The issue of missing variables when switching from a CSV to a JSON file as the data source in your ChatPromptTemplate might be due to a mismatch between how data is being read and passed. Here are some common reasons and solutions:

1. Data Structure Differences

Problem: JSON structures can vary in depth and keys, unlike a CSV file, which is flat with columns. - If the JSON contains nested objects or arrays, the variables may not be extracted as straightforwardly as they are from a CSV.

Solution: - Inspect the structure of the JSON file. - Ensure the variables in the JSON are accessible directly or via traversal (e.g., json['key1']['key2']). - Flatten the JSON data if required using libraries like pandas or a custom script.

```python import pandas as pd

Load JSON file and flatten if necessary

data = pd.json_normalize(your_json_data) ```

2. Key Mismatches

Problem: The variable names in your JSON might not exactly match those in your prompt template. - For example, in CSV you might have user_name, but the JSON key could be username.

Solution: - Align the keys in your JSON with the variables expected by the template. - Use a mapping function or rename the keys in your JSON.

```python

Example of renaming JSON keys

data = {'username': 'John', 'age': 30} mapped_data = {'user_name': data['username'], 'user_age': data['age']} ```

3. Data Loading Issues

Problem: When loading JSON, the data might not be parsed into a proper Python dictionary or DataFrame format.

Solution: - Ensure you’re correctly loading the JSON file into the program.

```python import json

Correct way to load JSON

with open('data.json', 'r') as file: data = json.load(file) ```

4. Template Expectations

Problem: ChatPromptTemplate expects specific variable names that are case-sensitive. If these names don’t exist in your JSON or have different cases, the template will fail.

Solution: - Confirm the required variables for the ChatPromptTemplate. - Match these names precisely in your JSON file or preprocess the JSON to ensure compatibility.

5. Debugging Tips

• Print the loaded JSON and check if all expected variables are present.
• Compare the keys in the JSON against the variables in the ChatPromptTemplate.

```python

Example debug print

print("Loaded JSON keys:", data.keys()) print("Expected variables:", chat_prompt_template.expected_variables()) ```

• Test a single entry from the JSON to isolate issues.

Final Workflow

1. Load JSON: python with open('data.json', 'r') as file: data = json.load(file)
2. Normalize JSON (if nested): python from pandas import json_normalize data = json_normalize(data)
3. Preprocess Keys: python data = {key.lower(): value for key, value in data.items()} # Example: Lowercase all keys
4. Pass to Template: python for entry in data: response = chat_prompt_template.format(**entry) print(response)

Please check out , our code examples.