Update README.md

README.md

@@ -1,27 +1,27 @@
----
-title: Hebrew Dentsit
-emoji: 🏢
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 5.10.0
-app_file: app.py
-pinned: false
-short_description: A RAG agent Hebrew Speaking Dentist
----
-
-Do you want to consult with a Dentist? Speaking Hebrew? Consulting with Dentist can be expensive... This is why I had built a Hebrew RAG Dentist Agent, which you can talk to. 
-
-Warning: The Agent (Chatbot) can still hallucinate and make up "fake" facts and shouldn’t be an alternative for an expert Dentist. the use of this Chatbot is on your responsibility only.  
-
-This RAG Agent based on Q&A data collected from 3 top Israeli forums. Data was collected using scraper, and saved into a SQL DB. Then, the titles & questions were embedded into vectors using free 'MPA/sambert' HuggingFace Encoder Model (this model found to be performing well on Hebrew Medical Jargon). The Vectors were stored a hundread at a time, into NoSQL Pinecone Vector Database, with answer_id as metadata.
-The answers were converted into vector embedding using the same free Encoder ('MPA/sambert'), and stored in Pinecone with different key and with the answer as metadata
-Now, all is left is the the RAG Agent which is composed from a Retriever, Reranker, and a Generator:
-4)	The Retriever embeds the user question (using the free 'MPA/sambert' HuggingFace Encoder Model) uses an ANN search with a cosine similarity metric and the top_k variable equals to 50.
-5)	The Reranker fetches the answers vectors suing their list of top_k ids and answers as metadata in a second scan from the PineCone database resorts the answers, then cosine similarity is calculated using the sklearn method. Afterwards, it selects the the top_n (equal to 5) answers, when each answer should be similar to the question embedding with a threshold of 0.7 or higher.
-6)	The Generator used is from a paid API -Anthropic Claude Sonnet 3.5 - a decoder that is not trained over the medical jargon - however with the right prompt and the right context the results are pretty good.
-
-The whole work from inception to completion was done by me (Eli Borodach)
-
-
+---
+title: Hebrew Dentist
+emoji: 🏢
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+short_description: A RAG agent Hebrew Speaking Dentist
+---
+
+Do you want to consult with a Dentist? Speaking Hebrew? Consulting with Dentist can be expensive... This is why I had built a Hebrew RAG Dentist Agent, which you can talk to. 
+
+Warning: The Agent (Chatbot) can still hallucinate and make up "fake" facts and shouldn’t be an alternative for an expert Dentist. the use of this Chatbot is on your responsibility only.  
+
+This RAG Agent based on Q&A data collected from 3 top Israeli forums. Data was collected using scraper, and saved into a SQL DB. Then, the titles & questions were embedded into vectors using free 'MPA/sambert' HuggingFace Encoder Model (this model found to be performing well on Hebrew Medical Jargon). The Vectors were stored a hundread at a time, into NoSQL Pinecone Vector Database, with answer_id as metadata.
+The answers were converted into vector embedding using the same free Encoder ('MPA/sambert'), and stored in Pinecone with different key and with the answer as metadata
+Now, all is left is the the RAG Agent which is composed from a Retriever, Reranker, and a Generator:
+4)	The Retriever embeds the user question (using the free 'MPA/sambert' HuggingFace Encoder Model) uses an ANN search with a cosine similarity metric and the top_k variable equals to 50.
+5)	The Reranker fetches the answers vectors suing their list of top_k ids and answers as metadata in a second scan from the PineCone database resorts the answers, then cosine similarity is calculated using the sklearn method. Afterwards, it selects the the top_n (equal to 5) answers, when each answer should be similar to the question embedding with a threshold of 0.7 or higher.
+6)	The Generator used is from a paid API -Anthropic Claude Sonnet 3.5 - a decoder that is not trained over the medical jargon - however with the right prompt and the right context the results are pretty good.
+
+The whole work from inception to completion was done by me (Eli Borodach)
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference