Just spent my evening prototyping a tool I've been putting off, and I'm genuinely impressed with how HuggingChat's function calling handled it. We have a pipeline that ingests messy CRM contact data, and validation was always a post-upload, batch job thing. I wanted to make it interactive for the support team.
The goal was a simple Python script that could take a contact record and call specific validation functions based on the user's natural language request, like "check this email and phone number" or "validate this address format."
Here's the core of how I set up the function definitions for HuggingChat:
```python
tools = [
{
"type": "function",
"function": {
"name": "validate_email",
"description": "Validate the format of an email address.",
"parameters": {
"type": "object",
"properties": {
"email": {"type": "string", "description": "The email address to validate."}
},
"required": ["email"]
}
}
},
{
"type": "function",
"function": {
"name": "validate_phone",
"description": "Validate the format of a US phone number.",
"parameters": {
"type": "object",
"properties": {
"phone_number": {"type": "string", "description": "The phone number to validate, ideally in E.164 or local US format."}
},
"required": ["phone_number"]
}
}
}
]
```
The cool part was the interaction. I could just paste a JSON blob and say "Can you check the email here?" and HuggingChat would reliably recognize the need for `validate_email`, extract the `email` parameter from the JSON, and return a structured call. I then fed that to my actual validation functions.
Some quick observations from a CI/CD perspective:
* The function description is **critical** for accurate routing. Be super clear.
* It's fantastic for building natural-language interfaces to existing API validation logic.
* I can see this being a game-changer for building lightweight, chat-based admin tools for internal teams, where you don't want to build a full UI.
Has anyone else used the function calling feature for similar internal tooling or data quality checks? I'm curious how the reliability feels compared to other providers for more complex, multi-step validation chains.
-pipelinepilot
Pipeline Pilot
Interesting use of an LLM as a natural language router. My immediate question is about the scale you're planning. How many validation requests per hour are you anticipating?
The reason I ask is that while this is a clever prototype for a handful of interactive checks, if this scales to the support team's full usage, you're paying for and waiting on an LLM API call every time you need to run a regex on an email. You could achieve the same user-facing flexibility with a simple keyword parser, like `"check email and phone for {data}"`, and skip the LLM overhead entirely.
It's a fun demo of function calling, but for a production script, the complexity and cost seem to invert the problem. You're using a sledgehammer to press a doorbell.
keep it simple