分類目錄：《大模型從入門到應用》總目錄

LangChain系列文章：

• 基礎(chǔ)知識
• 快速入門
  • 安裝與環(huán)境配置
  • 鏈（Chains）、代理（Agent:）和記憶（Memory）
  • 快速開發(fā)聊天模型
• 模型（Models）
  • 基礎(chǔ)知識
  • 大型語言模型（LLMs）
    • 基礎(chǔ)知識
    • LLM的異步API、自定義LLM包裝器、虛假LLM和人類輸入LLM（Human Input LLM）
    • 緩存LLM的調(diào)用結(jié)果
    • 加載與保存LLM類、流式傳輸LLM與Chat Model響應和跟蹤tokens使用情況
  • 聊天模型（Chat Models）
    • 基礎(chǔ)知識
    • 使用少量示例和響應流式傳輸
  • 文本嵌入模型
    • Aleph Alpha、Amazon Bedrock、Azure OpenAI、Cohere等
    • Embaas、Fake Embeddings、Google Vertex AI PaLM等
• 提示（Prompts）
  • 基礎(chǔ)知識
  • 提示模板
    • 基礎(chǔ)知識
    • 連接到特征存儲
    • 創(chuàng)建自定義提示模板和含有Few-Shot示例的提示模板
    • 部分填充的提示模板和提示合成
    • 序列化提示信息
  • 示例選擇器（Example Selectors）
  • 輸出解析器（Output Parsers）
• 記憶（Memory）
  • 基礎(chǔ)知識
  • 記憶的類型
    • 會話緩存記憶、會話緩存窗口記憶和實體記憶
    • 對話知識圖譜記憶、對話摘要記憶和會話摘要緩沖記憶
    • 對話令牌緩沖存儲器和基于向量存儲的記憶
  • 將記憶添加到LangChain組件中
  • 自定義對話記憶與自定義記憶類
  • 聊天消息記錄
  • 記憶的存儲與應用
• 索引（Indexes）
  • 基礎(chǔ)知識
  • 文檔加載器（Document Loaders）
  • 文本分割器（Text Splitters）
  • 向量存儲器（Vectorstores）
  • 檢索器（Retrievers）
• 鏈（Chains）
  • 基礎(chǔ)知識
  • 通用功能
    • 自定義Chain和Chain的異步API
    • LLMChain和RouterChain
    • SequentialChain和TransformationChain
    • 鏈的保存（序列化）與加載（反序列化）
  • 鏈與索引
    • 文檔分析和基于文檔的聊天
    • 問答的基礎(chǔ)知識
    • 圖問答（Graph QA）和帶來源的問答（Q&A with Sources）
    • 檢索式問答
    • 文本摘要（Summarization）、HyDE和向量數(shù)據(jù)庫的文本生成
• 代理（Agents）
  • 基礎(chǔ)知識
  • 代理類型
  • 自定義代理（Custom Agent）
  • 自定義MRKL代理
  • 帶有ChatModel的LLM聊天自定義代理和自定義多操作代理（Custom MultiAction Agent）
  • 工具
    • 基礎(chǔ)知識
    • 自定義工具（Custom Tools）
    • 多輸入工具和工具輸入模式
    • 人工確認工具驗證和Tools作為OpenAI函數(shù)
  • 工具包（Toolkit）
  • 代理執(zhí)行器（Agent Executor）
    • 結(jié)合使用Agent和VectorStore
    • 使用Agents的異步API和創(chuàng)建ChatGPT克隆
    • 處理解析錯誤、訪問中間步驟和限制最大迭代次數(shù)
    • 為代理程序設置超時時間和限制最大迭代次數(shù)和為代理程序和其工具添加共享內(nèi)存
  • 計劃與執(zhí)行
• 回調(diào)函數(shù)（Callbacks）

SequentialChain

在調(diào)用語言模型之后，下一步是對語言模型進行一系列的調(diào)用。若可以將一個調(diào)用的輸出作為另一個調(diào)用的輸入時則特別有用。在本節(jié)中，我們將介紹如何使用順序鏈來實現(xiàn)這一點。順序鏈被定義為一系列按確定順序調(diào)用的鏈條。有兩種類型的順序鏈：

• SimpleSequentialChain：最簡單的順序鏈形式，每個步驟具有單一的輸入和輸出，一個步驟的輸出作為下一個步驟的輸入。
• SequentialChain：更一般的順序鏈形式，允許多個輸入和輸出。

SimpleSequentialChain

在這個SimpleSequentialChain中，每個單獨的鏈都有一個單一的輸入和輸出，一個步驟的輸出被用作下一個步驟的輸入。我們通過一個玩具例子來演示這個過程，其中第一個鏈接受一個虛構(gòu)的劇本標題，然后生成該標題的簡介，第二個鏈條接受該劇本的簡介并生成一個虛構(gòu)的評論。

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# This is an LLMChain to write a synopsis given a title of a play.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.

Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)

# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template)

# This is the overall chain where we run these two chains in sequence.
from langchain.chains import SimpleSequentialChain
overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)
review = overall_chain.run("Tragedy at sunset on the beach")

日志輸出：

> Entering new SimpleSequentialChain chain...


Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future. 

The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever.

The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset.


Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. 

The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. 

The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.

> Finished chain.

輸入：

print(review)

輸出：

Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. 

The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. 

The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.

SequentialChain

并非所有的順序鏈都像將一個字符串作為參數(shù)傳遞并在鏈條的所有步驟中得到一個字符串輸出那樣簡單。在下面的例子中，我們將嘗試更復雜的鏈條，涉及多個輸入，同時也有多個最終輸出。重要的是如何命名輸入和輸出變量名。在上面的例子中，我們不需要考慮這個問題，因為我們只是將一個鏈條的輸出直接作為下一個鏈條的輸入傳遞，但是在這里我們需要關(guān)注這個問題，因為我們有多個輸入。

# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.

Title: {title}
Era: {era}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title", 'era'], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="synopsis")

# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review")

# This is the overall chain where we run these two chains in sequence.
from langchain.chains import SequentialChain
overall_chain = SequentialChain(
    chains=[synopsis_chain, review_chain],
    input_variables=["era", "title"],
    # Here we return multiple variables
    output_variables=["synopsis", "review"],
    verbose=True)
overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"})

日志輸出：

> Entering new SequentialChain chain...

> Finished chain.

輸出：

{'title': 'Tragedy at sunset on the beach',
 'era': 'Victorian England',
 'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn their journey, they make their way to a beach at sunset, where they plan to exchange their vows of love. Unbeknownst to them, their plans are overheard by John's father, who has been tracking them. He follows them to the beach and, in a fit of rage, confronts them. \n\nA physical altercation ensues, and in the struggle, John's father accidentally stabs Mary in the chest with his sword. The two are left in shock and disbelief as Mary dies in John's arms, her last words being a declaration of her love for him.\n\nThe tragedy of the play comes to a head when John, broken and with no hope of a future, chooses to take his own life by jumping off the cliffs into the sea below. \n\nThe play is a powerful story of love, hope, and loss set against the backdrop of 19th century England.",
 'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and start a new life together, and the audience is taken on a journey of hope and optimism for the future.\n\nUnfortunately, their dreams are cut short when John's father discovers them and in a fit of rage, fatally stabs Mary. The tragedy of the play is further compounded when John, broken and without hope, takes his own life. The storyline is not only realistic, but also emotionally compelling, drawing the audience in from start to finish.\n\nThe acting was also commendable, with the actors delivering believable and nuanced performances. The playwright and director have successfully crafted a timeless tale of love and loss that will resonate with audiences for years to come. Highly recommended."}

SequentialChain中的記憶

有時候，我們可能希望在鏈的每個步驟中或鏈條的后續(xù)部分中傳遞一些上下文信息，但是保持和鏈接輸入或輸出變量可能會變得混亂。使用SimpleMemory是一種方便的方式來管理這些上下文信息并簡化我們的鏈條。

例如，使用之前的劇本順序鏈，假設我們想在每個步驟中包含一些關(guān)于劇本的日期、時間和地點的上下文信息，并使用生成的簡介和評論創(chuàng)建一些社交媒體發(fā)布的文本。你可以將這些新的上下文變量添加為input_variables，或者我們可以在鏈條中添加一個SimpleMemory來管理這個上下文信息：

from langchain.chains import SequentialChain
from langchain.memory import SimpleMemory

llm = OpenAI(temperature=.7)
template = """You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.

Here is some context about the time and location of the play:
Date and Time: {time}
Location: {location}

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:
{review}

Social Media Post:
"""
prompt_template = PromptTemplate(input_variables=["synopsis", "review", "time", "location"], template=template)
social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="social_post_text")

overall_chain = SequentialChain(
    memory=SimpleMemory(memories={"time": "December 25th, 8pm PST", "location": "Theater in the Park"}),
    chains=[synopsis_chain, review_chain, social_chain],
    input_variables=["era", "title"],
    # Here we return multiple variables
    output_variables=["social_post_text"],
    verbose=True)

overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"})

日志輸出：

> Entering new SequentialChain chain...

> Finished chain.

輸出：

{'title': 'Tragedy at sunset on the beach',
 'era': 'Victorian England',
 'time': 'December 25th, 8pm PST',
 'location': 'Theater in the Park',
 'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland"}

TransformationChain

本節(jié)展示了如何使用TransformationChain。我們將創(chuàng)建一個虛擬的TransformationChain示例。它接受一個非常長的文本，將文本過濾為只包含前三個段落的內(nèi)容，然后將其傳遞給一個LLMChain來對其進行摘要。

from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()

def transform_func(inputs: dict) -> dict:
    text = inputs["text"]
    shortened_text = "\n\n".join(text.split("\n\n")[:3])
    return {"output_text": shortened_text}

transform_chain = TransformChain(input_variables=["text"], output_variables=["output_text"], transform=transform_func)
template = """Summarize this text:

{output_text}

Summary:"""
prompt = PromptTemplate(input_variables=["output_text"], template=template)
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)
sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])
sequential_chain.run(state_of_the_union)

輸出：

' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'

參考文獻：
[1] LangChain官方網(wǎng)站：https://www.langchain.com/
[2] LangChain ????? 中文網(wǎng)，跟著LangChain一起學LLM/GPT開發(fā)：https://www.langchain.com.cn/
[3] LangChain中文網(wǎng) - LangChain 是一個用于開發(fā)由語言模型驅(qū)動的應用程序的框架：http://www.cnlangchain.com/文章來源地址http://www.zghlxwxcb.cn/news/detail-665562.html

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