# 找到你的PINECONE_ENVIRONMENT PINECONE_ENV = os.environ.get('PINECONE_ENVIRONMENT') or 'PINECONE_ENVIRONMENT' pinecone.init( api_key=PINECONE_API_KEY, environment=PINECONE_ENV ) 如下图所示:第一个红色圆圈为 PINECONE_ENVIRONMENT 的参数取值。第二个圆圈为 PINECONE_API_KEY 的参数取值。复制粘贴到上述...
因为不管是pinecone还是后续要写的Langchain,python的支持程度目前都更好,所以demo是用python(不过语法和js都差不多,代码逻辑也很简单,一眼就懂) importopenaiimportpineconeopenai.api_key="Your key"pinecone.init(api_key="Your key",environment="Your env")# 提示词prompt="2022年卡塔尔世界杯的冠军是?"# 与...
1、加载环境变量 新建一个env的文件,然后填写如下内容: PINECONE_API_KEY="your PINECONE_API_KEY" QIANFAN_AK="your QIANFAN_AK" QIANFAN_SK="your QIANFAN_SK" Plain Text 收起 # 通过配置文件加载环境变量 import os from dotenv import load_dotenv load_dotenv(dotenv_path = "env",override = True...
pinecone.init( api_key="pinecone api key", environment="env" ) index_name = "langchain-demo" index = Pinecone.from_documents(docs, embeddings, index_name=index_name) 我们使用方法创建了一个新的 Pinecone 向量索引。这个方法接受三个参数:Pinecone.from_documents() 1、docs: 一个由 RecursiveCharact...
isEqualTo("env"); assertThat(props.getNamespace()).isEqualTo("namespace"); assertThat(props.getApiKey()).isEqualTo("key"); assertThat(props.getProjectId()).isEqualTo("project"); assertThat(props.getIndexName()).isEqualTo("index"); assertThat(props.getServerSideTimeout()).isEqualTo(...
在你得到的错误中，你会看到RequestError: connect ECONNREFUSED ::1:3000。我假设你已经把你的.env中...
在你得到的错误中，你会看到RequestError: connect ECONNREFUSED ::1:3000。我假设你已经把你的.env中...
import pinecone from langchain.llms import Cohere from langchain.retrievers.multi_query import MultiQueryRetriever from langchain.vectorstores import Pinecone pinecone.init(api_key=pine_api_key, environment=pinecone_env) index_name = "index_name" index = pinecone.Index(index_name) vectorstore = P...
# embedding example on random word embeddings = OpenAIEmbeddings() # initiate pinecondb pinecone.init( api_key="YOUR-API-KEY", envirnotallow="YOUR-ENV" ) # define index name index_name = "langchain-project" # store the data and embeddings into pinecone index index = Pinecone.from_document...
envirnotallow="YOUR-ENV" ) # define index name index_name = "langchain-project" # store the data and embeddings into pinecone index index = Pinecone.from_documents(docs, embeddings, index_name=index_name) 1. 2. 3. 4. 5. 6.