\n",
+    "    \"required\": [\"product_id\"]\n",
+    "    }}\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    response = completion(\n",
+    "        model=os.getenv(\"GPT_MODEL\", \"gpt-3.5-turbo-1106\"),\n",
+    "        messages=[{\"content\": prompt, \"role\": \"user\"}],\n",
+    "        max_tokens=1000,\n",
+    "        temperature=0\n",
+    "    )\n",
+    "\n",
+    "    product_id = response.choices[0].message.content.strip()\n",
+    "    return product_id\n",
+    "    \n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -483,16 +508,21 @@
     "def generate_stripe_payment_link(query: str) -> str:\n",
     "    \"\"\"Generate a stripe payment link for a customer based on a single query string.\"\"\"\n",
     "\n",
-    "    url = os.getenv(\"MINDWARE_URL\", \"\")\n",
-    "    api_key = os.getenv(\"MINDWARE_API_KEY\", \"\")\n",
+    "    # example testing payment gateway url\n",
+    "    PAYMENT_GATEWAY_URL = os.getenv(\"PAYMENT_GATEWAY_URL\", \"https://agent-payments-gateway.vercel.app/payment\")\n",
+    "    PRODUCT_PRICE_MAPPING = os.getenv(\"PRODUCT_PRICE_MAPPING\", \"example_product_price_id_mapping.json\")\n",
+    "    \n",
+    "    # use LLM to get the price_id from query\n",
+    "    price_id = get_product_id_from_query(query, PRODUCT_PRICE_MAPPING)\n",
     "\n",
-    "    payload = json.dumps({\"prompt\": query})\n",
+    "    payload = json.dumps({\"prompt\": query,\n",
+    "                          \"price_id\": price_id\n",
+    "                          })\n",
     "    headers = {\n",
     "        'Content-Type': 'application/json',\n",
-    "        'Authorization': f'Bearer {api_key}'\n",
     "    }\n",
     "\n",
-    "    response = requests.request(\"POST\", url, headers=headers, data=payload)\n",
+    "    response = requests.request(\"POST\", PAYMENT_GATEWAY_URL, headers=headers, data=payload)\n",
     "    return response.text"
    ]
   },