如何单独使用提示(不进行工具调用)进行提取
工具调用功能并非从LLM生成结构化输出所必需。能够很好地遵循提示指令的LLM可以被要求以给定格式输出信息。
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。
无需工具调用功能即可提取数据
- 指示LLM生成遵循预期格式(例如,具有特定模式的JSON)的文本;
- 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
选择 聊天模型
pip install -qU "langchain[google-genai]"
import getpass
import os
if not os.environ.get("GOOGLE_API_KEY"):
os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")
from langchain.chat_models import init_chat_model
model = init_chat_model("gemini-2.0-flash", model_provider="google_genai")
提示
本教程旨在简单化,但通常应该真正包含参考示例以提升性能!
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())
API 参考:PydanticOutputParser | ChatPromptTemplate
让我们看看发送给模型的信息
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、模型和输出解析器串联起来
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
- 在提示中,通过
parser.get_format_instructions()
; - 在链中,用于接收格式化输出并将其结构化为Python对象(在本例中为Pydantic对象
People
)。
自定义解析
如果需要,可以使用 LangChain
和 LCEL
轻松创建自定义提示和解析器。
要创建自定义解析器,请定义一个函数来将模型输出(通常是 AIMessage)解析为您选择的对象。
请参阅下面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
如果您正在考虑使用解析方法进行提取,请查看 Kor 库。它由一位 LangChain
维护者编写,有助于制作一个考虑示例的提示,允许控制格式(例如,JSON 或 CSV),并用 TypeScript 表达模式。它看起来非常有效!