分类目录：《大模型从入门到应用》总目录
LangChain系列文章：
- 基础知识
- 快速入门
- 安装与环境配置
- 链（Chains）、代理（Agent:）和记忆（Memory）
- 快速开发聊天模型
- 模型（Models）
- 基础知识
- 大型语言模型（LLMs）
- 基础知识
- LLM的异步API、自定义LLM包装器、虚假LLM和人类输入LLM（Human Input LLM）
- 缓存LLM的调用结果
- 加载与保存LLM类、流式传输LLM与Chat Model响应和跟踪tokens使用情况
- 聊天模型（Chat Models）
- 基础知识
- 使用少量示例和响应流式传输
- 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）
- 基础知识
- 提示模板
- 基础知识
- 连接到特征存储
- 创建自定义提示模板和含有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
- 示例选择器（Example Selectors）
- 输出解析器（Output Parsers）
- 记忆（Memory）
- 基础知识
- 记忆的类型
- 会话缓存记忆、会话缓存窗口记忆和实体记忆
- 对话知识图谱记忆、对话摘要记忆和会话摘要缓冲记忆
- 对话令牌缓冲存储器和基于向量存储的记忆
- 将记忆添加到LangChain组件中
- 自定义对话记忆与自定义记忆类
- 聊天消息记录
- 记忆的存储与应用
- 索引（Indexes）
- 基础知识
- 文档加载器（Document Loaders）
- 文本分割器（Text Splitters）
- 向量存储器（Vectorstores）
- 检索器（Retrievers）
- 链（Chains）
- 基础知识
- 通用功能
- 自定义Chain和Chain的异步API
- LLMChain和RouterChain
- SequentialChain和TransformationChain
- 链的保存（序列化）与加载（反序列化）
- 链与索引
- 文档分析和基于文档的聊天
- 问答的基础知识
- 图问答（Graph QA）和带来源的问答（Q&A with Sources）
- 检索式问答
- 文本摘要（Summarization）、HyDE和向量数据库的文本生成
- 代理（Agents）
- 基础知识
- 代理类型
- 自定义代理（Custom Agent）
- 自定义MRKL代理
- 带有ChatModel的LLM聊天自定义代理和自定义多操作代理（Custom MultiAction Agent）
- 工具
- 基础知识
- 自定义工具（Custom Tools）
- 多输入工具和工具输入模式
- 人工确认工具验证和Tools作为OpenAI函数
- 工具包（Toolkit）
- 代理执行器（Agent Executor）
- 结合使用Agent和VectorStore
- 使用Agents的异步API和创建ChatGPT克隆
- 处理解析错误、访问中间步骤和限制最大迭代次数
- 为代理程序设置超时时间和限制最大迭代次数和为代理程序和其工具添加共享内存
- 计划与执行
- 回调函数（Callbacks）
图问答（Graph QA）
创建图
在本节中，我们构建一个示例图。目前，这对于较小的文本片段效果最好，下面的示例中我们只使用一个小片段，因为提取知识三元组对硬件有一定要求：
from langchain.indexes import GraphIndexCreator
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader
index_creator = GraphIndexCreator(llm=OpenAI(temperature=0))
with open("../../state_of_the_union.txt") as f:
all_text = f.read()
text = "\n".join(all_text.split("\n\n")[105:108])
text
输出：
'It won’t look like much, but if you stop and look closely, you’ll see a “Field of dreams,” the ground on which America’s future will be built. \nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor “mega site”. \nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. '
我们可以创建图并查看：
graph.get_triples()
输出：
[('Intel', '$20 billion semiconductor "mega site"', 'is going to build'),
('Intel', 'state-of-the-art factories', 'is building'),
('Intel', '10,000 new good-paying jobs', 'is creating'),
('Intel', 'Silicon Valley', 'is helping build'),
('Field of dreams',
"America's future will be built",
'is the ground on which')]
查询图
现在我们可以使用图问答链来向图中提问：
from langchain.chains import GraphQAChain
chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)
chain.run("what is Intel going to build?")
日志输出：
> Entering new GraphQAChain chain...
Entities Extracted:
Intel
Full Context:
Intel is going to build $20 billion semiconductor "mega site"
Intel is building state-of-the-art factories
Intel is creating 10,000 new good-paying jobs
Intel is helping build Silicon Valley
> Finished chain.
输出：
' Intel is going to build a $20 billion semiconductor "mega site" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'
保存图
我们还可以保存和加载图：
from langchain.indexes.graph import NetworkxEntityGraph
graph.write_to_gml("graph.gml")
loaded_graph = NetworkxEntityGraph.from_gml("graph.gml")
loaded_graph.get_triples()
输出：
[('Intel', '$20 billion semiconductor "mega site"', 'is going to build'),
('Intel', 'state-of-the-art factories', 'is building'),
('Intel', '10,000 new good-paying jobs', 'is creating'),
('Intel', 'Silicon Valley', 'is helping build'),
('Field of dreams',
"America's future will be built",
'is the ground on which')]
带来源的问答（Q&A with Sources）
本节介绍如何使用LangChain在文档列表上进行带来源的问答。它涵盖了四种不同的链式类型：stuff、map_reduce、refine和map-rerank。有关这些链式类型的更详细解释，可以参考文章《大模型从入门到应用——LangChain：链（Chains）-[链与索引：问答的基础知识]》。
准备数据
首先，我们需要准备数据。在本示例中，我们对一个向量数据库进行相似性搜索，但这些文档可以以任何方式获取，本节的重点是强调在获取文档后的步骤。
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
日志输出：
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
输入：
query = "What did the president say about Justice Breyer"
docs = docsearch.similarity_search(query)
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
stuff类型的链
本节展示了使用stuff类型的链进行带来源的问答的结果。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
自定义提示
我们还可以在stuff类型的链中使用自定义提示，在下面这个示例中，我们将用意大利语回答：
template = """Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
Respond in Italian.
QUESTION: {question}
=========
{summaries}
=========
FINAL ANSWER IN ITALIAN:"""
PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'output_text': '\nNon so cosa abbia detto il presidente riguardo a Justice Breyer.\nSOURCES: 30, 31, 33'}
map_reduce 类型的链
本节展示了使用map_reduce 类型的链进行带来源的问答的结果。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
中间步骤
我们还可以返回map_reduce 类型的链的中间步骤，以供检查。这可以通过设置return_intermediate_steps变量来实现。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'intermediate_steps': [' "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service."',
' None',
' None',
' None'],
'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
自定义提示
我们还可以在map_reduce 类型的链中使用自定义提示，在下面这个示例中，我们将用意大利语回答：
question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.
Return any relevant text in Italian.
{context}
Question: {question}
Relevant text, if any, in Italian:"""
QUESTION_PROMPT = PromptTemplate(
template=question_prompt_template, input_variables=["context", "question"]
)
combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
Respond in Italian.
QUESTION: {question}
=========
{summaries}
=========
FINAL ANSWER IN ITALIAN:"""
COMBINE_PROMPT = PromptTemplate(
template=combine_prompt_template, input_variables=["summaries", "question"]
)
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.",
' Non pertinente.',
' Non rilevante.',
" Non c'è testo pertinente."],
'output_text': ' Non conosco la risposta. SOURCES: 30, 31, 33, 20.'}
批处理大小
在使用map_reduce链时，要注意的一点是在映射步骤中使用的批处理大小。如果批处理大小过大，可能会导致速率限制错误。您可以通过设置所使用的 LLM 的批处理大小来控制此参数。请注意，这仅适用于具有此参数的 LLM。以下是一个设置批处理大小的示例：
llm = OpenAI(batch_size=5, temperature=0)
refine类型的链
本部分展示了使用refine类型的链进行带来源的问答的结果。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="refine")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'output_text': "\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked him for his service and praised his career as a top litigator in private practice, a former federal public defender, and a family of public school educators and police officers. He noted Justice Breyer's reputation as a consensus builder and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of securing the border and fixing the immigration system in order to advance liberty and justice, and mentioned the new technology, joint patrols, dedicated immigration judges, and commitments to support partners in South and Central America that have been put in place. He also expressed his commitment to the LGBTQ+ community, noting the need for the bipartisan Equality Act and the importance of protecting transgender Americans from state laws targeting them. He also highlighted his commitment to bipartisanship, noting the 80 bipartisan bills he signed into law last year, and his plans to strengthen the Violence Against Women Act. Additionally, he announced that the Justice Department will name a chief prosecutor for pandemic fraud and his plan to lower the deficit by more than one trillion dollars in a"}
中间步骤
我们还可以返回refine类型的链的中间步骤，以供检查。这可以通过设置return_intermediate_steps变量来实现。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'intermediate_steps': ['\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service.',
'\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and a family of public school educators and police officers. He praised Justice Breyer for being a consensus builder and for receiving a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also noted that in order to advance liberty and justice, it was necessary to secure the border and fix the immigration system, and that the government was taking steps to do both. \n\nSource: 31',
'\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and a family of public school educators and police officers. He praised Justice Breyer for being a consensus builder and for receiving a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also noted that in order to advance liberty and justice, it was necessary to secure the border and fix the immigration system, and that the government was taking steps to do both. He also mentioned the need to pass the bipartisan Equality Act to protect LGBTQ+ Americans, and to strengthen the Violence Against Women Act that he had written three decades ago. \n\nSource: 31, 33',
'\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and a family of public school educators and police officers. He praised Justice Breyer for being a consensus builder and for receiving a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also noted that in order to advance liberty and justice, it was necessary to secure the border and fix the immigration system, and that the government was taking steps to do both. He also mentioned the need to pass the bipartisan Equality Act to protect LGBTQ+ Americans, and to strengthen the Violence Against Women Act that he had written three decades ago. Additionally, he mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole billions in relief money meant for small businesses and millions of Americans. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud. \n\nSource: 20, 31, 33'],
'output_text': '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and a family of public school educators and police officers. He praised Justice Breyer for being a consensus builder and for receiving a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also noted that in order to advance liberty and justice, it was necessary to secure the border and fix the immigration system, and that the government was taking steps to do both. He also mentioned the need to pass the bipartisan Equality Act to protect LGBTQ+ Americans, and to strengthen the Violence Against Women Act that he had written three decades ago. Additionally, he mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole billions in relief money meant for small businesses and millions of Americans. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud. \n\nSource: 20, 31, 33'}
自定义提示
我们还可以在refine类型的链中使用自定义提示，在下面这个示例中，我们将用意大利语回答：
refine_template = (
"The original question is as follows: {question}\n"
"We have provided an existing answer, including sources: {existing_answer}\n"
"We have the opportunity to refine the existing answer"
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question (in Italian)"
"If you do update it, please update the sources as well. "
"If the context isn't useful, return the original answer."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_template,
)
question_template = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question in Italian: {question}\n"
)
question_prompt = PromptTemplate(
input_variables=["context_str", "question"], template=question_template
)
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True, question_prompt=question_prompt, refine_prompt=refine_prompt)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
输出：
{'intermediate_steps': ['\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera.',
"\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione. Ha anche menzionato le nuove tecnologie come scanner all'avanguardia per rilevare meglio il traffico di droga, le pattuglie congiunte con Messico e Guatemala per catturare più trafficanti di esseri umani, l'istituzione di giudici di immigrazione dedicati per far sì che le famiglie che fuggono da per",
"\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione. Ha anche menzionato le nuove tecnologie come scanner all'avanguardia per rilevare meglio il traffico di droga, le pattuglie congiunte con Messico e Guatemala per catturare più trafficanti di esseri umani, l'istituzione di giudici di immigrazione dedicati per far sì che le famiglie che fuggono da per",
"\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione. Ha anche menzionato le nuove tecnologie come scanner all'avanguardia per rilevare meglio il traffico di droga, le pattuglie congiunte con Messico e Guatemala per catturare più trafficanti di esseri umani, l'istituzione di giudici di immigrazione dedicati per far sì che le famiglie che fuggono da per"],
'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione. Ha anche menzionato le nuove tecnologie come scanner all'avanguardia per rilevare meglio il traffico di droga, le pattuglie congiunte con Messico e Guatemala per catturare più trafficanti di esseri umani, l'istituzione di giudici di immigrazione dedicati per far sì che le famiglie che fuggono da per"}
map-rerank 类型的链
本节展示了使用map-rerank 类型的链进行带来源的问答的结果。
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_rerank", metadata_keys=['source'], return_intermediate_steps=True)
query = "What did the president say about Justice Breyer"
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
result["output_text"]
输出：
' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'
输入：
result["intermediate_steps"]
输出：
[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
'score': '100'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'}]
自定义提示
我们还可以在map-rerank 类型的链中使用自定义提示，在下面这个示例中，我们将用意大利语回答：
from langchain.output_parsers import RegexParser
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
Question: [question here]
Helpful Answer In Italian: [answer here]
Score: [score between 0 and 100]
Begin!
Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
output_parser=output_parser,
)
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_rerank", metadata_keys=['source'], return_intermediate_steps=True, prompt=PROMPT)
query = "What did the president say about Justice Breyer"
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
result
输出：文章来源：https://www.toymoban.com/news/detail-677408.html
{'source': 30,
'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',
'score': '100'},
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
'score': '100'},
{'answer': ' Non so.', 'score': '0'},
{'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',
'score': '100'}],
'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}
参考文献：
[1] LangChain官方网站：https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发：https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架：http://www.cnlangchain.com/文章来源地址https://www.toymoban.com/news/detail-677408.html
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