peachdb 0.0.2

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PeachDB

PeachDB - the AI-First, Embeddings Database

Build memory for your AI products in minutes!

Getting Started

pip install peachdb

Setup Modal

• Create an account at modal.com
• Install the modal-client package: pip install modal-client
• Setup token: modal token new

(optional) To use S3 URIs for your dataset and embedding storage, configure your .env file as below.

# Setup your AWS CLI, if you haven't already done so
# Fetch the below values from ~/.aws/credentials
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=

Our core API has 3 functions

from peachdb import PeachDB

# Create a new PeachDB instance
# Embeddings are automatically computed at scale using the selected `embedding_generator` model on Modal.
db = PeachDB.create(
    project_name="my_app",
    csv_path="/path/to/local/csv",  # or "s3://path/to/csv"
    column_to_embed="foo",
    id_column_name="id",
    max_rows=None, # or N, to process N rows of the dataset 
    distance_metric="cosine",  # or "l2"
    embedding_backend="exact_cpu",  # or "exact_gpu" or "approx"
    embedding_generator="sentence_transformer_L12",
    embeddings_output_s3_bucket_uri=None, # required when using S3 URI for `csv_path`
)
# Once the database has been successfully setup, you can reference it in the future via
# db = PeachDB(project_name="my_app")

# Query/search 5 most similar results
results_df = db.query(text='An example query', top_k=5)

# Deploy the database as a publicly available FastAPI server
# Call GET /query?text='An example query'&top_k=5 to fetch 5 most similar results
db.deploy()

Why another embedding database?

PeachDB puts embeddings at the forefront! We've streamlined the entire end-to-end lifecycle of managing embeddings, making it developer-friendly, seamless, and cost-effective. You no longer have to build custom pipelines or fret over hardware setups & scalability issues. PeachDB ensures you can get started within minutes, leaving the worries of cost optimization and scale to us.

Our key features include:

• Automated, cost-effective & large-scale embedding computation: We leverage serverless GPU functions (through Modal) to compute embeddings on a large scale efficiently and affordably.
  • For instance, we processed the Kaggle 5M song lyrics dataset in just 12 minutes at a cost of $4.90, using sentence transformers!
  • We've developed a Modal wrapper for Sentence Transformer L12.
    • Coming soon: Modal wrappers for OpenAI, Cohere, ImageBind, and more.
  • Coming soon: Bring your own embeddings.
  • Coming soon: Custom embedding functions for even more flexibility.
• Highly Customizable: PeachDB allows you to tailor its features to your needs. You can customize:
  • Embedding computation: as described above.
  • Backend: choose between exact_cpu (numpy), exact_gpu (torch), or approx (HNSW).
  • Distance metrics: cosine or l2.
• Effortless Deployment: Deploy PeachDB as a publicly available server with a single API. No need to worry about nginx or SSL certs.
  • Coming soon: Managed, scalable deployment.
• Consistent API: Experience the same API across all environments - dev, test, and prod.
• Open Source: Apache 2.0.

Use-cases

• Build web apps like - clip.audio & awesome-movies.life
• Build ChatGPT for X
• Build ChatGPT plugins

Example

Below is an example of creating a web server for a music recommendation app. To power the app, we are using the Kaggle 5M song lyric dataset

• Ssh into your remote instance (doesn't need GPU)
• Create & activate a new conda environment conda create -n spoti_vibe python=3.10 & conda activate spoti_vibe
• Setup modal as outlined above
• Install Peachdb pip install peachdb
• mkdir spoti_vibe/
• Create a new module inside the directory server.py
• Add the following code

import kaggle # make sure you've run `pip install kaggle`
import os

from peachdb import PeachDB

os.environ['KAGGLE_USERNAME'] = None # set user name
os.environ['KAGGLE_KEY'] = None # set key

kaggle.api.authenticate()
kaggle.api.dataset_download_files('nikhilnayak123/5-million-song-lyrics-dataset', path='/path/to/save', unzip=True)


db = PeachDB.create(
    project_name="spoti_vibe",
    csv_path="/path/to/local/csv",
    column_to_embed="lyrics",
    id_column_name="id",
    max_rows=None,
    distance_metric="cosine",
    embedding_backend="exact_cpu",
    embedding_generator="sentence_transformer_L12",
)

db.deploy() # Public URL will be printed to console

And that's it! You should now have a publicly available server that can listen to query requests from the user on: GET <PUBLIC_URL>/query?text=''&top_k=5

FAQs

Q) How can I delete a project? Run db.delete(project_name="my_app")

Get Involved

We welcome PR contributors and ideas for how to improve the project.

License

Apache 2.0