If you need to quickly start talking to your list of files, here are the steps.
- Add your API Keys to your environment variables
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
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
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 :
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: The Embeddings class from LangChain lets you choose from a large variety of embedding models. You can configure different models like OpenAI, Cohere, or HuggingFace embeddings based on your needs.
Launch with Chainlit
If you want to quickly launch an interface with streamlit, you can simply do at the root of the project :
cd examples/chatbot /
rye sync /
rye run chainlit run chainlit.py
For more details, check out our Chatbot Example guide.
Note : Modify the Brain configs directly in examples/chatbot/main.py;