本地大模型编程实战(06)从文本中提取重要信息(2)

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

from typing import Optional
from pydantic import BaseModel, Field

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"
    )

from langchain_core.utils.function_calling import tool_example_to_messages

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))

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.

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