pull model via Ollama first however. OP's codefrom langchain_openai import ChatOpenAI llm = ChatOpenAI( api_key="ollama", model="llama3:8b-instruct-fp16", base_url="http://localhost:11434/v1", )should work as l
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 查询语句 write_query = create_sql_query_chain(llm...
接下来我们需要先加载一下在之前的博客 让Langchain与你的数据对话(二):向量存储 与嵌入中我们在本地创建的关于吴恩达老师的机器学习课程cs229课程讲义(pdf)的向量数据库: from langchain.vectorstores import Chroma from langchain.embeddings.openai import OpenAIEmbeddings persist_directory = 'docs/chroma/' embed...
from langchain_openai import ChatOpenAI 这里假设 langchain-openai 库中有一个名为 ChatOpenAI 的类或函数,这是基于参考信息中的示例代码推测的。实际导入内容应根据库的实际情况来确定。 使用langchain-openai 库: 一旦你成功安装了库并导入了必要的类或函数,你就可以开始使用它了。以下是一个简单的示例,展...
from langchain.chat_models.openai import ChatOpenAI from langchain.utilities import GoogleSearchAPIWrapper os.environ[“OPENAI_API_KEY”] = ‘my_key’ vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory=“./chroma_db_oai”) ...
import os os.environ["OPENAI_API_KEY"] = "..." from langchain.chains import LLMChain from langchain.chat_models import ChatOpenAI from langchain.prompts import PromptTemplate from langchain.prompts.chat import ChatPromptTemplate from langchain.schema import messages_from_dict role_strings = [ ...
importopenaifrom"../../openai.app.mjs";importcommonfrom"../common/common-assistants.mjs";exportdefault{...common,key:"openai-chat-with-assistant",name:"Chat with Assistant",description:"Sends a message and generates a response, storing the message history for a continuous co...
from openai import ChatCompletion 要求的python版本,#如何在Python中实现“fromopenaiimportChatCompletion”所需的版本在当今科技时代,开发者使用OpenAIAPI的需求日益增加,特别是在Python环境中。对于刚入行的小白来说,可能会对如何准备环境并导入OpenAI库感到困惑。
importopenaifrom"../../openai.app.mjs";importcommonfrom"../common/common-assistants.mjs";exportdefault{...common,key:"openai-chat-with-assistant",name:"Chat with Assistant",description:"Sends a message and generates a response, storing the message history for a continuous ...
如LangChain 快速入门所示,我正在尝试以下 Python 代码:from langchain.prompts.chat import ChatPromptTemplate template = "You are a helpful assistant that translates {input_language} to {output_language}." human_template = "{text}" chat_prompt = ChatPromptTemplate.from_messages([ ("system", ...