| Name | RAGatouille JSON |
| Version |
0.0.9
JSON |
| download |
| home_page | None |
| Summary | Library to facilitate the use of state-of-the-art retrieval models in common RAG contexts. |
| upload_time | 2025-02-11 04:29:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| 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. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
reranking
retrieval
rag
nlp
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Welcome to RAGatouille
_Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research._
[](https://github.com/bclavie/ragatouille/stargazers)
[](https://pepy.tech/project/ragatouille)
[](https://ben.clavie.eu/ragatouille/)
[](https://twitter.com/bclavie)
<p align="center"><img width=500 alt="The RAGatouille logo, it's a cheerful rat on his laptop (branded with a slightly eaten piece of cheese) and a pile of books he's looking for information in." src="RAGatouille.png"/></p>
---
The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To get the best performance, you need to optimise for many components: among them, a very important one is the models you use for retrieval.
Dense retrieval, i.e. using embeddings such as OpenAI's `text-ada-002`, is a good baseline, but there's a lot of research [showing dense embeddings might not be the](https://arxiv.org/abs/2104.08663) [best fit for **your** usecase](https://arxiv.org/abs/2204.11447).
The Information Retrieval research field has recently been booming, and models like ColBERT have been shown to [generalise better](https://arxiv.org/abs/2203.10053) [to new or complex domains](https://aclanthology.org/2022.findings-emnlp.78/) [than dense embeddings](https://arxiv.org/abs/2205.02870), are [ridiculously data-efficient](https://arxiv.org/abs/2309.06131) and are even [better suited to efficiently being trained](https://arxiv.org/abs/2312.09508) [on non-English languages with low amount of data](https://arxiv.org/abs/2312.16144)! Unfortunately, most of those new approaches aren't very well known, and are much harder to use than dense embeddings.
This is where __RAGatouille__ comes in: RAGatouille's purpose is to bridge this gap: make it easy to use state-of-the-art methods in your RAG pipeline, without having to worry about the details or the years of literature! At the moment, RAGatouille focuses on making ColBERT simple to use. If you want to check out what's coming next, you can check out our [broad roadmap](https://ben.clavie.eu/ragatouille/roadmap)!
_If you want to read more about the motivations, philosophy, and why the late-interaction approach used by ColBERT works so well, check out the [introduction in the docs](https://ben.clavie.eu/ragatouille/)._
Want to give it a try? Nothing easier, just run `pip install ragatouille` and you're good to go!
⚠️ Running notes/requirements: ⚠️
- If running inside a script, you must run it inside `if __name__ == "__main__"`
- Windows is not supported. RAGatouille doesn't appear to work outside WSL and has issues with WSL1. Some users have had success running RAGatouille in WSL2.
## Get Started
RAGatouille makes it as simple as can be to use ColBERT! We want the library to work on two levels:
- Strong, but parameterizable defaults: you should be able to get started with just a few lines of code and still leverage the full power of ColBERT, and you should be able to tweak any relevant parameter if you need to!
- Powerful yet simple re-usable components under-the-hood: any part of the library should be usable stand-alone. You can use our DataProcessor or our negative miners outside of `RAGPretrainedModel` and `RagTrainer`, and you can even write your own negative miner and use it in the pipeline if you want to!
<!-- (more on [components](https://ben.clavie.eu/ragatouille/components)). -->
In this section, we'll quickly walk you through the three core aspects of RAGatouille:
- [🚀 Training and Fine-Tuning ColBERT models](#-training-and-fine-tuning)
- [🗄️ Embedding and Indexing Documents](#%EF%B8%8F-indexing)
- [🔎 Retrieving documents](#-retrieving-documents)
➡️ If you want just want to see fully functional code examples, head over to the [examples](https://github.com/bclavie/RAGatouille/tree/main/examples)⬅️
### 🚀 Training and fine-tuning
_If you're just prototyping, you don't need to train your own model! While finetuning can be useful, one of the strength of ColBERT is that the pretrained models are particularly good at generalisation, and [ColBERTv2](https://huggingface.co/colbert-ir/colbertv2.0) has [repeatedly been shown to be extremely strong](https://arxiv.org/abs/2303.00807) at zero-shot retrieval in new domains!_
#### Data Processing
RAGatouille's RAGTrainer has a built-in `TrainingDataProcessor`, which can take most forms of retrieval training data, and automatically convert it to training triplets, with data enhancements. The pipeline works as follows:
- Accepts pairs, labelled pairs and various forms of triplets as inputs (strings or list of strings) -- transparently!
- Automatically remove all duplicates and maps all positives/negatives to their respective query.
- By default, mine hard negatives: this means generating negatives that are hard to distinguish from positives, and that are therefore more useful for training.
This is all handled by `RAGTrainer.prepare_training_data()`, and is as easy as doing passing your data to it:
```python
from ragatouille import RAGTrainer
my_data = [
("What is the meaning of life ?", "The meaning of life is 42"),
("What is Neural Search?", "Neural Search is a terms referring to a family of ..."),
...
] # Unlabelled pairs here
trainer = RAGTrainer()
trainer.prepare_training_data(raw_data=my_data)
```
ColBERT prefers to store processed training data on-file, which also makes easier to properly version training data via `wandb` or `dvc`. By default, it will write to `./data/`, but you can override this by passing a `data_out_path` argument to `prepare_training_data()`.
Just like all things in RAGatouille, `prepare_training_data` uses strong defaults, but is also fully parameterizable.
<!-- Check out the [Data Processing](https://ben.clavie.eu/ragatouille/data-processing) section of the docs! -->
#### Running the Training/Fine-Tuning
Training and Fine-Tuning follow the exact same process. When you instantiate `RAGTrainer`, you must pass it a `pretrained_model_name`. If this pretrained model is a ColBERT instance, the trainer will be in fine-tuning mode, if it's another kind of transformer, it will be in training mode to begin training a new ColBERT initialised from the model's weights!
```python
from ragatouille import RAGTrainer
from ragatouille.utils import get_wikipedia_page
pairs = [
("What is the meaning of life ?", "The meaning of life is 42"),
("What is Neural Search?", "Neural Search is a terms referring to a family of ..."),
# You need many more pairs to train! Check the examples for more details!
...
]
my_full_corpus = [get_wikipedia_page("Hayao_Miyazaki"), get_wikipedia_page("Studio_Ghibli")]
trainer = RAGTrainer(model_name = "MyFineTunedColBERT",
pretrained_model_name = "colbert-ir/colbertv2.0") # In this example, we run fine-tuning
# This step handles all the data processing, check the examples for more details!
trainer.prepare_training_data(raw_data=pairs,
data_out_path="./data/",
all_documents=my_full_corpus)
trainer.train(batch_size=32) # Train with the default hyperparams
```
When you run `train()`, it'll by default inherit its parent ColBERT hyperparameters if fine-tuning, or use the default training parameters if training a new ColBERT. Feel free to modify them as you see fit (check the example and API reference for more details!)
### 🗄️ Indexing
To create an index, you'll need to load a trained model, this can be one of your own or a pretrained one from the hub! Creating an index with the default configuration is just a few lines of code:
```python
from ragatouille import RAGPretrainedModel
from ragatouille.utils import get_wikipedia_page
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
my_documents = [get_wikipedia_page("Hayao_Miyazaki"), get_wikipedia_page("Studio_Ghibli")]
index_path = RAG.index(index_name="my_index", collection=my_documents)
```
You can also optionally add document IDs or document metadata when creating the index:
```python
document_ids = ["miyazaki", "ghibli"]
document_metadatas = [
{"entity": "person", "source": "wikipedia"},
{"entity": "organisation", "source": "wikipedia"},
]
index_path = RAG.index(
index_name="my_index_with_ids_and_metadata",
collection=my_documents,
document_ids=document_ids,
document_metadatas=document_metadatas,
)
```
Once this is done running, your index will be saved on-disk and ready to be queried! RAGatouille and ColBERT handle everything here:
- Splitting your documents
- Tokenizing your documents
- Identifying the individual terms
- Embedding the documents and generating the bags-of-embeddings
- Compressing the vectors and storing them on disk
Curious about how this works? Check out the [Late-Interaction & ColBERT concept explainer](https://ben.clavie.eu/ragatouille/#late-interaction)
<!-- or find out more about [indexing](https://ben.clavie.eu/ragatouille/indexing)! -->
### 🔎 Retrieving Documents
Once an index is created, querying it is just as simple as creating it! You can either load the model you need directly from an index's configuration:
```python
from ragatouille import RAGPretrainedModel
query = "ColBERT my dear ColBERT, who is the fairest document of them all?"
RAG = RAGPretrainedModel.from_index("path_to_your_index")
results = RAG.search(query)
```
This is the preferred way of doing things, since every index saves the full configuration of the model used to create it, and you can easily load it back up.
`RAG.search` is a flexible method! You can set the `k` value to however many results you want (it defaults to `10`), and you can also use it to search for multiple queries at once:
```python
RAG.search(["What manga did Hayao Miyazaki write?",
"Who are the founders of Ghibli?"
"Who is the director of Spirited Away?"],)
```
`RAG.search` returns results in the form of a list of dictionaries, or a list of list of dictionaries if you used multiple queries:
```python
# single-query result
[
{"content": "blablabla", "score": 42.424242, "rank": 1, "document_id": "x"},
...,
{"content": "albalbalba", "score": 24.242424, "rank": k, "document_id": "y"},
]
# multi-query result
[
[
{"content": "blablabla", "score": 42.424242, "rank": 1, "document_id": "x"},
...,
{"content": "albalbalba", "score": 24.242424, "rank": k, "document_id": "y"},
],
[
{"content": "blablabla", "score": 42.424242, "rank": 1, "document_id": "x"},
...,
{"content": "albalbalba", "score": 24.242424, "rank": k, "document_id": "y"},
],
],
```
If your index includes document metadata, it'll be returned as a dictionary in the `document_metadata` key of the result dictionary:
```python
[
{"content": "blablabla", "score": 42.424242, "rank": 1, "document_id": "x", "document_metadata": {"A": 1, "B": 2}},
...,
{"content": "albalbalba", "score": 24.242424, "rank": k, "document_id": "y", "document_metadata": {"A": 3, "B": 4}},
]
```
## I'm sold, can I integrate late-interaction RAG into my project?
To get started, RAGatouille bundles everything you need to build a ColBERT native index and query it. Just look at the docs! RAGatouille persists indices on disk in compressed format, and a very viable production deployment is to simply integrate the index you need into your project and query it directly. Don't just take our word for it, this is what Spotify does in production with their own vector search framework, serving dozens of millions of users:
> Statelessness: Many of Spotify’s systems use nearest-neighbor search in memory, enabling stateless deployments (via Kubernetes) and almost entirely removing the maintenance and cost burden of maintaining a stateful database cluster. (_[Spotify, announcing Voyager](https://engineering.atspotify.com/2023/10/introducing-voyager-spotifys-new-nearest-neighbor-search-library/)_)
### Integrations
If you'd like to use more than RAGatouille, ColBERT has a growing number of integrations, and they all fully support models trained or fine-tuned with RAGatouille!
- The [official ColBERT implementation](https://github.com/stanford-futuredata/ColBERT) has a built-in query server (using Flask), which you can easily query via API requests and does support indexes generated with RAGatouille! This should be enough for most small applications, so long as you can persist the index on disk.
- [Vespa](https://vespa.ai) offers a fully managed RAG engine with ColBERT support: it's essentially just like a vector DB, except with many more retrieval options! Full support for ColBERT models will be released in the next couple weeks, and using a RAGatouille-trained model will be as simple as loading it from the huggingface hub! **Vespa is a well-tested, widely used framework and is [fully-supported in LangChain](https://python.langchain.com/docs/integrations/providers/vespa), making it the ideal slot-in replacement to replace your current RAG pipeline with ColBERT!**
- [Intel's FastRAG](https://github.com/IntelLabs/fastRAG) supports ColBERT models for RAG, and is fully compatible with RAGatouille-trained models.
- [LlamaIndex](https://www.llamaindex.ai) is building ColBERT integrations and already [has early ColBERT support, with active development continuing](https://github.com/run-llama/llama_index/pull/9656).
Raw data
{
"_id": null,
"home_page": null,
"name": "RAGatouille",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": "Ben Clavi\u00e9 <bc@answer.ai>",
"keywords": "reranking, retrieval, rag, nlp",
"author": null,
"author_email": "Ben Clavi\u00e9 <bc@answer.ai>",
"download_url": "https://files.pythonhosted.org/packages/4f/aa/c452d447b92be81e4be6d38f9051b073a67b9b746145db8c66675990dcd1/ragatouille-0.0.9.tar.gz",
"platform": null,
"description": "# Welcome to RAGatouille\n\n_Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research._\n\n[](https://github.com/bclavie/ragatouille/stargazers)\n\n[](https://pepy.tech/project/ragatouille)\n[](https://ben.clavie.eu/ragatouille/)\n[](https://twitter.com/bclavie)\n\n<p align=\"center\"><img width=500 alt=\"The RAGatouille logo, it's a cheerful rat on his laptop (branded with a slightly eaten piece of cheese) and a pile of books he's looking for information in.\" src=\"RAGatouille.png\"/></p>\n\n---\n\nThe main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To get the best performance, you need to optimise for many components: among them, a very important one is the models you use for retrieval.\n\nDense retrieval, i.e. using embeddings such as OpenAI's `text-ada-002`, is a good baseline, but there's a lot of research [showing dense embeddings might not be the](https://arxiv.org/abs/2104.08663) [best fit for **your** usecase](https://arxiv.org/abs/2204.11447).\n\nThe Information Retrieval research field has recently been booming, and models like ColBERT have been shown to [generalise better](https://arxiv.org/abs/2203.10053) [to new or complex domains](https://aclanthology.org/2022.findings-emnlp.78/) [than dense embeddings](https://arxiv.org/abs/2205.02870), are [ridiculously data-efficient](https://arxiv.org/abs/2309.06131) and are even [better suited to efficiently being trained](https://arxiv.org/abs/2312.09508) [on non-English languages with low amount of data](https://arxiv.org/abs/2312.16144)! Unfortunately, most of those new approaches aren't very well known, and are much harder to use than dense embeddings.\n\nThis is where __RAGatouille__ comes in: RAGatouille's purpose is to bridge this gap: make it easy to use state-of-the-art methods in your RAG pipeline, without having to worry about the details or the years of literature! At the moment, RAGatouille focuses on making ColBERT simple to use. If you want to check out what's coming next, you can check out our [broad roadmap](https://ben.clavie.eu/ragatouille/roadmap)!\n\n_If you want to read more about the motivations, philosophy, and why the late-interaction approach used by ColBERT works so well, check out the [introduction in the docs](https://ben.clavie.eu/ragatouille/)._\n\n\n\n\nWant to give it a try? Nothing easier, just run `pip install ragatouille` and you're good to go!\n\n\u26a0\ufe0f Running notes/requirements: \u26a0\ufe0f\n\n- If running inside a script, you must run it inside `if __name__ == \"__main__\"`\n- Windows is not supported. RAGatouille doesn't appear to work outside WSL and has issues with WSL1. Some users have had success running RAGatouille in WSL2.\n\n## Get Started\n\nRAGatouille makes it as simple as can be to use ColBERT! We want the library to work on two levels:\n\n- Strong, but parameterizable defaults: you should be able to get started with just a few lines of code and still leverage the full power of ColBERT, and you should be able to tweak any relevant parameter if you need to!\n- Powerful yet simple re-usable components under-the-hood: any part of the library should be usable stand-alone. You can use our DataProcessor or our negative miners outside of `RAGPretrainedModel` and `RagTrainer`, and you can even write your own negative miner and use it in the pipeline if you want to!\n<!-- (more on [components](https://ben.clavie.eu/ragatouille/components)). -->\n\nIn this section, we'll quickly walk you through the three core aspects of RAGatouille:\n\n- [\ud83d\ude80 Training and Fine-Tuning ColBERT models](#-training-and-fine-tuning)\n- [\ud83d\uddc4\ufe0f Embedding and Indexing Documents](#%EF%B8%8F-indexing)\n- [\ud83d\udd0e Retrieving documents](#-retrieving-documents)\n\n\u27a1\ufe0f If you want just want to see fully functional code examples, head over to the [examples](https://github.com/bclavie/RAGatouille/tree/main/examples)\u2b05\ufe0f\n\n### \ud83d\ude80 Training and fine-tuning\n\n_If you're just prototyping, you don't need to train your own model! While finetuning can be useful, one of the strength of ColBERT is that the pretrained models are particularly good at generalisation, and [ColBERTv2](https://huggingface.co/colbert-ir/colbertv2.0) has [repeatedly been shown to be extremely strong](https://arxiv.org/abs/2303.00807) at zero-shot retrieval in new domains!_\n\n#### Data Processing\n\nRAGatouille's RAGTrainer has a built-in `TrainingDataProcessor`, which can take most forms of retrieval training data, and automatically convert it to training triplets, with data enhancements. The pipeline works as follows:\n\n- Accepts pairs, labelled pairs and various forms of triplets as inputs (strings or list of strings) -- transparently!\n- Automatically remove all duplicates and maps all positives/negatives to their respective query.\n- By default, mine hard negatives: this means generating negatives that are hard to distinguish from positives, and that are therefore more useful for training.\n\nThis is all handled by `RAGTrainer.prepare_training_data()`, and is as easy as doing passing your data to it:\n\n```python\nfrom ragatouille import RAGTrainer\n\nmy_data = [\n (\"What is the meaning of life ?\", \"The meaning of life is 42\"),\n (\"What is Neural Search?\", \"Neural Search is a terms referring to a family of ...\"),\n ...\n] # Unlabelled pairs here\ntrainer = RAGTrainer()\ntrainer.prepare_training_data(raw_data=my_data)\n```\n\nColBERT prefers to store processed training data on-file, which also makes easier to properly version training data via `wandb` or `dvc`. By default, it will write to `./data/`, but you can override this by passing a `data_out_path` argument to `prepare_training_data()`.\n\nJust like all things in RAGatouille, `prepare_training_data` uses strong defaults, but is also fully parameterizable.\n<!-- Check out the [Data Processing](https://ben.clavie.eu/ragatouille/data-processing) section of the docs! -->\n\n#### Running the Training/Fine-Tuning\n\nTraining and Fine-Tuning follow the exact same process. When you instantiate `RAGTrainer`, you must pass it a `pretrained_model_name`. If this pretrained model is a ColBERT instance, the trainer will be in fine-tuning mode, if it's another kind of transformer, it will be in training mode to begin training a new ColBERT initialised from the model's weights!\n\n\n```python\nfrom ragatouille import RAGTrainer\nfrom ragatouille.utils import get_wikipedia_page\n\npairs = [\n (\"What is the meaning of life ?\", \"The meaning of life is 42\"),\n (\"What is Neural Search?\", \"Neural Search is a terms referring to a family of ...\"),\n # You need many more pairs to train! Check the examples for more details!\n ...\n]\n\nmy_full_corpus = [get_wikipedia_page(\"Hayao_Miyazaki\"), get_wikipedia_page(\"Studio_Ghibli\")]\n\n\ntrainer = RAGTrainer(model_name = \"MyFineTunedColBERT\",\n pretrained_model_name = \"colbert-ir/colbertv2.0\") # In this example, we run fine-tuning\n\n# This step handles all the data processing, check the examples for more details!\ntrainer.prepare_training_data(raw_data=pairs,\n data_out_path=\"./data/\",\n all_documents=my_full_corpus)\n\ntrainer.train(batch_size=32) # Train with the default hyperparams\n```\n\nWhen you run `train()`, it'll by default inherit its parent ColBERT hyperparameters if fine-tuning, or use the default training parameters if training a new ColBERT. Feel free to modify them as you see fit (check the example and API reference for more details!)\n\n\n### \ud83d\uddc4\ufe0f Indexing\n\nTo create an index, you'll need to load a trained model, this can be one of your own or a pretrained one from the hub! Creating an index with the default configuration is just a few lines of code:\n\n```python\nfrom ragatouille import RAGPretrainedModel\nfrom ragatouille.utils import get_wikipedia_page\n\nRAG = RAGPretrainedModel.from_pretrained(\"colbert-ir/colbertv2.0\")\nmy_documents = [get_wikipedia_page(\"Hayao_Miyazaki\"), get_wikipedia_page(\"Studio_Ghibli\")]\nindex_path = RAG.index(index_name=\"my_index\", collection=my_documents)\n```\nYou can also optionally add document IDs or document metadata when creating the index:\n\n```python\ndocument_ids = [\"miyazaki\", \"ghibli\"]\ndocument_metadatas = [\n {\"entity\": \"person\", \"source\": \"wikipedia\"},\n {\"entity\": \"organisation\", \"source\": \"wikipedia\"},\n]\nindex_path = RAG.index(\n index_name=\"my_index_with_ids_and_metadata\",\n collection=my_documents,\n document_ids=document_ids,\n document_metadatas=document_metadatas,\n)\n```\n\nOnce this is done running, your index will be saved on-disk and ready to be queried! RAGatouille and ColBERT handle everything here:\n- Splitting your documents\n- Tokenizing your documents\n- Identifying the individual terms\n- Embedding the documents and generating the bags-of-embeddings\n- Compressing the vectors and storing them on disk\n\nCurious about how this works? Check out the [Late-Interaction & ColBERT concept explainer](https://ben.clavie.eu/ragatouille/#late-interaction)\n<!-- or find out more about [indexing](https://ben.clavie.eu/ragatouille/indexing)! -->\n\n### \ud83d\udd0e Retrieving Documents\n\nOnce an index is created, querying it is just as simple as creating it! You can either load the model you need directly from an index's configuration:\n\n```python\nfrom ragatouille import RAGPretrainedModel\n\nquery = \"ColBERT my dear ColBERT, who is the fairest document of them all?\"\nRAG = RAGPretrainedModel.from_index(\"path_to_your_index\")\nresults = RAG.search(query)\n```\n\nThis is the preferred way of doing things, since every index saves the full configuration of the model used to create it, and you can easily load it back up.\n\n`RAG.search` is a flexible method! You can set the `k` value to however many results you want (it defaults to `10`), and you can also use it to search for multiple queries at once:\n\n```python\nRAG.search([\"What manga did Hayao Miyazaki write?\",\n\"Who are the founders of Ghibli?\"\n\"Who is the director of Spirited Away?\"],)\n```\n\n`RAG.search` returns results in the form of a list of dictionaries, or a list of list of dictionaries if you used multiple queries: \n\n```python\n# single-query result\n[\n {\"content\": \"blablabla\", \"score\": 42.424242, \"rank\": 1, \"document_id\": \"x\"},\n ...,\n {\"content\": \"albalbalba\", \"score\": 24.242424, \"rank\": k, \"document_id\": \"y\"},\n]\n# multi-query result\n[\n [\n {\"content\": \"blablabla\", \"score\": 42.424242, \"rank\": 1, \"document_id\": \"x\"},\n ...,\n {\"content\": \"albalbalba\", \"score\": 24.242424, \"rank\": k, \"document_id\": \"y\"},\n ],\n [\n {\"content\": \"blablabla\", \"score\": 42.424242, \"rank\": 1, \"document_id\": \"x\"},\n ...,\n {\"content\": \"albalbalba\", \"score\": 24.242424, \"rank\": k, \"document_id\": \"y\"},\n ],\n ],\n```\nIf your index includes document metadata, it'll be returned as a dictionary in the `document_metadata` key of the result dictionary:\n \n```python\n[\n {\"content\": \"blablabla\", \"score\": 42.424242, \"rank\": 1, \"document_id\": \"x\", \"document_metadata\": {\"A\": 1, \"B\": 2}},\n ...,\n {\"content\": \"albalbalba\", \"score\": 24.242424, \"rank\": k, \"document_id\": \"y\", \"document_metadata\": {\"A\": 3, \"B\": 4}},\n]\n```\n\n## I'm sold, can I integrate late-interaction RAG into my project?\n\nTo get started, RAGatouille bundles everything you need to build a ColBERT native index and query it. Just look at the docs! RAGatouille persists indices on disk in compressed format, and a very viable production deployment is to simply integrate the index you need into your project and query it directly. Don't just take our word for it, this is what Spotify does in production with their own vector search framework, serving dozens of millions of users:\n\n> Statelessness: Many of Spotify\u2019s systems use nearest-neighbor search in memory, enabling stateless deployments (via Kubernetes) and almost entirely removing the maintenance and cost burden of maintaining a stateful database cluster. (_[Spotify, announcing Voyager](https://engineering.atspotify.com/2023/10/introducing-voyager-spotifys-new-nearest-neighbor-search-library/)_)\n\n\n### Integrations\n\nIf you'd like to use more than RAGatouille, ColBERT has a growing number of integrations, and they all fully support models trained or fine-tuned with RAGatouille!\n\n- The [official ColBERT implementation](https://github.com/stanford-futuredata/ColBERT) has a built-in query server (using Flask), which you can easily query via API requests and does support indexes generated with RAGatouille! This should be enough for most small applications, so long as you can persist the index on disk.\n- [Vespa](https://vespa.ai) offers a fully managed RAG engine with ColBERT support: it's essentially just like a vector DB, except with many more retrieval options! Full support for ColBERT models will be released in the next couple weeks, and using a RAGatouille-trained model will be as simple as loading it from the huggingface hub! **Vespa is a well-tested, widely used framework and is [fully-supported in LangChain](https://python.langchain.com/docs/integrations/providers/vespa), making it the ideal slot-in replacement to replace your current RAG pipeline with ColBERT!**\n- [Intel's FastRAG](https://github.com/IntelLabs/fastRAG) supports ColBERT models for RAG, and is fully compatible with RAGatouille-trained models.\n- [LlamaIndex](https://www.llamaindex.ai) is building ColBERT integrations and already [has early ColBERT support, with active development continuing](https://github.com/run-llama/llama_index/pull/9656).\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. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
"summary": "Library to facilitate the use of state-of-the-art retrieval models in common RAG contexts.",
"version": "0.0.9",
"project_urls": {
"Homepage": "https://github.com/answerdotai/ragatouille"
},
"split_keywords": [
"reranking",
" retrieval",
" rag",
" nlp"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3b9c407e467a783d2bc43bd026203c4ea889bcd71de723c8f66b2638a007059a",
"md5": "344931c63fae56eff14e630e0ab63657",
"sha256": "88b7dd882f58deb793853f0c04d0508690f1963847cd8aad9acb43dae0ad5736"
},
"downloads": -1,
"filename": "RAGatouille-0.0.9-py3-none-any.whl",
"has_sig": false,
"md5_digest": "344931c63fae56eff14e630e0ab63657",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 45707,
"upload_time": "2025-02-11T04:29:51",
"upload_time_iso_8601": "2025-02-11T04:29:51.668817Z",
"url": "https://files.pythonhosted.org/packages/3b/9c/407e467a783d2bc43bd026203c4ea889bcd71de723c8f66b2638a007059a/RAGatouille-0.0.9-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4faac452d447b92be81e4be6d38f9051b073a67b9b746145db8c66675990dcd1",
"md5": "39cd22ea32312287dbd846644a40f001",
"sha256": "67957d84686c8b37229f67bb882a9beb5360ba2ddc05aa9590a40eafa201a760"
},
"downloads": -1,
"filename": "ragatouille-0.0.9.tar.gz",
"has_sig": false,
"md5_digest": "39cd22ea32312287dbd846644a40f001",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 45548,
"upload_time": "2025-02-11T04:29:53",
"upload_time_iso_8601": "2025-02-11T04:29:53.871714Z",
"url": "https://files.pythonhosted.org/packages/4f/aa/c452d447b92be81e4be6d38f9051b073a67b9b746145db8c66675990dcd1/ragatouille-0.0.9.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-02-11 04:29:53",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "answerdotai",
"github_project": "ragatouille",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "ragatouille"
}
