| 3 | import json |
| 4 | |
| 5 | def run_llm(prompt, temperature, max_tokens, opeani_api_keys, engine="gpt-3.5-turbo"): |
| 6 | if "llama" not in engine.lower(): |
| 7 | openai.api_key = "EMPTY" |
| 8 | openai.api_base = "http://localhost:8000/v1" # your local llama server port |
| 9 | engine = openai.Model.list()["data"][0]["id"] |
| 10 | else: |
| 11 | openai.api_key = opeani_api_keys |
| 12 | |
| 13 | messages = [{"role":"system","content":"You are an AI assistant that helps people find information."}] |
| 14 | message_prompt = {"role":"user","content":prompt} |
| 15 | messages.append(message_prompt) |
| 16 | print("start openai") |
| 17 | while(f == 0): |
| 18 | try: |
| 19 | response = openai.ChatCompletion.create( |
| 20 | model=engine, |
| 21 | messages = messages, |
| 22 | temperature=temperature, |
| 23 | max_tokens=max_tokens, |
| 24 | frequency_penalty=0, |
| 25 | presence_penalty=0) |
| 26 | result = response["choices"][0]['message']['content'] |
| 27 | f = 1 |
| 28 | except: |
| 29 | print("openai error, retry") |
| 30 | time.sleep(2) |
| 31 | print("end openai") |
| 32 | return result |
| 33 | |
| 34 | def prepare_dataset(dataset_name): |
| 35 | if dataset_name == 'cwq': |