Update README.md

README.md

@@ -8,7 +8,7 @@
 
 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_similarity`, 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).
+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.
@@ -73,8 +73,8 @@ Watch the the automated setup process in action [here](https://asciinema.org/a/6
 
 1. **Text Embedding Computation**: Utilizes pre-trained LLama3 and other LLMs via llama_cpp to generate embeddings for any provided text.
 2. **Embedding Caching**: Efficiently stores and retrieves computed embeddings in SQLite, minimizing redundant computations.
-3. **Advanced Similarity Measurements and Retrieval**: Utilizes the author's own `fast_vector_similarity` library written in Rust to offer highly optimized advanced similarity measures such as `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_similarity`, and `hoeffding_d`. Semantic search across cached embeddings is also supported using FAISS vector searching.
-4. **Two-Step Advanced Semantic Search**: The API first leverages FAISS and cosine similarity for rapid filtering, and then applies additional similarity measures like `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_similarity`, and `hoeffding_d` for a more nuanced comparison.
+3. **Advanced Similarity Measurements and Retrieval**: Utilizes the author's own `fast_vector_similarity` library written in Rust to offer highly optimized advanced similarity measures such as `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_dependency_measure`, and `hoeffding_d`. Semantic search across cached embeddings is also supported using FAISS vector searching.
+4. **Two-Step Advanced Semantic Search**: The API first leverages FAISS and cosine similarity for rapid filtering, and then applies additional similarity measures like `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_dependency_measure`, and `hoeffding_d` for a more nuanced comparison.
 5. **File Processing for Documents**: The library now accepts a broader range of file types including plaintext, PDFs, MS Word documents, and images. It can also handle OCR automatically. Returned embeddings for each sentence are organized in various formats like records, table, etc., using Pandas to_json() function.
 6. **Advanced Text Preprocessing**: The library now employs a more advanced sentence splitter to segment text into meaningful sentences. It handles cases where periods are used in abbreviations, domain names, or numbers and also ensures complete sentences even when quotes are used. It also takes care of pagination issues commonly found in scanned documents, such as awkward newlines and hyphenated line breaks.
 7. **Audio Transcription and Embedding**: Upload an audio file in MP3 or WAV format. The library uses OpenAI's Whisper model for transcription. Optionally, sentence embeddings can be computed for the transcript.
@@ -512,7 +512,7 @@ Compute the similarity between two given input strings using specified model emb
 - `text1`: The first input text.
 - `text2`: The second input text.
 - `llm_model_name`: The model used to calculate embeddings (optional).
-- `similarity_measure`: The similarity measure to be used. Supported measures include `all`, `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_similarity`, and `hoeffding_d` (optional, default is `all`).
+- `similarity_measure`: The similarity measure to be used. Supported measures include `all`, `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_dependency_measure`, and `hoeffding_d` (optional, default is `all`).
 
 #### Workflow
 1. **Retrieve Embeddings**: The embeddings for `text1` and `text2` are retrieved or computed using the specified or default model.