如何在提取时使用参考示例
通过向 LLM 提供参考示例,通常可以提高提取的质量。
数据提取尝试生成文本和其他非结构化或半结构化格式中信息的结构化表示。工具调用 LLM 功能通常在此上下文中使用。本指南演示如何构建工具调用的少样本示例,以帮助引导提取和类似应用程序的行为。
虽然本指南侧重于如何在工具调用模型中使用示例,但此技术通常适用,也适用于 JSON 或基于提示的技术。
LangChain 在来自 LLM 的消息中实现了一个 工具调用属性,这些消息包含工具调用。有关更多详细信息,请参阅我们的 关于工具调用的操作指南。为了构建数据提取的参考示例,我们构建了一个包含以下序列的聊天历史记录:
- 包含示例输入的 HumanMessage;
- 包含示例工具调用的 AIMessage;
- 包含示例工具输出的 ToolMessage。
LangChain 采用此约定来构建跨 LLM 模型提供商的对话中的工具调用。
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}"),
]
)
测试模板
from langchain_core.messages import (
HumanMessage,
)
prompt.invoke(
{"text": "this is some text", "examples": [HumanMessage(content="testing 1 2 3")]}
)
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={})])
定义模式
让我们重用提取教程中的人员模式。
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]
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输入
文本和一个示例输出
,显示应该从文本中提取的内容。
这有点深入,所以可以随意跳过。
示例的格式需要与使用的 API 相匹配(例如,工具调用或 JSON 模式等)。
这里,格式化的示例将与工具调用 API 预期的格式相匹配,因为我们正在使用它。
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
接下来,让我们定义我们的示例,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 LLM。因为我们正在使用工具调用,所以我们需要一个支持工具调用功能的模型。请参阅此表格以了解可用的 LLM。
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_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
按照提取教程,我们使用.with_structured_output
方法根据所需的模式构造模型输出
runnable = prompt | llm.with_structured_output(
schema=Data,
method="function_calling",
include_raw=False,
)
没有示例 😿
请注意,即使是功能强大的模型也可能会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=[]
有示例 😻
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 跟踪中将少样本示例视为工具调用。
我们在积极的样本中保留了性能
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)])