superlinked

 

Experiment in a notebook | Run in production | Use-cases | Supported VDBs | Resources

Why use Superlinked

Improve your vector search relevance by encoding your metadata together with your data into your vector embeddings.

What is Superlinked

Superlinked is a framework AND a self-hostable REST API server that helps you make better vectors, that sits between your data, vector database and backend services.

How does it work

Superlinked makes it easy to construct custom data & query embedding models from pre-trained encoders, see the feature and use-case notebooks below for examples.

If you like what we do, give us a star! ⭐

Visit Superlinked for more information about the company behind this product and our other initiatives.

Features

• Embed structured and unstructured data (Text | Number | Category | Time | Event)
• Combine encoders to build a custom model (notebook)
• Add a custom encoder (notebook)
• Update your vectors with behavioral events & relationships (notebook)
• Use query-time weights (notebook)
• Query with natural language (notebook)
• Filter your results (notebook)
• Export vectors for analysis (notebook)

You can check a full list of our features or head to our reference section for more information.

Use-cases

Dive deeper with our notebooks into how each use-case benefits from the Superlinked framework.

• RAG: HR Knowledgebase
• Semantic Search: Movies, Business News, Product Images & Descriptions
• Recommendation Systems: E-commerce
• Analytics: User Acquisition, Keyword expansion

You can check a full list of examples here.

Experiment in a notebook

Example on combining Text with Numerical encoders to get correct results with LLMs.

Install the superlinked library

%pip install superlinked

Run the example:

First run will take slightly longer as it has to download the embedding model.

import json
import os

from superlinked import framework as sl


class Product(sl.Schema):
    id: sl.IdField
    description: sl.String
    rating: sl.Integer


product = Product()

description_space = sl.TextSimilaritySpace(
    text=product.description, model="Alibaba-NLP/gte-large-en-v1.5"
)
rating_maximizer_space = sl.NumberSpace(
    number=product.rating, min_value=1, max_value=5, mode=sl.Mode.MAXIMUM
)
index = sl.Index([description_space, rating_maximizer_space], fields=[product.rating])

# fill this with your API key - this will drive param extraction
openai_config = sl.OpenAIClientConfig(
    api_key=os.environ["OPEN_AI_API_KEY"], model="gpt-4o"
)

# it is possible now to add descriptions to a `Param` to aid the parsing of information from natural language queries.
text_similar_param = sl.Param(
    "query_text",
    description="The text in the user's query that refers to product descriptions.",
)

# Define your query using dynamic parameters for query text and weights.
# we will have our LLM fill them based on our natural language query
query = (
    sl.Query(
        index,
        weights={
            description_space: sl.Param("description_weight"),
            rating_maximizer_space: sl.Param("rating_maximizer_weight"),
        },
    )
    .find(product)
    .similar(
        description_space,
        text_similar_param,
    )
    .limit(sl.Param("limit"))
    .with_natural_query(sl.Param("natural_query"), openai_config)
)

# Run the app.
source = sl.InMemorySource(product)
executor = sl.InMemoryExecutor(sources=[source], indices=[index])
app = executor.run()

# Download dataset.
data = [
    {"id": 1, "description": "Budget toothbrush in black color.", "rating": 1},
    {"id": 2, "description": "High-end toothbrush created with no compromises.", "rating": 5},
    {"id": 3, "description": "A toothbrush created for the smart 21st century man.", "rating": 3},
]

# Ingest data to the framework.
source.put(data)

result = app.query(query, natural_query="best toothbrushes", limit=1)

# examine the extracted parameters from your query
print(json.dumps(result.knn_params, indent=2))
# the result is the 5 star rated product
result.to_pandas()

Run in production

Superlinked Server allows you to leverage the power of Superlinked in deployable projects. With a single script, you can deploy a Superlinked-powered app instance that creates REST endpoints and connects to external Vector Databases. This makes it an ideal solution for those seeking an easy-to-deploy environment for their Superlinked projects.

If your are interested in learning more about running at scale, Book a demo for an early access to our managed cloud.

Supported VDBs

We have started partnering with vector database providers to allow you to use Superlinked with your VDB of choice. If you are unsure, which VDB to chose, check-out our Vector DB Comparison.

• Redis
• MongoDB

Missing your favorite VDB? Tell us which vector database we should support next!

Reference

1. Describe your data using Python classes with the @schema decorator.
2. Describe your vector embeddings from building blocks with Spaces.
3. Combine your embeddings into a queryable Index.
4. Define your search with dynamic parameters and weights as a Query.
5. Load your data using a Source.
6. Define your transformations with a Parser (e.g.: from pd.DataFrame).
7. Run your configuration with an Executor.

You can check all references here.

Logging

Contextual information is automatically included in log messages, such as the process ID and package scope. Personally Identifiable Information (PII) is filtered out by default but can be exposed with the SUPERLINKED_EXPOSE_PII environment variable to true.

Resources

• Vector DB Comparison: Open-source collaborative comparison of vector databases by Superlinked.
• Vector Hub: VectorHub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack

Support

If you encounter any challenges during your experiments, feel free to create an issue, request a feature or to start a discussion. Make sure to group your feedback in separate issues and discussions by topic. Thank you for your feedback!