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如何仅使用提示(不使用工具调用)进行提取

工具调用功能不是从 LLM 生成结构化输出所必需的。能够很好地遵循提示指令的 LLM 可以被任务以给定的格式输出信息。

这种方法依赖于设计良好的提示,然后解析 LLM 的输出,使其能够很好地提取信息。

要在没有工具调用功能的情况下提取数据

  1. 指示 LLM 生成遵循预期格式的文本(例如,具有特定模式的 JSON);
  2. 使用输出解析器将模型响应结构化为所需的 Python 对象。

首先,我们选择一个 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

model = ChatOpenAI(model="gpt-4o-mini")
提示

本教程旨在简单易懂,但通常应该包含参考示例,以充分发挥性能!

使用 PydanticOutputParser

以下示例使用内置的 PydanticOutputParser 来解析聊天模型的输出。

from typing import List, Optional

from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field, validator


class Person(BaseModel):
"""Information about a person."""

name: str = Field(..., description="The name of the person")
height_in_meters: float = Field(
..., description="The height of the person expressed in meters."
)


class People(BaseModel):
"""Identifying information about all people in a text."""

people: List[Person]


# Set up a parser
parser = PydanticOutputParser(pydantic_object=People)

# Prompt
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"Answer the user query. Wrap the output in `json` tags\n{format_instructions}",
),
("human", "{query}"),
]
).partial(format_instructions=parser.get_format_instructions())

让我们看看哪些信息被发送到模型

query = "Anna is 23 years old and she is 6 feet tall"
print(prompt.format_prompt(query=query).to_string())
System: Answer the user query. Wrap the output in `json` tags
The output should be formatted as a JSON instance that conforms to the JSON schema below.

As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}
the object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.

Here is the output schema:
\`\`\`
{"$defs": {"Person": {"description": "Information about a person.", "properties": {"name": {"description": "The name of the person", "title": "Name", "type": "string"}, "height_in_meters": {"description": "The height of the person expressed in meters.", "title": "Height In Meters", "type": "number"}}, "required": ["name", "height_in_meters"], "title": "Person", "type": "object"}}, "description": "Identifying information about all people in a text.", "properties": {"people": {"items": {"$ref": "#/$defs/Person"}, "title": "People", "type": "array"}}, "required": ["people"]}
\`\`\`
Human: Anna is 23 years old and she is 6 feet tall

定义好提示后,我们只需将提示、模型和输出解析器链接在一起

chain = prompt | model | parser
chain.invoke({"query": query})
People(people=[Person(name='Anna', height_in_meters=1.83)])

查看相关的 Langsmith 追踪

请注意,模式出现在两个地方

  1. 在提示中,通过 parser.get_format_instructions()
  2. 在链中,接收格式化的输出并将其结构化为 Python 对象(在本例中是 Pydantic 对象 People)。

自定义解析

如果需要,可以使用 LangChainLCEL 轻松创建自定义提示和解析器。

要创建自定义解析器,请定义一个函数来解析模型的输出(通常是 AIMessage),并将其转换为您选择的对象。

请参阅下面的 JSON 解析器的简单实现。

import json
import re
from typing import List, Optional

from langchain_anthropic.chat_models import ChatAnthropic
from langchain_core.messages import AIMessage
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field, validator


class Person(BaseModel):
"""Information about a person."""

name: str = Field(..., description="The name of the person")
height_in_meters: float = Field(
..., description="The height of the person expressed in meters."
)


class People(BaseModel):
"""Identifying information about all people in a text."""

people: List[Person]


# Prompt
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"Answer the user query. Output your answer as JSON that "
"matches the given schema: \`\`\`json\n{schema}\n\`\`\`. "
"Make sure to wrap the answer in \`\`\`json and \`\`\` tags",
),
("human", "{query}"),
]
).partial(schema=People.schema())


# Custom parser
def extract_json(message: AIMessage) -> List[dict]:
"""Extracts JSON content from a string where JSON is embedded between \`\`\`json and \`\`\` tags.

Parameters:
text (str): The text containing the JSON content.

Returns:
list: A list of extracted JSON strings.
"""
text = message.content
# Define the regular expression pattern to match JSON blocks
pattern = r"\`\`\`json(.*?)\`\`\`"

# Find all non-overlapping matches of the pattern in the string
matches = re.findall(pattern, text, re.DOTALL)

# Return the list of matched JSON strings, stripping any leading or trailing whitespace
try:
return [json.loads(match.strip()) for match in matches]
except Exception:
raise ValueError(f"Failed to parse: {message}")
query = "Anna is 23 years old and she is 6 feet tall"
print(prompt.format_prompt(query=query).to_string())
System: Answer the user query. Output your answer as JSON that  matches the given schema: \`\`\`json
{'$defs': {'Person': {'description': 'Information about a person.', 'properties': {'name': {'description': 'The name of the person', 'title': 'Name', 'type': 'string'}, 'height_in_meters': {'description': 'The height of the person expressed in meters.', 'title': 'Height In Meters', 'type': 'number'}}, 'required': ['name', 'height_in_meters'], 'title': 'Person', 'type': 'object'}}, 'description': 'Identifying information about all people in a text.', 'properties': {'people': {'items': {'$ref': '#/$defs/Person'}, 'title': 'People', 'type': 'array'}}, 'required': ['people'], 'title': 'People', 'type': 'object'}
\`\`\`. Make sure to wrap the answer in \`\`\`json and \`\`\` tags
Human: Anna is 23 years old and she is 6 feet tall
chain = prompt | model | extract_json
chain.invoke({"query": query})
/Users/bagatur/langchain/.venv/lib/python3.11/site-packages/pydantic/_internal/_fields.py:201: UserWarning: Field name "schema" in "PromptInput" shadows an attribute in parent "BaseModel"
warnings.warn(
[{'people': [{'name': 'Anna', 'height_in_meters': 1.83}]}]

其他库

如果您正在寻找使用解析方法进行提取,请查看 Kor 库。它由 LangChain 的一位维护者编写,它可以帮助您制作考虑示例的提示,允许控制格式(例如,JSON 或 CSV),并以 TypeScript 表达模式。它看起来效果不错!


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