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

Before using RAPTOR, ensure Python 3.8 is installed. Clone the RAPTOR repository and install necessary dependencies:

git clone https://github.com/parthsarthi03/raptor.git
cd raptor
pip install -r requirements.txt

To get started with RAPTOR, follow these steps:

First, set your OpenAI API key and initialize the RAPTOR configuration:

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()

Add your text documents to RAPTOR for indexing:

with open('sample.txt', 'r') as file:
    text = file.read()
RA.add_documents(text)

You can now use RAPTOR to answer questions based on the indexed documents:

question = "How did Cinderella reach her happy ending?"
answer = RA.answer_question(question=question)
print("Answer: ", answer)

Save the constructed tree to a specified path:

SAVE_PATH = "demo/cinderella"
RA.save(SAVE_PATH)

Load the saved tree back into RAPTOR:

RA = RetrievalAugmentation(tree=SAVE_PATH)
answer = RA.answer_question(question=question)

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:

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:

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

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:

from raptor import BaseQAModel

class CustomQAModel(BaseQAModel):
    def __init__(self):
        # Initialize your model here
        pass

    def answer_question(self, context, question):
        # Implement your QA logic here
        # Return the answer as a string
        answer = "Your answer here"
        return answer

To use a different embedding model, extend the BaseEmbeddingModel class. Implement the create_embedding method, which should return a vector representation of the input text:

from raptor import BaseEmbeddingModel

class CustomEmbeddingModel(BaseEmbeddingModel):
    def __init__(self):
        # Initialize your model here
        pass

    def create_embedding(self, text):
        # Implement your embedding logic here
        # Return the embedding as a numpy array or a list of floats
        embedding = [0.0] * embedding_dim  # Replace with actual embedding logic
        return embedding

After implementing your custom models, integrate them with RAPTOR as follows:

from raptor import RetrievalAugmentation, RetrievalAugmentationConfig

# Initialize your custom models
custom_summarizer = CustomSummarizationModel()
custom_qa = CustomQAModel()
custom_embedding = CustomEmbeddingModel()

# Create a config with your custom models
custom_config = RetrievalAugmentationConfig(
    summarization_model=custom_summarizer,
    qa_model=custom_qa,
    embedding_model=custom_embedding
)

# Initialize RAPTOR with your custom config
RA = RetrievalAugmentation(config=custom_config)

Check out demo.ipynb for examples on how to specify your own summarization/QA models, such as Llama/Mistral/Gemma, and Embedding Models such as SBERT, for use with RAPTOR.

Note: More examples and ways to configure RAPTOR are forthcoming. Advanced usage and additional features will be provided in the documentation and repository updates.

RAPTOR is an open-source project, and contributions are welcome. Whether you're fixing bugs, adding new features, or improving documentation, your help is appreciated.

RAPTOR is released under the MIT License. See the LICENSE file in the repository for full details.

If RAPTOR assists in your research, please cite it as follows:

@inproceedings{sarthi2024raptor,
    title={RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval},
    author={Sarthi, Parth and Abdullah, Salman and Tuli, Aditi and Khanna, Shubh and Goldie, Anna and Manning, Christopher D.},
    booktitle={International Conference on Learning Representations (ICLR)},
    year={2024}
}

Stay tuned for more examples, configuration guides, and updates.