DashVector 向量存储¶

如果您在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%pip install llama-index-vector-stores-dashvector

%pip install llama-index-vector-stores-dashvector

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!pip install llama-index

!pip install llama-index

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import logging
import sys
import os

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import logging import sys import os logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

创建一个 DashVector Collection¶

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import dashvector

import dashvector

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api_key = os.environ["DASHVECTOR_API_KEY"]
client = dashvector.Client(api_key=api_key)

api_key = os.environ["DASHVECTOR_API_KEY"] client = dashvector.Client(api_key=api_key)

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# dimensions are for text-embedding-ada-002
client.create("llama-demo", dimension=1536)

# dimensions are for text-embedding-ada-002 client.create("llama-demo", dimension=1536)

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{"code": 0, "message": "", "requests_id": "82b969d2-2568-4e18-b0dc-aa159b503c84"}

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dashvector_collection = client.get("quickstart")

dashvector_collection = client.get("quickstart")

下载数据¶

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!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'

!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'

加载文档，构建 DashVectorStore 和 VectorStoreIndex¶

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.dashvector import DashVectorStore
from IPython.display import Markdown, display

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.

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# load documents
documents = SimpleDirectoryReader("./data/paul_graham").load_data()

# load documents documents = SimpleDirectoryReader("./data/paul_graham").load_data()

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# initialize without metadata filter
from llama_index.core import StorageContext

vector_store = DashVectorStore(dashvector_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# initialize without metadata filter from llama_index.core import StorageContext vector_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¶

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# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")

# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine() response = query_engine.query("What did the author do growing up?")

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display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))

作者在学校之外进行写作和编程。他们写短篇故事，并尝试在 IBM 1401 计算机上编写程序。他们还构建了一台微型计算机，并开始在其上编程，编写简单的游戏和文字处理器。