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add raptor (run-llama#11527)
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logan-markewich authored Mar 1, 2024
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1 change: 1 addition & 0 deletions .github/workflows/publish_sub_package.yml
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push:
branches:
- main

env:
POETRY_VERSION: "1.6.1"
PYTHON_VERSION: "3.10"
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153 changes: 153 additions & 0 deletions llama-index-packs/llama-index-packs-raptor/.gitignore
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llama_index/_static
.DS_Store
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
bin/
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# PyInstaller
# Usually these files are written by a python script from a template
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4 changes: 4 additions & 0 deletions llama-index-packs/llama-index-packs-raptor/BUILD
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poetry_requirements(
name="poetry",
module_mapping={"umap-learn": ["umap"], "scikit-learn": ["sklearn"]}
)
17 changes: 17 additions & 0 deletions llama-index-packs/llama-index-packs-raptor/Makefile
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GIT_ROOT ?= $(shell git rev-parse --show-toplevel)

help: ## Show all Makefile targets.
@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'

format: ## Run code autoformatters (black).
pre-commit install
git ls-files | xargs pre-commit run black --files

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78 changes: 78 additions & 0 deletions llama-index-packs/llama-index-packs-raptor/README.md
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# Raptor Retriever LlamaPack

This LlamaPack shows how to use an implementation of RAPTOR with llama-index, leveraging the RAPTOR pack.

RAPTOR works by recursively clustering and summarizing clusters in layers for retrieval.

There two retrieval modes:

- tree_traversal -- traversing the tree of clusters, performing top-k at each level in the tree.
- collapsed -- treat the entire tree as a giant pile of nodes, perform simple top-k.

See [the paper](https://arxiv.org/abs/2401.18059) for full algorithm details.

## CLI Usage

You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:

```bash
llamaindex-cli download-llamapack RaptorPack --download-dir ./raptor_pack
```

You can then inspect/modify the files at `./raptor_pack` and use them as a template for your own project.

## Code Usage

You can alternaitvely install the package:

`pip install llama-index-packs-raptor`

Then, you can import and initialize the pack! This will perform clustering and summarization over your data.

```python
from llama_index.packs.raptor import RaptorPack

pack = RaptorPack(documents, llm=llm, embed_model=embed_model)
```

The `run()` function is a light wrapper around `retriever.retrieve()`.

```python
nodes = pack.run(
"query",
mode="collapsed", # or tree_traversal
)
```

You can also use modules individually.

```python
# get the retriever
retriever = pack.retriever
```

## Persistence

The `RaptorPack` comes with the `RaptorRetriever`, which offers ways of saving/reloading!

If you are using a remote vector-db, just pass it in

```python
# Pack usage
pack = RaptorPack(..., vector_store=vector_store)

# RaptorRetriever usage
retriever = RaptorRetriever(..., vector_store=vector_store)
```

Then, to re-connect, just pass in the vector store again and an empty list of documents

```python
# Pack usage
pack = RaptorPack([], ..., vector_store=vector_store)

# RaptorRetriever usage
retriever = RaptorRetriever([], ..., vector_store=vector_store)
```

Check out the [notebook here for complete details!](https://github.com/run-llama/llama_index/blob/main/llama-index-packs/llama-index-packs-raptor/examples/raptor.ipynb).
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