我们将了解如何使用 LlamaIndex 与 Upstash Vector 对接！

! pip install -q llama-index upstash-vector

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.vector_stores import UpstashVectorStore
from llama_index.core import StorageContext
import textwrap
import openai

# Setup the OpenAI API
openai.api_key = "sk-..."

# Download data
! mkdir -p 'data/paul_graham/'
! wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

--2024-02-03 20:04:25--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.01s

2024-02-03 20:04:25 (5.96 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

现在，我们可以使用 LlamaIndex SimpleDirectoryReader 加载文档

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

print("# Documents:", len(documents))

# Documents: 1

要在 Upstash 上创建索引，请访问 https://console.upstash.com/vector，创建一个具有 1536 维度和 Cosine 距离度量的索引。复制下面的 URL 和 token

vector_store = UpstashVectorStore(url="https://...", token="...")

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

现在我们已经成功创建索引，并用文章中的向量填充了它！数据需要一点时间建立索引，然后就可以进行查询了。

query_engine = index.as_query_engine()
res1 = query_engine.query("What did the author learn?")
print(textwrap.fill(str(res1), 100))

print("\n")

res2 = query_engine.query("What is the author's opinion on startups?")
print(textwrap.fill(str(res2), 100))

The author learned that the study of philosophy in college did not live up to their expectations.
They found that other fields took up most of the space of ideas, leaving little room for what they
perceived as the ultimate truths that philosophy was supposed to explore. As a result, they decided
to switch to studying AI.

The author's opinion on startups is that they are in need of help and support, especially in the
beginning stages. The author believes that founders of startups are often helpless and face various
challenges, such as getting incorporated and understanding the intricacies of running a company. The
author's investment firm, Y Combinator, aims to provide seed funding and comprehensive support to
startups, offering them the guidance and resources they need to succeed.

元数据过滤

你可以将 MetadataFilters 与 VectorStoreQuery 一起传入，以过滤 Upstash 向量存储返回的节点。

import os

from llama_index.vector_stores.upstash import UpstashVectorStore
from llama_index.core.vector_stores.types import (
    MetadataFilter,
    MetadataFilters,
    FilterOperator,
)

vector_store = UpstashVectorStore(
    url=os.environ.get("UPSTASH_VECTOR_URL") or "",
    token=os.environ.get("UPSTASH_VECTOR_TOKEN") or "",
)

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

filters = MetadataFilters(
    filters=[
        MetadataFilter(
            key="author", value="Marie Curie", operator=FilterOperator.EQ
        )
    ],
)

retriever = index.as_retriever(filters=filters)

retriever.retrieve("What is inception about?")

我们还可以使用 AND 或 OR 条件组合多个 MetadataFilters

from llama_index.core.vector_stores import FilterOperator, FilterCondition

filters = MetadataFilters(
    filters=[
        MetadataFilter(
            key="theme",
            value=["Fiction", "Horror"],
            operator=FilterOperator.IN,
        ),
        MetadataFilter(key="year", value=1997, operator=FilterOperator.GT),
    ],
    condition=FilterCondition.AND,
)

retriever = index.as_retriever(filters=filters)
retriever.retrieve("Harry Potter?")