base on Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want. # 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 ", Assign "at most 3 tags" to the expected json: {"id":"7703","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"