I just set up a new dedicated AI server that is quite fast by my standards. I have it running with OpenWebUI and would like to integrate it with other services. I think it would be cool to have something like copilot where I can be writing code in a text editor and have it add a readme function or something like that. I have also used some RAG stuff and like it, but I think it would be cool to have a RAG that can access live data, like having the most up to date docker compose file and nginx configs for when I ask it about server stuff. So, what are you integrating your AI stuff with, and how can I get started?
I looked into roo, and was able to get it to interact with ollama but not actually work. From looking into roo, I found Cline which works a lot better. I would like to figure out a way to get it to work with the authenticated proxy api hosted to openwebui so I can access models externally but it is still pretty cool.
If your running into the issue of an app wanting an api key for your local ollamas openai-compatable web interface API and refuses to work without one, I found that any random characters work. If you port forward your host computer you should be able to access the webui interface on an external network using the public IP.
Heres the dead simple python program I used to send and recieve text to kobold.cpp engine through the web API. Not sure how similar ollama but afaik openai-compatable API means it all should works close to the same for compatibility(I think? lol!) if you give it a shot Make sure to set the .py file you make as executable and run it from a terminal doing ./filename.py to see the output in real time. It should make a log text file in same dir as the program too. Just use your host computers local ip if the python script pc is on same network.
spoiler
import requests # Configuration API_URL = "http://10.0.0.xx:5001/api/v1/generate" PROMPT = "Tell me a short story about a robot learning to dance." OUTPUT_FILE = "output.txt" # Define the API request data data = { "prompt": PROMPT, "max_length": 200, # Adjust response length "temperature": 0.7, # Control randomness (0=deterministic, 1=creative) "top_p": 0.9, # Focus on high-probability tokens } # Send the request to kobold.cpp response = requests.post(API_URL, json=data) if response.status_code == 200: # Extract the generated text result = response.json() generated_text = result["results"][0]["text"] # Save to a text file with open(OUTPUT_FILE, "w") as f: f.write(generated_text) print(f"Response saved to {OUTPUT_FILE}!") else: print(f"Error: {response.status_code} - {response.text}")