quivr AI技能包

Prerequisites 📋

Ensure you have the following installed:

• Python 3.10 or newer

Getting Started 🚀

You can find everything on the documentation.

30 seconds Installation 💽

• Step 1: Install the package

  pip install quivr-core # Check that the installation worked
  

• Step 2: Create a RAG with 5 lines of code

  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

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

brain = Brain.from_files(name = "my smart brain", file_paths = ["./my_first_doc.pdf", "./my_second_doc.txt"], )

4. Launch a Chat

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() ```

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

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

import os
os.environ["OPENAI_API_KEY"] = "myopenai_apikey"

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"]