初探轻量级LLM应用开发框架ell

1import ell
2
3@ell.simple(model="gpt-4o-mini")
4def hello(world: str):
5    """You are a helpful assistant""" # System prompt
6    name = world.capitalize()
7    return f"Say hello to {name}!" # User prompt
8
9hello("sam altman") # just a str, "Hello Sam Altman! ..."

1import ell
2
3ell.init(store='./logdir')  # Versions your LMPs and their calls
4
5# ... define your lmps
6
7hello("strawberry") # the source code of the LMP the call is saved to the store

1ell-studio --storage-dir ./logdir

 1import ell
 2@ell.simple(model="gpt-4o-mini", temperature=1.0, n=10)
 3def write_ten_drafts(idea : str):
 4  """You are an adept story writer. The story should only be 3 paragraphs"""
 5  return f"Write a story about {idea}."
 6
 7@ell.simple(model="gpt-4o", temperature=0.1)
 8def choose_the_best_draft(drafts : List[str]):
 9  """You are an expert fiction editor."""
10  return f"Choose the best draft from the following list: {'\n'.join(drafts)}."
11
12drafts = write_ten_drafts(idea)
13
14best_draft = choose_the_best_draft(drafts) # Best of 10 sampling.

 1import ell
 2
 3@ell.tool()
 4def scrape_website(url : str):
 5   return requests.get(url).text
 6
 7@ell.complex(model="gpt-5-omni", tools=[scrape_website])
 8def get_news_story(topic : str):
 9   return [
10      ell.system("""Use the web to find a news story about the topic"""),
11      ell.user(f"Find a news story about {topic}.")
12   ]
13
14message_response = get_news_story("stock market")
15if message_response.tool_calls:
16   for tool_call in message_response.tool_calls:
17      #...
18if message_response.text:
19   print(message_response.text)
20if message_response.audio:
21   # message_response.play_audio() supprot for multimodal outputs will work as soon as the LLM supports it
22   pass

 1from PIL import Image
 2import ell
 3
 4
 5@ell.simple(model="gpt-4o", temperature=0.1)
 6def describe_activity(image: Image.Image):
 7   return [
 8      ell.system("You are VisionGPT. Answer <5 words all lower case."),
 9      ell.user(["Describe what the person in the image is doing:", image])
10   ]
11
12# Capture an image from the webcam
13describe_activity(capture_webcam_image()) # "they are holding a book"

1poetry add ell-ai -vv

1[tool.poetry.dependencies]
2python = "^3.11"
3ell-ai = "^0.0.3"

1OPENAI_API_KEY=<your key>
2OPENAI_BASE_URL=<your base url>

 1from dotenv import load_dotenv
 2import os
 3from openai import OpenAI
 4
 5assert load_dotenv()
 6assert os.environ.get("OPENAI_BASE_URL")
 7assert os.environ.get("OPENAI_API_KEY")
 8
 9messages = [
10    {
11        "role": "system",
12        "content": """你是用户的生活助理-小智。
13识别用户的意图并用简洁的语言给出合适的回应。
14        """,
15    },
16    {"role": "user", "content": "我想明天早上5点起床"},
17]
18
19client = OpenAI()
20
21response = client.chat.completions.create(
22    model="gpt-4o-mini", messages=messages, temperature=0.9, max_tokens=1000
23)
24
25print(response.choices[0].message.content)

1好的，明天早上5点起床。建议你今晚早点睡，确保充足的休息！需要我设置闹钟提醒吗？

 1from dotenv import load_dotenv
 2import os
 3from openai import OpenAI
 4import ell
 5
 6assert load_dotenv()
 7assert os.environ.get("OPENAI_BASE_URL")
 8assert os.environ.get("OPENAI_API_KEY")
 9
10client = OpenAI()
11
12
13@ell.simple(model="gpt-4o-mini", client=client, temperature=0.9, max_tokens=1000)
14def reconize_intent(user_input: str):
15    """你是用户的生活助理-小智。
16    识别用户的意图并用简洁的语言给出合适的回应。
17    """  # System prompt
18    return user_input  # User prompt
19
20
21content = reconize_intent("我想明天早上5点起床")
22print(content)

1好的，建议你今晚早点睡，设定一个闹钟在5点。确保把手机放在能听到的地方哦！

1ell.init(verbose=True)

 1╔══════════════════════════════════════════════════════════════════════════════════════╗
 2║ reconize_intent(我想明天早上5点..)
 3╠══════════════════════════════════════════════════════════════════════════════════════╣
 4║ Prompt:
 5╟──────────────────────────────────────────────────────────────────────────────────────╢
 6│      system: 你是用户的生活助理-小智。
 7│                  识别用户的意图并用简洁的语言给出合适的回应。
 8│
 9│        user: 我想明天早上5点起床
10╟──────────────────────────────────────────────────────────────────────────────────────╢
11║ Output:
12╟──────────────────────────────────────────────────────────────────────────────────────╢
13│   assistant: 好的，建议你今晚早点睡，这样明天早上能更好地起床。要我帮你设定闹钟吗？
14╚══════════════════════════════════════════════════════════════════════════════════════╝
15好的，建议你今晚早点睡，这样明天早上能更好地起床。要我帮你设定闹钟吗？

1ell.init(store='./logdir', autocommit=True, verbose=True)

1ell-studio --storage-dir ./logdir

 1from dotenv import load_dotenv
 2import os
 3from openai import OpenAI
 4import ell
 5
 6assert load_dotenv()
 7assert os.environ.get("OPENAI_BASE_URL")
 8assert os.environ.get("OPENAI_API_KEY")
 9
10client = OpenAI()
11
12
13@ell.simple(model="gpt-4o-mini", client=client, temperature=0.9, max_tokens=1000)
14def reconize_intent(user_input: str):
15    """你是用户的生活助理-小智。
16    识别用户的意图并用简洁的语言给出合适的回应。
17    回应的格式是一个json数组,每个对象json包含字段intent和parameters
18    如果无法识别用户的意图, json对象中intent字段的值固定为unknown, 并将user_input字段设置为用户的输入
19    """  # System prompt
20    return user_input  # User prompt
21
22
23ell.init(store="./logdir", autocommit=True, verbose=True)
24content = reconize_intent("我想明天早上5点起床")
25print(content)

1[
2    {
3        "intent": "set_alarm",
4        "parameters": {
5            "time": "5:00 AM"
6        }
7    }
8]