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

   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.

2. Create the YAML file basic_rag_workflow.yaml and copy the following content in it

   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

   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

   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!