StrOutputParser 输出解析器 字符串输出处理, 可以直接获取到content的内容。 from langchain_core.output_parsers import StrOutputParser output_parser = StrOutputParser() chain = prompt | llm | output_parser chain.invoke({"input": "
from langchain_core.output_parsers import PydanticOutputParser, JsonOutputParser from langchain_core.prompts import PromptTemplate from langchain_openai import OpenAI, ChatOpenAI from pydantic import BaseModel, Field, model_validator 1. 2. 3. 4. 5. 6. 7. 8. 9. 3.2 定义模型 model = ChatOpen...
StrOutputParser 类的正确导入方式应该是 from langchain_core.output_parsers import StrOutputParser。 在LangChain 框架中,StrOutputParser 是一个用于解析模型输出并提取字符串内容的输出解析器。如果你尝试使用 from langchain_core.output_parsers import stroutputparser 来导入这个类,Python 解释器会抛出一个 Import...
fromtypingimportListfromlangchain.output_parsersimportPydanticOutputParserfromlangchain.promptsimportChatPromptTemplatefromlangchain.schemaimportHumanMessagefromlangchain_core.pydantic_v1importBaseModel, Fieldfromlangchain_openaiimportChatOpenAIclassBookInfo(BaseModel): book_name:str= Field(description="书籍的名字"...
#用 Pydantic 定义输出的 JSON 格式fromlangchain_core.pydantic_v1importBaseModel, Fieldfromlangchain_core.output_parsersimportJsonOutputParserfromlangchain_openaiimportChatOpenAIfromlangchain_core.promptsimportChatPromptTemplate# Define your desired data structure.classJoke(BaseModel): ...
from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.output_parsers import StrOutputParser llm = Ollama(model="llama3:8b") ## 提示词输入 messages = [ SystemMessage(content="Translate the following from English into Italian"), ...
fromtypingimportListfromlangchain.output_parsersimportPydanticOutputParserfromlangchain.promptsimportChatPromptTemplatefromlangchain.schemaimportHumanMessagefromlangchain_core.pydantic_v1importBaseModel, Fieldfromlangchain_openaiimportChatOpenAIclassBookInfo(BaseModel): ...
from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough from langchain_openai import OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter # Load, chunk and index the contents of the blog. ...
from langchain_core.output_parsers import StrOutputParserfrom langchain_core.prompts import PromptTemplatefrom langchain_openai import ChatOpenAIllm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.5, max_tokens=200)summarizing_prompt_template = """输出为 JSON 格式,包含字段 content、summary。
from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from langchain.output_parsers.json import SimpleJsonOutputParser model = ChatOpenAI( model="gpt-4o", model_kwargs={ "response_format": { "type": "json_object" } }, ...