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rag_tutorials/llm_app_hybrid_RAG_claude/README.md

@@ -1,13 +1,16 @@
-# Hybrid RAG Claude Chat 🤖
+# LLM Hybrid Search-RAG Assistant - Claude 🤖
 
-A powerful document Q&A application that combines Hybrid Search (RAG) with Claude's general knowledge. This is built on the RAGLite framework and Chainlit for the UI. 
+A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Chainlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses.
+
+## Demo
+
+![Demo](rag_tutorials/llm_app_hybrid_RAG_claude/LLM-Hybrid-RagLite-Claude.mp4)
 
 ## Features
 
-- **Hybrid Question Answering**
+- **Hybrid Search Question Answering**
     - RAG-based answers for document-specific queries
     - Fallback to Claude for general knowledge questions
-    - Seamless switching between modes
 
 - **Document Processing**:
   - PDF document upload and processing
@@ -15,16 +18,10 @@ A powerful document Q&A application that combines Hybrid Search (RAG) with Claud
   - Hybrid search combining semantic and keyword matching
   - Reranking for better context selection
 
-- **Interactive Chat Interface**:
-  - Real-time streaming responses
-  - Chat history preservation
-  - Error handling with retry options
-  - File upload validation
-
 - **Multi-Model Integration**:
-  - Claude for text generation
+  - Claude for text generation - tested with Claude 3 Opus 
-  - OpenAI for embeddings
+  - OpenAI for embeddings - tested with text-embedding-3-large
-  - Cohere for reranking (tried using the new Cohere 3.5 reranker)
+  - Cohere for reranking - tested with Cohere 3.5 reranker
 
 ## Prerequisites
 
@@ -40,11 +37,11 @@ You'll need the following API keys and database setup:
    - [Anthropic API key](https://console.anthropic.com/settings/keys) for Claude
    - [Cohere API key](https://dashboard.cohere.com/api-keys) for reranking
 
-## Installation
+## How to get Started?
 
 1. **Clone the Repository**:
    ```bash
-   git clone <repository-url>
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
    cd rag_tutorials/llm_app_hybrid_RAG_claude
    ```
 
@@ -66,7 +63,7 @@ You'll need the following API keys and database setup:
 ## Usage
 
 1. Start the application
-2. When prompted, enter your:
+2. Enter your important keys in the ChatSettings widget:
    - OpenAI API key
    - Anthropic API key
    - Cohere API key

rag_tutorials/llm_app_hybrid_RAG_claude/chainlit.yaml

@@ -11,8 +11,8 @@ project:
     - DB_URL
 
 ui:
-  name: "LLM App with Hybrid Search and RAG - Claude"
+  name: "🤖 RAG-Powered Document Assistant"
-  description: "by unwind ai"
+  description: "Upload documents and ask questions using hybrid RAG with Claude"
   hide_cot: false
   default_collapse_content: true
   default_expand_messages: true
@@ -25,6 +25,7 @@ ui:
     centered: true
   show_sidebar: true
   sidebar_position: "left"
+  theme: light
 
 # Configure the environment variables UI
 user_env:
@@ -68,3 +69,29 @@ theme:
     primary: "#2563eb"
     background: "#1a1a1a"
     sidebar: "#1e1e1e"
+
+chat_profiles: []
+default_expand_messages: false
+markdown: true
+message_html: true
+show_readme_as_default: true
+default_collapse_content: true
+theme: light
+
+features:
+  speech_to_text: false
+  task_list: true
+
+ui:
+  name: Hybrid RAG Assistant
+  description: Ask questions about your documents
+  hide_cot: false
+  show_docs: false
+  show_debugger: false
+  show_file_upload: true
+
+file_upload:
+  max_size_mb: 20
+  max_files: 5
+  accept:
+    - application/pdf

rag_tutorials/llm_app_hybrid_RAG_claude/main.py

@@ -10,14 +10,13 @@ from chainlit.input_widget import TextInput
 from chainlit import AskUserMessage
 import anthropic
 
-# Configure logging
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize global config variable
 my_config = None
 
-# Define RAG system prompt
+# Defining the  RAG system prompt
 RAG_SYSTEM_PROMPT = """
 You are a friendly and knowledgeable assistant that provides complete and insightful answers.
 Answer the user's question using only the context below.
@@ -48,7 +47,7 @@ def process_document(file_path: str) -> None:
         import time
         start_time = time.time()
 
-        # Insert document into PostgreSQL database
+        # Inserting document into PostgreSQL database
         insert_document(Path(file_path), config=my_config)
 
         processing_time = time.time() - start_time
@@ -181,7 +180,16 @@ async def start() -> None:
         logger.info("Chat session started")
         cl.user_session.set("chat_history", [])
         
-        # Show settings form first
+        # Add header with markdown formatting
+        await cl.Message(
+            content="""# 🤖 LLM-Powered Hybrid Search-RAG Assistant powered by Claude
+            
+            Welcome! To get started:
+            Enter your API keys below on the ChatSettings widget
+            """
+        ).send()
+        
+        # Show settings form
         await cl.ChatSettings([
             TextInput(
                 id="OpenAIApiKey",
@@ -280,28 +288,53 @@ async def handle_fallback(query: str, msg: cl.Message) -> None:
     """Handle fallback to Claude when RAG is not available or fails."""
     try:
         user_env = cl.user_session.get("env")
+        if not user_env or "ANTHROPIC_API_KEY" not in user_env:
+            raise ValueError("Missing Anthropic API key in session")
+            
         client = anthropic.Anthropic(api_key=user_env["ANTHROPIC_API_KEY"])
         
+        logger.info("Falling back to Claude for response")
+        
+        # Get response from Claude
         response = client.messages.create(
-            model="claude-3-5-sonnet-20241022",
+            model="claude-3-sonnet-20240229",
-            max_tokens=1024,
             messages=[
-                {"role": "user", "content": query}
+                {
-            ]
+                    "role": "system",
+                    "content": """You are a helpful AI assistant. When asked questions, provide informative responses 
+                    based on your general knowledge. You don't need to reference any specific documents or context.
+                    Just answer naturally as a knowledgeable assistant."""
+                },
+                {
+                    "role": "user",
+                    "content": query
+                }
+            ],
+            max_tokens=1024,
+            temperature=0.7
         )
         
+        # Get the complete response
         full_response = response.content[0].text
-        await msg.send(content=full_response)
+        
+        # Create a new message for streaming
+        msg = cl.Message(content="")
+        await msg.send()
+        
+        # Stream the response word by word
+        for word in full_response.split():
+            await msg.stream_token(word + " ")
         
         # Update chat history
         chat_history = cl.user_session.get("chat_history", [])
         chat_history.append((query, full_response))
         cl.user_session.set("chat_history", chat_history)
+        logger.info("Successfully generated fallback response")
         
     except Exception as e:
         error_msg = f"Fallback error: {str(e)}"
         logger.error(error_msg)
-        await msg.send(content=error_msg)
+        await cl.Message(content=f"❌ {error_msg}").send()
 
 if __name__ == "__main__":
     cl.run()