Neum AI

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Neum AI is a data platform that helps developers leverage their data to contextualize Large Language Models through Retrieval Augmented Generation (RAG) This includes extracting data from existing data sources like document storage and NoSQL, processing the contents into vector embeddings and ingesting the vector embeddings into vector databases for similarity search.

It provides you a comprehensive solution for RAG that can scale with your application and reduce the time spent integrating services like data connectors, embedding models and vector databases.

Features

• 🏭 High throughput distributed architecture to handle billions of data points. Allows high degrees of parallelization to optimize embedding generation and ingestion.
• 🧱 Built-in data connectors to common data sources, embedding services and vector stores.
• 🔄 Real-time synchronization of data sources to ensure your data is always up-to-date.
• ♻ Customizable data pre-processing in the form of loading, chunking and selecting.
• 🤝 Cohesive data management to support hybrid retrieval with metadata. Neum AI automatically augments and tracks metadata to provide rich retrieval experience.

Talk to us

You can reach our team either through email (founders@tryneum.com), on discord or by scheduling a call wit us.

Getting Started

Neum AI Cloud

Sign up today at dashboard.neum.ai. See our quickstart to get started.

The Neum AI Cloud supports a large-scale, distributed architecture to run millions of documents through vector embedding. For the full set of features see: Cloud vs Local

Local Development

Install the neumai package:

pip install neumai

To create your first data pipelines visit our quickstart.

At a high level, a pipeline consists of one or multiple sources to pull data from, one embed connector to vectorize the content, and one sink connector to store said vectors. With this snippet of code we will craft all of these and run a pipeline:

Creating and running a pipeline

from neumai.DataConnectors.WebsiteConnector import WebsiteConnector
from neumai.Shared.Selector import Selector
from neumai.Loaders.HTMLLoader import HTMLLoader
from neumai.Chunkers.RecursiveChunker import RecursiveChunker
from neumai.Sources.SourceConnector import SourceConnector
from neumai.EmbedConnectors import OpenAIEmbed
from neumai.SinkConnectors import WeaviateSink
from neumai.Pipelines import Pipeline

website_connector =  WebsiteConnector(
    url = "https://www.neum.ai/post/retrieval-augmented-generation-at-scale",
    selector = Selector(
        to_metadata=['url']
    )
)
source = SourceConnector(
    data_connector = website_connector, 
    loader = HTMLLoader(), 
    chunker = RecursiveChunker()
)

openai_embed = OpenAIEmbed(
    api_key = "<OPEN AI KEY>",
)

weaviate_sink = WeaviateSink(
    url = "your-weaviate-url",
    api_key = "your-api-key",
    class_name = "your-class-name",
)

pipeline = Pipeline(
    sources=[source], 
    embed=openai_embed, 
    sink=weaviate_sink
)
pipeline.run()

results = pipeline.search(
    query="What are the challenges with scaling RAG?", 
    number_of_results=3
)

for result in results:
    print(result.metadata)

Creating and running a pipeline - Postgres connector

from neumai.DataConnectors.PostgresConnector import PostgresConnector
from neumai.Shared.Selector import Selector
from neumai.Loaders.JSONLoader import JSONLoader
from neumai.Chunkers.RecursiveChunker import RecursiveChunker
from neumai.Sources.SourceConnector import SourceConnector
from neumai.EmbedConnectors import OpenAIEmbed
from neumai.SinkConnectors import WeaviateSink
from neumai.Pipelines import Pipeline

website_connector =  PostgresConnector(
    connection_string = 'postgres',
    query = 'Select * from ...'
)
source = SourceConnector(
    data_connector = website_connector, 
    loader = JSONLoader(
        id_key='<your id key of your jsons>',
        selector=Selector(
            to_embed=['property1_to_embed','property2_to_embed'],
            to_metadata=['property3_to_include_in_metadata_in_vector']
        )
    ),
    chunker = RecursiveChunker()
)

openai_embed = OpenAIEmbed(
    api_key = "<OPEN AI KEY>",
)

weaviate_sink = WeaviateSink(
    url = "your-weaviate-url",
    api_key = "your-api-key",
    class_name = "your-class-name",
)

pipeline = Pipeline(
    sources=[source], 
    embed=openai_embed, 
    sink=weaviate_sink
)

pipeline.run()

results = pipeline.search(
    query="...", 
    number_of_results=3
)

for result in results:
    print(result.metadata)

Publishing pipeline to Neum Cloud

from neumai.Client.NeumClient import NeumClient
client = NeumClient(
    api_key='<your neum api key, get it from https://dashboard.neum.ai',
)
client.create_pipeline(pipeline=pipeline)

Self-host

If you are interested in deploying Neum AI to your own cloud contact us at founders@tryneum.com.

We have a sample backend architecture published on GitHub which you can use as a starting point.

Available Connectors

For an up-to-date list please visit our docs

Source connectors

1. Postgres
2. Hosted Files
3. Websites
4. S3
5. Azure Blob
6. Sharepoint
7. Singlestore
8. Supabase Storage

Embed Connectors

1. OpenAI embeddings
2. Azure OpenAI embeddings

Sink Connectors

1. Supabase postgres
2. Weaviate
3. Qdrant
4. Pinecone
5. Singlestore

Roadmap

Our roadmap is evolving with asks, so if there is anything missing feel free to open an issue or send us a message.

Connectors

• MySQL - Source
• GitHub - Source
• Google Drive - Source
• Hugging Face - Embedding
• LanceDB - Sink
• Marqo - Sink
• Milvus - Sink
• Chroma - Sink

Search

• Retrieval feedback
• Filter support
• Unified Neum AI filters
• Smart routing (w/ embedding based classification)
• Smart routing (w/ LLM based classification)
• Self-Query Retrieval (w/ Metadata attributes generation)

Extensibility

• Langchain / Llama Index Document to Neum Document converter
• Custom chunking and loading

Experimental

• Async metadata augmentation
• Chat history connector
• Structured (SQL and GraphQL) search connector

Neum Tools

Additional tooling for Neum AI can be found here:

• neumai-tools: contains pre-processing tools for loading and chunking data before generating vector embeddings.