Upstash Vector Store
Source: llama_index

# Upstash Vector StoreWe're going to look at how to use LlamaIndex to interface with Upstash Vector!```python! pip install -q llama-index upstash-vector``````pythonfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReaderfrom llama_index.core.vector_stores import UpstashVectorStorefrom llama_index.core import StorageContextimport textwrapimport openai``````python# Setup the OpenAI APIopenai.api_key = "sk-..."``````python# Download data! 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'```    --2024-02-03 20:04:25--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/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]    Now, we can load the documents using the LlamaIndex SimpleDirectoryReader```pythondocuments = SimpleDirectoryReader("./data/paul_graham/").load_data()print("# Documents:", len(documents))```    # Documents: 1To create an index on Upstash, visit https://console.upstash.com/vector, create an index with 1536 dimensions and `Cosine` distance metric. Copy the URL and token below```pythonvector_store = UpstashVectorStore(url="https://...", token="...")storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context)```Now we've successfully created an index and populated it with vectors from the essay! The data will take a second to index and then it'll be ready for querying.```pythonquery_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.### Metadata FilteringYou can pass `MetadataFilters` with your `VectorStoreQuery` to filter the nodes returned from Upstash vector store.```pythonimport osfrom llama_index.vector_stores.upstash import UpstashVectorStorefrom 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?")```We can also combine multiple `MetadataFilters` with `AND` or `OR` condition```pythonfrom llama_index.core.vector_stores import FilterOperator, FilterConditionfilters = 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?")```