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import json | |
from openai import OpenAI | |
# Point to the local server | |
client1 = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio") # html to json | |
model = r"lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF/Meta-Llama-3-8B-Instruct-Q8_0.gguf" | |
# model = "MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF/Mistral-7B-Instruct-v0.3.Q4_K_M.gguf:2" | |
def get_completion(prompt, client=client1, model=model): | |
""" | |
given the prompt, obtain the response from LLM hosted by LM Studio as a server | |
:param prompt: prompt to be sent to LLM server | |
:return: response from the LLM | |
""" | |
prompt = [ | |
{"role": "user", "content": prompt} | |
] | |
completion = client.chat.completions.create( | |
model=model, | |
messages=prompt, | |
temperature=0.0, | |
stream=True, | |
) | |
new_message = {"role": "assistant", "content": ""} | |
for chunk in completion: | |
if chunk.choices[0].delta.content: | |
# print(chunk.choices[0].delta.content, end="", flush=True) | |
val = chunk.choices[0].delta.content | |
new_message["content"] += val | |
# print(type() | |
val = new_message["content"] # .split("<end_of_turn>")[0] | |
return val | |
if __name__ == '__main__': | |
prompt = """ | |
You are a political leader and your party is trying to win the general elections in India. | |
You are given an LLM that can provide you the analytics using the past historical data given to it. | |
In particular the LLM has been provided data on which party won each constituency out of 545 and which assembly segment within the main constituency is more favorable. | |
It also has details of votes polled by every candidate. | |
Tell me 10 questions that you want to ask the LLM. | |
""" | |
results = get_completion(prompt) | |
print(results) | |