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Add first retrieval documentation. (#3117)
Adds a file for documenting research in the retrieval direction.
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# Retrieval Directions and Research Papers | ||
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## 1. Retrieval-Index | ||
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At first, either a rule-based search a fixed encoder for sematic vector-based | ||
retrieval (e.g. BERT, Contriever) could be used. | ||
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### Relevant Papers | ||
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1. FAISS: [https://arxiv.org/abs/1702.08734](https://arxiv.org/abs/1702.08734) - | ||
vector index by Facebook | ||
2. SCaNN: [https://arxiv.org/abs/1908.10396](https://arxiv.org/abs/1908.10396) - | ||
vector index by Google | ||
3. BEIR: | ||
[https://arxiv.org/abs/2104.08663v4](https://arxiv.org/abs/2104.08663v4) - | ||
Benchmark for Information Retrieval | ||
4. MS MARCO | ||
[https://arxiv.org/abs/1611.09268v3](https://arxiv.org/abs/1611.09268v3) - | ||
Machine Reading Comprehension Dataset / Retrieval Benchmark | ||
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### Links | ||
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- ElasticSearch: | ||
[https://www.elastic.co/elasticsearch](https://www.elastic.co/elasticsearch) | ||
- Apache Lucene: [https://lucene.apache.org/](https://lucene.apache.org/) | ||
- Meta Faiss: | ||
[https://github.com/facebookresearch/faiss](https://github.com/facebookresearch/faiss) | ||
- Google Scann: | ||
[https://github.com/google-research/google-research/tree/master/scann](https://github.com/google-research/google-research/tree/master/scann) | ||
- Qdrant Vector DB: | ||
[https://github.com/qdrant/qdrant](https://github.com/qdrant/qdrant) | ||
- Milvus Vector DB: [https://milvus.io/](https://milvus.io/) | ||
- Open Retrieval Index Code: | ||
[https://github.com/kenhktsui/open-information-retrieval](https://github.com/kenhktsui/open-information-retrieval) | ||
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## 2. Plugin-based approach | ||
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In this approach, the retrieval is used on top of a language model. It acts as | ||
an additional tool, like a search engine for a human. | ||
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### Links | ||
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- LangChain: | ||
[https://github.com/hwchase17/langchain](https://github.com/hwchase17/langchain) - | ||
Plugins around any language model | ||
- LlamaIndex: | ||
[https://github.com/jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) - | ||
General Retrieval System for LMs and external data | ||
- LlamaHub: [https://llamahub.ai/](https://llamahub.ai/) - Data Source Plugins | ||
for LlamaIndex | ||
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### Relevant Papers | ||
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- Toolformer: [http://arxiv.org/abs/2302.04761](http://arxiv.org/abs/2302.04761) | ||
- ... | ||
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## 3. Embedding-based approach | ||
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The embedding-based approach ingests retrieved information directly into the | ||
model, e.g. via an additional encoder and cross-attention. | ||
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### Relevant papers | ||
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- RETRO: [http://arxiv.org/abs/2112.04426](http://arxiv.org/abs/2112.04426) | ||
- ... |