Byaldi

Name	Byaldi JSON
Version	0.0.7 JSON
	download
home_page	None
Summary	Use late-interaction multi-modal models such as ColPali in just a few lines of code.
upload_time	2024-11-13 08:06:29
maintainer	None
docs_url	None
author	None
requires_python	>=3.9
license	Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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keywords	reranking retrieval rag nlp colpali colbert multi-modal
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requirements	No requirements were recorded.
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# Welcome to Byaldi
_Did you know? In the movie RAGatouille, the dish Remy makes is not actually a ratatouille, but a refined version of the dish called "Confit Byaldi"._

⚠️ This is the pre-release version of Byaldi. Please report any issue you encounter, there will likely be quite a few quirks to iron out!

Byaldi is [RAGatouille](https://github.com/answerdotai/ragatouille)'s mini sister project. It is a simple wrapper around the [ColPali](https://github.com/illuin-tech/colpali) repository to make it easy to use late-interaction multi-modal models such as ColPALI with a familiar API.

## Getting started

First, a warning: This is a pre-release library, using uncompressed indexes and lacking other kinds of refinements.

Currently, we support all models supported by the underlying [colpali-engine](https://github.com/illuin-tech/colpali), including the newer, and better, ColQwen2 checkpoints, such as `vidore/colqwen2-v1.0`. Broadly, the aim is for byaldi to support all ColVLM models.

Additional backends will be supported in future updates. As byaldi exists to facilitate the adoption of multi-modal retrievers, we intend to also add support for models such as [VisRAG](https://github.com/openbmb/visrag).

Eventually, we'll add an HNSW indexing mechanism, pooling, and, who knows, maybe 2-bit quantization?

It will get updated as the multi-modal ecosystem develops further!

### Pre-requisites

#### Poppler

To convert pdf to images with a friendly license, we use the `pdf2image` library. This library requires `poppler` to be installed on your system. Poppler is very easy to install by following the instructions [on their website](https://poppler.freedesktop.org/). The tl;dr is:

__MacOS with homebrew__

```bash
brew install poppler
```

__Debian/Ubuntu__

```bash
sudo apt-get install -y poppler-utils
```

#### Flash-Attention

Gemma uses a recent version of flash attention. To make things run as smoothly as possible, we'd recommend that you install it after installing the library:

```bash
pip install --upgrade byaldi
pip install flash-attn
```

#### Hardware

ColPali uses multi-billion parameter models to encode documents. We recommend using a GPU for smooth operations, though weak/older GPUs are perfectly fine! Encoding your collection would suffer from poor performance on CPU or MPS.

## Using `byaldi`

Byaldi is largely modeled after RAGatouille, meaning that everything is designed to take the fewest lines of code possible, so you can very quickly build on top of it rather than spending time figuring out how to create a retrieval pipeline.

### Loading a model

Loading a model with `byaldi` is extremely straightforward:

If you've already got an index, and wish to load it along with the model necessary to query it, you can do so just as easily:

```python3
from byaldi import RAGMultiModalModel
# Optionally, you can specify an `index_root`, which is where it'll look for the index. It defaults to ".byaldi/".
RAG = RAGMultiModalModel.from_index("your_index_name")
```

### Creating an index
Creating an index with `byaldi` is simple and flexible. **You can index a single PDF file, a single image file, or a directory containing multiple of those**. Here's how to create an index:

```python3
from byaldi import RAGMultiModalModel
# Optionally, you can specify an `index_root`, which is where it'll save the index. It defaults to ".byaldi/".
RAG = RAGMultiModalModel.from_pretrained("vidore/colqwen2-v1.0")
RAG.index(
input_path="docs/", # The path to your documents
index_name=index_name, # The name you want to give to your index. It'll be saved at `index_root/index_name/`.
store_collection_with_index=False, # Whether the index should store the base64 encoded documents.
doc_ids=[0, 1, 2], # Optionally, you can specify a list of document IDs. They must be integers and match the number of documents you're passing. Otherwise, doc_ids will be automatically created.
metadata=[{"author": "John Doe", "date": "2021-01-01"}], # Optionally, you can specify a list of metadata for each document. They must be a list of dictionaries, with the same length as the number of documents you're passing.
overwrite=True # Whether to overwrite an index if it already exists. If False, it'll return None and do nothing if `index_root/index_name` exists.
)
```

And that's it! The model will start spinning and create your index, exporting all the necessary information to disk when it's done. You can then use the `RAGMultiModalModel.from_index("your_index_name")` method presented above to load it whenever needed (you don't need to do this right after creating it -- it's already loaded in memory and ready to go!).

The main decision you'll have to make here is whether you want to set `store_collection_with_index` to True or not. If set to true, it greatly simplifies your workflow: the base64-encoded version of relevant documents will be returned as part of the query results, so you can immediately pipe it to your LLM. However, it adds considerable memory and storage requirements to your index, so you might want to set it to False (the default setting) if you're short on those resources, and create the base64 encoded versions yourself whenever needed.

### Searching

Once you've created or loaded an index, you can start searching for relevant documents. Again, it's a single, very straightforward command:

```python3
results = RAG.search(query, k=3)
```

Results will be a list of `Result` objects, which you can also treat as normal dictionaries. Each result will be in this format:
```python3
[
{
"doc_id": 0,
"page_num": 10,
"score": 12.875,
"metadata": {},
"base64": None
},
...
]
```

`page_num` are 1-indexed, while doc_ids are 0-indexed. This is to make simpler to operate with other PDF manipulation tools, where the 1st page is generally page 1. `page_num` for images and single-page PDFs will always be 1, it's only useful for longer PDFs.

If you've passed metadata or encoded with the flag to store the base64 versions, these fields will be populated. Results are sorted by score, so item 0 from the list will always be the most relevant document, etc...

### Adding documents to an existing index

Since indexes are in-memory, they're addition-friendly! If you need to ingest some new pdfs, just load your index with `from_index`, and then, call `add_to_index`, with similar parameters to the original `index()` method:

```python3
RAG.add_to_index("path_to_new_docs",
store_collection_with_index: bool = False,
...
)
```

Raw data

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    "_id": null,
    "home_page": null,
    "name": "Byaldi",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": "Ben Clavi\u00e9 <bc@answer.ai>, Tony Wu <tony.wu@illuin.tech>",
    "keywords": "reranking, retrieval, rag, nlp, colpali, colbert, multi-modal",
    "author": null,
    "author_email": "Ben Clavi\u00e9 <bc@answer.ai>",
    "download_url": "https://files.pythonhosted.org/packages/83/29/9824d48581d1e85f5cb4fb6618f64664847ec41104766ae45437ac1a803c/byaldi-0.0.7.tar.gz",
    "platform": null,
    "description": "# Welcome to Byaldi\n_Did you know? In the movie RAGatouille, the dish Remy makes is not actually a ratatouille, but a refined version of the dish called \"Confit Byaldi\"._\n\n<p align=\"center\"><img width=350 alt=\"The Byaldi logo, it's a cheerful rat using a magnifying glass to look at a complex document. It says 'byaldi' in the middle of a circle around the rat.\" src=\"byaldi.webp\"/></p>\n\n\u26a0\ufe0f This is the pre-release version of Byaldi. Please report any issue you encounter, there will likely be quite a few quirks to iron out!\n\nByaldi is [RAGatouille](https://github.com/answerdotai/ragatouille)'s mini sister project. It is a simple wrapper around the [ColPali](https://github.com/illuin-tech/colpali) repository to make it easy to use late-interaction multi-modal models such as ColPALI with a familiar API.\n\n## Getting started\n\nFirst, a warning: This is a pre-release library, using uncompressed indexes and lacking other kinds of refinements.\n\nCurrently, we support all models supported by the underlying [colpali-engine](https://github.com/illuin-tech/colpali), including the newer, and better, ColQwen2 checkpoints, such as `vidore/colqwen2-v1.0`.  Broadly, the aim is for byaldi to support all ColVLM models.\n\nAdditional backends will be supported in future updates. As byaldi exists to facilitate the adoption of multi-modal retrievers, we intend to also add support for models such as [VisRAG](https://github.com/openbmb/visrag).\n\nEventually, we'll add an HNSW indexing mechanism, pooling, and, who knows, maybe 2-bit quantization?\n\nIt will get updated as the multi-modal ecosystem develops further!\n\n### Pre-requisites\n\n#### Poppler\n\nTo convert pdf to images with a friendly license, we use the `pdf2image` library. This library requires `poppler` to be installed on your system. Poppler is very easy to install by following the instructions [on their website](https://poppler.freedesktop.org/). The tl;dr is:\n\n__MacOS with homebrew__\n\n```bash\nbrew install poppler\n```\n\n__Debian/Ubuntu__\n\n```bash\nsudo apt-get install -y poppler-utils\n```\n\n#### Flash-Attention\n\nGemma uses a recent version of flash attention. To make things run as smoothly as possible, we'd recommend that you install it after installing the library:\n\n```bash\npip install --upgrade byaldi\npip install flash-attn\n```\n\n\n#### Hardware\n\nColPali uses multi-billion parameter models to encode documents. We recommend using a GPU for smooth operations, though weak/older GPUs are perfectly fine! Encoding your collection would suffer from poor performance on CPU or MPS.\n\n## Using `byaldi`\n\nByaldi is largely modeled after RAGatouille, meaning that everything is designed to take the fewest lines of code possible, so you can very quickly build on top of it rather than spending time figuring out how to create a retrieval pipeline.\n\n### Loading a model\n\nLoading a model with `byaldi` is extremely straightforward:\n\n```python3\nfrom byaldi import RAGMultiModalModel\n# Optionally, you can specify an `index_root`, which is where it'll save the index. It defaults to \".byaldi/\".\nRAG = RAGMultiModalModel.from_pretrained(\"vidore/colqwen2-v1.0\")\n```\n\nIf you've already got an index, and wish to load it along with the model necessary to query it, you can do so just as easily:\n\n```python3\nfrom byaldi import RAGMultiModalModel\n# Optionally, you can specify an `index_root`, which is where it'll look for the index. It defaults to \".byaldi/\".\nRAG = RAGMultiModalModel.from_index(\"your_index_name\")\n```\n\n### Creating an index\nCreating an index with `byaldi` is simple and flexible. **You can index a single PDF file, a single image file, or a directory containing multiple of those**. Here's how to create an index:\n\n```python3\nfrom byaldi import RAGMultiModalModel\n# Optionally, you can specify an `index_root`, which is where it'll save the index. It defaults to \".byaldi/\".\nRAG = RAGMultiModalModel.from_pretrained(\"vidore/colqwen2-v1.0\")\nRAG.index(\n    input_path=\"docs/\", # The path to your documents\n    index_name=index_name, # The name you want to give to your index. It'll be saved at `index_root/index_name/`.\n    store_collection_with_index=False, # Whether the index should store the base64 encoded documents.\n    doc_ids=[0, 1, 2], # Optionally, you can specify a list of document IDs. They must be integers and match the number of documents you're passing. Otherwise, doc_ids will be automatically created.\n    metadata=[{\"author\": \"John Doe\", \"date\": \"2021-01-01\"}], # Optionally, you can specify a list of metadata for each document. They must be a list of dictionaries, with the same length as the number of documents you're passing.\n    overwrite=True # Whether to overwrite an index if it already exists. If False, it'll return None and do nothing if `index_root/index_name` exists.\n)\n```\n\nAnd that's it! The model will start spinning and create your index, exporting all the necessary information to disk when it's done. You can then use the `RAGMultiModalModel.from_index(\"your_index_name\")` method presented above to load it whenever needed (you don't need to do this right after creating it -- it's already loaded in memory and ready to go!).\n\nThe main decision you'll have to make here is whether you want to set `store_collection_with_index` to True or not. If set to true, it greatly simplifies your workflow: the base64-encoded version of relevant documents will be returned as part of the query results, so you can immediately pipe it to your LLM. However, it adds considerable memory and storage requirements to your index, so you might want to set it to False (the default setting) if you're short on those resources, and create the base64 encoded versions yourself whenever needed.\n\n\n### Searching\n\nOnce you've created or loaded an index, you can start searching for relevant documents. Again, it's a single, very straightforward command:\n\n```python3\nresults = RAG.search(query, k=3)\n```\n\nResults will be a list of `Result` objects, which you can also treat as normal dictionaries. Each result will be in this format:\n```python3\n[\n    {\n        \"doc_id\": 0,\n        \"page_num\": 10,\n        \"score\": 12.875,\n        \"metadata\": {},\n        \"base64\": None\n    },\n    ...\n]\n```\n\n`page_num` are 1-indexed, while doc_ids are 0-indexed. This is to make simpler to operate with other PDF manipulation tools, where the 1st page is generally page 1. `page_num` for images and single-page PDFs will always be 1, it's only useful for longer PDFs.\n\nIf you've passed metadata or encoded with the flag to store the base64 versions, these fields will be populated. Results are sorted by score, so item 0 from the list will always be the most relevant document, etc...\n\n### Adding documents to an existing index\n\nSince indexes are in-memory, they're addition-friendly! If you need to ingest some new pdfs, just load your index with `from_index`, and then, call `add_to_index`, with similar parameters to the original `index()` method:\n\n```python3\nRAG.add_to_index(\"path_to_new_docs\",\n        store_collection_with_index: bool = False,\n        ...\n    )\n```\n",
    "bugtrack_url": null,
    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.  \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License.  \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.  \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.  \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).  \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.  \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\"  \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.  2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.  3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:  (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and  (b) You must cause any modified files to carry prominent notices stating that You changed the files; and  (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and  (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.  You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.  5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. 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