LangChain#

1
from langchain_core.prompts import PromptTemplate
2

3
# 方法1：使用from_template（推荐）
4
prompt = PromptTemplate.from_template("Tell me a {adjective} joke about {topic}")
5

6
# 方法2：使用构造函数
7
prompt = PromptTemplate(
8
    input_variables=["adjective", "topic"],
9
    template="Tell me a {adjective} joke about {topic}"
10
)

1
# 方法1：format方法
2
formatted_string = prompt.format(adjective="funny", topic="programming")
3

4
# 方法2：invoke方法（兼容LCEL）
5
formatted_string = prompt.invoke({"adjective": "funny", "topic": "programming"}).text

1
from langchain_core.prompts import ChatPromptTemplate
2

3
# 方法1：使用元组格式
4
chat_prompt = ChatPromptTemplate.from_messages([
5
    ("system", "You are a helpful assistant specialized in {domain}"),
6
    ("user", "Please help me with: {request}"),
7
    ("assistant", "I'll help you with {request}. Let me think about this..."),
8
    ("user", "{follow_up}")
9
])
10

11
# 方法2：使用消息模板对象
12
from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate
13

14
system_template = SystemMessagePromptTemplate.from_template(
15
    "You are a {role} with expertise in {domain}"
16
)
17
human_template = HumanMessagePromptTemplate.from_template("Help me with: {task}")
18

19
chat_prompt = ChatPromptTemplate.from_messages([system_template, human_template])

1
# 生成消息列表
2
messages = chat_prompt.format_messages(
3
    domain="web development",
4
    request="optimize my React app",
5
    follow_up="What about performance monitoring?"
6
)
7

8
# 消息类型: [SystemMessage, HumanMessage, AIMessage, HumanMessage]

1
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
2

3
# 在模板中插入消息占位符
4
prompt = ChatPromptTemplate([
5
    ("system", "You are a helpful assistant"),
6
    MessagesPlaceholder("conversation_history"),
7
    ("user", "现在请回答: {question}")
8
])

1
from langchain_core.messages import HumanMessage, AIMessage
2

3
# 准备历史消息
4
history = [
5
    HumanMessage(content="你好"),
6
    AIMessage(content="您好！有什么可以帮助您的吗？"),
7
    HumanMessage(content="我想学习Python")
8
]
9

10
# 格式化包含历史的对话
11
messages = prompt.format_messages(
12
    conversation_history=history,
13
    question="什么是列表推导式？"
14
)

1
from langchain_core.prompts import PromptTemplate
2

3
# 指定使用Jinja2模板格式
4
prompt = PromptTemplate.from_template(
5
    template="Hello {{ name }}! Welcome to {{ place }}.",
6
    template_format="jinja2"
7
)

1
conditional_template = PromptTemplate.from_template(
2
    """
3
{%- if user.is_vip -%}
4
尊敬的VIP用户 {{ user.name }}，您享有专属服务！
5
{%- else -%}
6
您好 {{ user.name }}，欢迎使用我们的服务！
7
{%- endif -%}
8
    """,
9
    template_format="jinja2"
10
)

1
loop_template = PromptTemplate.from_template(
2
    """
3
任务清单：
4
{%- for task in tasks %}
5
{{ loop.index }}. {{ task.name }} - 优先级：{{ task.priority }}
6
{%- endfor %}
7
    """,
8
    template_format="jinja2"
9
)

1
filter_template = PromptTemplate.from_template(
2
    """
3
- 姓名：{{ name | title }}
4
- 邮箱：{{ email | lower }}
5
- 分数：{{ score | round(2) }}
6
- 标签：{{ tags | join(", ") }}
7
    """,
8
    template_format="jinja2"
9
)

1
macro_template = PromptTemplate.from_template(
2
    """
3
{%- macro render_skill(skill_name, level, description="") -%}
4
🔧 {{ skill_name }}
5
   等级：{{ "⭐" * level }}{{ "☆" * (5 - level) }} ({{ level }}/5)
6
   {%- if description %}
7
   说明：{{ description }}
8
   {%- endif %}
9
{%- endmacro -%}
10

11
{{ render_skill("Python", 4, "熟练掌握面向对象编程") }}
12
{{ render_skill("JavaScript", 3) }}
13
    """,
14
    template_format="jinja2"
15
)

1
from langchain_core.example_selectors.base import BaseExampleSelector
2
from typing import Dict, List
3

4
class CustomExampleSelector(BaseExampleSelector):
5
    """基于输入长度选择最相似长度的示例"""
6

7
    def __init__(self, examples: List[Dict[str, str]]):
8
        self.examples = examples
9

10
    def add_example(self, example: Dict[str, str]) -> None:
11
        """添加新示例"""
12
        self.examples.append(example)
13

14
    def select_examples(self, input_variables: Dict[str, str]) -> List[Dict[str, str]]:
15
        """根据输入变量选择示例"""
16
        new_word = input_variables["input"]
17
        new_word_length = len(new_word)
18

19
        # 找到长度最接近的示例
20
        best_match = None
21
        smallest_diff = float("inf")
22

23
        for example in self.examples:
24
            current_diff = abs(len(example["input"]) - new_word_length)
25
            if current_diff < smallest_diff:
26
                smallest_diff = current_diff
27
                best_match = example
28

29
        return [best_match] if best_match else []

1
from langchain_core.example_selectors import LengthBasedExampleSelector
2
from langchain_core.prompts import PromptTemplate, FewShotPromptTemplate
3

4
# 创建示例
5
examples = [
6
    {"input": "happy", "output": "sad"},
7
    {"input": "tall", "output": "short"},
8
    {"input": "energetic", "output": "lethargic"},
9
    {"input": "sunny", "output": "gloomy"},
10
]
11

12
# 示例格式模板
13
example_prompt = PromptTemplate(
14
    input_variables=["input", "output"],
15
    template="Input: {input}\nOutput: {output}",
16
)
17

18
# 创建长度基础选择器
19
selector = LengthBasedExampleSelector(
20
    examples=examples,
21
    example_prompt=example_prompt,
22
    max_length=25  # token限制
23
)
24

25
# 在Few-Shot模板中使用
26
dynamic_prompt = FewShotPromptTemplate(
27
    example_selector=selector,
28
    example_prompt=example_prompt,
29
    prefix="Give the antonym of every input",
30
    suffix="Input: {adjective}\nOutput:",
31
)

1
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
2
from langchain_openai import OpenAIEmbeddings
3
from langchain_community.vectorstores import FAISS
4

5
# 创建语义相似度选择器
6
selector = SemanticSimilarityExampleSelector.from_examples(
7
    examples=examples,
8
    embeddings=OpenAIEmbeddings(model="text-embedding-3-small"),
9
    vectorstore_cls=FAISS,
10
    k=2  # 选择最相似的2个示例
11
)
12

13
# 使用选择器
14
selected = selector.select_examples({"input": "joyful"})

1
from langchain_core.example_selectors import MaxMarginalRelevanceExampleSelector
2

3
# 创建MMR选择器
4
mmr_selector = MaxMarginalRelevanceExampleSelector.from_examples(
5
    examples=examples,
6
    embeddings=OpenAIEmbeddings(model="text-embedding-3-small"),
7
    vectorstore_cls=FAISS,
8
    k=2  # 平衡相关性和多样性
9
)

1
from langchain_community.example_selectors import NGramOverlapExampleSelector
2

3
# 创建N-gram重叠选择器
4
example_selector = NGramOverlapExampleSelector(
5
    examples=examples,
6
    example_prompt=example_prompt,
7
    threshold=-1.0,  # 阈值，低于此值的示例将被排除
8
)
9

10
# 设置阈值排除低重叠示例
11
example_selector.threshold = 0.0