LangChain--如何使用大模型

【🍊易编橙·终身成长社群🍊】

大家好，我是小森( ﹡ˆoˆ﹡ ) ！易编橙·终身成长社群创始团队嘉宾，橙似锦计划领衔成员、阿里云专家博主、腾讯云内容共创官、CSDN人工智能领域优质创作者。

LangChain是一个用于构建和管理语言模型链的开源框架，旨在帮助开发者更高效地构建和部署复杂的自然语言处理（NLP）应用。LangChain自身并不开发LLMs，它的核心理念是为各种LLMs实现通用的接口，把LLMs相关的组件“链接”在一起，简化LLMs应用的开发难度

LangChain的应用场景非常广泛：智能问答系统、文本生成、信息提取、智能客服等等。

LangChain主要组件

Models：模型
Prompts：提示
Memory：记忆
Indexes：索引
Chains：链，一系列对各种组件的调用
Agents：代理，决定模型采取哪些行动

Models

Models模块提供了与不同类型语言模型进行交互的接口和能力。这些语言模型包括但不限于OpenAI的GPT系列、Google的LaMDA、Meta的LLaMa等。通过Models模块，开发者可以轻松地接入和使用这些强大的语言模型，构建复杂的自然语言处理任务。

在LangChain中，语言模型被分为LLMs、Chat Models和Text Embedding三类。Models模块针对不同类型的模型提供了相应的类和操作方法，以满足不同的应用场景需求。

常用大模型的下载库：huggingface、modelscope

pip install langchain
pip install qianfan

我们可以去百度千帆大模型平台，创建模型，调用API Key 和 Secret Key：

我们可以通过langchain调用文心一言4.0模型：

import os
from langchain_community.llms import QianfanLLMEndpoint

os.environ['QIANFAN_AK'] = "*********"
os.environ['QIANFAN_SK'] = "*********"

llm = QianfanLLMEndpoint(model="ERNIE-Bot-turbo")
res = llm("请写一首诗把")
print(res)

输出：

好的，以下是一首关于春天的诗：

春风轻拂绿意浓，
花开满园醉人心。
蝴蝶翩翩飞舞处，
小鸟欢歌乐不停。

田野间麦浪滚滚，
桃花笑语映日红。
万物复苏生机起，
春日美景入诗中。

Chat Models

Chat Models模块专注于处理和交互基于聊天消息格式的语言模型。与传统的LLMs（大型语言模型）不同，Chat Models的输入和输出是格式化的聊天消息，这使得它们更适合处理对话式任务和生成更加自然、上下文相关的响应。

import os
from langchain_community.chat_models import QianfanChatEndpoint
from langchain.schema.messages import HumanMessage

os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"

chat = QianfanChatEndpoint(model="ERNIE-Bot-turbo")
messages = [HumanMessage(content="给我写一首唐诗")]
res = chat(messages)
print(res)

输出：

content='当然可以。这是一首名为《秋日思乡》的唐诗：\n\n秋风吹过菊花黄，\n归心似箭逐月行。\n故土犹在梦难留，\n望断天涯泪满衣。' additional_kwargs={'finish_reason': '', 'request_id': 'as-508nd0m8r2', 'object': 'chat.completion', 'search_info': [], 'usage': {'prompt_tokens': 5, 'completion_tokens': 50, 'total_tokens': 55}} response_metadata={'token_usage': {'prompt_tokens': 5, 'completion_tokens': 50, 'total_tokens': 55}, 'model_name': 'ERNIE-Bot-turbo', 'finish_reason': 'stop', 'id': 'as-508nd0m8r2', 'object': 'chat.completion', 'created': 1722134892, 'result': '当然可以。这是一首名为《秋日思乡》的唐诗：\n\n秋风吹过菊花黄，\n归心似箭逐月行。\n故土犹在梦难留，\n望断天涯泪满衣。', 'is_truncated': False, 'need_clear_history': False, 'usage': {'prompt_tokens': 5, 'completion_tokens': 50, 'total_tokens': 55}} id='run-871f99c6-248e-48d9-91c6-7e7779bb59b2-0' usage_metadata={'input_tokens': 5, 'output_tokens': 50, 'total_tokens': 55}

提示模板

提示模板就是把一些常见的提示整理成模板，用户只需要修改模板中特定的词语，就能快速准确地告诉模型自己的需求。

import os
from langchain_community.chat_models import QianfanChatEndpoint
from langchain.prompts import ChatPromptTemplate


os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"


template_str = """你是一位专业的小红书运营官。\n
对于售价为 {price} 元的 {flower_name} ，您能提供一个吸引人的简短描述吗？ 一句话"""

promp_emplate = ChatPromptTemplate.from_template(template_str)
prompt = promp_emplate.format_messages(flower_name=["玫瑰"], price='50')


print('prompt-->', prompt)

chat = QianfanChatEndpoint(
    streaming=True,model="ERNIE-Bot-turbo"
)

result = chat(prompt)
print(result)

输出：

content='珍贵的玫瑰花瓣，如同初升的朝阳般温暖而甜美，为您的生活增添一丝独特的香气和温暖。🌹 💕 （仅供参考，请根据实际情况调整话语）' response_metadata={'token_usage': {'prompt_tokens': 35, 'completion_tokens': 37, 'total_tokens': 72}, 'model_name': 'ERNIE-Bot-turbo', 'finish_reason': 'stop'} id='run-c15bff74-741d-4004-9412-ad6003e8f96c-0'

Embeddings Models

Embeddings Models可以为文本创建向量映射，这样就能在向量空间里去考虑文本；在NLP中，Embedding的作用就是将数据进行文本向量化。

import os
from langchain_community.embeddings import QianfanEmbeddingsEndpoint


os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"


embed = QianfanEmbeddingsEndpoint()
res1 = embed.embed_query('我爱你')
print(res1)

res2 = embed.embed_documents(['我爱你', '我喜欢你'])
print(res2)

输出：

[INFO][2024-07-28 10:56:16.725] oauth.py:228 [t:5544]: trying to refresh access_token for ak `CNzMCb***`
[INFO][2024-07-28 10:56:16.988] oauth.py:243 [t:5544]: sucessfully refresh access_token
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Prompts

Prompt是指当用户输入信息给模型时加入的提示，这个提示的形式可以是zero-shot或者few-shot等方式，目的是让模型理解更为复杂的业务场景以便更好的解决问题。

import os
from langchain import PromptTemplate
from langchain_community.llms import QianfanLLMEndpoint

os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"

template = "我的邻居姓{lastname}，他生了个儿子，给他儿子起个名字"

prompt = PromptTemplate(
    input_variables=["lastname"],
    template=template,
)

prompt_text = prompt.format(lastname="贾")
print(prompt_text)


llm = QianfanLLMEndpoint()

result = llm(prompt_text)
print(result)

输出：

我的邻居姓贾，他生了个儿子，给他儿子起个名字
D:\apollo\anaconda\lib\site-packages\langchain_core\_api\deprecation.py:139: LangChainDeprecationWarning: The method `BaseLLM.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.
  warn_deprecated(
[INFO][2024-07-28 11:03:13.756] oauth.py:228 [t:29636]: trying to refresh access_token for ak `CNzMCb***`
[INFO][2024-07-28 11:03:14.095] oauth.py:243 [t:29636]: sucessfully refresh access_token
给邻居家的新生儿起名字是一件非常重要的事情，需要考虑到很多因素，如家庭传统、文化背景、父母的偏好等等。基于您提供的信息，我为您提供以下几个名字供您参考：

1. 贾瑞安：这个名字寓意着平安、吉祥，是一个非常美好的名字。
2. 贾俊豪：这个名字寓意着英俊、豪爽，是一个很有男子气概的名字。
3. 贾宇航：这个名字寓意着广阔的天地、无限的可能，适合有远大抱负的孩子。
4. 贾浩然：这个名字寓意着浩渺、自然，是一个很自然的名字。
5. 贾睿诚：这个名字寓意着睿智、诚实，是一个很有品行的名字。
6. 贾梓轩：这个名字适合有艺术天赋或喜欢音乐的孩子，寓意着广阔的舞台和无限的可能。

zero-shot学习通常通过精心设计的提示（prompt）来实现。可以构建一个包含适当上下文和指令的提示，然后将其传递给LLM，引导模型在没有任何特定任务示例的情况下完成任务。
few-shot场景中为LLM提供少量的任务示例作为上下文。这些示例展示了如何执行任务，并帮助模型快速理解任务的要求。
Few-shot学习在LangChain中特别有用，因为它可以在少量样本的情况下提高模型的性能。

few-shot来使用提示

from langchain_community.llms import QianfanLLMEndpoint
import os
from langchain import PromptTemplate, FewShotPromptTemplate

os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"


examples = [
    {"word": "开心", "fanyici": "难过"},
    {"word": "黑", "fanyici": "白"},
]

example_template = """
单词: {word}
反义词: {fanyici}\\n
"""

example_prompt = PromptTemplate(
    input_variables=["word", "fanyici"],
    template=example_template,
)

few_shot_prompt = FewShotPromptTemplate(                  
    examples=examples,
    example_prompt=example_prompt,
    prefix="给出每个单词的反义词",
    suffix="单词: {input}\\n反义词:",
    input_variables=["input"],
    example_separator="\\n",
)

prompt_text = few_shot_prompt.format(input="亮")
print(prompt_text)
print('*'*30)

llm = QianfanLLMEndpoint(model='Qianfan-Chinese-Llama-2-7B')
print(llm(prompt_text))

输出：

给出每个单词的反义词\n
单词: 开心
反义词: 难过\n
\n
单词: 黑
反义词: 白\n
\n单词: 亮\n反义词:
******************************
暗

我们可以通过打印出来的提示词观察到，prefix参数就是前缀的提示词，examples是给出的实例，example_prompt定义了如何将每个示例格式化为字符串（规定了实例怎么被输出出来）。

Chains(链)

Chains可以简单理解为对组件的调用序列，其中可以包括其他Chains。在LangChain中，Chains可以是非常简单的，如只包含一个Prompt模板和大型语言模型（LLM）的LLMChain；也可以是更复杂的，涉及多个步骤和多种组件的调用。Chains的主要特点是其输出会成为下一个组件或Chain的输入，从而实现功能的串联。

Chains的类型

LangChain中Chains的类型多样，以满足不同的需求。以下是一些常见的Chains类型：

LLMChain：最基本的Chains类型，整合了大型语言模型和提示模板。它接受用户输入，通过提示模板格式化后传递给LLM，并返回LLM的响应。
TransformChain：用于处理Chains之间的输入和输出，支持自定义的转换函数，便于Chains之间的数据传输。
SequentialChain：顺序链，允许将多个Chains按顺序组合起来，每个Chain的输出成为下一个Chain的输入。这可以用于执行多步骤的任务。
SimpleSequentialChain：与SequentialChain类似，但每个步骤都有一个单一的输入/输出。
异步Chains：用于执行异步函数和任务，如异步LLMChain等。
实用工具Chains：包含各种实用工具的Chains，如SQL数据库Chains、Bash Chains等，允许将自然语言转换为SQL查询、运行Bash命令等。

from langchain_community.llms import QianfanLLMEndpoint
import os
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.chains import LLMChain
from langchain.chains import SimpleSequentialChain

os.environ['QIANFAN_AK'] = "***"
os.environ['QIANFAN_SK'] = "***"

template = "我的邻居姓{lastname}，他生了个儿子，给他儿子起个名字"

first_prompt = PromptTemplate(
    input_variables=["lastname"],
    template=template,
)
llm = QianfanLLMEndpoint()

first_chain = LLMChain(llm=llm, prompt=first_prompt)

# 创建第二条链
second_prompt = PromptTemplate(
    input_variables=["child_name"],
    template="邻居的儿子名字叫{child_name}，给他起一个小名",
)

second_chain = LLMChain(llm=llm, prompt=second_prompt)

# 链接两条链
overall_chain = SimpleSequentialChain(chains=[first_chain, second_chain], verbose=True)

print(overall_chain)

catchphrase = overall_chain.run("孙")
print(catchphrase)

输出：