quivr
# Quivr - Your Second Brain, Empowered by Generative AI <div align="center"> <img src="./logo.png" alt="Quivr-logo" width="31%" style="border-radius: 50%; padding-bottom: 20px"/> </div> [](https://discord.gg/HUpRgp2HG8) [](https://github.com/quivrhq/quivr) [](https://twitter.com/_StanGirard) Quivr, helps you build your second brain, utilizes the power of GenerativeAI to be your personal assistant ! ## Key Features 🎯 - **Opiniated RAG**: We created a RAG that is opinionated, fast and efficient so you can focus on your product - **LLMs**: Quivr works with any LLM, you can use it with OpenAI, Anthropic, Mistral, Gemma, etc. - **Any File**: Quivr works with any file, you can use it with PDF, TXT, Markdown, etc and even add your own parsers. - **Customize your RAG**: Quivr allows you to customize your RAG, add internet search, add tools, etc. - **Integrations with Megaparse**: Quivr works with [Megaparse](https://github.com/quivrhq/megaparse), so you can ingest your files with Megaparse and use the RAG with Quivr. >We take care of the RAG so you can focus on your product. Simply install quivr-core and add it to your project. You can now ingest your files and ask questions.* **We will be improving the RAG and adding more features, stay tuned!** This is the core of Quivr, the brain of Quivr.com. <!-- ## Demo Highlight 🎥 https://github.com/quivrhq/quivr/assets/19614572/a6463b73-76c7-4bc0-978d-70562dca71f5 --> ## Getting Started 🚀 You can find everything on the [documentation](https://core.quivr.com/). ### Prerequisites 📋 Ensure you have the following installed: - Python 3.10 or newer ### 30 seconds Installation 💽 - **Step 1**: Install the package ```bash pip install quivr-core # Check that the installation worked ``` - **Step 2**: Create a RAG with 5 lines of code ```python import tempfile from quivr_core import Brain if __name__ == "__main__": with tempfile.NamedTemporaryFile(mode="w", suffix=".txt") as temp_file: temp_file.write("Gold is a liquid of blue-like colour.") temp_file.flush() brain = Brain.from_files( name="test_brain", file_paths=[temp_file.name], ) answer = brain.ask( "what is gold? asnwer in french" ) print("answer:", answer) ``` ## Configuration ### Workflows #### Basic RAG  Creating a basic RAG workflow like the one above is simple, here are the steps: 1. Add your API Keys to your environment variables ```python import os os.environ["OPENAI_API_KEY"] = "myopenai_apikey" ``` Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama. 1. Create the YAML file ``basic_rag_workflow.yaml`` and copy the following content in it ```yaml workflow_config: name: "standard RAG" nodes: - name: "START" edges: ["filter_history"] - name: "filter_history" edges: ["rewrite"] - name: "rewrite" edges: ["retrieve"] - name: "retrieve" edges: ["generate_rag"] - name: "generate_rag" # the name of the last node, from which we want to stream the answer to the user edges: ["END"] # Maximum number of previous conversation iterations # to include in the context of the answer max_history: 10 # Reranker configuration reranker_config: # The reranker supplier to use supplier: "cohere" # The model to use for the reranker for the given supplier model: "rerank-multilingual-v3.0" # Number of chunks returned by the reranker top_n: 5 # Configuration for the LLM llm_config: # maximum number of tokens passed to the LLM to generate the answer max_input_tokens: 4000 # temperature for the LLM temperature: 0.7 ``` 3. Create a Brain with the default configuration ```python from quivr_core import Brain brain = Brain.from_files(name = "my smart brain", file_paths = ["./my_first_doc.pdf", "./my_second_doc.txt"], ) ``` 4. Launch a Chat ```python brain.print_info() from rich.console import Console from rich.panel import Panel from rich.prompt import Prompt from quivr_core.config import RetrievalConfig config_file_name = "./basic_rag_workflow.yaml" retrieval_config = RetrievalConfig.from_yaml(config_file_name) 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, retrieval_config=retrieval_config) # Print the answer with typing effect console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}") console.print("-" * console.width) brain.print_info() ``` 5. You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file! ## Go further You can go further with Quivr by adding internet search, adding tools, etc. Check the [documentation](https://core.quivr.com/) for more information. ## Contributors ✨ Thanks go to these wonderful people: <a href="https://github.com/quivrhq/quivr/graphs/contributors"> <img src="https://contrib.rocks/image?repo=quivrhq/quivr" /> </a> ## Contribute 🤝 Did you get a pull request? Open it, and we'll review it as soon as possible. Check out our project board [here](https://github.com/users/StanGirard/projects/5) to see what we're currently focused on, and feel free to bring your fresh ideas to the table! - [Open Issues](https://github.com/quivrhq/quivr/issues) - [Open Pull Requests](https://github.com/quivrhq/quivr/pulls) - [Good First Issues](https://github.com/quivrhq/quivr/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) ## Partners ❤️ This project would not be possible without the support of our partners. Thank you for your support! <a href="https://ycombinator.com/"> <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/b/b2/Y_Combinator_logo.svg/1200px-Y_Combinator_logo.svg.png" alt="YCombinator" style="padding: 10px" width="70px"> </a> <a href="https://www.theodo.fr/"> <img src="https://avatars.githubusercontent.com/u/332041?s=200&v=4" alt="Theodo" style="padding: 10px" width="70px"> </a> ## License 📄 This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details