File size: 2,117 Bytes
31d9d4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import argparse
import sys
import json
from .model_manager import OVModelManager
def main():
parser = argparse.ArgumentParser(description="OpenVinayaka: Hallucination-Free AI Runner")
parser.add_argument("command", choices=["run", "serve"], help="Command to execute")
parser.add_argument("--model", type=str, default="gpt2", help="HuggingFace model ID (e.g., meta-llama/Llama-2-7b)")
parser.add_argument("--memory", type=str, help="Path to JSON memory file (Truth Source)")
args = parser.parse_args()
if args.command == "run":
print(f"🚀 OpenVinayaka CLI v1.0")
print(f" Model: {args.model}")
# Initialize Model
manager = OVModelManager(args.model)
manager.attach_ov_hooks()
# Load Memory if provided
memory_data = None
if args.memory:
try:
with open(args.memory, "r") as f:
memory_data = json.load(f)
print(f"📂 Memory Loaded: {len(memory_data)} facts.")
except Exception as e:
print(f"⚠️ Could not load memory: {e}")
print("\n💬 Ready! Type your query (or 'exit'):")
while True:
try:
user_input = input("> ")
if user_input.lower() in ["exit", "quit"]:
break
# Simple Memory Retrieval (Mocked for CLI speed)
relevant_memory = None
if memory_data:
# In a real app, we run the Vector Search + Metadata Priority here
# For now, just grab the first item as a demo
relevant_memory = memory_data[0]
response = manager.generate(user_input, relevant_memory)
print(f"\n🤖 {response}\n")
except KeyboardInterrupt:
break
elif args.command == "serve":
print("🌐 API Server starting on port 8000... (Not implemented in demo)")
if __name__ == "__main__":
main()
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