Release 4.5.0: Added demo script, fixed HDV JSON bugs, applied HF YAML tags

README.md

@@ -1,3 +1,22 @@
+---
+title: MnemoCore
+emoji: 🧠
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+pinned: false
+license: mit
+language:
+  - en
+tags:
+  - memory
+  - vector-database
+  - cognitive-architecture
+  - agents
+  - hyperdimensional-computing
+  - vsa
+---
+
 # MnemoCore
 
 ### Infrastructure for Persistent Cognitive Memory

demo_wow.py

@@ -0,0 +1,66 @@
+"""
+MnemoCore 60-Second "Wow" Demo
+Showcasing Persistent Cognitive Memory, Analogy Reasoning, and Decay.
+Run this directly: `python demo_wow.py`
+"""
+
+import asyncio
+import tempfile
+import os
+from mnemocore.core.engine import HAIMEngine
+
+async def main():
+    print("[1] Booting MnemoCore HAIM Engine...")
+    
+    # We use a temporary directory for this quick demo so we don't leave files behind.
+    temp_dir = tempfile.mkdtemp()
+    demo_db_path = os.path.join(temp_dir, "demo_memory.jsonl")
+    
+    engine = HAIMEngine(persist_path=demo_db_path)
+    
+    # Temporarily override the conceptual memory storage to avoid corrupting the real ConceptualMemory
+    from mnemocore.core.holographic import ConceptualMemory
+    engine.soul = ConceptualMemory(dimension=engine.dimension, storage_dir=temp_dir)
+    
+    await engine.initialize()
+    
+    print("\n[2] Teaching the AI facts and procedures...")
+    await engine.store("The company's main language is Python.", goal_id="onboarding")
+    await engine.store("If the database locks, restart the Redis pod.", metadata={"type": "procedure"})
+    
+    # Wait for the async worker to process the events
+    print("   [Syncing memory tiers...]")
+    await asyncio.sleep(0.5)
+    
+    print("\n[3] Retrieving context associatively...")
+    results = await engine.query("If the database locks, what should I do?")
+    print(f"   [Debug: raw query results: {results}]")
+    if results:
+        mem = await engine.get_memory(results[0][0])
+        print(f"   -> Top memory retrieved: '{mem.content}' (Score: {results[0][1]:.2f})")
+    else:
+        print("   -> No memories found.")
+
+    print("\n[4] VSA Analogical Reasoning (Math in 16k-dimensions)...")
+    await engine.define_concept("CTO", {"role": "technical_leader", "gender": "neutral"})
+    await engine.define_concept("CEO", {"role": "business_leader", "gender": "neutral"})
+    await engine.define_concept("code", {"domain": "technical_leader"})
+    
+    print("   If CTO : code :: CEO : ?")
+    analogy = await engine.reason_by_analogy(src="CTO", val="code", tgt="CEO")
+    print(f"   -> The engine deduced: {analogy[0][0]} (Confidence: {analogy[0][1]:.2f})")
+
+    print("\n[Success] MnemoCore transforms data storage into cognitive reasoning. Try it in your agents today!")
+    await engine.close()
+    
+    # Clean up temp file
+    if os.path.exists(demo_db_path):
+        os.remove(demo_db_path)
+    if os.path.exists(os.path.join(temp_dir, "codebook.json")):
+        os.remove(os.path.join(temp_dir, "codebook.json"))
+    if os.path.exists(os.path.join(temp_dir, "concepts.json")):
+        os.remove(os.path.join(temp_dir, "concepts.json"))
+    os.rmdir(temp_dir)
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/mnemocore/core/holographic.py

@@ -167,7 +167,7 @@ class ConceptualMemory:
         codebook_data = {}
         for k, v in self.symbols.items():
             codebook_data[k] = {
-                "data": list(v.data),  # Store as list of uint8
+                "data": v.data.tolist(),  # Store as standard Python ints
                 "dimension": v.dimension
             }
         with open(self.codebook_path, 'w') as f:
@@ -177,7 +177,7 @@ class ConceptualMemory:
         concepts_data = {}
         for k, v in self.concepts.items():
             concepts_data[k] = {
-                "data": list(v.data),
+                "data": v.data.tolist(),
                 "dimension": v.dimension
             }
         with open(self.concepts_path, 'w') as f:

upload_hf.py

@@ -0,0 +1,34 @@
+import sys
+from huggingface_hub import HfApi
+
+def upload_to_hf():
+    api = HfApi()
+    
+    # We are uploading the whole repo but excluding internal/unwanted files
+    # User explicitly requested to NOT include the newsletter
+    exclude_patterns = [
+        ".git/**",
+        ".venv/**",
+        ".pytest_cache/**",
+        "__pycache__/**",
+        "data/**",
+        "demo_error*.log",
+        "*newsletter*",
+        "*nyhetsbrev*",
+        ".gemini/**",
+        ".claude/**",
+        ".tmp_phase43_tests/**"
+    ]
+    
+    print("Uploading to HF...")
+    api.upload_folder(
+        folder_path="c:/Users/Robin/MnemoCore-Infrastructure-for-Persistent-Cognitive-Memory",
+        repo_id="Granis87/MnemoCore",
+        repo_type="model",
+        ignore_patterns=exclude_patterns,
+        commit_message="Release 4.5.0: Added demo script, fixed HDV JSON bugs, applied HF YAML tags"
+    )
+    print("Upload completed successfully!")
+
+if __name__ == "__main__":
+    upload_to_hf()