Aidocumentlibrarychat
A project to show howto use SpringAI with OpenAI to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Lda2vec Tf | 407 | 7 years ago | 10 | Python | ||||||
| tensorflow port of the lda2vec model for unsupervised learning of document + topic + word embeddings | ||||||||||
| Vectorai | 299 |  | 4 | 6 months ago | 9 | June 04, 2021 | 12 | apache-2.0 | Python | |
| Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors. | ||||||||||
| Eeap Examples | 241 | 5 years ago | 4 | Jupyter Notebook | ||||||
| Code for Document Similarity on Reuters dataset using Encode, Embed, Attend, Predict recipe | ||||||||||
| Spherical Text Embedding | 121 | 4 years ago | 1 | apache-2.0 | C | |||||
| [NeurIPS 2019] Spherical Text Embedding | ||||||||||
| Coreference Resolution | 118 | 4 years ago | 11 | Perl | ||||||
| Efficient and clean PyTorch reimplementation of "End-to-end Neural Coreference Resolution" (Lee et al., EMNLP 2017). | ||||||||||
| Cedr | 103 | 4 years ago | 6 | mit | Python | |||||
| Code for CEDR: Contextualized Embeddings for Document Ranking, accepted at SIGIR 2019. | ||||||||||
| Average Word2vec | 52 | 3 years ago | Jupyter Notebook | |||||||
| 🔤 Calculate average word embeddings (word2vec) from documents for transfer learning | ||||||||||
| Semantic Search | 36 | 4 years ago | Python | |||||||
| Semantic search using Transformers and others | ||||||||||
| Dv Cosine | 35 | 2 years ago | Java | |||||||
| Word Embeddings And Document Vectors | 25 | 5 years ago | 1 | Python | ||||||
| An evaluation of word-embeddings for classification | ||||||||||
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