指南:在现有的Weaviate向量存储中使用向量存储索引¶
如果您在colab上打开这个笔记本,您可能需要安装LlamaIndex 🦙。
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%pip install llama-index-vector-stores-weaviate
%pip install llama-index-embeddings-openai
%pip install llama-index-vector-stores-weaviate
%pip install llama-index-embeddings-openai
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!pip install llama-index
!pip install llama-index
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import weaviate
import weaviate
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client = weaviate.Client("https://test-cluster-bbn8vqsn.weaviate.network")
client = weaviate.Client("https://test-cluster-bbn8vqsn.weaviate.network")
准备样本“现有” Weaviate 向量存储库¶
定义模式¶
我们为“Book”类创建了一个模式,包括4个属性:标题(str)、作者(str)、内容(str)和年份(int)。
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try:
client.schema.delete_class("Book")
except:
pass
try:
client.schema.delete_class("Book")
except:
pass
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schema = {
"classes": [
{
"class": "Book",
"properties": [
{"name": "title", "dataType": ["text"]},
{"name": "author", "dataType": ["text"]},
{"name": "content", "dataType": ["text"]},
{"name": "year", "dataType": ["int"]},
],
},
]
}
if not client.schema.contains(schema):
client.schema.create(schema)
schema = {
"classes": [
{
"class": "Book",
"properties": [
{"name": "title", "dataType": ["text"]},
{"name": "author", "dataType": ["text"]},
{"name": "content", "dataType": ["text"]},
{"name": "year", "dataType": ["int"]},
],
},
]
}
if not client.schema.contains(schema):
client.schema.create(schema)
定义样本数据¶
我们创建了4本样本书籍
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books = [
{
"title": "To Kill a Mockingbird",
"author": "Harper Lee",
"content": (
"To Kill a Mockingbird is a novel by Harper Lee published in"
" 1960..."
),
"year": 1960,
},
{
"title": "1984",
"author": "George Orwell",
"content": (
"1984 is a dystopian novel by George Orwell published in 1949..."
),
"year": 1949,
},
{
"title": "The Great Gatsby",
"author": "F. Scott Fitzgerald",
"content": (
"The Great Gatsby is a novel by F. Scott Fitzgerald published in"
" 1925..."
),
"year": 1925,
},
{
"title": "Pride and Prejudice",
"author": "Jane Austen",
"content": (
"Pride and Prejudice is a novel by Jane Austen published in"
" 1813..."
),
"year": 1813,
},
]
books = [
{
"title": "To Kill a Mockingbird",
"author": "Harper Lee",
"content": (
"To Kill a Mockingbird is a novel by Harper Lee published in"
" 1960..."
),
"year": 1960,
},
{
"title": "1984",
"author": "George Orwell",
"content": (
"1984 is a dystopian novel by George Orwell published in 1949..."
),
"year": 1949,
},
{
"title": "The Great Gatsby",
"author": "F. Scott Fitzgerald",
"content": (
"The Great Gatsby is a novel by F. Scott Fitzgerald published in"
" 1925..."
),
"year": 1925,
},
{
"title": "Pride and Prejudice",
"author": "Jane Austen",
"content": (
"Pride and Prejudice is a novel by Jane Austen published in"
" 1813..."
),
"year": 1813,
},
]
添加数据¶
我们将样本书籍添加到我们的Weaviate "Book"类中(带有内容字段的嵌入)。
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from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding()
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding()
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with client.batch as batch:
for book in books:
vector = embed_model.get_text_embedding(book["content"])
batch.add_data_object(
data_object=book, class_name="Book", vector=vector
)
with client.batch as batch:
for book in books:
vector = embed_model.get_text_embedding(book["content"])
batch.add_data_object(
data_object=book, class_name="Book", vector=vector
)
查询现有的 Weaviate 向量存储库¶
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from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_source_node
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_source_node
您必须正确指定与所需的Weaviate类匹配的“index_name”,并选择一个类属性作为“text”字段。
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vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="Book", text_key="content"
)
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="Book", text_key="content"
)
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retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(
similarity_top_k=1
)
retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(
similarity_top_k=1
)
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nodes = retriever.retrieve("What is that book about a bird again?")
nodes = retriever.retrieve("What is that book about a bird again?")
让我们检查一下检索到的节点。我们可以看到书籍数据被加载为LlamaIndex Node
对象,其中“content”字段是主要文本。
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pprint_source_node(nodes[0])
pprint_source_node(nodes[0])
Document ID: cf927ce7-0672-4696-8aae-7e77b33b9659 Similarity: None Text: author: Harper Lee title: To Kill a Mockingbird year: 1960 To Kill a Mockingbird is a novel by Harper Lee published in 1960......
剩余的字段应该被加载为元数据(存储在 metadata
中)
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nodes[0].node.metadata
nodes[0].node.metadata
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{'author': 'Harper Lee', 'title': 'To Kill a Mockingbird', 'year': 1960}