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README.md

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-# Text Retrieval and Summarizer ChatBot Framework 
-
-### RAG (Retrieval-Augmented Generation) System
-
-**Project Summary**:
-
-This project is a text retrieval and summarization system that allows users to input a question and receive a concise summary based on relevant content.
-
-It works by first converting the user’s input into numerical embeddings using a sentence transformer model. These embeddings are then compared against a pre-built vector index (FAISS) to identify the most relevant text chunks from your dataset. The retrieved content is combined and passed to a transformer-based summarization model BART, which generates a concise summary as the final output.
-
-The entire pipeline is integrated into an interactive user interface built using Gradio, allowing users to easily input queries and view summarized results in real time.
-
-Steps:
-1. Retrieves relevant text from a User's Document (FAISS)
-2. Converts Corpus to Sentences (Sentence Transformer)
-3. Generates a Summarized output (HuggingFace Text Summarizer)
-
-**Use of SBERT**:
-
-Sentence Transformers(SBERT), uses pretrained "Embedding" models, all we do is provide them our chunks from previous step and it creates vectors. (huggingface)
-Embeddings are dense, lower-dimensional, numerical vector representations of data such as text, images, or audio that capture semantic meaning and relationships.(soucre: google)
-
-Steps:
-1. Load an embedding model
-2. Feed text chunks into the model
-3. Convert each chunk into a vector of numbers
-
-Transformer Model (all-MiniLM-L6-v2):
-This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.(huggingface)
-
-**Use of FAISS**:
-
-FAISS as a super-fast “vector search engine”, stands for Facebook AI Similarity Search. 
-It is an open-source library developed by Meta's Fundamental AI Research group (formerly Facebook AI Research) designed for the efficient similarity search and clustering of dense vectors. (google)
-
-Takes chunks of text from the document
-As each chunk is previously converted to a 384-dimensional embedding by MiniLM
-This store all embeddings in FAISS
-so when a user asks a question, the question is converted to a vector and FAISS finds the nearest embeddings (most similar chunks of text from the document)
-Then we pass those chunks to your LLM to generate the answer
-
-
-**Final Pipeline**: 
-Take PDF -> Get chunks -> Make embeddings -> Ask Question -> Retrieve Answer -> Summarize Result and Display Metrics
-
-*--by Murk Asad*
+---
+title: RAGDeepLearningChatbot
+emoji: 💬
+python_version: '3.10'
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 6.5.1
+app_file: app.py
+pinned: false
+hf_oauth: true
+hf_oauth_scopes:
+  - inference-api
+short_description: Deep Learning Information Support Chatbot
+---