Summarization
在Colab中打开
Use case
假设您有一组文件（PDF，Notion页面，客户问题等），并且希望总结其中的内容。
LLM在理解和综合文本方面非常擅长，因此是一个很好的工具。
在这个教程中，我们将讨论如何使用LLMs进行文档摘要。
Overview
构建摘要器的一个核心问题是如何将您的文档传递到LLM的上下文窗口中。两种常见的方法是：
- 将您所有的文件都简单地“stuff”到一个单一提示中。 这是最简单的方法（有关create_stuff_documents_chain构造函数的更多信息，请参阅此处，该构造函数用于此方法）。
- 将每个文档都在“映射”步骤中总结，并将这些摘要“减少”到最终总结中（请参阅MapReduceDocumentsChain，该方法中使用了此方法）。
Quickstart
为了给您一个预览，任何一个管道都可以被包装在一个单一的对象中：load_summarize_chain。
假设我们想要总结一篇博客文章。我们可以用几行代码来实现。
首先设置环境变量并安装软件包：
%pip install --upgrade --quiet langchain-openai tiktoken chromadb langchain
# Set env var OPENAI_API_KEY or load from a .env file
# import dotenv
# dotenv.load_dotenv()
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我们可以使用 "chain_type="stuff"" ，尤其是在使用较大上下文窗口模型时。
- 16k token 是 OpenAI 推出的神经网络模型名称为 gpt-3.5-turbo-1106。
- 100k 代币 Anthropic Claude-2
我们还可以提供 chain_type="map_reduce" 或 chain_type="refine"。
from langchain.chains.summarize import load_summarize_chain
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import ChatOpenAI
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
docs = loader.load()
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-1106")
chain = load_summarize_chain(llm, chain_type="stuff")
chain.run(docs)
'The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains. It also highlights the challenges and limitations of using LLMs in agent systems.'
Option 1. Stuff
当我们使用 load_summarize_chain 时，带 chain_type="stuff"，我们将使用 StuffDocumentsChain。
该链将获取一个文档列表，将它们全部插入到一个提示中，并将该提示传递给一个LLM：
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain_core.prompts import PromptTemplate
# Define prompt
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
# Define LLM chain
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k")
llm_chain = LLMChain(llm=llm, prompt=prompt)
# Define StuffDocumentsChain
stuff_chain = StuffDocumentsChain(llm_chain=llm_chain, document_variable_name="text")
docs = loader.load()
print(stuff_chain.run(docs))
The article discusses the concept of building autonomous agents powered by large language models (LLMs). It explores the components of such agents, including planning, memory, and tool use. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains, such as scientific discovery and generative agents simulation. It also highlights the challenges and limitations of using LLMs in agent systems.
很好！我们可以看到使用 load_summarize_chain 复现了之前的结果。
Go deeper
- 您可以轻松自定义提示。
- 您可以通过 llm 参数轻松尝试不同的 LLMs（例如，Claude）。
Option 2. Map-Reduce
让我们来解析 Map Reduce 方法。为此，我们首先将每个文档通过 LLMChain 映射到一个个体摘要。然后我们将使用 ReduceDocumentsChain 将这些摘要合并为一个全局摘要。
首先，我们指定用于将每个文档映射到个人摘要的 LLMChain：
from langchain.chains import MapReduceDocumentsChain, ReduceDocumentsChain
from langchain_text_splitters import CharacterTextSplitter
llm = ChatOpenAI(temperature=0)
# Map
map_template = """The following is a set of documents
{docs}
Based on this list of docs, please identify the main themes
Helpful Answer:"""
map_prompt = PromptTemplate.from_template(map_template)
map_chain = LLMChain(llm=llm, prompt=map_prompt)
我们也可以使用提示中心来存储和获取提示。
这将适用于您的LangSmith API密钥。
例如，查看地图提示 这里 。
from langchain import hub
map_prompt = hub.pull("rlm/map-prompt")
map_chain = LLMChain(llm=llm, prompt=map_prompt)
减少文档链 处理 将文档映射结果归约为单个输出的过程。它包装了一个通用的 组合文档链（类似 StuffDocumentsChain），但增加了在将其传递给 组合文档链之前折叠文档的能力，如果它们的累积大小超过了 token_max。在这个例子中，我们实际上可以重复使用用于组合我们的文档的链，也可以用来折叠我们的文档。
如果我们映射的文件中的累积令牌数超过4000个，则我们将递归地将文档分批处理，每批\< 4000个令牌，以创建批处理摘要。 一旦这些批处理摘要的累积令牌少于4000个，我们将最后一次将它们全部传递给StuffDocumentsChain来创建最终摘要。
# Reduce
reduce_template = """The following is set of summaries:
{docs}
Take these and distill it into a final, consolidated summary of the main themes.
Helpful Answer:"""
reduce_prompt = PromptTemplate.from_template(reduce_template)
# Note we can also get this from the prompt hub, as noted above
reduce_prompt = hub.pull("rlm/map-prompt")
reduce_prompt
ChatPromptTemplate(input_variables=['docs'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['docs'], template='The following is a set of documents:\n{docs}\nBased on this list of docs, please identify the main themes \nHelpful Answer:'))])
# Run chain
reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
# Takes a list of documents, combines them into a single string, and passes this to an LLMChain
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_chain, document_variable_name="docs"
)
# Combines and iteratively reduces the mapped documents
reduce_documents_chain = ReduceDocumentsChain(
# This is final chain that is called.
combine_documents_chain=combine_documents_chain,
# If documents exceed context for `StuffDocumentsChain`
collapse_documents_chain=combine_documents_chain,
# The maximum number of tokens to group documents into.
token_max=4000,
)
将我们的 map 和 reduce 链组合成一个：
# Combining documents by mapping a chain over them, then combining results
map_reduce_chain = MapReduceDocumentsChain(
# Map chain
llm_chain=map_chain,
# Reduce chain
reduce_documents_chain=reduce_documents_chain,
# The variable name in the llm_chain to put the documents in
document_variable_name="docs",
# Return the results of the map steps in the output
return_intermediate_steps=False,
)
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=0
)
split_docs = text_splitter.split_documents(docs)
Created a chunk of size 1003, which is longer than the specified 1000
print(map_reduce_chain.run(split_docs))
Based on the list of documents provided, the main themes can be identified as follows:
1. LLM-powered autonomous agents: The documents discuss the concept of building agents with LLM as their core controller and highlight the potential of LLM beyond generating written content. They explore the capabilities of LLM as a general problem solver.
2. Agent system overview: The documents provide an overview of the components that make up a LLM-powered autonomous agent system, including planning, memory, and tool use. Each component is explained in detail, highlighting its role in enhancing the agent's capabilities.
3. Planning: The documents discuss how the agent breaks down large tasks into smaller subgoals and utilizes self-reflection to improve the quality of its actions and results.
4. Memory: The documents explain the importance of both short-term and long-term memory in an agent system. Short-term memory is utilized for in-context learning, while long-term memory allows the agent to retain and recall information over extended periods.
5. Tool use: The documents highlight the agent's ability to call external APIs for additional information and resources that may be missing from its pre-trained model weights. This includes accessing current information, executing code, and retrieving proprietary information.
6. Case studies and proof-of-concept examples: The documents provide examples of how LLM-powered autonomous agents can be applied in various domains, such as scientific discovery and generative agent simulations. These case studies serve as examples of the capabilities and potential applications of such agents.
7. Challenges: The documents acknowledge the challenges associated with building and utilizing LLM-powered autonomous agents, although specific challenges are not mentioned in the given set of documents.
8. Citation and references: The documents include a citation and reference section, indicating that the information presented is based on existing research and sources.
Overall, the main themes in the provided documents revolve around LLM-powered autonomous agents, their components and capabilities, planning, memory, tool use, case studies, and challenges.
Go deeper
定制化
- 如上所示，您可以自定义LLMs和用于映射和reduce阶段的提示信息。
现实世界使用案例
- 请查看这篇博客文章，分析用户互动（关于LangChain文档的问题）的案例研究！
- 这篇博客文章和相关 repo 还介绍了聚类作为一种总结手段。
- 这打开了第三条值得考虑的路径，超越了`stuff`或`map-reduce`方法。
Option 3. Refine
RefineDocumentsChain类似于map-reduce:
精炼文档链通过循环遍历输入的文档，并迭代地更新其答案来构建响应。对于每个文档，它将所有非文档输入、当前文档和最新的中间答案传递给LLM链来获取新的答案。
这个可以很容易地通过指定 chain_type="refine" 来运行。
chain = load_summarize_chain(llm, chain_type="refine")
chain.run(split_docs)
'The article explores the concept of building autonomous agents powered by large language models (LLMs) and their potential as problem solvers. It discusses different approaches to task decomposition, the integration of self-reflection into LLM-based agents, and the use of external classical planners for long-horizon planning. The new context introduces the Chain of Hindsight (CoH) approach and Algorithm Distillation (AD) for training models to produce better outputs. It also discusses different types of memory and the use of external memory for fast retrieval. The article explores the concept of tool use and introduces the MRKL system and experiments on fine-tuning LLMs to use external tools. It introduces HuggingGPT, a framework that uses ChatGPT as a task planner, and discusses the challenges of using LLM-powered agents in real-world scenarios. The article concludes with case studies on scientific discovery agents and the use of LLM-powered agents in anticancer drug discovery. It also introduces the concept of generative agents that combine LLM with memory, planning, and reflection mechanisms. The conversation samples provided discuss the implementation of a game architecture and the challenges in building LLM-centered agents. The article provides references to related research papers and resources for further exploration.'
可以提供提示并返回中间步骤。
prompt_template = """Write a concise summary of the following:
{text}
CONCISE SUMMARY:"""
prompt = PromptTemplate.from_template(prompt_template)
refine_template = (
"Your job is to produce a final summary\n"
"We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary in Italian"
"If the context isn't useful, return the original summary."
)
refine_prompt = PromptTemplate.from_template(refine_template)
chain = load_summarize_chain(
llm=llm,
chain_type="refine",
question_prompt=prompt,
refine_prompt=refine_prompt,
return_intermediate_steps=True,
input_key="input_documents",
output_key="output_text",
)
result = chain({"input_documents": split_docs}, return_only_outputs=True)
print(result["output_text"])
Il presente articolo discute il concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. Esplora i diversi componenti di un sistema di agenti alimentato da LLM, tra cui la pianificazione, la memoria e l'uso degli strumenti. Dimostrazioni di concetto come AutoGPT mostrano il potenziale di LLM come risolutore generale di problemi. Approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorarsi iterativamente. Tuttavia, ci sono sfide da affrontare, come la limitata capacità di contesto che limita l'inclusione di informazioni storiche dettagliate e la difficoltà di pianificazione a lungo termine e decomposizione delle attività. Inoltre, l'affidabilità dell'interfaccia di linguaggio naturale tra LLM e componenti esterni come la memoria e gli strumenti è incerta, poiché i LLM possono commettere errori di formattazione e mostrare comportamenti ribelli. Nonostante ciò, il sistema AutoGPT viene menzionato come esempio di dimostrazione di concetto che utilizza LLM come controller principale per agenti autonomi. Questo articolo fa riferimento a diverse fonti che esplorano approcci e applicazioni specifiche di LLM nell'ambito degli agenti autonomi.
print("\n\n".join(result["intermediate_steps"][:3]))
This article discusses the concept of building autonomous agents using LLM (large language model) as the core controller. The article explores the different components of an LLM-powered agent system, including planning, memory, and tool use. It also provides examples of proof-of-concept demos and highlights the potential of LLM as a general problem solver.
Questo articolo discute del concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. L'articolo esplora i diversi componenti di un sistema di agenti alimentato da LLM, inclusa la pianificazione, la memoria e l'uso degli strumenti. Vengono forniti anche esempi di dimostrazioni di proof-of-concept e si evidenzia il potenziale di LLM come risolutore generale di problemi. Inoltre, vengono presentati approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion che consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorare iterativamente.
Questo articolo discute del concetto di costruire agenti autonomi utilizzando LLM (large language model) come controller principale. L'articolo esplora i diversi componenti di un sistema di agenti alimentato da LLM, inclusa la pianificazione, la memoria e l'uso degli strumenti. Vengono forniti anche esempi di dimostrazioni di proof-of-concept e si evidenzia il potenziale di LLM come risolutore generale di problemi. Inoltre, vengono presentati approcci come Chain of Thought, Tree of Thoughts, LLM+P, ReAct e Reflexion che consentono agli agenti autonomi di pianificare, riflettere su se stessi e migliorare iterativamente. Il nuovo contesto riguarda l'approccio Chain of Hindsight (CoH) che permette al modello di migliorare autonomamente i propri output attraverso un processo di apprendimento supervisionato. Viene anche presentato l'approccio Algorithm Distillation (AD) che applica lo stesso concetto alle traiettorie di apprendimento per compiti di reinforcement learning.
Splitting and summarizing in a single chain
为了方便起见，我们可以将长文档的文本拆分和摘要汇总封装在一个 AnalyzeDocumentsChain 中。
from langchain.chains import AnalyzeDocumentChain
summarize_document_chain = AnalyzeDocumentChain(
combine_docs_chain=chain, text_splitter=text_splitter
)
summarize_document_chain.run(docs[0].page_content)