AutoRAG Agent工作流

Quick Install

We recommend using Python version 3.10 or higher for AutoRAG.

pip install AutoRAG

If you want to use the local models, you need to install gpu version.

pip install "AutoRAG[gpu]"

Or for parsing, you can use the parsing version.

pip install "AutoRAG[gpu,parse]"

4. Deploy your optimal RAG pipeline

YouTube Tutorial

https://github.com/Marker-Inc-Korea/AutoRAG/assets/96727832/c0d23896-40c0-479f-a17b-aa2ec3183a26

Muted by default, enable sound for voice-over

You can see on YouTube

Colab Tutorial

• Step 1: Basic of AutoRAG | Optimizing your RAG pipeline
• Step 2: Data Creation | Create your own Data for RAG Optimization
• Step 3: Use Custom LLM & Embedding Model | Use Custom Model

Quick Start

Quick Start

4-2. Run as an API server

You can run this pipeline as an API server.

Check out the API endpoint at here.

import nest_asyncio
from autorag.deploy import ApiRunner

nest_asyncio.apply()

runner = ApiRunner.from_trial_folder('/your/path/to/trial_dir')
runner.run_api_server()

autorag run_api --trial_dir your/path/to/trial_dir --host 0.0.0.0 --port 8000

The cli command uses extracted config YAML file. If you want to know it more, check out here.

4-3. Run as a Web Interface

you can run this pipeline as a web interface.

Check out the web interface at here.

autorag run_web --trial_path your/path/to/trial_path

sample web interface

<img width="1491" alt="web_interface" src="https://github.com/Marker-Inc-Korea/AutoRAG/assets/96727832/f6b00353-f6bb-4d8f-8740-1c264c0acbb8">

📌 Supporting Data Creation Modules

• Supporting Parsing Modules List
• Supporting Chunking Modules List

How AutoRAG optimizes RAG pipeline?

Here is the AutoRAG RAG Structure that only show Nodes.

Here is the image showing all the nodes and modules.

📌 Supporting RAG Optimization Nodes & modules

• Supporting RAG Modules list

3. QA Creation

You can create QA dataset with just a few lines of code.

import pandas as pd
from llama_index.llms.openai import OpenAI

from autorag.data.qa.filter.dontknow import dontknow_filter_rule_based
from autorag.data.qa.generation_gt.llama_index_gen_gt import (
	make_basic_gen_gt,
	make_concise_gen_gt,
)
from autorag.data.qa.schema import Raw, Corpus
from autorag.data.qa.query.llama_gen_query import factoid_query_gen
from autorag.data.qa.sample import random_single_hop

llm = OpenAI()
raw_df = pd.read_parquet("your/path/to/parsed.parquet")
raw_instance = Raw(raw_df)

corpus_df = pd.read_parquet("your/path/to/corpus.parquet")
corpus_instance = Corpus(corpus_df, raw_instance)

initial_qa = (
	corpus_instance.sample(random_single_hop, n=3)
	.map(
		lambda df: df.reset_index(drop=True),
	)
	.make_retrieval_gt_contents()
	.batch_apply(
		factoid_query_gen,  # query generation
		llm=llm,
	)
	.batch_apply(
		make_basic_gen_gt,  # answer generation (basic)
		llm=llm,
	)
	.batch_apply(
		make_concise_gen_gt,  # answer generation (concise)
		llm=llm,
	)
	.filter(
		dontknow_filter_rule_based,  # filter don't know
		lang="en",
	)
)

initial_qa.to_parquet('./qa.parquet', './corpus.parquet')

☎️ FaQ

💻 Hardware Specs

⭐ Running AutoRAG

🍯 Tips/Tricks

☎️ TroubleShooting