Update README.md

README.md

@@ -54,7 +54,57 @@ print(response)
 ### Basic
 
 ```python
+import json
 
+def get_prompt(tool: str, user_input: str) -> str:
+  system = "You are a helpful assistant with access to the following tools. Use them if required - \n```json\n{}\n```"
+  messages = [
+    {"role": "system", "content": system.format(tool)},
+    {"role": "user", "content": 'Extract the information from the following - \n{}'.format(user_input)}
+  ]
+  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+  return prompt
+
+tool = {
+  "type": "function",
+  "function": {
+    "name": "get_company_info",
+    "description": "Correctly extracted company information with all the required parameters with correct types",
+    "parameters": {
+      "properties": {
+        "name": {"title": "Name", "type": "string"},
+        "investors": {
+          "items": {"type": "string"},
+          "title": "Investors",
+          "type": "array"
+        },
+        "valuation": {"title": "Valuation", "type": "string"},
+        "source": {"title": "Source", "type": "string"}
+      },
+      "required": ["investors", "name", "source", "valuation"],
+      "type": "object"
+    }
+  }
+}
+input_text = "Founded in 2021, Pluto raised $4 million across multiple seed funding rounds, valuing the company at $12 million (pre-money), according to PitchBook. The startup was backed by investors including Switch Ventures, Caffeinated Capital and Maxime Seguineau."
+prompt = get_prompt(json.dumps(tool), input_text)
+response = inference(prompt)
+print(response)
+# ```json
+# {
+#   "name": "get_company_info",
+#   "arguments": {
+#     "name": "Pluto",
+#     "investors": [
+#       "Switch Ventures",
+#       "Caffeinated Capital",
+#       "Maxime Seguineau"
+#     ],
+#     "valuation": "pre-money $12M",
+#     "source": "PitchBook"
+#   }
+# }
+# ```
 ```
 
 ### Advanced
@@ -65,15 +115,6 @@ from enum import Enum
 from pydantic import BaseModel, Field # pip install pydantic
 from instructor.function_calls import openai_schema # pip install instructor
 
-def get_prompt(tool: str, user_input: str) -> str:
-  system = "You are a helpful assistant with access to the following tools. Use them if required - \n```json\n{}\n```"
-  messages = [
-    {"role": "system", "content": system.format(tool)},
-    {"role": "user", "content": 'Extract the information from the following - \n{}'.format(user_input)}
-  ]
-  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-  return prompt
-
 # Define functions using pydantic classes
 class PaperCategory(str, Enum):
   TYPE_1_DIABETES = 'Type 1 Diabetes'
@@ -90,7 +131,13 @@ prompt = get_prompt(json.dumps(tool), input_text)
 output = inference(prompt)
 print(output)
 # ```json
-# {"name": "Classification", "arguments": {"label": "Type 1 Diabetes", "reason": "The study investigated the effect of vitamin D status and treatment with 1,25(OH)(2)D(3) on diabetes onset in non-obese diabetic (NOD) mice. It also concluded that vitamin D deficiency leads to an increase in diabetes incidence and that the addition of 1,25(OH)(2)D(3) can prevent diabetes onset in NOD mice."}}
+# {
+#     "name": "Classification", 
+#     "arguments": {
+#         "label": "Type 1 Diabetes", 
+#         "reason": "The study investigated the effect of vitamin D status and treatment with 1,25(OH)(2)D(3) on diabetes onset in non-obese diabetic (NOD) mice. It also concluded that vitamin D deficiency leads to an increase in diabetes incidence and that the addition of 1,25(OH)(2)D(3) can prevent diabetes onset in NOD mice."
+#     }
+# }
 # ```
 # Extract JSON string using regex
 output = re.search(r'```json\s*(\{.*?\})\s*```', output).group(1)