fromlangchain.chat_modelsimportChatOpenAI#创建llmllm=ChatOpenAI(temperature=0)llm llm的相关参数 这里我们创建的openai的llm默认使用了“gpt-3.5-turbo”模型,同时我们还设置了temperature参数为0,这样做是为了降低llm给出答案的随机性。下面我们来创建一个检索问答链(RetrievalQA),然后我们将llm和检索器(retriever)...
from langchain.chains import create_sql_query_chain from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool # 执行查询动作 execute_query = QuerySQLDataBaseTool(db=db) # 获取sql...
import os from dotenv import load_dotenv,find_dotenv load_dotenv(find_dotenv()) prompt = "Your Prompt Here" OpenAI_key = os.environ.get("OPEN_AI_KEY") print(OpenAI_token) from langchain.llms import OpenAI llm=openai.Completion.create(model_name="text-davinci-003",temperature...
Error 2: Invalid response object from API Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.._completion_with_retry in 4.0 seconds as it raised APIError: Invalid response object from API: '{"object":"error","message":"Expecting value: line 1 column 1 (char ...
from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain.vectorstores import Weaviate from langchain.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain.prompts import PromptTemplate from langchain.embeddings.voyageai import VoyageEmbedding...
So, the correct import statement should be: fromlangchain.chat_models.azure_openaiimportAzureChatOpenAI Please replace 'AzureOpenAI' with 'AzureChatOpenAI' in your code and try again. If you're still facing issues, it might be due to a problem with your Python environment or a bug in the...
You should have a very good reason and know why you are using langchain. Not just because you stumbled upon it. You can look at the API reference links on the sidebar of the forum for ways of interacting directly with OpenAI models with little extra code. ...
您的示例来自LangChain 快速入门教程的提示模板部分。我没有发现任何差异,所以它应该按给定的方式工作。 我自己尝试了该示例,并使用一个附加循环来输出由以下命令创建的消息chat_prompt.format_messages: from langchain.prompts.chat import ChatPromptTemplate ...
from langchain.embeddings.openai import OpenAIEmbeddings embedding = OpenAIEmbeddings(openai_api_key=api_key) db = Chroma(persist_directory="embeddings\\",embedding_function=embedding) The embedding_function parameter accepts OpenAI embedding object that serves the purpose. ...
classLangChainService:def__init__(self, api_key): self.llm = OpenAI(api_key=api_key) self.chat_model = ChatOpenAI(api_key=api_key) self.memory = ConversationBufferMemory() self.prompt_template = ChatPromptTemplate(messages=[ SystemMessagePromptTemplate.from_template("Prompt here"), ...