分類目錄：《大模型從入門到應(yīng)用》總目錄

LangChain系列文章：

• 基礎(chǔ)知識(shí)
• 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(shí)
  • 大型語言模型（LLMs）
    • 基礎(chǔ)知識(shí)
    • 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(yīng)和跟蹤tokens使用情況
  • 聊天模型（Chat Models）
    • 基礎(chǔ)知識(shí)
    • 使用少量示例和響應(yīng)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(shí)
  • 提示模板
    • 基礎(chǔ)知識(shí)
    • 連接到特征存儲(chǔ)
    • 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(shí)
  • 記憶的類型
    • 會(huì)話緩存記憶、會(huì)話緩存窗口記憶和實(shí)體記憶
    • 對(duì)話知識(shí)圖譜記憶、對(duì)話摘要記憶和會(huì)話摘要緩沖記憶
    • 對(duì)話令牌緩沖存儲(chǔ)器和基于向量存儲(chǔ)的記憶
  • 將記憶添加到LangChain組件中
  • 自定義對(duì)話記憶與自定義記憶類
  • 聊天消息記錄
  • 記憶的存儲(chǔ)與應(yīng)用
• 索引（Indexes）
  • 基礎(chǔ)知識(shí)
  • 文檔加載器（Document Loaders）
  • 文本分割器（Text Splitters）
  • 向量存儲(chǔ)器（Vectorstores）
  • 檢索器（Retrievers）
• 鏈（Chains）
  • 基礎(chǔ)知識(shí)
  • 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(shí)
    • 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(shí)
  • 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(shí)
    • 自定義工具（Custom Tools）
    • 多輸入工具和工具輸入模式
    • 人工確認(rèn)工具驗(yàn)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(cuò)誤、訪問中間步驟和限制最大迭代次數(shù)
    • 為代理程序設(shè)置超時(shí)時(shí)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(jì)劃與執(zhí)行
• 回調(diào)函數(shù)（Callbacks）

自定義對(duì)話記憶

本節(jié)介紹了幾種自定義對(duì)話記憶的方法：

from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = OpenAI(temperature=0)

AI前綴

第一種方法是通過更改對(duì)話摘要中的AI前綴來實(shí)現(xiàn)。默認(rèn)情況下，它設(shè)置為AI，但你可以將其設(shè)置為任何你想要的內(nèi)容。需要注意的是，如果我們更改了這個(gè)前綴，我們還應(yīng)該相應(yīng)地更改鏈條中使用的提示來反映這個(gè)命名更改。讓我們通過下面的示例來演示這個(gè)過程。

# Here it is by default set to "AI"
conversation = ConversationChain(
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI:

> Finished ConversationChain chain.

輸出：

" Hi there! It's nice to meet you. How can I help you today?"

輸入：

conversation.predict(input="What's the weather?")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI:  Hi there! It's nice to meet you. How can I help you today?
Human: What's the weather?
AI:

> Finished ConversationChain chain.

輸出：

' The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the next few days is sunny with temperatures in the mid-70s.'

輸入：

# Now we can override it and set it to "AI Assistant"
from langchain.prompts.prompt import PromptTemplate

template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:
{history}
Human: {input}
AI Assistant:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=template
)
conversation = ConversationChain(
    prompt=PROMPT,
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory(ai_prefix="AI Assistant")
)
conversation.predict(input="Hi there!")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI Assistant:

> Finished ConversationChain chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="What's the weather?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Human: Hi there!
AI Assistant:  Hi there! It's nice to meet you. How can I help you today?
Human: What's the weather?
AI Assistant:

> Finished ConversationChain chain.

輸出：

The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is sunny with a high of 78 degrees and a low of 65 degrees.'

人類前綴

第二種方法是通過更改對(duì)話摘要中的人類前綴來實(shí)現(xiàn)。默認(rèn)情況下，它設(shè)置為Human，但我們可以將其設(shè)置為任何我們想要的內(nèi)容。需要注意的是，如果我們更改了這個(gè)前綴，我們還應(yīng)該相應(yīng)地更改鏈條中使用的提示來反映這個(gè)命名更改。讓我們通過下面的示例來演示這個(gè)過程。

# Now we can override it and set it to "Friend"
from langchain.prompts.prompt import PromptTemplate

template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:
{history}
Friend: {input}
AI:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=template
)
conversation = ConversationChain(
    prompt=PROMPT,
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory(human_prefix="Friend")
)
conversation.predict(input="Hi there!")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Friend: Hi there!
AI:

> Finished ConversationChain chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="What's the weather?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:

Friend: Hi there!
AI:  Hi there! It's nice to meet you. How can I help you today?
Friend: What's the weather?
AI:

> Finished ConversationChain chain.

輸出：

  ' The weather right now is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is mostly sunny with a high of 82 degrees.'

創(chuàng)建自定義記憶類

盡管在LangChain中有幾種預(yù)定義的記憶類型，但我們很可能希望添加自己的記憶類型，以使其適用于我們的應(yīng)用程序。在本節(jié)中，我們將向ConversationChain添加一個(gè)自定義的記憶類型。為了添加自定義的記憶類，我們需要導(dǎo)入基本的記憶類并對(duì)其進(jìn)行子類化。

from langchain import OpenAI, ConversationChain
from langchain.schema import BaseMemory
from pydantic import BaseModel
from typing import List, Dict, Any

在這個(gè)示例中，我們將編寫一個(gè)自定義的記憶類，使用spacy提取實(shí)體并將有關(guān)它們的信息保存在一個(gè)簡(jiǎn)單的哈希表中。然后，在對(duì)話過程中，我們將查看輸入文本，提取任何實(shí)體，并將關(guān)于它們的任何信息放入上下文中。需要注意的是，這種實(shí)現(xiàn)相當(dāng)簡(jiǎn)單且脆弱，可能在生產(chǎn)環(huán)境中不太有用。它的目的是展示我們可以添加自定義的記憶實(shí)現(xiàn)。為此，我們需要首先安裝spacy。

# !pip install spacy
# !python -m spacy download en_core_web_lg
import spacy
nlp = spacy.load('en_core_web_lg')
class SpacyEntityMemory(BaseMemory, BaseModel):
    """Memory class for storing information about entities."""

    # Define dictionary to store information about entities.
    entities: dict = {}
    # Define key to pass information about entities into prompt.
    memory_key: str = "entities"
        
    def clear(self):
        self.entities = {}

    @property
    def memory_variables(self) -> List[str]:
        """Define the variables we are providing to the prompt."""
        return [self.memory_key]

    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """Load the memory variables, in this case the entity key."""
        # Get the input text and run through spacy
        doc = nlp(inputs[list(inputs.keys())[0]])
        # Extract known information about entities, if they exist.
        entities = [self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities]
        # Return combined information about entities to put into context.
        return {self.memory_key: "\n".join(entities)}

    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save context from this conversation to buffer."""
        # Get the input text and run through spacy
        text = inputs[list(inputs.keys())[0]]
        doc = nlp(text)
        # For each entity that was mentioned, save this information to the dictionary.
        for ent in doc.ents:
            ent_str = str(ent)
            if ent_str in self.entities:
                self.entities[ent_str] += f"\n{text}"
            else:
                self.entities[ent_str] = text

我們現(xiàn)在定義一個(gè)提示，其中包含有關(guān)實(shí)體的信息以及用戶的輸入：

from langchain.prompts.prompt import PromptTemplate

template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:
{entities}

Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(
    input_variables=["entities", "input"], template=template
)

現(xiàn)在，我們把它們整合起來：

llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory())

在第一個(gè)例子中，由于對(duì)Harrison沒有先前的了解，"Relevant entity information"部分是空的：

conversation.predict(input="Harrison likes machine learning")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:


Conversation:
Human: Harrison likes machine learning
AI:

> Finished ConversationChain chain.

輸出：

" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?"

現(xiàn)在在第二個(gè)例子中，我們可以看到它提取了關(guān)于Harrison的信息。

conversation.predict(input="What do you think Harrison's favorite subject in college was?")

日志輸出：

> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:
Harrison likes machine learning

Conversation:
Human: What do you think Harrison's favorite subject in college was?
AI:

> Finished ConversationChain chain.

輸出：

' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'

這個(gè)實(shí)現(xiàn)方式相對(duì)簡(jiǎn)單且容易出錯(cuò)，可能在實(shí)際生產(chǎn)環(huán)境中沒有太大的用途，但它展示了我們可以添加自定義的內(nèi)存實(shí)現(xiàn)方式。

參考文獻(xiàn)：
[1] LangChain官方網(wǎng)站：https://www.langchain.com/
[2] LangChain ????? 中文網(wǎng)，跟著LangChain一起學(xué)LLM/GPT開發(fā)：https://www.langchain.com.cn/
[3] LangChain中文網(wǎng) - LangChain 是一個(gè)用于開發(fā)由語言模型驅(qū)動(dòng)的應(yīng)用程序的框架：http://www.cnlangchain.com/文章來源地址http://www.zghlxwxcb.cn/news/detail-648286.html

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