| Name | AutoRAG JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | Automatically Evaluate RAG pipelines with your own data. Find optimal structure for new RAG product. |
| upload_time | 2024-05-28 05:00:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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| keywords |
rag
autorag
autorag
rag-evaluation
evaluation
rag-auto
automl
automl-rag
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
pandas
tqdm
tiktoken
openai
rank_bm25
transformers
swifter
pyyaml
pyarrow
fastparquet
sacrebleu
evaluate
rouge_score
rich
chromadb
click
fastapi
uvicorn
torch
sentencepiece
guidance
cohere
tokenlog
aiohttp
bert_score
sentence-transformers
FlagEmbedding
ragas
ray
kiwipiepy
llmlingua
llama-index
llama-index-core
llama-index-embeddings-openai
llama-index-embeddings-huggingface
llama-index-llms-openai
llama-index-llms-huggingface
llama-index-llms-openai-like
llama-index-retrievers-bm25
streamlit
langchain-core
panel
seaborn
ipykernel
ipywidgets
ipywidgets_bokeh
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# AutoRAG
RAG AutoML tool for automatically finds an optimal RAG pipeline for your data.
Explore our đź“– [Document](https://marker-inc-korea.github.io/AutoRAG/)!!
Plus, join our đź“ž [Discord](https://discord.gg/P4DYXfmSAs) Community.
---
đź“Ś Colab Tutorial
- [Step 1: Basic of AutoRAG | Optimizing your RAG pipeline](https://colab.research.google.com/drive/19OEQXO_pHN6gnn2WdfPd4hjnS-4GurVd?usp=sharing)
- [Step 2: Create evaluation dataset](https://colab.research.google.com/drive/1HXjVHCLTaX7mkmZp3IKlEPt0B3jVeHvP#scrollTo=cgFUCuaUZvTr)
---
🚨 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](https://youtu.be/2ojK8xjyXAU?feature=shared)
# đź“‘ Index
- [Introduction](#introduction)
- [Quick Install](#-quick-install)
- [Index](#-index)
- [Strengths](#-strengths)
- [QuickStart](#-quickstart)
- [1. Prepare your evaluation data](#1-prepare-your-evaluation-data)
- [2. Evaluate your data to various RAG modules](#2-evaluate-your-data-to-various-rag-modules)
- [3. Use a found optimal RAG pipeline](#3-use-a-found-optimal-rag-pipeline)
- [4. Run Dashboard to see the result](#4-run-dashboard)
- [5. Share your RAG pipeline](#5-share-your-rag-pipeline)
- [+ Config yaml file](#-create-your-own-config-yaml-file)
- [Supporting RAG modules](#supporting-nodes--modules)
- [Roadmap](#roadmap)
- [Contribution](#contribution)
# Introduction
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 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.
# ⚡ Quick Install
```bash
pip install AutoRAG
```
# đź’Ş Strengths
### **1. Find your RAG baseline**
Benchmark RAG pipelines with few lines of code. You can quickly get a high-performance RAG
pipeline just for your data. Don’t waste time dealing with complex RAG modules and academic paper. Focus on your data.
### **2. Analyze where is wrong**
Sometimes it is hard to keep tracking where is the major problem within your RAG pipeline.
AutoRAG gives you the data of it, so you can analyze and focus where is the major problem and where you to focus on.
### **3. Quick Starter Pack for your new RAG product**
Get the most effective RAG workflow among many pipelines, and start from
there.
Don’t start at toy-project level, start from advanced level.
### **4. Share your experiment to others**
It's really easy to share your experiment to others. Share your config yaml file and
summary csv files. Plus, check out others result and adapt to your use-case.
# ⚡ QuickStart
### 1. Prepare your evaluation data
For evaluation, you need to prepare just three files.
- QA dataset file (qa.parquet)
- Corpus dataset file (corpus.parquet)
- Config yaml file (config.yaml)
There is a template for your evaluation data for using AutoRAG.
- Check out how to make evaluation data
at [here](https://marker-inc-korea.github.io/AutoRAG/data_creation/tutorial.html).
- Check out the evaluation data rule
at [here](https://marker-inc-korea.github.io/AutoRAG/data_creation/data_format.html).
- Plus, you can get example datasets for testing AutoRAG at [here](./sample_dataset).
### 2. Evaluate your data to various RAG modules
You can get various config yaml files at [here](./sample_config).
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](#-create-your-own-config-yaml-file)
section.
You can evaluate your RAG pipeline with just a few lines of code.
```python
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 command line interface
```bash
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 at 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](https://marker-inc-korea.github.io/AutoRAG/optimization/folder_structure.html).
### 3. Use a found optimal RAG pipeline
You can use a found optimal RAG pipeline right away.
It needs just a few lines of code, and you are ready to use!
First, you need to build pipeline yaml file from your evaluated trial folder.
You can find the trial folder in your current directory.
Just looking folder like '0' or other numbers.
```python
from autorag.deploy import Runner
runner = Runner.from_trial_folder('your/path/to/trial_folder')
runner.run('your question')
```
Or, you can run this pipeline as api server.
You can use python code or CLI command.
Check out API endpoint at [here](https://marker-inc-korea.github.io/AutoRAG/deploy/api_endpoint.html).
```python
from autorag.deploy import Runner
runner = Runner.from_trial_folder('your/path/to/trial_folder')
runner.run_api_server()
```
You can run api server with CLI command.
```bash
autorag run_api --config_path your/path/to/pipeline.yaml --host 0.0.0.0 --port 8000
```
### 4. Run Dashboard
You can run dashboard to easily see the result.
```bash
autorag dashboard --trial_dir /your/path/to/trial_dir
```
### 5. Share your RAG pipeline
You can use your RAG pipeline from extracted pipeline yaml file.
This extracted pipeline is great for sharing your RAG pipeline to others.
You must run this at project folder, which contains datas in data folder, and ingested corpus for retrieval at resources
folder.
```python
from autorag.deploy import extract_best_config
pipeline_dict = extract_best_config(trial_path='your/path/to/trial_folder', output_path='your/path/to/pipeline.yaml')
```
### âž• Create your own Config yaml file
You can build your own evaluation process with config yaml file.
You can check detailed explanation how to configure each module and node
at [here](https://marker-inc-korea.github.io/AutoRAG/nodes/index.html#).
There is a simple yaml file example.
It evaluates two retrieval modules which are BM25 and Vector Retriever, and three reranking modules.
Lastly, it generates prompt and makes generation with two other LLM models and three temperatures.
```yaml
node_lines:
- node_line_name: retrieve_node_line
nodes:
- node_type: retrieval
strategy:
metric: retrieval_f1
top_k: 50
modules:
- module_type: bm25
- module_type: vector
embedding_model: [ openai, openai_embed_3_large ]
- module_type: hybrid_rrf
target_modules: ('bm25', 'vectordb')
rrf_k: [ 3, 5, 10 ]
- node_type: reranker
strategy:
metric: retrieval_precision
speed_threshold: 5
top_k: 3
modules:
- module_type: upr
- module_type: tart
prompt: Arrange the following sentences in the correct order.
- module_type: monoT5
- node_line_name: generate_node_line
nodes:
- node_type: prompt_maker
modules:
- module_type: fstring
prompt: "This is a news dataset, crawled from finance news site. You need to make detailed question about finance news. Do not make questions that not relevant to economy or finance domain.\n{retrieved_contents}\n\nQ: {query}\nA:"
- node_type: generator
strategy:
metric:
- metric_name: meteor
- metric_name: rouge
- metric_name: sem_score
embedding_model: openai
- metric_name: g_eval
model: gpt-3.5-turbo
modules:
- module_type: llama_index_llm
llm: openai
model: [ gpt-3.5-turbo-16k, gpt-3.5-turbo-1106 ]
temperature: [ 0.5, 1.0, 1.5 ]
```
# âť—Supporting Nodes & modules
You can check our all supporting Nodes & modules
at [here](https://edai.notion.site/Supporting-Nodes-modules-0ebc7810649f4e41aead472a92976be4?pvs=4)
# âť—Supporting Evaluation Metrics
You can check our all supporting Evaluation Metrics
at [here](https://edai.notion.site/Supporting-metrics-867d71caefd7401c9264dd91ba406043?pvs=4)
- [Retrieval Metrics](https://edai.notion.site/Retrieval-Metrics-dde3d9fa1d9547cdb8b31b94060d21e7?pvs=4)
- [Retrieval Token Metrics](https://edai.notion.site/Retrieval-Token-Metrics-c3e2d83358e04510a34b80429ebb543f?pvs=4)
- [Generation Metrics](https://edai.notion.site/Retrieval-Token-Metrics-c3e2d83358e04510a34b80429ebb543f?pvs=4)
# 🛣Roadmap
- [ ] Policy Module for modular RAG pipeline
- [x] Visualize evaluation result
- [ ] Visualize config yaml file
- [x] More RAG modules support
- [x] Token usage strategy
- [ ] Multi-modal support
- [x] More evaluation metrics
- [ ] Answer Filtering Module
- [x] Restart optimization from previous trial
# Contribution
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](https://marker-inc-korea.github.io/AutoRAG/index.html).
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"description": "# AutoRAG\n\nRAG AutoML tool for automatically finds an optimal RAG pipeline for your data.\n\nExplore our \ud83d\udcd6 [Document](https://marker-inc-korea.github.io/AutoRAG/)!!\n\nPlus, join our \ud83d\udcde [Discord](https://discord.gg/P4DYXfmSAs) Community.\n\n---\n\n\ud83d\udccc Colab Tutorial\n\n- [Step 1: Basic of AutoRAG | Optimizing your RAG pipeline](https://colab.research.google.com/drive/19OEQXO_pHN6gnn2WdfPd4hjnS-4GurVd?usp=sharing)\n- [Step 2: Create evaluation dataset](https://colab.research.google.com/drive/1HXjVHCLTaX7mkmZp3IKlEPt0B3jVeHvP#scrollTo=cgFUCuaUZvTr)\n\n---\n\ud83d\udea8 YouTube Tutorial\n\nhttps://github.com/Marker-Inc-Korea/AutoRAG/assets/96727832/c0d23896-40c0-479f-a17b-aa2ec3183a26\n\n_Muted by default, enable sound for voice-over_\n\nYou can see on [YouTube](https://youtu.be/2ojK8xjyXAU?feature=shared)\n\n# \ud83d\udcd1 Index\n\n- [Introduction](#introduction)\n- [Quick Install](#-quick-install)\n- [Index](#-index)\n- [Strengths](#-strengths)\n- [QuickStart](#-quickstart)\n - [1. Prepare your evaluation data](#1-prepare-your-evaluation-data)\n - [2. Evaluate your data to various RAG modules](#2-evaluate-your-data-to-various-rag-modules)\n - [3. Use a found optimal RAG pipeline](#3-use-a-found-optimal-rag-pipeline)\n - [4. Run Dashboard to see the result](#4-run-dashboard)\n - [5. Share your RAG pipeline](#5-share-your-rag-pipeline)\n - [+ Config yaml file](#-create-your-own-config-yaml-file)\n- [Supporting RAG modules](#supporting-nodes--modules)\n- [Roadmap](#roadmap)\n- [Contribution](#contribution)\n\n# Introduction\n\nThere are many RAG pipelines and modules out there,\nbut you don\u2019t know what pipeline is great for \u201cyour own data\u201d and \"your own use-case.\"\nMaking and evaluating all RAG modules is very time-consuming and hard to do.\nBut without it, you will never know which RAG pipeline is the best for your own use-case.\n\nAutoRAG is a tool for finding optimal RAG pipeline for \u201cyour data.\u201d\nYou can evaluate various RAG modules automatically with your own evaluation data,\nand find the best RAG pipeline for your own use-case.\n\nAutoRAG supports a simple way to evaluate many RAG module combinations.\nTry now and find the best RAG pipeline for your own use-case.\n\n# \u26a1 Quick Install\n\n```bash\npip install AutoRAG\n```\n\n# \ud83d\udcaa Strengths\n\n### **1. Find your RAG baseline**\n\nBenchmark RAG pipelines with few lines of code. You can quickly get a high-performance RAG\npipeline just for your data. Don\u2019t waste time dealing with complex RAG modules and academic paper. Focus on your data.\n\n### **2. Analyze where is wrong**\n\nSometimes it is hard to keep tracking where is the major problem within your RAG pipeline.\nAutoRAG gives you the data of it, so you can analyze and focus where is the major problem and where you to focus on.\n\n### **3. Quick Starter Pack for your new RAG product**\n\nGet the most effective RAG workflow among many pipelines, and start from\nthere.\nDon\u2019t start at toy-project level, start from advanced level.\n\n### **4. Share your experiment to others**\n\nIt's really easy to share your experiment to others. Share your config yaml file and\nsummary csv files. Plus, check out others result and adapt to your use-case.\n\n# \u26a1 QuickStart\n\n### 1. Prepare your evaluation data\n\nFor evaluation, you need to prepare just three files.\n\n- QA dataset file (qa.parquet)\n- Corpus dataset file (corpus.parquet)\n- Config yaml file (config.yaml)\n\nThere is a template for your evaluation data for using AutoRAG.\n\n- Check out how to make evaluation data\n at [here](https://marker-inc-korea.github.io/AutoRAG/data_creation/tutorial.html).\n- Check out the evaluation data rule\n at [here](https://marker-inc-korea.github.io/AutoRAG/data_creation/data_format.html).\n- Plus, you can get example datasets for testing AutoRAG at [here](./sample_dataset).\n\n### 2. Evaluate your data to various RAG modules\n\nYou can get various config yaml files at [here](./sample_config).\nWe highly recommend using pre-made config yaml files for starter.\n\nIf you want to make your own config yaml files, check out the [Config yaml file](#-create-your-own-config-yaml-file)\nsection.\n\nYou can evaluate your RAG pipeline with just a few lines of code.\n\n```python\nfrom autorag.evaluator import Evaluator\n\nevaluator = Evaluator(qa_data_path='your/path/to/qa.parquet', corpus_data_path='your/path/to/corpus.parquet')\nevaluator.start_trial('your/path/to/config.yaml')\n```\n\nor you can use command line interface\n\n```bash\nautorag evaluate --config your/path/to/default_config.yaml --qa_data_path your/path/to/qa.parquet --corpus_data_path your/path/to/corpus.parquet\n```\n\nOnce it is done, you can see several files and folders created at your current directory.\nAt the trial folder named to numbers (like 0),\nyou can check `summary.csv` file that summarizes the evaluation results and the best RAG pipeline for your data.\n\nFor more details, you can check out how the folder structure looks like\nat [here](https://marker-inc-korea.github.io/AutoRAG/optimization/folder_structure.html).\n\n### 3. Use a found optimal RAG pipeline\n\nYou can use a found optimal RAG pipeline right away.\nIt needs just a few lines of code, and you are ready to use!\n\nFirst, you need to build pipeline yaml file from your evaluated trial folder.\nYou can find the trial folder in your current directory.\nJust looking folder like '0' or other numbers.\n\n```python\nfrom autorag.deploy import Runner\n\nrunner = Runner.from_trial_folder('your/path/to/trial_folder')\nrunner.run('your question')\n```\n\nOr, you can run this pipeline as api server.\nYou can use python code or CLI command.\nCheck out API endpoint at [here](https://marker-inc-korea.github.io/AutoRAG/deploy/api_endpoint.html).\n\n```python\nfrom autorag.deploy import Runner\n\nrunner = Runner.from_trial_folder('your/path/to/trial_folder')\nrunner.run_api_server()\n```\n\nYou can run api server with CLI command.\n\n```bash\nautorag run_api --config_path your/path/to/pipeline.yaml --host 0.0.0.0 --port 8000\n```\n\n### 4. Run Dashboard\n\nYou can run dashboard to easily see the result.\n\n```bash\nautorag dashboard --trial_dir /your/path/to/trial_dir\n```\n\n### 5. Share your RAG pipeline\n\nYou can use your RAG pipeline from extracted pipeline yaml file.\nThis extracted pipeline is great for sharing your RAG pipeline to others.\n\nYou must run this at project folder, which contains datas in data folder, and ingested corpus for retrieval at resources\nfolder.\n\n```python\nfrom autorag.deploy import extract_best_config\n\npipeline_dict = extract_best_config(trial_path='your/path/to/trial_folder', output_path='your/path/to/pipeline.yaml')\n```\n\n### \u2795 Create your own Config yaml file\n\nYou can build your own evaluation process with config yaml file.\nYou can check detailed explanation how to configure each module and node\nat [here](https://marker-inc-korea.github.io/AutoRAG/nodes/index.html#).\n\nThere is a simple yaml file example.\n\nIt evaluates two retrieval modules which are BM25 and Vector Retriever, and three reranking modules.\nLastly, it generates prompt and makes generation with two other LLM models and three temperatures.\n\n```yaml\nnode_lines:\n - node_line_name: retrieve_node_line\n nodes:\n - node_type: retrieval\n strategy:\n metric: retrieval_f1\n top_k: 50\n modules:\n - module_type: bm25\n - module_type: vector\n embedding_model: [ openai, openai_embed_3_large ]\n - module_type: hybrid_rrf\n target_modules: ('bm25', 'vectordb')\n rrf_k: [ 3, 5, 10 ]\n - node_type: reranker\n strategy:\n metric: retrieval_precision\n speed_threshold: 5\n top_k: 3\n modules:\n - module_type: upr\n - module_type: tart\n prompt: Arrange the following sentences in the correct order.\n - module_type: monoT5\n - node_line_name: generate_node_line\n nodes:\n - node_type: prompt_maker\n modules:\n - module_type: fstring\n prompt: \"This is a news dataset, crawled from finance news site. You need to make detailed question about finance news. Do not make questions that not relevant to economy or finance domain.\\n{retrieved_contents}\\n\\nQ: {query}\\nA:\"\n - node_type: generator\n strategy:\n metric:\n - metric_name: meteor\n - metric_name: rouge\n - metric_name: sem_score\n embedding_model: openai\n - metric_name: g_eval\n model: gpt-3.5-turbo\n modules:\n - module_type: llama_index_llm\n llm: openai\n model: [ gpt-3.5-turbo-16k, gpt-3.5-turbo-1106 ]\n temperature: [ 0.5, 1.0, 1.5 ]\n\n```\n\n# \u2757Supporting Nodes & modules\n\nYou can check our all supporting Nodes & modules\nat [here](https://edai.notion.site/Supporting-Nodes-modules-0ebc7810649f4e41aead472a92976be4?pvs=4)\n\n# \u2757Supporting Evaluation Metrics\n\nYou can check our all supporting Evaluation Metrics\nat [here](https://edai.notion.site/Supporting-metrics-867d71caefd7401c9264dd91ba406043?pvs=4)\n\n- [Retrieval Metrics](https://edai.notion.site/Retrieval-Metrics-dde3d9fa1d9547cdb8b31b94060d21e7?pvs=4)\n- [Retrieval Token Metrics](https://edai.notion.site/Retrieval-Token-Metrics-c3e2d83358e04510a34b80429ebb543f?pvs=4)\n- [Generation Metrics](https://edai.notion.site/Retrieval-Token-Metrics-c3e2d83358e04510a34b80429ebb543f?pvs=4)\n\n# \ud83d\udee3Roadmap\n\n- [ ] Policy Module for modular RAG pipeline\n- [x] Visualize evaluation result\n- [ ] Visualize config yaml file\n- [x] More RAG modules support\n- [x] Token usage strategy\n- [ ] Multi-modal support\n- [x] More evaluation metrics\n- [ ] Answer Filtering Module\n- [x] Restart optimization from previous trial\n\n# Contribution\n\nWe are developing AutoRAG as open-source.\n\nSo this project welcomes contributions and suggestions. Feel free to contribute to this project.\n\nPlus, check out our detailed documentation at [here](https://marker-inc-korea.github.io/AutoRAG/index.html).\n",
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