base on The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval <!-- <p align="center"> <img align="center" src="raptor.jpg" width="1000px" /> </p> <p align="left"> --> <!-- <picture> <source media="(prefers-color-scheme: dark)" srcset="raptor.jpg" width="1000px"> <source media="(prefers-color-scheme: light)" srcset="raptor_dark.png" width="1000px"> </picture> --> <picture> <source media="(prefers-color-scheme: dark)" srcset="raptor_dark.png"> <img alt="Shows an illustrated sun in light color mode and a moon with stars in dark color mode." src="raptor.jpg"> </picture> ## 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 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 ``` #### Custom Embedding Model 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: ```python 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 ``` #### Integrating Custom Models with RAPTOR After implementing your custom models, integrate them with RAPTOR as follows: ```python 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. ## Contributing 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. ## License RAPTOR is released under the MIT License. See the LICENSE file in the repository for full details. ## Citation If RAPTOR assists in your research, please cite it as follows: ```bibtex @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. 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