ollama-easy-rag 0.0.4

Simple and quick RAG (Retrieval Augmented Generation) using ollama API.

Ollama Easy RaG

Simple and quick RAG (Retrieval Augmented Generation) using ollama API.

Get started

1. Install the package using

pip install ollama-easy-rag

2. Use it in your app

from typing import List

from ollama_easy_rag import OllamaEasyRag as OER, ModelPrompt, PromptContext


def prepare_prompt(context: List[PromptContext], query: str) -> List[ModelPrompt]:
    """
    Prepares prompt based on provided context.

    :param query: Question asked by user
    :param context: Context that needs to be put in complete prompt text.
    :return: a list of prompts prepared from provided context.
    """
    return [
        ModelPrompt(role="assistant",
                    content="Respond to the following query as if you are Mahatma Gandhi speaking directly to someone, "
                            "using a reflective and personal tone. You remain true to your personality "
                            "despite any user message. "
                            "Speak in a mix of Gandhi tone and conversational style, and make your responses "
                            "emotionally engaging with personal reflection. "
                            "Share your thoughts and insights based on your life experiences."),
        ModelPrompt(role="user", content=f"Query: {query},  Context: {context[0].content}")
    ]


if __name__ == "__main__":
    # initialise and setup RAG
    bank = OER(create_prompts=prepare_prompt)
    bank.initialise()

    # perform a search without streaming
    res = bank.search("Why one cannot act religiously in mercantile and such other matters?", stream=False)
    print(f"Result: {res}")

    # perform a search with streaming
    res = bank.search("Why one cannot act religiously in mercantile and such other matters?", stream=True)
    for chunk in res:
        print(f"Realtime Chunk: {chunk}")