updated layout

pages/1_📊_About.py

@@ -7,63 +7,61 @@ st.sidebar.header("About Semantic Search with LLMs")
 
 tab1, tab2 = st.tabs(["Product Discovery", "Question And Answering"])
 with tab1:
-    st.header("Product Discovery")
-    with st.expander("Details"):
-        st.markdown("**Application:** Gofynd.com")
-        st.markdown("**Total Products:** 14,000")
-        st.markdown("**Data Used:**")
-        st.markdown("- Text fields - Name, Brand, Category, Gender")
-        st.markdown("- Images - Single Images (First image of the product)")
-        st.markdown("Embedding Model: Used CLIP model for Generating the Embeddings for Text and Image")
-        st.markdown("Weightage [Text, Image]: [0.1, 0.9]")
-        st.markdown("Vector Database: Milvus")
-        st.markdown("ML Pipeline Orchestration: Vertex AI Pipeline")
-        st.markdown("Supported Input data format:")
-        st.markdown("Similarity Metric: Inner Product")
-        st.markdown("Type of Index: FLAT_L2")
-        
-        st.subheader("Input Sample Data:")
-        st.markdown("Sample data can be displayed here.")
-        
-        st.subheader("STEPS")
-        st.markdown("**TRAINING:**")
-        st.markdown("1. Pull the Catalog data from Fynd Platform")
-        st.markdown("2. Parse Textual and Image data from Catalog data")
-        st.markdown("3. Generate Embeddings for Textual fields")
-        st.markdown("4. Generate Embeddings for Image")
-        st.markdown("5. Aggregate Embeddings with help of defined weightage")
-        st.markdown("6. Store it to Vector DB - Milvus")
-        
-        st.markdown("**SERVING:**")
-        st.markdown("1. Convert the Users’s query as Input text string to Embeddings with CLIP model")
-        st.markdown("2. Pass the user’s search query to LLM - OpenAI’S GPT3 to detect the search entities - Query, Brand, Category, Price, Sorting type")
-        st.markdown("3. Prepare and Apply Search Query on Collection of Milvus DB on which the data is indexed")
-        st.markdown("4. Fetch top N results")
-        st.markdown("5. Apply filters and return the results")
+    st.header("Product Discovery")    
+    st.markdown("**Application:** Gofynd.com")
+    st.markdown("**Total Products:** 14,000")
+    st.markdown("**Data Used:**")
+    st.markdown("- Text fields - Name, Brand, Category, Gender")
+    st.markdown("- Images - Single Images (First image of the product)")
+    st.markdown("Embedding Model: Used CLIP model for Generating the Embeddings for Text and Image")
+    st.markdown("Weightage [Text, Image]: [0.1, 0.9]")
+    st.markdown("Vector Database: Milvus")
+    st.markdown("ML Pipeline Orchestration: Vertex AI Pipeline")
+    st.markdown("Supported Input data format:")
+    st.markdown("Similarity Metric: Inner Product")
+    st.markdown("Type of Index: FLAT_L2")
+    
+    st.subheader("Input Sample Data:")
+    st.markdown("Sample data can be displayed here.")
+    
+    st.subheader("STEPS")
+    st.markdown("**TRAINING:**")
+    st.markdown("1. Pull the Catalog data from Fynd Platform")
+    st.markdown("2. Parse Textual and Image data from Catalog data")
+    st.markdown("3. Generate Embeddings for Textual fields")
+    st.markdown("4. Generate Embeddings for Image")
+    st.markdown("5. Aggregate Embeddings with help of defined weightage")
+    st.markdown("6. Store it to Vector DB - Milvus")
+    
+    st.markdown("**SERVING:**")
+    st.markdown("1. Convert the Users’s query as Input text string to Embeddings with CLIP model")
+    st.markdown("2. Pass the user’s search query to LLM - OpenAI’S GPT3 to detect the search entities - Query, Brand, Category, Price, Sorting type")
+    st.markdown("3. Prepare and Apply Search Query on Collection of Milvus DB on which the data is indexed")
+    st.markdown("4. Fetch top N results")
+    st.markdown("5. Apply filters and return the results")
 
 with tab2:
     st.header("Question And Answering")
-    with st.expander("Details"):
-        st.markdown("**Application:** Gofynd.com")
-        st.markdown("**Total Documents:** FAQ, Return, Shipping, T&C")
-        st.markdown("**Framework:** Langchain")
-        st.markdown("Embedding Model: Used OpenAI’s GPT3.5 model for Generating the Embeddings for Text docus")
-        st.markdown("Vector Database: Milvus")
-        st.markdown("ML Pipeline Orchestration: Vertex AI Pipeline")
-        st.markdown("Supported Input data format: Strict JSON format (Other formats to be added shortly)")
-        st.markdown("Support file type: JSON")
-        st.markdown("Similarity Metric: Inner Product")
-        st.markdown("Type of Index: FLAT_L2")
-        
-        st.subheader("STEPS")
-        st.markdown("**TRAINING:**")
-        st.markdown("1. Pull Textual policy documents from FDK")
-        st.markdown("2. Parse the file data and make all documents at flat level")
-        st.markdown("3. Apply splitters to convert the documents to chunks")
-        st.markdown("4. Generate the Embeddings for those chunks")
-        st.markdown("5. Store Embeddings to Milvus DB and create Index")
-        
-        st.markdown("**SERVING:**")
-        st.markdown("1. Convert users’ question to embeddings with help of OpenAI’s GPT3.5 model")
-        st.markdown("2. Apply Embedding Search Query on Collection of Milvus DB on which the data is indexed and Fetch top 10 Docs")
-        st.markdown("3. Use PROMPT template to prepare PROMPT = TEMPLATE + CONTEXT and use OpenAI’s to generate the response")
+    st.markdown("**Application:** Gofynd.com")
+    st.markdown("**Total Documents:** FAQ, Return, Shipping, T&C")
+    st.markdown("**Framework:** Langchain")
+    st.markdown("Embedding Model: Used OpenAI’s GPT3.5 model for Generating the Embeddings for Text docus")
+    st.markdown("Vector Database: Milvus")
+    st.markdown("ML Pipeline Orchestration: Vertex AI Pipeline")
+    st.markdown("Supported Input data format: Strict JSON format (Other formats to be added shortly)")
+    st.markdown("Support file type: JSON")
+    st.markdown("Similarity Metric: Inner Product")
+    st.markdown("Type of Index: FLAT_L2")
+    
+    st.subheader("STEPS")
+    st.markdown("**TRAINING:**")
+    st.markdown("1. Pull Textual policy documents from FDK")
+    st.markdown("2. Parse the file data and make all documents at flat level")
+    st.markdown("3. Apply splitters to convert the documents to chunks")
+    st.markdown("4. Generate the Embeddings for those chunks")
+    st.markdown("5. Store Embeddings to Milvus DB and create Index")
+    
+    st.markdown("**SERVING:**")
+    st.markdown("1. Convert users’ question to embeddings with help of OpenAI’s GPT3.5 model")
+    st.markdown("2. Apply Embedding Search Query on Collection of Milvus DB on which the data is indexed and Fetch top 10 Docs")
+    st.markdown("3. Use PROMPT template to prepare PROMPT = TEMPLATE + CONTEXT and use OpenAI’s to generate the response")