首先,你需要确保已经安装了langchain库。如果尚未安装,可以使用pip进行安装: bash pip install langchain 然后,在你的Python代码中导入langchain库中的openai模块: python from langchain import openai 2. 设置OpenAI API的密钥 为了使用OpenAI的API,你需要一个有效的API密钥。你可以从OpenAI的官方网站获取这个密钥...
from typing import List, Tuple from dotenv import load_dotenv from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.schema import Document from langchain_openai import AzureOpenAIEmbeddings from langchain_community.vectorstores import FAISS from langchain_openai import AzureChatOp...
步骤2:使用Embbeding类为每个句子生成一个嵌入 from langchain.embeddings.openai import OpenAIEmbeddings embedding = OpenAIEmbeddings() embedding1 = embedding.embed_query(sentence1) embedding2 = embedding.embed_query(sentence2) embedding3 = embedding.embed_query(sentence3) 步骤3:用点积(dot product)来计算...
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...
from langchain_openai import ChatOpenAI llm = ChatOpenAI( api_key="ollama", model="llama3:8b-instruct-fp16", base_url="http://localhost:11434/v1", ) Description Using Model from Ollama in ChatOpenAI doesnt invoke the tools with bind_tools System Info .. 3 Replies...
pip install qdrant-client langchain_community langchain_openai langchain_text_splitters -q# 这里没有 import 任何 qdrant 的东西fromlangchain_community.document_loadersimportTextLoaderfromlangchain_commnunity.vectorstoresimportQdrantfromlangchain_openaiimportOpenAIEmbeddingsfromlangchain_text_splitersimportCharacter...
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. ...
10 #from langchain import OpenAI ---> 11 from langchain.chains import ConversationalRetrievalChain 12 #from langchain.chains.question_answering import load_qa_chain 13 from langchain.llms import OpenAI File c:\Users\matthewwalter\Anaconda3\envs\yhi_langchain\lib\site-packages\langchain_init_....
from langchain.vectorstores import Chroma from langchain.embeddings import OpenAIEmbeddings from langchain.chat_models.openai import ChatOpenAI from langchain.utilities import GoogleSearchAPIWrapper os.environ[“OPENAI_API_KEY”] = ‘my_key’
environ['OPENAI_API_KEY'] 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_...