创建公司网站需要注意什么,wordpress 导航 代码,东营会计信息网官网,网站开发合同受托方分类目录#xff1a;《自然语言处理从入门到应用》总目录 本文介绍了如何将链保存#xff08;序列化#xff09;到磁盘和从磁盘加载#xff08;反序列化#xff09;。我们使用的序列化格式是json或yaml。目前#xff0c;只有一些链支持这种类型的序列化。随着时间的推移《自然语言处理从入门到应用》总目录 本文介绍了如何将链保存序列化到磁盘和从磁盘加载反序列化。我们使用的序列化格式是json或yaml。目前只有一些链支持这种类型的序列化。随着时间的推移我们将增加支持的链条数量。
将链保存序列化到磁盘
首先让我们可以使用.save方法将链保存到磁盘并指定一个带有json或yaml扩展名的文件路径。
from langchain import PromptTemplate, OpenAI, LLMChain
template Question: {question}Answer: Lets think step by step.
prompt PromptTemplate(templatetemplate, input_variables[question])
llm_chain LLMChain(promptprompt, llmOpenAI(temperature0), verboseTrue)llm_chain.save(llm_chain.json)现在让我们来看看保存的文件中的内容
!cat llm_chain.json输出
{memory: null,verbose: true,prompt: {input_variables: [question],output_parser: null,template: Question: {question}\n\nAnswer: Lets think step by step.,template_format: f-string},llm: {model_name: text-davinci-003,temperature: 0.0,max_tokens: 256,top_p: 1,frequency_penalty: 0,presence_penalty: 0,n: 1,best_of: 1,request_timeout: null,logit_bias: {},_type: openai},output_key: text,_type: llm_chain
}从磁盘加载反序列化链
我们可以使用load_chain方法从磁盘加载链
from langchain.chains import load_chain
chain load_chain(llm_chain.json)
chain.run(whats 2 2)日志输出 Entering new LLMChain chain...
Prompt after formatting:
Question: whats 2 2Answer: Lets think step by step. Finished chain.输出 2 2 4分别保存组件
在上面的例子中我们可以看到提示和LLM配置信息与整个链条保存在同一个json中但我们也可以将它们分开保存。这通常有助于使保存的组件更加模块化。为了做到这一点我们只需要指定llm_path而不是llm组件并且指定prompt_path而不是prompt组件。
llm_chain.prompt.save(prompt.json)输入
!cat prompt.json输出
{input_variables: [question],output_parser: null,template: Question: {question}\n\nAnswer: Lets think step by step.,template_format: f-string
}输入
llm_chain.llm.save(llm.json)输入
!cat llm.json输出
{model_name: text-davinci-003,temperature: 0.0,max_tokens: 256,top_p: 1,frequency_penalty: 0,presence_penalty: 0,n: 1,best_of: 1,request_timeout: null,logit_bias: {},_type: openai
}输入
config {memory: None,verbose: True,prompt_path: prompt.json,llm_path: llm.json,output_key: text,_type: llm_chain
}import jsonwith open(llm_chain_separate.json, w) as f:json.dump(config, f, indent2)输入
!cat llm_chain_separate.json输出
{memory: null,verbose: true,prompt_path: prompt.json,llm_path: llm.json,output_key: text,_type: llm_chain
}我们可以以相同的方式加载它
chain load_chain(llm_chain_separate.json)
chain.run(whats 2 2)日志输出 Entering new LLMChain chain...
Prompt after formatting:
Question: whats 2 2Answer: Lets think step by step. Finished chain.输出 2 2 4从LangChainHub加载
本节介绍如何从LangChainHub加载链。
from langchain.chains import load_chainchain load_chain(lc://chains/llm-math/chain.json)
chain.run(whats 2 raised to .12)日志输出 Entering new LLMMathChain chain...
whats 2 raised to .12
Answer: 1.0791812460476249Finished chain.输出
Answer: 1.0791812460476249有时候链会需要额外的参数这些参数在链序列化时未包含在内。例如一个用于对向量数据库进行问答的链条将需要一个向量数据库作为参数。
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader
loader TextLoader(../../state_of_the_union.txt)
documents loader.load()
text_splitter CharacterTextSplitter(chunk_size1000, chunk_overlap0)
texts text_splitter.split_documents(documents)embeddings OpenAIEmbeddings()
vectorstore Chroma.from_documents(texts, embeddings)
# Running Chroma using direct local API.
# Using DuckDB in-memory for database. Data will be transient.chain load_chain(lc://chains/vector-db-qa/stuff/chain.json, vectorstorevectorstore)
query What did the president say about Ketanji Brown Jackson
chain.run(query)输出 The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nations top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyers legacy of excellence.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/
