Follow the instructions below to create the agentic RAG workflow shown above, which includes some advanced capabilities such as:
  • user intention detection - the agent can detect if the user wants to activate the web search tool to look for information not present in the documents;
  • dynamic chunk retrieval - the number of retrieved chunks is not fixed, but determined dynamically using the reranker’s relevance scores and the user-provided relevance_score_threshold;
  • web search - the agent can search the web for more information if needed.
  1. Add your API Keys to your environment variables
import os
os.environ["OPENAI_API_KEY"] = "my_openai_api_key"
os.environ["TAVILY_API_KEY"] = "my_tavily_api_key"
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.
  1. Create the YAML file rag_with_web_search_workflow.yaml and copy the following content in it
workflow_config:
  name: "RAG with web search"

  # List of tools that the agent can activate if the user instructions require it
  available_tools:
    - "web search"

  nodes:
    - name: "START"
      conditional_edge:
        routing_function: "routing_split"
        conditions: ["edit_system_prompt", "filter_history"]

    - name: "edit_system_prompt"
      edges: ["filter_history"]

    - name: "filter_history"
      edges: ["dynamic_retrieve"]

    - name: "dynamic_retrieve"
      conditional_edge:
        routing_function: "tool_routing"
        conditions: ["run_tool", "generate_rag"]

    - name: "run_tool"
      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"]
      tools:
        - name: "cited_answer"

# Maximum number of previous conversation iterations
# to include in the context of the answer
max_history: 10

# Number of chunks returned by the retriever
k: 40

# 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

  # Among the chunks returned by the reranker, only those with relevance
  # scores equal or above the relevance_score_threshold will be returned
  # to the LLM to generate the answer (allowed values are between 0 and 1,
  # a value of 0.1 works well with the cohere and jina rerankers)
  relevance_score_threshold: 0.01

# LLM configuration
llm_config:

  # maximum number of tokens passed to the LLM to generate the answer
  max_input_tokens: 8000

  # temperature for the LLM
  temperature: 0.7
  1. 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"],
                        )
  1. 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 = "./rag_with_web_search_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()
  1. You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file!