Basic RAG

Creating a basic RAG workflow like the one above is simple, 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 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

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"],
                        )

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()

You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file!

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Basic RAG

Basic RAG

Creating a basic RAG workflow like the one above is simple, 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 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

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"],
                        )

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()

You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file!

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Basic RAG

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