RAG AutoML tool for automatically finding an optimal RAG pipeline for your data.

There are many RAG pipelines and modules out there, but you don’t know what pipeline is great for “your own data” and "your own use-case." Making and evaluating all RAG modules is very time-consuming and hard to do. But without it, you will never know which RAG pipeline is the best for your own use-case.

AutoRAG is a tool for finding the optimal RAG pipeline for “your data.” You can evaluate various RAG modules automatically with your own evaluation data and find the best RAG pipeline for your own use-case.

AutoRAG supports a simple way to evaluate many RAG module combinations. Try now and find the best RAG pipeline for your own use-case.

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Do you have any difficulties in optimizing your RAG pipeline? Or is it hard to set up things to use AutoRAG? Try AutoRAG Cloud beta. We will help you to run AutoRAG and optimize. Plus, we can help you to build RAG evaluation dataset.

Starts with 9.99$ per optimization.

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You can see on YouTube

• 💬 Naive RAG Chatbot
• ✏️ AutoRAG Data Creation
• 🚀 AutoRAG RAG Pipeline Optimization

• 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 Install
• 🐳 AutoRAG Docker Guide
• Data Creation
  • Parsing
  • Chunking
  • QA Creation
• RAG Optimization
  • Set YAML File
  • Run AutoRAG
  • Run Dashboard
  • Deploy your optimal RAG pipeline (for testing)
• Supporting Data Creation Modules
• Supporting RAG Optimization Nodes & modules
• Supporting Evaluation Metrics
• FaQ

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]"

RAG Optimization requires two types of data: QA dataset and Corpus dataset.

1. QA dataset file (qa.parquet)
2. Corpus dataset file (corpus.parquet)

QA dataset is important for accurate and reliable evaluation and optimization.

Corpus dataset is critical to the performance of RAGs. This is because RAG uses the corpus to retrieve documents and generate answers using it.

modules:
  - module_type: langchain_parse
    parse_method: pdfminer

You can also use multiple Parse modules at once. However, in this case, you'll need to return a new process for each parsed result.

You can parse your raw documents with just a few lines of code.

from autorag.parser import Parser

parser = Parser(data_path_glob="your/data/path/*")
parser.start_parsing("your/path/to/parse_config.yaml")

modules:
  - module_type: llama_index_chunk
    chunk_method: Token
    chunk_size: 1024
    chunk_overlap: 24
    add_file_name: en

You can also use multiple Chunk modules at once. In this case, you need to use one corpus to create QA and then map the rest of the corpus to QA Data. If the chunk method is different, the retrieval_gt will be different, so we need to remap it to the QA dataset.

You can chunk your parsed results with just a few lines of code.

from autorag.chunker import Chunker

chunker = Chunker.from_parquet(parsed_data_path="your/parsed/data/path")
chunker.start_chunking("your/path/to/chunk_config.yaml")

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')

This guide provides a quick overview of building and running the AutoRAG Docker container for production, with instructions on setting up the environment for evaluation using your configuration and data paths.

Tip: If you want to build an image for a gpu version, you can use autoraghq/autorag:gpu or autoraghq/autorag:gpu-parsing

python sample_dataset/eli5/load_eli5_dataset.py --save_path projects/tutorial_1

Note: This step may take a long time to complete and involves OpenAI API calls, which may cost approximately $0.30.

docker run --rm -it \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $(pwd)/projects:/usr/src/app/projects \
  -e OPENAI_API_KEY=${OPENAI_API_KEY} \
  autoraghq/autorag:api evaluate \
  --config /usr/src/app/projects/tutorial_1/config.yaml \
  --qa_data_path /usr/src/app/projects/tutorial_1/qa_test.parquet \
  --corpus_data_path /usr/src/app/projects/tutorial_1/corpus.parquet \
  --project_dir /usr/src/app/projects/tutorial_1/

docker run --rm -it \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $(pwd)/projects:/usr/src/app/projects \
  -e OPENAI_API_KEY=${OPENAI_API_KEY} \
  autoraghq/autorag:api validate \
  --config /usr/src/app/projects/tutorial_1/config.yaml \
  --qa_data_path /usr/src/app/projects/tutorial_1/qa_test.parquet \
  --corpus_data_path /usr/src/app/projects/tutorial_1/corpus.parquet

docker run --rm -it \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $(pwd)/projects:/usr/src/app/projects \
  -e OPENAI_API_KEY=${OPENAI_API_KEY} \
  -p 8502:8502 \
  autoraghq/autorag:api dashboard \
    --trial_dir /usr/src/app/projects/tutorial_1/0

docker run --rm -it \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $(pwd)/projects:/usr/src/app/projects \
  -e OPENAI_API_KEY=${OPENAI_API_KEY} \
  -p 8501:8501 \
  autoraghq/autorag:api run_web --trial_path ./projects/tutorial_1/0

• -v ~/.cache/huggingface:/cache/huggingface: Mounts the host machine’s Hugging Face cache to /cache/huggingface in the container, enabling access to pre-downloaded models.
• -e OPENAI_API_KEY: ${OPENAI_API_KEY}: Passes the OPENAI_API_KEY from your host environment.

For more detailed instructions, refer to the Docker Installation Guide.

First, you need to set the config YAML file for your RAG optimization.

You can get various config YAML files at here. We highly recommend using pre-made config YAML files for starter.

If you want to make your own config YAML files, check out the Config YAML file section.

Here is an example of the config YAML file to use retrieval, prompt_maker, and generator nodes.

node_lines:
- node_line_name: retrieve_node_line  # Set Node Line (Arbitrary Name)
  nodes:
    - node_type: retrieval  # Set Retrieval Node
      strategy:
        metrics: [retrieval_f1, retrieval_recall, retrieval_ndcg, retrieval_mrr]  # Set Retrieval Metrics
      top_k: 3
      modules:
        - module_type: vectordb
          vectordb: default
        - module_type: bm25
        - module_type: hybrid_rrf
          weight_range: (4,80)
- node_line_name: post_retrieve_node_line  # Set Node Line (Arbitrary Name)
  nodes:
    - node_type: prompt_maker  # Set Prompt Maker Node
      strategy:
        metrics:   # Set Generation Metrics
          - metric_name: meteor
          - metric_name: rouge
          - metric_name: sem_score
            embedding_model: openai
      modules:
        - module_type: fstring
          prompt: "Read the passages and answer the given question. \n Question: {query} \n Passage: {retrieved_contents} \n Answer : "
    - node_type: generator  # Set Generator Node
      strategy:
        metrics:  # Set Generation Metrics
          - metric_name: meteor
          - metric_name: rouge
          - metric_name: sem_score
            embedding_model: openai
      modules:
        - module_type: openai_llm
          llm: gpt-4o-mini
          batch: 16

You can evaluate your RAG pipeline with just a few lines of code.

from autorag.evaluator import Evaluator

evaluator = Evaluator(qa_data_path='your/path/to/qa.parquet', corpus_data_path='your/path/to/corpus.parquet')
evaluator.start_trial('your/path/to/config.yaml')

or you can use the command line interface

autorag evaluate --config your/path/to/default_config.yaml --qa_data_path your/path/to/qa.parquet --corpus_data_path your/path/to/corpus.parquet

Once it is done, you can see several files and folders created in your current directory. At the trial folder named to numbers (like 0), you can check summary.csv file that summarizes the evaluation results and the best RAG pipeline for your data.

For more details, you can check out how the folder structure looks like at here.

You can run a dashboard to easily see the result.

autorag dashboard --trial_dir /your/path/to/trial_dir

You can use an optimal RAG pipeline right away from the trial folder. The trial folder is the directory used in the running dashboard. (like 0, 1, 2, ...)

from autorag.deploy import Runner

runner = Runner.from_trial_folder('/your/path/to/trial_dir')
runner.run('your question')

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.

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

You can deploy the advanced web interface featured by Kotaemon to the fly.io. Go here to use it and deploy to the fly.io.

Example :

• You can check our all Parsing Modules at here
• You can check our all Chunk Modules at here

You can check our all supporting Nodes & modules at here

You can check our all supporting Evaluation Metrics at here

• Retrieval Metrics
• Retrieval Token Metrics
• Generation Metrics

🛣️ Roadmap

💻 Hardware Specs

⭐ Running AutoRAG

🍯 Tips/Tricks

☎️ TroubleShooting

Talk with us! We are always open to talk with you.

• 🎤 Talk with Jeffrey

• 🦜 Talk with Bwook

Thanks go to these wonderful people:

We are developing AutoRAG as open-source.

So this project welcomes contributions and suggestions. Feel free to contribute to this project.

Plus, check out our detailed documentation at here.