Quick start

If you need to quickly start talking to your list of files, here are the steps.

Add your API Keys to your environment variables

python

import os
os.environ["OPENAI_API_KEY"] = "myopenai_apikey"

Check our .env.example file to see the possible environment variables you can configure. Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama.

Create a Brain with Quivr default configuration

python

from quivr_core import Brain

brain = Brain.from_files(name = "my smart brain",
                        file_paths = ["/my_smart_doc.pdf", "/my_intelligent_doc.txt"],
                        )

Launch a Chat

python

brain.print_info()

from rich.console import Console
from rich.panel import Panel
from rich.prompt import Prompt

console = Console()
console.print(Panel.fit("Ask your brain !", style="bold magenta"))

while True:
    # Get user input
    question = Prompt.ask("[bold cyan]Question[/bold cyan]")

    # Check if user wants to exit
    if question.lower() == "exit":
        console.print(Panel("Goodbye!", style="bold yellow"))
        break

    answer = brain.ask(question)
    # Print the answer with typing effect
    console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}")

    console.print("-" * console.width)

brain.print_info()

And now you are all set up to talk with your brain !

Custom Brain

If you want to change the language or embeddings model, you can modify the parameters of the brain.

Let's say you want to use a LLM from Mistral and a specific embedding model :

python

from quivr_core import Brain
from langchain_core.embeddings import Embeddings

brain = Brain.from_files(name = "my smart brain",
                        file_paths = ["/my_smart_doc.pdf", "/my_intelligent_doc.txt"],
                        llm=LLMEndpoint(
                            llm_config=LLMEndpointConfig(model="mistral-small-latest", llm_base_url="https://api.mistral.ai/v1/chat/completions"),
                        ),
                        embedder=Embeddings(size=64),
                        )

Note : Embeddings is a langchain class that lets you chose from a large variety of embedding models. Please check out the following docs to know the panel of models you can try.

Launch with Chainlit

If you want to quickly launch an interface with streamlit, you can simply do at the root of the project :

bash

cd examples/chatbot /
rye sync /
rye run chainlit run chainlit.py

For more detail, go in examples/chatbot/chainlit.md

Note : Modify the Brain configs directly in examples/chatbot/main.py;