进行提取时使用参考示例的方法

通过向大语言模型提供参考示例，通常可以提高提取的质量。

数据提取旨在生成结构化表示，用于描述从文本及其他非结构化或半结构化格式中发现的信息。工具调用 LLM 功能常在此类场景中使用。本指南演示如何构建少样本工具调用示例，以帮助引导提取及类似应用的行为。

提示

本指南重点介绍如何使用示例与工具调用模型配合，但该技术通常适用于更广泛的场景，也能更好地与基于 JSON 或提示词的技术协同工作。

LangChain 为包含工具调用的 LLM 消息实现了一个工具调用属性。有关更多详细信息，请参阅我们的工具调用指南。为了构建数据提取的参考示例，我们构建一个包含以下序列的聊天历史：

HumanMessage 包含示例输入；
AIMessage 包含示例工具调用；
工具消息包含示例工具输出。

LangChain 采用此约定来跨 LLM 模型提供商将工具调用结构化到对话中。

首先，我们构建一个提示模板，其中包含这些消息的占位符：

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

# Define a custom prompt to provide instructions and any additional context.
# 1) You can add examples into the prompt template to improve extraction quality
# 2) Introduce additional parameters to take context into account (e.g., include metadata
#    about the document from which the text was extracted.)
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are an expert extraction algorithm. "
            "Only extract relevant information from the text. "
            "If you do not know the value of an attribute asked "
            "to extract, return null for the attribute's value.",
        ),
        # ↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓
        MessagesPlaceholder("examples"),  # <-- EXAMPLES!
        # ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
        ("human", "{text}"),
    ]
)

API 参考:聊天提示模板 |消息占位符

测试该模板：

from langchain_core.messages import (
    HumanMessage,
)

prompt.invoke(
    {"text": "this is some text", "examples": [HumanMessage(content="testing 1 2 3")]}
)

API 参考:人类消息

ChatPromptValue(messages=[SystemMessage(content="You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.", additional_kwargs={}, response_metadata={}), HumanMessage(content='testing 1 2 3', additional_kwargs={}, response_metadata={}), HumanMessage(content='this is some text', additional_kwargs={}, response_metadata={})])

定义架构

让我们重新使用来自提取教程的人物模式。

from typing import List, Optional

from langchain_openai import ChatOpenAI
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(..., description="The name of the person")
    hair_color: Optional[str] = Field(
        ..., description="The color of the person's hair if known"
    )
    height_in_meters: Optional[str] = Field(..., description="Height in METERs")


class Data(BaseModel):
    """Extracted data about people."""

    # Creates a model so that we can extract multiple entities.
    people: List[Person]

API 参考:ChatOpenAI

定义参考示例

示例可以定义为输入 - 输出对的列表。

每个示例都包含一个示例 input 文本和一个示例 output，展示应从该文本中提取的内容。

重要

这部分内容较为深入，您可以选择跳过。

示例的格式需要与所使用的 API 匹配（例如，工具调用或 JSON 模式等）。

在此，格式化后的示例将与工具调用 API 所期望的格式相匹配，因为我们正在使用该 API。

import uuid
from typing import Dict, List, TypedDict

from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    ToolMessage,
)
from pydantic import BaseModel, Field


class Example(TypedDict):
    """A representation of an example consisting of text input and expected tool calls.

    For extraction, the tool calls are represented as instances of pydantic model.
    """

    input: str  # This is the example text
    tool_calls: List[BaseModel]  # Instances of pydantic model that should be extracted


def tool_example_to_messages(example: Example) -> List[BaseMessage]:
    """Convert an example into a list of messages that can be fed into an LLM.

    This code is an adapter that converts our example to a list of messages
    that can be fed into a chat model.

    The list of messages per example corresponds to:

    1) HumanMessage: contains the content from which content should be extracted.
    2) AIMessage: contains the extracted information from the model
    3) ToolMessage: contains confirmation to the model that the model requested a tool correctly.

    The ToolMessage is required because some of the chat models are hyper-optimized for agents
    rather than for an extraction use case.
    """
    messages: List[BaseMessage] = [HumanMessage(content=example["input"])]
    tool_calls = []
    for tool_call in example["tool_calls"]:
        tool_calls.append(
            {
                "id": str(uuid.uuid4()),
                "args": tool_call.dict(),
                # The name of the function right now corresponds
                # to the name of the pydantic model
                # This is implicit in the API right now,
                # and will be improved over time.
                "name": tool_call.__class__.__name__,
            },
        )
    messages.append(AIMessage(content="", tool_calls=tool_calls))
    tool_outputs = example.get("tool_outputs") or [
        "You have correctly called this tool."
    ] * len(tool_calls)
    for output, tool_call in zip(tool_outputs, tool_calls):
        messages.append(ToolMessage(content=output, tool_call_id=tool_call["id"]))
    return messages

API 参考:AI 消息 |BaseMessage |人类消息 |系统消息 |工具消息

接下来让我们定义示例，然后将它们转换为消息格式。

examples = [
    (
        "The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.",
        Data(people=[]),
    ),
    (
        "Fiona traveled far from France to Spain.",
        Data(people=[Person(name="Fiona", height_in_meters=None, hair_color=None)]),
    ),
]


messages = []

for text, tool_call in examples:
    messages.extend(
        tool_example_to_messages({"input": text, "tool_calls": [tool_call]})
    )

让我们测试一下这个提示

example_prompt = prompt.invoke({"text": "this is some text", "examples": messages})

for message in example_prompt.messages:
    print(f"{message.type}: {message}")

system: content="You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value." additional_kwargs={} response_metadata={}
human: content="The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it." additional_kwargs={} response_metadata={}
ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': []}, 'id': '240159b1-1405-4107-a07c-3c6b91b3d5b7', 'type': 'tool_call'}]
tool: content='You have correctly called this tool.' tool_call_id='240159b1-1405-4107-a07c-3c6b91b3d5b7'
human: content='Fiona traveled far from France to Spain.' additional_kwargs={} response_metadata={}
ai: content='' additional_kwargs={} response_metadata={} tool_calls=[{'name': 'Data', 'args': {'people': [{'name': 'Fiona', 'hair_color': None, 'height_in_meters': None}]}, 'id': '3fc521e4-d1d2-4c20-bf40-e3d72f1068da', 'type': 'tool_call'}]
tool: content='You have correctly called this tool.' tool_call_id='3fc521e4-d1d2-4c20-bf40-e3d72f1068da'
human: content='this is some text' additional_kwargs={} response_metadata={}

创建一个提取器

让我们选择一个大语言模型（LLM）。由于我们使用了工具调用功能，我们需要一个支持工具调用的模型。有关可用的 LLM 列表，请参见此表格。

选择聊天模型:

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gpt-4-0125-preview", model_provider="openai", temperature=0)

遵循提取教程，我们使用 .with_structured_output 方法根据所需模式结构化模型输出：

runnable = prompt | llm.with_structured_output(
    schema=Data,
    method="function_calling",
    include_raw=False,
)

没有示例 😿

请注意，即使是能力强大的模型也可能在非常简单的测试用例中失败！

for _ in range(5):
    text = "The solar system is large, but earth has only 1 moon."
    print(runnable.invoke({"text": text, "examples": []}))

people=[Person(name='earth', hair_color='null', height_in_meters='null')]
``````output
people=[Person(name='earth', hair_color='null', height_in_meters='null')]
``````output
people=[]
``````output
people=[Person(name='earth', hair_color='null', height_in_meters='null')]
``````output
people=[]

带示例 😻

参考示例有助于修复失败！

for _ in range(5):
    text = "The solar system is large, but earth has only 1 moon."
    print(runnable.invoke({"text": text, "examples": messages}))

people=[]
``````output
people=[]
``````output
people=[]
``````output
people=[]
``````output
people=[]

请注意，我们可以在 LangSmith 追踪中将少样本示例视为工具调用。

并且我们在正样本上保持了性能：

runnable.invoke(
    {
        "text": "My name is Harrison. My hair is black.",
        "examples": messages,
    }
)

Data(people=[Person(name='Harrison', hair_color='black', height_in_meters=None)])

定义架构​

定义参考示例​

创建一个提取器​

没有示例 😿​

带示例 😻​

定义架构

定义参考示例

创建一个提取器

没有示例 😿

带示例 😻