upstash-search 0.1.0

An HTTP/REST based AI Search client built on top of Upstash REST API.

Upstash AI Search Python Client

[!NOTE] This project is in GA Stage.

The Upstash Professional Support fully covers this project. It receives regular updates, and bug fixes. The Upstash team is committed to maintaining and improving its functionality.

It is a connectionless (HTTP based) AI Search client and designed for:

• Serverless functions (AWS Lambda ...)
• Cloudflare Workers
• Next.js, Jamstack ...
• Client side web/mobile applications
• WebAssembly
• and other environments where HTTP is preferred over TCP.

Quick Start

Install

Python

pip install upstash-search

Create Database

Create a new database on Upstash

Basic Usage:

from upstash_search import Search

client = Search(
    url="<UPSTASH_SEARCH_REST_URL>",
    token="<UPSTASH_SEARCH_REST_TOKEN>",
)

# Access the index of a database
index = client.index("movies")

# Upsert documents into index
index.upsert(
    documents=[
        {
            "id": "movie-0",
            "content": {
                "title": "Star Wars",
                "overview": "Sci-fi space opera",
                "genre": "sci-fi",
                "category": "classic",
            },
            "metadata": {
                "poster": "https://poster.link/starwars.jpg",
            },
        },
        {
            "id": "movie-1",
            "content": {
                "title": "Inception",
                "overview": "Mind-bending thriller",
                "genre": "sci-fi",
                "category": "modern",
            },
            "metadata": {
                "poster": "https://poster.link/inception.jpg",
            },
        },
    ],
)

# Fetch documents by ids
documents = index.fetch(
    ids=["movie-0", "movie-1"],
)
print(documents)

# AI search
scores = index.search(
    query="space opera",
    limit=2,
)
print(scores)

# AI search with reranking
scores = index.search(
    query="space opera",
    limit=2,
    reranking=True,
)
print(scores)

# AI search with filtering
scores = index.search(
    query="space opera",
    limit=2,
    filter="category = 'classic'",
)
print(scores)

# Range over documents
range_documents = index.range(
    cursor="",
    limit=1,
)
print(range_documents.documents)

# Range over the next page of documents
range_documents = index.range(
    cursor=range_documents.next_cursor,
    limit=3,
)

# Delete a document by id
index.delete(
    ids=["movie-0"],
)

# Reset the index (delete all documents)
index.reset()

# Get database and index info
info = client.info()
print(info)