Bagel 向量存储¶

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

输入 [ ]

%pip install llama-index-vector-stores-bagel
%pip install llama-index
%pip install bagelML

%pip install llama-index-vector-stores-bagel %pip install llama-index %pip install bagelML

输入 [ ]

import logging
import sys

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

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

输入 [ ]

# set up OpenAI
import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai

openai.api_key = os.environ["OPENAI_API_KEY"]

# 设置 OpenAI import os import getpass os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API 密钥:") import openai openai.api_key = os.environ["OPENAI_API_KEY"]

输入 [ ]

import os

# Set environment variable
os.environ["BAGEL_API_KEY"] = getpass.getpass("Bagel API Key:")

import os # 设置环境变量 os.environ["BAGEL_API_KEY"] = getpass.getpass("Bagel API 密钥:")

输入 [ ]

import bagel
from bagel import Settings

import bagel from bagel import Settings

输入 [ ]

server_settings = Settings(
    bagel_api_impl="rest", bagel_server_host="api.bageldb.ai"
)

client = bagel.Client(server_settings)

collection = client.get_or_create_cluster(
    "testing_embeddings_3", embedding_model="custom", dimension=1536
)

server_settings = Settings( bagel_api_impl="rest", bagel_server_host="api.bageldb.ai" ) client = bagel.Client(server_settings) collection = client.get_or_create_cluster( "testing_embeddings_3", embedding_model="custom", dimension=1536 )

输入 [ ]

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.bagel import BagelVectorStore

from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.bagel import BagelVectorStore

输入 [ ]

from llama_index.core.schema import TextNode

nodes = [
    TextNode(
        text=(
            "Michael Jordan is a retired professional basketball player,"
            " widely regarded as one of the greatest basketball players of all"
            " time."
        ),
        metadata={
            "category": "Sports",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Angelina Jolie is an American actress, filmmaker, and"
            " humanitarian. She has received numerous awards for her acting"
            " and is known for her philanthropic work."
        ),
        metadata={
            "category": "Entertainment",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Elon Musk is a business magnate, industrial designer, and"
            " engineer. He is the founder, CEO, and lead designer of SpaceX,"
            " Tesla, Inc., Neuralink, and The Boring Company."
        ),
        metadata={
            "category": "Business",
            "country": "United States",
        },
    ),
    TextNode(
        text=(
            "Rihanna is a Barbadian singer, actress, and businesswoman. She"
            " has achieved significant success in the music industry and is"
            " known for her versatile musical style."
        ),
        metadata={
            "category": "Music",
            "country": "Barbados",
        },
    ),
    TextNode(
        text=(
            "Cristiano Ronaldo is a Portuguese professional footballer who is"
            " considered one of the greatest football players of all time. He"
            " has won numerous awards and set multiple records during his"
            " career."
        ),
        metadata={
            "category": "Sports",
            "country": "Portugal",
        },
    ),
]

from llama_index.core.schema import TextNode nodes = [ TextNode( text=( "Michael Jordan is a retired professional basketball player," " widely regarded as one of the greatest basketball players of all" " time." ), metadata={ "category": "体育", "country": "美国", }, ), TextNode( text=( "Angelina Jolie is an American actress, filmmaker, and" " humanitarian. She has received numerous awards for her acting" " and is known for her philanthropic work." ), metadata={ "category": "娱乐", "country": "美国", }, ), TextNode( text=( "Elon Musk is a business magnate, industrial designer, and" " engineer. He is the founder, CEO, and lead designer of SpaceX," " Tesla, Inc., Neuralink, and The Boring Company." ), metadata={ "category": "商业", "country": "美国", }, ), TextNode( text=( "Rihanna is a Barbadian singer, actress, and businesswoman. She" " has achieved significant success in the music industry and is" " known for her versatile musical style." ), metadata={ "category": "音乐", "country": "巴巴多斯", }, ), TextNode( text=( "Cristiano Ronaldo is a Portuguese professional footballer who is" " considered one of the greatest football players of all time. He" " has won numerous awards and set multiple records during his" " career." ), metadata={ "category": "体育", "country": "葡萄牙", }, ), ]

输入 [ ]

vector_store = BagelVectorStore(collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

vector_store = BagelVectorStore(collection=collection) storage_context = StorageContext.from_defaults(vector_store=vector_store)

输入 [ ]

index = VectorStoreIndex(nodes, storage_context=storage_context)

index = VectorStoreIndex(nodes, storage_context=storage_context)

输入 [ ]

from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo


vector_store_info = VectorStoreInfo(
    content_info="brief biography of celebrities",
    metadata_info=[
        MetadataInfo(
            name="category",
            type="str",
            description=(
                "Category of the celebrity, one of [Sports, Entertainment,"
                " Business, Music]"
            ),
        ),
        MetadataInfo(
            name="country",
            type="str",
            description=(
                "Country of the celebrity, one of [United States, Barbados,"
                " Portugal]"
            ),
        ),
    ],
)
retriever = VectorIndexAutoRetriever(
    index, vector_store_info=vector_store_info
)

from llama_index.core.retrievers import VectorIndexAutoRetriever from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo vector_store_info = VectorStoreInfo( content_info="名人的简短传记", metadata_info=[ MetadataInfo( name="category", type="str", description=( "名人所属类别，可选值：[体育, 娱乐," " 商业, 音乐]" ), ), MetadataInfo( name="country", type="str", description=( "名人所在国家，可选值：[美国, 巴巴多斯," " 葡萄牙]" ), ), ], ) retriever = VectorIndexAutoRetriever( index, vector_store_info=vector_store_info )

输入 [ ]

retriever.retrieve("celebrity")

retriever.retrieve("celebrity")