Hey folks! Been tinkering with HuggingChat's API for a few client projects, and while it's fantastic, those per-call costs can add up fast, especially for repetitive queries or when you're prototyping. I started thinking: what if we could cache common responses locally?
The core idea is a simple local proxy server that sits between your app and the HuggingChat API. It checks if a similar request has been made before, serves the cached response if so, and only calls the real API for new stuff. It's a game-changer for internal tools or workflows where you ask similar questions often.
Here's a basic Python/Flask example to illustrate the concept:
```python
from flask import Flask, request, jsonify
import requests
import hashlib
import json
import redis # or use a simple dict for testing
app = Flask(__name__)
cache = redis.Redis(host='localhost', port=6379, decode_responses=True)
API_ENDPOINT = "https://api.huggingface.co/chat"
API_KEY = "your_hf_key_here"
@app.route('/proxy/chat', methods=['POST'])
def proxy_chat():
request_data = request.json
# Create a unique hash of the request (prompt + parameters)
request_hash = hashlib.md5(json.dumps(request_data, sort_keys=True).encode()).hexdigest()
# Check cache
cached_response = cache.get(request_hash)
if cached_response:
return jsonify(json.loads(cached_response))
# If not cached, call HuggingChat API
headers = {"Authorization": f"Bearer {API_KEY}"}
api_response = requests.post(API_ENDPOINT, json=request_data, headers=headers)
response_data = api_response.json()
# Store in cache (expire after, say, 24 hours or longer)
cache.setex(request_hash, 86400, json.dumps(response_data))
return jsonify(response_data)
if __name__ == '__main__':
app.run(port=5000)
```
Now, instead of calling `api.huggingface.co` directly, your Zapier/Make/Node app calls ` http://localhost:5000/proxy/chat`.
Some considerations and extensions I've implemented:
- **Cache key strategy:** Hashing the full prompt + parameters (like `temperature`, `max_tokens`) ensures different settings don't get wrong hits.
- **Storage:** Redis is great for persistence and speed, but you can start with a simple file or SQLite.
- **Advanced patterns:** You could add a TTL (time-to-live) per cache item, or invalidate caches for certain topics when your knowledge base updates.
- **Beyond prototyping:** For production, you'd want to add request queuing and rate limiting to respect HuggingChat's limits.
This is especially useful for:
- Caching FAQ-style responses in a customer support pipeline.
- Saving outputs from common data transformation prompts.
- Reducing costs during development and testing of new workflows.
Has anyone else tried a similar approach? I'm curious about other caching strategies or if you've integrated this into a larger automation platform like Make or n8n. The savings on repetitive operational queries have been significant for us.