Create model_registry.py

src/utils/model_registry.py

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+"""
+Models registry for the Auto-Analyst application.
+This file serves as the single source of truth for all model information.
+"""
+
+# Model providers
+PROVIDERS = {
+    "openai": "OpenAI",
+    "anthropic": "Anthropic",
+    "groq": "GROQ",
+    "gemini": "Google Gemini"
+}
+
+# Cost per 1K tokens for different models
+MODEL_COSTS = {
+    "openai": {
+        "gpt-4.1": {"input": 0.002, "output": 0.008},
+        "gpt-4.1-mini": {"input": 0.0004, "output": 0.0016},
+        "gpt-4.1-nano": {"input": 0.00010, "output": 0.0004},
+        "gpt-4.5-preview": {"input": 0.075, "output": 0.15},
+        "gpt-4o": {"input": 0.0025, "output": 0.01},
+        "gpt-4o-mini": {"input": 0.00015, "output": 0.0006},  
+        "o1": {"input": 0.015, "output": 0.06},  
+        "o1-pro": {"input": 0.015, "output": 0.6},
+        "o1-mini": {"input": 0.00011, "output": 0.00044}, 
+        "o3": {"input": 0.001, "output": 0.04},
+        "o3-mini": {"input": 0.00011, "output": 0.00044},
+        "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},  
+    },
+    "anthropic": {
+        "claude-3-opus-latest": {"input": 0.015, "output": 0.075},  
+        "claude-3-7-sonnet-latest": {"input": 0.003, "output": 0.015},   
+        "claude-3-5-sonnet-latest": {"input": 0.003, "output": 0.015}, 
+        "claude-3-5-haiku-latest": {"input": 0.0008, "output": 0.0004},
+    },
+    "groq": {
+        "deepseek-r1-distill-llama-70b": {"input": 0.00075, "output": 0.00099},
+        "llama-3.3-70b-versatile": {"input": 0.00059, "output": 0.00079},
+        "llama3-8b-8192": {"input": 0.00005, "output": 0.00008},
+        "llama3-70b-8192": {"input": 0.00059, "output": 0.00079},
+        "mistral-saba-24b": {"input": 0.00079, "output": 0.00079},
+        "gemma2-9b-it": {"input": 0.0002, "output": 0.0002},
+        "qwen-qwq-32b": {"input": 0.00029, "output": 0.00039},
+        "meta-llama/llama-4-maverick-17b-128e-instruct": {"input": 0.0002, "output": 0.0006},
+        "meta-llama/llama-4-scout-17b-16e-instruct": {"input": 0.00011, "output": 0.00034},
+        "deepseek-r1-distill-qwen-32b": {"input": 0.00075, "output": 0.00099},
+        "llama-3.1-70b-versatile": {"input": 0.00059, "output": 0.00079},
+    },
+    "gemini": {
+        "gemini-2.5-pro-preview-03-25": {"input": 0.00015, "output": 0.001}
+    }
+}
+
+# Tiers based on cost per 1K tokens
+MODEL_TIERS = {
+    "tier1": {
+        "name": "Basic",
+        "credits": 1,
+        "models": [
+            "llama3-8b-8192",
+            "llama-3.1-8b-instant",
+            "gemma2-9b-it",
+            "meta-llama/llama-4-scout-17b-16e-instruct",
+            "llama-3.2-1b-preview",
+            "llama-3.2-3b-preview",
+            "llama-3.2-11b-text-preview",
+            "llama-3.2-11b-vision-preview",
+            "llama3-groq-8b-8192-tool-use-preview"
+        ]
+    },
+    "tier2": {
+        "name": "Standard",
+        "credits": 3,
+        "models": [
+            "gpt-4.1-nano",
+            "gpt-4o-mini",
+            "o1-mini",
+            "o3-mini",
+            "qwen-qwq-32b",
+            "meta-llama/llama-4-maverick-17b-128e-instruct"
+        ]
+    },
+    "tier3": {
+        "name": "Premium",
+        "credits": 5,
+        "models": [
+            "gpt-4.1",
+            "gpt-4.1-mini",
+            "gpt-4.5-preview",
+            "gpt-4o",
+            "o1",
+            "o1-pro",
+            "o3",
+            "gpt-3.5-turbo",
+            "claude-3-opus-latest",
+            "claude-3-7-sonnet-latest",
+            "claude-3-5-sonnet-latest",
+            "claude-3-5-haiku-latest",
+            "deepseek-r1-distill-llama-70b",
+            "llama-3.3-70b-versatile",
+            "llama3-70b-8192",
+            "mistral-saba-24b",
+            "deepseek-r1-distill-qwen-32b",
+            "llama-3.2-90b-text-preview",
+            "llama-3.2-90b-vision-preview",
+            "llama-3.3-70b-specdec",
+            "llama2-70b-4096",
+            "llama-3.1-70b-versatile",
+            "llama-3.1-405b-reasoning",
+            "llama3-groq-70b-8192-tool-use-preview",
+            "gemini-2.5-pro-preview-03-25"
+        ]
+    }
+}
+
+
+# Model metadata (display name, context window, etc.)
+MODEL_METADATA = {
+    # OpenAI
+    "gpt-4.1": {"display_name": "GPT-4.1", "context_window": 128000},
+    "gpt-4.1-mini": {"display_name": "GPT-4.1 Mini", "context_window": 128000},
+    "gpt-4.1-nano": {"display_name": "GPT-4.1 Nano", "context_window": 128000},
+    "gpt-4o": {"display_name": "GPT-4o", "context_window": 128000},
+    "gpt-4.5-preview": {"display_name": "GPT-4.5 Preview", "context_window": 128000},
+    "gpt-4o-mini": {"display_name": "GPT-4o Mini", "context_window": 128000},
+    "gpt-3.5-turbo": {"display_name": "GPT-3.5 Turbo", "context_window": 16385},
+    "o1": {"display_name": "o1", "context_window": 128000},
+    "o1-pro": {"display_name": "o1 Pro", "context_window": 128000},
+    "o1-mini": {"display_name": "o1 Mini", "context_window": 128000},
+    "o3": {"display_name": "o3", "context_window": 128000},
+    "o3-mini": {"display_name": "o3 Mini", "context_window": 128000},
+    # Anthropic
+    "claude-3-opus-latest": {"display_name": "Claude 3 Opus", "context_window": 200000},
+    "claude-3-7-sonnet-latest": {"display_name": "Claude 3.7 Sonnet", "context_window": 200000},
+    "claude-3-5-sonnet-latest": {"display_name": "Claude 3.5 Sonnet", "context_window": 200000},
+    "claude-3-5-haiku-latest": {"display_name": "Claude 3.5 Haiku", "context_window": 200000},
+    
+    # GROQ
+    "deepseek-r1-distill-llama-70b": {"display_name": "DeepSeek R1 Distill Llama 70b", "context_window": 32768},
+    "llama-3.3-70b-versatile": {"display_name": "Llama 3.3 70b", "context_window": 8192},
+    "llama3-8b-8192": {"display_name": "Llama 3 8b", "context_window": 8192},
+    "llama3-70b-8192": {"display_name": "Llama 3 70b", "context_window": 8192},
+    "mistral-saba-24b": {"display_name": "Mistral Saba 24b", "context_window": 32768},
+    "gemma2-9b-it": {"display_name": "Gemma 2 9b", "context_window": 8192},
+    "qwen-qwq-32b": {"display_name": "Qwen QWQ 32b | Alibaba", "context_window": 32768},
+    "meta-llama/llama-4-maverick-17b-128e-instruct": {"display_name": "Llama 4 Maverick 17b", "context_window": 128000},
+    "meta-llama/llama-4-scout-17b-16e-instruct": {"display_name": "Llama 4 Scout 17b", "context_window": 16000},
+    "llama-3.1-70b-versatile": {"display_name": "Llama 3.1 70b Versatile", "context_window": 8192},
+    
+    # Gemini
+    "gemini-2.5-pro-preview-03-25": {"display_name": "Gemini 2.5 Pro", "context_window": 1000000},
+}
+
+# Helper functions
+
+def get_provider_for_model(model_name):
+    """Determine the provider based on model name"""
+    if not model_name:
+        return "Unknown"
+        
+    model_name = model_name.lower()
+    return next((provider for provider, models in MODEL_COSTS.items() 
+                if any(model_name in model for model in models)), "Unknown")
+
+def get_model_tier(model_name):
+    """Get the tier of a model"""
+    for tier_id, tier_info in MODEL_TIERS.items():
+        if model_name in tier_info["models"]:
+            return tier_id
+    return "tier1"  # Default to tier1 if not found
+
+def calculate_cost(model_name, input_tokens, output_tokens):
+    """Calculate the cost for using the model based on tokens"""
+    if not model_name:
+        return 0
+        
+    # Convert tokens to thousands
+    input_tokens_in_thousands = input_tokens / 1000
+    output_tokens_in_thousands = output_tokens / 1000
+    
+    # Get model provider
+    model_provider = get_provider_for_model(model_name)
+    
+    # Handle case where model is not found
+    if model_provider == "Unknown" or model_name not in MODEL_COSTS.get(model_provider, {}):
+        return 0
+        
+    return (input_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["input"] + 
+            output_tokens_in_thousands * MODEL_COSTS[model_provider][model_name]["output"])
+
+def get_credit_cost(model_name):
+    """Get the credit cost for a model"""
+    tier_id = get_model_tier(model_name)
+    return MODEL_TIERS[tier_id]["credits"]
+
+def get_display_name(model_name):
+    """Get the display name for a model"""
+    return MODEL_METADATA.get(model_name, {}).get("display_name", model_name)
+
+def get_context_window(model_name):
+    """Get the context window size for a model"""
+    return MODEL_METADATA.get(model_name, {}).get("context_window", 4096)
+
+def get_all_models_for_provider(provider):
+    """Get all models for a specific provider"""
+    if provider not in MODEL_COSTS:
+        return []
+    return list(MODEL_COSTS[provider].keys())
+
+def get_models_by_tier(tier_id):
+    """Get all models for a specific tier"""
+    return MODEL_TIERS.get(tier_id, {}).get("models", [])