Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (12.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: lexiforest
- License: mit
- Language: Python
- Default Branch: main
- Size: 10.7 KB
Statistics
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
🐻 Ragbear
ragbear, short for RAG Bear, is an academic oriented RAG framework for building retrieval
enhanced applications.
Why
existing solutions are too heavy.
But RAG should be simple, as simple as the following lines of code:
```py DEFAULT_PROMPT = """Please answer the question according to the references.
Question: {question}
References: {docs} """
question = "Has tiktok been banned in the US?"
docs = get_docs(question)
prompt = DEFAULT_PROMPT.format(question=question, "\n".join(docs))
completion = openai.chat.completion.create({..., prompt, ...})
print(completion.choices[0].message.content) ```
With ragbear, it's even simpler:
py
ragbear.query("Has tiktok been banned in the US?")
But a lot fancier, too:
py
ragbear.query(
"Who is the father of Luke Skywalker?",
model="http://localhost:8964",
source=[
{"data": "http://localhost:8888", "type": "meilisearch"}, # query a meilisearch database
],
rerank="newest",
rewrite="query2doc",
template=template,
)
But you will need to optimize towards your academic or commercial goals, you need a framework.
Enter Ragbear, we follow the above pattern closely, but give you options to swap each parts of the pipeline. The code is concise and straightforward, no useless wrappers around wrappers. Just read it.
Unlike LangChain, we do not try to encapsulate every solution out there, because there is actually not too much to wrap at all. We would like to call ragbear a patter, rather than a framework.
Implemented algorithms
With ragbear, we want to incorporate the latest research ideas and put it into production. We have implemented the following algorithms, which you can easily apply in your app.
- HyDE
- REPLUG
- Query2Doc
For example, to use the REPLUG method, it's as easy as:
```py import ragbear
ans = ragbear.query("Where was Steve Jobs born?", rewrite="replug") ```
Data
We support the following data query backend
- DuckDuckGo, via python package
duckduckgo_search - Local Dataset, via ANN engines, aks vector databases
- hnswlib + sqlite
- faiss + sqlite
- pg_vector
Dependencies
- An LLM API endpoint, you can use OpenAI API or local models with Ollama.
- An data backend, use
duckduckgo_searchor local dataset.
Citation
If you find this project useful, please cite it as below:
@software{Lyonnet2024,
author = {Alex Lyonnet, Shang Yu},
title = {RAGBear - various RAG methods implemented in one package},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/oysteroil/ragbear}},
}
Owner
- Login: lexiforest
- Kind: user
- Company: Riverside AI LLC
- Website: https://impersonate.pro
- Repositories: 16
- Profile: https://github.com/lexiforest
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Lyonnet" given-names: "Alex" - family-names: "Yu" given-names: "Shang" title: "RAGBear - various RAG methods implemented in one package" version: 0.0.1 date-released: 2024-05-04 url: "https://github.com/oysteroil/ragbear"
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0