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README.md

@@ -1,5 +1,5 @@
 ---
-title: Socratic Aid
+title: Socratic Llama
 emoji: 🤖
 colorFrom: blue
 colorTo: red
@@ -9,7 +9,7 @@ app_file: app.py
 pinned: false
 ---
 
-# Socratic Aid
+# Socratic Llama
 
 
 
@@ -53,7 +53,7 @@ pinned: false
 
 ## Configuration
 
-- **Model**: google/gemini-2.0-flash-001
-- **Temperature**: 0.7
-- **Max Tokens**: 1500
+- **Model**: nvidia/llama-3.1-nemotron-70b-instruct
+- **Temperature**: 0.9
+- **Max Tokens**: 1000
 - **API Key Variable**: OPENROUTER_API_KEY

app.py

@@ -10,16 +10,15 @@ import urllib.parse
 
 
 # Configuration
-SPACE_NAME = "Socratic Aid"
+SPACE_NAME = "Socratic Llama"
 SPACE_DESCRIPTION = ""
 SYSTEM_PROMPT = """You are a pedagogically-minded academic assistant designed for introductory courses. Your approach follows constructivist learning principles: build on students' prior knowledge, scaffold complex concepts through graduated questioning, and use Socratic dialogue to guide discovery. Provide concise, evidence-based explanations that connect theory to lived experiences. Each response should model critical thinking by acknowledging multiple perspectives, identifying assumptions, and revealing conceptual relationships. Conclude with open-ended questions that promote higher-order thinking—analysis, synthesis, or evaluation—rather than recall."""
-MODEL = "google/gemini-2.0-flash-001"
+MODEL = "nvidia/llama-3.1-nemotron-70b-instruct"
 GROUNDING_URLS = []
 # Get access code from environment variable for security
 ACCESS_CODE = os.environ.get("SPACE_ACCESS_CODE", "")
 ENABLE_DYNAMIC_URLS = False
-ENABLE_VECTOR_RAG = False
-RAG_DATA = None
+# RAG functionality removed
 
 # Get API key from environment - customizable variable name with validation
 API_KEY = os.environ.get("OPENROUTER_API_KEY")
@@ -175,10 +174,31 @@ def export_conversation_to_markdown(conversation_history):
     markdown_content = f"""# Conversation Export
 Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
 
----
+## Configuration Information
 
+**Assistant Name:** Socratic Llama
+**Description:** 
+**Model:** {MODEL}
+**Temperature:** 0.9
+**Max Tokens:** 1000
+**API Key Variable:** OPENROUTER_API_KEY
 """
     
+    # Add URL grounding information
+    if GROUNDING_URLS:
+        markdown_content += f"\n**URL Grounding ({len(GROUNDING_URLS)} URLs):**\n"
+        for i, url in enumerate(GROUNDING_URLS, 1):
+            markdown_content += f"- URL {i}: {url}\n"
+    
+    # Add feature flags
+    if ENABLE_DYNAMIC_URLS:
+        markdown_content += f"\n**Dynamic URL Fetching:** Enabled\n"
+    
+    # Add system prompt
+    markdown_content += f"\n**System Prompt:**\n```\n{SYSTEM_PROMPT}\n```\n"
+    
+    markdown_content += "\n---\n\n"
+    
     message_pair_count = 0
     for i, message in enumerate(conversation_history):
         if isinstance(message, dict):
@@ -201,35 +221,7 @@ Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
     
     return markdown_content
 
-# Initialize RAG context if enabled
-if ENABLE_VECTOR_RAG and RAG_DATA:
-    try:
-        import faiss
-        import numpy as np
-        import base64
-        
-        class SimpleRAGContext:
-            def __init__(self, rag_data):
-                # Deserialize FAISS index
-                index_bytes = base64.b64decode(rag_data['index_base64'])
-                self.index = faiss.deserialize_index(index_bytes)
-                
-                # Restore chunks and mappings
-                self.chunks = rag_data['chunks']
-                self.chunk_ids = rag_data['chunk_ids']
-            
-            def get_context(self, query, max_chunks=3):
-                """Get relevant context - simplified version"""
-                # In production, you'd compute query embedding here
-                # For now, return a simple message
-                return "\n\n[RAG context would be retrieved here based on similarity search]\n\n"
-        
-        rag_context_provider = SimpleRAGContext(RAG_DATA)
-    except Exception as e:
-        print(f"Failed to initialize RAG: {e}")
-        rag_context_provider = None
-else:
-    rag_context_provider = None
+# RAG functionality removed
 
 def generate_response(message, history):
     """Generate response using OpenRouter API"""
@@ -249,11 +241,7 @@ def generate_response(message, history):
     # Get grounding context
     grounding_context = get_grounding_context()
     
-    # Add RAG context if available
-    if ENABLE_VECTOR_RAG and rag_context_provider:
-        rag_context = rag_context_provider.get_context(message)
-        if rag_context:
-            grounding_context += rag_context
+    # RAG functionality removed
     
     # If dynamic URLs are enabled, check message for URLs to fetch
     if ENABLE_DYNAMIC_URLS:
@@ -306,8 +294,8 @@ def generate_response(message, history):
             json={
                 "model": MODEL,
                 "messages": messages,
-                "temperature": 0.7,
-                "max_tokens": 1500
+                "temperature": 0.9,
+                "max_tokens": 1000
             },
             timeout=30
         )
@@ -501,17 +489,14 @@ def get_configuration_status():
         status_parts.append("❌ **API Key:** Not configured - Set `OPENROUTER_API_KEY` in Space secrets")
     
     status_parts.append(f"🤖 **Model:** {MODEL}")
-    status_parts.append(f"🌡️ **Temperature:** 0.7")
-    status_parts.append(f"📝 **Max Tokens:** 1500")
+    status_parts.append(f"🌡️ **Temperature:** 0.9")
+    status_parts.append(f"📝 **Max Tokens:** 1000")
     
     if GROUNDING_URLS:
         status_parts.append(f"��� **URL Grounding:** {len(GROUNDING_URLS)} URLs configured")
     
     if ENABLE_DYNAMIC_URLS:
         status_parts.append("🔄 **Dynamic URLs:** Enabled")
-        
-    if ENABLE_VECTOR_RAG:
-        status_parts.append("📚 **Document RAG:** Enabled")
     
     if ACCESS_CODE:
         status_parts.append("🔐 **Access Control:** Enabled")

config.json

@@ -1,15 +1,13 @@
 {
-  "name": "Socratic Aid",
+  "name": "Socratic Llama",
   "description": "",
   "system_prompt": "You are a pedagogically-minded academic assistant designed for introductory courses. Your approach follows constructivist learning principles: build on students' prior knowledge, scaffold complex concepts through graduated questioning, and use Socratic dialogue to guide discovery. Provide concise, evidence-based explanations that connect theory to lived experiences. Each response should model critical thinking by acknowledging multiple perspectives, identifying assumptions, and revealing conceptual relationships. Conclude with open-ended questions that promote higher-order thinking\u2014analysis, synthesis, or evaluation\u2014rather than recall.",
-  "model": "google/gemini-2.0-flash-001",
+  "model": "nvidia/llama-3.1-nemotron-70b-instruct",
   "api_key_var": "OPENROUTER_API_KEY",
-  "temperature": 0.7,
-  "max_tokens": 1500,
-  "examples": "[\"Hello! How can you help me?\", \"Tell me something interesting\", \"What can you do?\"]",
+  "temperature": 0.9,
+  "max_tokens": 1000,
+  "examples": "[\"Help me solve a problem?\", \"Where do I start with databases?\", \"Why is the unexamined life not worth living?\"]",
   "grounding_urls": "[]",
   "access_code": "",
-  "enable_dynamic_urls": false,
-  "enable_vector_rag": false,
-  "rag_data_json": "None"
+  "enable_dynamic_urls": false
 }