本文将演示使用大语言模型从文本中提炼结构化信息。这次我们不直接使用提示词，而是使用大模型的 `few-shot prompting` 特性，即使用很少的例子来引导大模型做推理。
我们将用 `llama3.1` 和 `deepseek` 做一个简单的对比。
> 由于 `langchain` 可能对不同大模型支持程度不同，不同大模型的特点也不同，所以这个对比并不能说明哪个模型更好。
准备
在正式开始撸代码之前，需要准备一下编程环境。
1. 计算机
本文涉及的所有代码可以在没有显存的环境中执行。 我使用的机器配置为：
- CPU: Intel i5-8400 2.80GHz
- 内存: 16GB
2. Visual Studio Code 和 venv
这是很受欢迎的开发工具，相关文章的代码可以在 `Visual Studio Code` 中开发和调试。 我们用 `python` 的 `venv` 创建虚拟环境, 详见：
3. Ollama
在 `Ollama` 平台上部署本地大模型非常方便，基于此平台，我们可以让 `langchain` 使用 `llama3.1`、`qwen2.5` 等各种本地大模型。详见：
简单推理
下面我们给大模型举几个简单的例子，看看到模型能否根据这些例子，推理出结果。
def reference(model_name):
messages = [
{"role": "user", "content": "2 🦜 2"},
{"role": "assistant", "content": "4"},
{"role": "user", "content": "2 🦜 3"},
{"role": "assistant", "content": "5"},
{"role": "user", "content": "3 🦜 4"},
]
response = ChatOllama(model=model_name,temperature=0.5,verbose=True).invoke(messages)
return response.content
我们使用 `llama3.1` 和`deepseek` 做一下测试：
|
llama3.1
|
deepseek-r1-tool-calling
|
| 7 |
... \boxed{7}
|
我运行了3次，`llama3.1` 每次都给出了简洁的正确结果，`deepseek` 输出的内容比较多，推理时间也略长，只有一次推理成功，不知道是否是 `langchain` 的原因。
下面我们再让大模型执行复杂一点的任务。
定义数据格式
我们先用 `Pydantic` 来定义要提取的数据格式。
class Person(BaseModel):
"""Information about a person."""
# ^ Doc-string for the entity Person.
# This doc-string is sent to the LLM as the description of the schema Person,
# and it can help to improve extraction results.
# Note that:
# 1. Each field is an `optional` -- this allows the model to decline to extract it!
# 2. Each field has a `description` -- this description is used by the LLM.
# Having a good description can help improve extraction results.
name: Optional[str] = Field(default=None, description="The name of the person")
hair_color: Optional[str] = Field(
default=None, description="The color of the person's hair if known"
)
height_in_meters: Optional[float] = Field(
default=None, description="Height measured in meters"
)
准备消息
我们使用 `LangChain` 的实用函数 `tool_example_to_messages` 将样例转化为消息序列，用于后面的推理任务。
examples = [
(
"The ocean is vast and blue. It's more than 20,000 feet deep.",
Person(),
),
(
"Fiona traveled far from France to Spain.",
Person(name="Fiona", height_in_meters=None, hair_color=None),
),
(
"Alan Smith is 1.83 meters tall and has blond hair.",
Person(name="Alan Smith", height_in_meters=1.83, hair_color="blond"),
),
]
messages = []
for txt, tool_call in examples:
if tool_call.name is None:
# This final message is optional for some providers
ai_response = "Detected people."
else:
ai_response = "Detected no people."
messages.extend(tool_example_to_messages(txt, [tool_call], ai_response=ai_response))
经过处理后，这些例子将转化为三种消息：
- User message
- AI message with tool call
- Tool message with result
我们打印出转化后的消息看一下：
for message in messages:
message.pretty_print()
================================ Human Message =================================
The ocean is vast and blue. It's more than 20,000 feet deep.
================================== Ai Message ==================================
Tool Calls:
Person (16513a89-ca85-46b4-8fac-db02a47b03fe)
Call ID: 16513a89-ca85-46b4-8fac-db02a47b03fe
Args:
name: None
hair_color: None
height_in_meters: None
================================= Tool Message =================================
You have correctly called this tool.
================================== Ai Message ==================================
Detected people.
================================ Human Message =================================
Fiona traveled far from France to Spain.
================================== Ai Message ==================================
Tool Calls:
Person (84e608e4-f444-49e1-b983-c28c38a1c870)
Call ID: 84e608e4-f444-49e1-b983-c28c38a1c870
Args:
name: Fiona
hair_color: None
height_in_meters: None
================================= Tool Message =================================
You have correctly called this tool.
================================== Ai Message ==================================
Detected no people.
================================ Human Message =================================
Alan Smith is 1.83 meters tall and has blond hair.
================================== Ai Message ==================================
Tool Calls:
Person (e464cd1f-fca8-48fb-85a0-5fc722328ec0)
Call ID: e464cd1f-fca8-48fb-85a0-5fc722328ec0
Args:
name: Alan Smith
hair_color: blond
height_in_meters: 1.83
================================= Tool Message =================================
You have correctly called this tool.
================================== Ai Message ==================================
Detected no people.
提取Person
我们定义两个方法：extract方法不使用前面生成的messages；而extract_with_messages则使用前面的messages。
def extract(model_name,text):
structured_llm = ChatOllama(model=model_name,temperature=0,verbose=True).with_structured_output(schema=Person)
user_message = {"role": "user", "content":text}
response = structured_llm.invoke([user_message])
return response
def extract_with_messages(model_name,text):
structured_llm = ChatOllama(model=model_name,temperature=0,verbose=True).with_structured_output(schema=Person)
user_message = {"role": "user", "content":text}
structured_llm.invoke(messages + [user_message])
return response
现在使用两个字符串做一下测试：
- Roy is 1.73 meters tall and has black hair.
| llama3.1 | deepseek-r1-tool-calling | |
| extract | name='Roy' hair_color='black' height_in_meters=1.73 | name='' hair_color='black' height_in_meters=1.73 |
| extract_with_messages | name='Roy' hair_color='black' height_in_meters=1.73 | name='' hair_color='black' height_in_meters=1.73 |
- John Doe is 1.72 meters tall and has brown hair.
| llama3.1 | deepseek-r1-tool-calling | |
| extract | name='John Doe' hair_color='brown' height_in_meters=1.72 | name='' hair_color='brown' height_in_meters=1.72 |
| extract_with_messages | name='John Doe' hair_color='brown' height_in_meters=1.72 | name='' hair_color='brown' height_in_meters=1.72 |
通过以上简单的推理任务，我们发现 `deepseek` 的表现没有 `llama3.1` 准确；`few-shot prompting` 对结果影响不大。
总结
通过以上的演练，我们可以看出 `llama3.1` 和 `deepseek` 在根据例子做推理时表现不一样：
- `deepseek` 似乎推理过程更加复杂一点
- `llama3.1` 推理更加准确一些
- `few-shot prompting` 对此类简单任务结果没什么影响
> 以上的所谓对比，可能因为 `langchain` 对不同模型的支持不同而失真，不能作为评定大模型好坏的标准，仅供参考。
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