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RAG Splitter System


What does it do?
The RAG Splitter System allows you to:
• Select from various text splitters: Recursive Splitter, HTML Splitter, Markdown Splitter, Code Splitter, Token Splitter, Character Splitter, and Semantic Chunker.
• Process your data: Easily split and analyze text data based on your chosen method.
• View outputs clearly: Each splitter’s result is displayed in an organized manner, providing clarity and insight.

Key Features:
• User-friendly interface: Choose your splitter type and see the results instantly.
• Informative: Each splitter comes with a brief description to help you understand its purpose and functionality.
• Aesthetic design: With a streamlined layout and customizable styling, the app is both functional and visually appealing.
<h2 style="font-family: 'poppins'; font-weight: bold; color: Green;">👨💻 Author: Irfan Ullah Khan</h2>
My Portfolio :https://flowcv.me/ikm

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Rag[[:space:]]System[[:space:]]Profile.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,101 @@
+import streamlit as st
+import re
+
+# Define the split functions
+def recursive_splitter(data):
+    paragraphs = data.split('\n\n')
+    sentences = [sentence for para in paragraphs for sentence in para.split('.')]
+    return [sentence.strip() + '.' for sentence in sentences if sentence.strip()]
+
+def html_splitter(data):
+    parts = re.split(r'(<[^>]+>)', data)
+    return [part for part in parts if part.strip()]
+
+def markdown_splitter(data):
+    parts = re.split(r'(^#{1,6} .*$)', data, flags=re.MULTILINE)
+    return [part.strip() for part in parts if part.strip()]
+
+def code_splitter(data):
+    parts = re.split(r'(?m)^def ', data)
+    return [f'def {part.strip()}' if idx > 0 else part.strip() for idx, part in enumerate(parts) if part.strip()]
+
+def token_splitter(data):
+    tokens = re.findall(r'\b\w+\b', data)
+    return tokens
+
+def character_splitter(data):
+    return list(data)
+
+def semantic_chunker(data):
+    sentences = re.split(r'(?<=\.)\s+', data)
+    return [sentence.strip() for sentence in sentences if sentence.strip()]
+
+# Mapping splitter names to functions and descriptions
+splitter_details = {
+    "Recursive Splitter": {
+        "function": recursive_splitter,
+        "description": "Recursively splits the data into smaller chunks, like paragraphs into sentences. Useful for processing text at different levels of granularity."
+    },
+    "HTML Splitter": {
+        "function": html_splitter,
+        "description": "Splits data based on HTML tags, making it easier to work with structured web content, such as isolating specific sections of HTML code."
+    },
+    "Markdown Splitter": {
+        "function": markdown_splitter,
+        "description": "Splits markdown content based on headings (e.g., '# ', '## '). Useful for processing documents written in Markdown format."
+    },
+    "Code Splitter": {
+        "function": code_splitter,
+        "description": "Splits programming code into logical blocks like functions or classes. Useful for code analysis and documentation."
+    },
+    "Token Splitter": {
+        "function": token_splitter,
+        "description": "Splits data into individual tokens/words, which is often the first step in natural language processing (NLP) tasks."
+    },
+    "Character Splitter": {
+        "function": character_splitter,
+        "description": "Splits text into individual characters. Useful for character-level analysis or encoding tasks."
+    },
+    "Semantic Chunker": {
+        "function": semantic_chunker,
+        "description": "Splits data based on semantic meaning, typically by sentences. Ensures that related information stays together."
+    },
+}
+
+# Streamlit app
+st.sidebar.title("Splitter Settings")
+st.sidebar.subheader("Data Input")
+user_data = st.sidebar.text_area("Enter the data you want to split:", "This is a sample text. Enter your data here...")
+
+st.sidebar.subheader("Splitter Type")
+splitter_type = st.sidebar.selectbox(
+    "Choose a splitter type:",
+    list(splitter_details.keys())
+)
+
+st.sidebar.subheader("Options")
+show_info = st.sidebar.checkbox("Show information about all splitter types")
+
+st.title("RAG Splitter System")
+st.markdown('<p class="title">Developed By: Irfan Ullah Khan</p>', unsafe_allow_html=True)
+
+# Display selected splitter description
+st.subheader(f"Selected Splitter: {splitter_type}")
+st.write(splitter_details[splitter_type]["description"])
+
+# Processing
+if st.button("Split Data"):
+    with st.spinner('Processing data...'):
+        splitter_function = splitter_details[splitter_type]["function"]
+        split_output = splitter_function(user_data)
+        
+        if split_output:
+            st.subheader(f"Output using {splitter_type}")
+            for idx, part in enumerate(split_output):
+                st.write(f"**Part {idx + 1}:**")
+                st.write(part)
+
+if show_info:
+    for name, details in splitter_details.items():
+        st.subheader(name)
+        st.write(details["description"])