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The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
| Date | Stars |
|---|---|
| 2026-07-24 | 1728 |
| 2026-07-25 | 1728 |
| 2026-07-28 | 1728 |
| 2026-07-30 | 1728 |
| 2026-08-06 | 1728 |
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## RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
**RAPTOR** introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models.
For detailed methodologies and implementations, refer to the original paper:
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://arxiv.org/abs/2401.18059)
[](https://huggingface.co/papers/2401.18059)
[](https://paperswithcode.com/sota/question-answering-on-quality?p=raptor-recursive-abstractive-processing-for)
## Installation
Before using RAPTOR, ensure Python 3.8+ is installed. Clone the RAPTOR repository and install necessary dependencies:
```bash
git clone https://github.com/parthsarthi03/raptor.git
cd raptor
pip install -r requirements.txt
```
## Basic Usage
To get started with RAPTOR, follow these steps:
### Setting Up RAPTOR
First, set your OpenAI API key and initialize the RAPTOR configuration:
```python
import os
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
from raptor import RetrievalAugmentation
# Initialize with default configuration. For advanced configurations, check the documentation. [WIP]
RA = RetrievalAugmentation()
```
### Adding Documents to the Tree
Add your text documents to RAPTOR for indexing:
```python
with open('sample.txt', 'r') as file:
text = file.read()
RA.add_documents(text)
```
### Answering Questions
You can now use RAPTOR to answer questions based on the indexed documents:
```python
question = "How did Cinderella reach her happy ending?"
answer = RA.answer_question(question=question)
print("Answer: ", answer)
```
### Saving and Loading the Tree
Save the constructed tree to a specified path:
```python
SAVE_PATH = "demo/cinderella"
RA.save(SAVE_PATH)
```
Load the saved tree back into RAPTOR:
```python
RA = RetrievalAugmentation(tree=SAVE_PATH)
answer = RA.answer_question(question=question)
```
### Extending RAPTOR with other Models
RAPTOR is designed to be flexible and allows you to integrate any models for summarization, question-answering (QA), and embedding generation. Here is how to extend RAPTOR with your own models:
#### Custom Summarization Model
If you wish to use a different language model for summarization, you can do so by extending the `BaseSummarizationModel` class. Implement the `summarize` method to integrate your custom summarization logic:
```python
from raptor import BaseSummarizationModel
class CustomSummarizationModel(BaseSummarizationModel):
def __init__(self):
# Initialize your model here
pass
def summarize(self, context, max_tokens=150):
# Implement your summarization logic here
# Return the summary as a string
summary = "Your summary here"
return summary
```
#### Custom QA Model
For custom QA models, extend the `BaseQAModel` class and implement the `answer_question` method. This method should return the best answer found by your model given a context and a question:
```python
from raptor import BaseQAModel
class CustomQExcerpt of 6,715 characters
Read on GitHub
16
Alphabet boys
2
1
1
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matched fp:0a59de32788b6ee6, topic:rag, topic:retrieval-augmented-generation, readme:retrieval augmented
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