RAG Core

A Retrieval-Augmented Generation library with a CLI interface. Build RAG applications with just a few commands and a configuration file.

Supported setups

For more details see the documentation.

Installation

To install, run

pip install ragcore

or clone and build from source

git clone https://github.com/daved01/ragcore.git
cd ragcore
pip install .

If everything worked, running

ragcore -h

should show you some information about ragcore.

A Simple Example

To build an application with OpenAI or AzureOpenAI LLMs and embeddings, and a local database, first set your OpenAI API key as described here:

export OPENAI_API_KEY=[your token]

Then, create a config file config.yaml like this in the root of your project:

database:
  provider: "chroma"
  number_search_results: 5
  base_dir: "data/database"

splitter:
  chunk_overlap: 256
  chunk_size: 1024

embedding:
  provider: "openai"
  model: "text-embedding-model"

llm:
  provider: "openai"
  model: "gpt-model"

And finally, create your application using this config file:

from ragcore import RAGCore


app = RAGCore() # pass config=<path-to-config.yaml> if not in root

# Upload a document "My_Book.pdf"
app.add(path="My_Book.pdf")

# Now you can ask questions
answer = app.query(query="What did the elk say?")

print(answer.content)

# List the document's title and content on which the response is based
for doc in answer.documents:
  print(doc.title, " | ", doc.content)

# List all documents in the database
print(app.get_titles())

# You can delete by title
app.delete(title="My_Book")

And that's it! For more information, as well as an overview of supported integrations check out the documentation.