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。 在langchain中,你可以通过以下方式设置这个密钥: python import os os.environ["OPENAI_API_KEY"] = "你的OpenAI API密钥" 注意:在实际代码中,请确保将"你的OpenAI API密钥"替换为你从OpenAI获取的实际密钥。同时,为了避免...
from langchain_openai import AzureChatOpenAI from langchain.prompts import ChatPromptTemplate from rank_bm25 import BM25Okapi import cohere import logging import time from llama_parse import LlamaParse from azure.ai.documentintelligence.models import DocumentAnalysisFeature from langchain_community.document_...
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...
步骤1:创建上下文压缩检索器 from langchain.retrievers import ContextualCompressionRetriever from langchain.retrievers.document_compressors import LLMChainExtractor # 包装我们的向量存储 llm = OpenAI(temperature=0) compressor = LLMChainExtractor.from_llm(llm) compression_retriever = ContextualCompressionRetriever(...
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 long as he has the an Ollama instance active when using ChatOpenAI()....
qdrant 的东西fromlangchain_community.document_loadersimportTextLoaderfromlangchain_commnunity.vectorstoresimportQdrantfromlangchain_openaiimportOpenAIEmbeddingsfromlangchain_text_splitersimportCharacterTextSplitter## openai keyimportgetpassimportosos.environ['OPENAI_API_KEY']=getpass.getpass("OPEN API Key: ")...
13 from langchain.llms import OpenAI File c:\Users\matthewwalter\Anaconda3\envs\yhi_langchain\lib\site-packages\langchain_init_.py:6 3 from importlib import metadata 4 from typing import Optional ---> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain 7...
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. ...
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”) ...
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...