ragcore 1.0.4

A library to build Retrieval-Augmented Generation applications with only a few lines of code.

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

Databases	LLMs	Embeddings	Document types
Chroma (local)	OpenAI	OpenAI	PDF
Pinecone (remote)	AzureOpenAI	AzureOpenAI

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.