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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
| Date | Stars |
|---|---|
| 2026-07-24 | 1054 |
| 2026-07-25 | 1054 |
| 2026-07-28 | 1054 |
| 2026-07-30 | 1054 |
| 2026-08-06 | 1054 |
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# 🇨🇭🎖️🦙 Swiss Army Llama <div align="center"> <img src="https://github.com/Dicklesworthstone/swiss_army_llama/raw/main/image_files/swiss_army_llama_logo.webp" width="500"> </div> ## Introduction The Swiss Army Llama is designed to facilitate and optimize the process of working with local LLMs by using FastAPI to expose convenient REST endpoints for various tasks, including obtaining text embeddings and completions using different LLMs via llama_cpp, as well as automating the process of obtaining all the embeddings for most common document types, including PDFs (even ones that require OCR), Word files, etc; it even allows you to submit an audio file and automatically transcribes it with the Whisper model, cleans up the resulting text, and then computes the embeddings for it. To avoid wasting computation, these embeddings are cached in SQlite and retrieved if they have already been computed before. To speed up the process of loading multiple LLMs, optional RAM Disks can be used, and the process for creating and managing them is handled automatically for you. With a quick and easy setup process, you will immediately get access to a veritable "Swiss Army Knife" of LLM related tools, all accessible via a convenient Swagger UI and ready to be integrated into your own applications with minimal fuss or configuration required. Some additional useful endpoints are provided, such as computing semantic similarity between submitted text strings. The service leverages a high-performance Rust-based library, `fast_vector_similarity`, to offer a range of similarity measures including `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_dependency_measure`, and [`hoeffding_d`](https://blogs.sas.com/content/iml/2021/05/03/examples-hoeffding-d.html). Additionally, semantic search across all your cached embeddings is supported using FAISS vector searching. You can either use the built in cosine similarity from FAISS, or supplement this with a second pass that computes the more sophisticated similarity measures for the most relevant subset of the stored vectors found using cosine similarity (see the advanced semantic search endpoint for this functionality). Also, we now support multiple embedding pooling methods for combining token-level embedding vectors into a single fixed-length embedding vector for any length of input text, including the following: - `mean`: Mean pooling of token embeddings. - `mins_maxes`: Concatenation of the minimum and maximum values of each dimension of the token embeddings. - `svd`: Concatenation of the first two singular vectors obtained from the Singular Value Decomposition (SVD) of the token embeddings matrix. - `svd_first_four`: Concatenation of the first four singular vectors obtained from the Singular Value Decomposition (SVD) of the token embeddings matrix. - `ica`: Flattened independent components obtained from Independent Component Analysis (ICA) of the token embeddings. - `factor_analysis`: Flattened factors obtained from Factor Analysis of the token embeddings. - `gaussian_random_projection`: Flattened embeddings obtained from Gaussian Random Projection of the token embeddings. As mentioned above, you can now submit not only plaintext and fully digital PDFs but also MS Word documents, images, and other file types supported by the textract library. The library can automatically apply OCR using Tesseract for scanned text. The returned embeddings for each sentence in a document can be organized in various formats like records, table, etc., using the Pandas to_json() function. The results can be returned either as a ZIP file containing a JSON file or as a direct JSON response. You can now also submit audio files in MP3 or WAV formats. The library uses OpenAI's Whisper model, as optimized by the Faster Whisper Python library, to transcribe the audio into text. Optionally, this transcript can be treated like any other document, with each sentence's embedding
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Jeff Emanuel
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dd08d740c83a288d, topic:embeddings, readme:text embeddings
matched fp:dd08d740c83a288d, topic:llamacpp
matched fp:dd08d740c83a288d, topic:semantic-search