代码如下: PYTHON_CODE="""def hello_langchain():print("Hello, Langchain!")# Call the functionhello_langchain()"""python_splitter=RecursiveCharacterTextSplitter.from_language(language=Language.PYTHON,chunk_size=50,chunk_overlap=0)python_docs=python_splitter.create_documents([PYTHON_CODE])python_doc...
首先,让我们导入一些通用类,我们肯定会用到的。 from langchain.chains import RetrievalQA from langchain.llms import OpenAI 然后,在通用设置中,让我们指定我们想要使用的文档加载程序。您可以在这里下载state_of_the_union.txt文件。 from langchain.document_loaders import TextLoader loader = TextLoader('../s...
openai_api_key= openai_gpt_key, )# Iterating through the questions listforquestionin(questionList): qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs={"prompt": custom_prompt_template}, ) res = qa({'query...
RetrievalQAWithSourcesChain is an extension of RetrievalQA that chained together multiple sources of information, providing context and transparency in constructing comprehensive answers. Load_qa_chain loads a pre-trained question-answering chain, specifying language model and chain type, suitable for appli...
。当我使用查询输入运行 QA 链时,此错误不断出现: ---> result = qa_chain({'query': question}) ValueError: Missing some input keys: {'query'} 这是我的代码: from langchain.chains import RetrievalQA from langchain.prompts import PromptTemplate # Build prompt template = """Given the followi...
5、构建并初始化RetrievalQA 准备好提示模板和C Transformers LLM后，我们还需要编写了三个函数来构建LangChain RetrievalQA对象，该对象使我们能够执行文档问答。from langchain import PromptTemplatefrom langchain.chains import RetrievalQAfrom langchain.embeddings import HuggingFaceEmbeddingsfrom langchain.vectorstores ...
chains import RetrievalQA from langchain.document_loaders import TextLoader from langchain.embeddings.openai import OpenAIEmbeddings llm = OpenAI(temperature=0, openai_api_key=openai_api_key) 代码语言:javascript 代码运行次数:0 复制Cloud Studio 代码运行 loader = TextLoader('wonderland.txt') # 载入...
创建RetrievalQA 检索 在文本分割这个任务中,LangChain 支持了多种分割方式,例如按字符数的 CharacterTextSplitter,针对 Markdown 文档的 MarkdownTextSplitter,以及利用递归方法的 RecursiveCharacterTextSplitter,当然你也可以通过继成 TextSplitter 父类来实现自定义的 split_text 方法,例如在中文文档中,我们可以采用按每...
4)自定义QA的prompt,通过RetrievalQA回答相关的问题 from langchain.chains import RetrievalQA from langchain.document_loaders import WebBaseLoader from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.prompts import PromptTemplate ...
4)自定义QA的prompt,通过RetrievalQA回答相关的问题 from langchain.chains import RetrievalQA from langchain.document_loaders import WebBaseLoader from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.prompts import PromptTemplate ...