如果您在colab上打开这个笔记本,您可能需要安装LlamaIndex 🦙。
%pip install llama-index-vector-stores-dashvector
!pip install llama-index
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import dashvector
api_key = os.environ["DASHVECTOR_API_KEY"]
client = dashvector.Client(api_key=api_key)
# 文本嵌入ada-002的维度client.create("llama-demo", dimension=1536)
{"code": 0, "message": "", "requests_id": "82b969d2-2568-4e18-b0dc-aa159b503c84"}
dashvector_collection = client.get("quickstart")
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.dashvector import DashVectorStore
from IPython.display import Markdown, display
INFO:numexpr.utils:Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8. INFO:numexpr.utils:NumExpr defaulting to 8 threads. NumExpr defaulting to 8 threads.
# 加载文档documents = SimpleDirectoryReader("./data/paul_graham").load_data()
# 初始化,不带元数据过滤器from llama_index.core import StorageContextvector_store = DashVectorStore(dashvector_collection)storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents( documents, storage_context=storage_context)
# 查询索引
这个代码段演示了如何在Python中查询列表中的元素索引。
# 将日志级别设置为DEBUG,以获得更详细的输出query_engine = index.as_query_engine()response = query_engine.query("作者在成长过程中做了什么?")
display(Markdown(f"<b>{response}</b>"))
The author worked on writing and programming outside of school. They wrote short stories and tried writing programs on the IBM 1401 computer. They also built a microcomputer and started programming on it, writing simple games and a word processor.