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)