Rent Deposit Maintainance Prompt

Analyze the provided text and extract the following details. Return the results in a JSON object with the specified format and conditions:

1. contact

   • Must include the country code "+91".
   • Remove unnecessary spaces or non-numeric characters, ensuring a valid contact format.
   • Example: "+91 98765 43210" → "+919876543210"

2. rent

   • Normalize values:
     • k → multiply by 1000.
     • lakh → multiply by 100000.
     • Single digits (e.g., "2") are treated as 2 * 1000 (i.e., 2000).

3. deposit

   • Apply the same normalization rules as for rent.

4. maintenance

   • Apply the same normalization rules as for rent.

5. furnished

   • Possible values: "No", "Yes", "Semi".
   • Extract and map based on keywords in the text, e.g., "fully furnished" → "Yes", "semi-furnished" → "Semi".

6. number_of_rooms

   • Identify the number of rooms based on mentions of BHK or RK.
   • Extract the numeric value directly (e.g., "2 BHK" → 2).

7. number_of_bathrooms

   • Extract the numeric value representing bathrooms, ensuring clarity if mentioned explicitly.

8. address

   • Extract the full address as mentioned in the text.

9. property_type

   • Possible values: "Entire house" or "Shared Space".
   • Infer based on text description.

10. floor

    • Extract the floor number and return as a numeric value.

11. Tenant Type

• Extract the type of tenant required in the post - Possible : ["Male", "Female", "Family", "Bachelor"]

General Conditions:

• For any field not mentioned in the text, return "Info Not Available".

• Ensure the JSON output has clear and consistent formatting.

• Avoid hallucinating details or making unsupported assumptions.

  Example Output:
  
  "contact": "+919876543210",
  "rent": 15000,
  "deposit": 50000,
  "maintenance": 2000,
  "furnished": "Semi",
  "number_of_rooms": 2,
  "number_of_bathrooms": 2,
  "address": "123, ABC Street, Mumbai",
  "property_type": "Entire house",
  "floor": 5,
  "tenant_type" : "Family"
  

• Notes for Processing:

1. Use keyword detection and context analysis for accurate mappings (e.g., identifying "shared space" from the description).
2. Ensure numeric normalization adheres to the rules defined above.
3. Maintain clarity and precision in output, returning "Info Not Available" for any missing information.

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