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core/SAVANT_CORE/README.md

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+
+SAVANT_CORE
+===========
+Core funcional del motor Savant Simbiótico RRF (scaffold).
+
+Cómo usar:
+1. Copia la carpeta SAVANT_CORE a tu Drive o a tu repo.
+2. Instala dependencias: pip install -r SAVANT_CORE/requirements.txt
+3. Ejecuta demo: python SAVANT_CORE/run_demo.py
+
+Contenido:
+- core/: módulos engine, memory, mappings, resonance, music, self_improvement, trainer
+- run_demo.py: demo CLI simple

core/SAVANT_CORE/SAVANT_CORE/README.md

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+
+SAVANT_CORE
+===========
+Core funcional del motor Savant Simbiótico RRF (scaffold).
+
+Cómo usar:
+1. Copia la carpeta SAVANT_CORE a tu Drive o a tu repo.
+2. Instala dependencias: pip install -r SAVANT_CORE/requirements.txt
+3. Ejecuta demo: python SAVANT_CORE/run_demo.py
+
+Contenido:
+- core/: módulos engine, memory, mappings, resonance, music, self_improvement, trainer, api_helpers
+- run_demo.py: demo CLI simple

core/SAVANT_CORE/SAVANT_CORE/core/__init__.py

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+
+# SAVANT_CORE - núcleo del motor Savant Simbiótico RRF
+from .engine import SavantEngine

core/SAVANT_CORE/SAVANT_CORE/core/api_helpers.py

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+
+"""
+api_helpers.py
+Funciones helper para integrar con una UI (Gradio/Flask).
+Provee: chat_refine(text), map_text(text), music_from_text(text)
+""" # Added closing triple quotes
+from .mappings import IcosaMap, DodecaMap
+from .resonance import ResonanceSimulator
+from .music import MusicAdapter
+from .memory import MemoryStore
+from .self_improvement import SelfImprover
+
+# instantiate shared small objects (lightweight)
+_mem = MemoryStore()
+_icosa = IcosaMap()
+_dodeca = DodecaMap()
+_res = ResonanceSimulator()
+_music = MusicAdapter()
+
+def map_text(text):
+    node = _icosa.closest_node(text)
+    _mem.add({"type":"map", "query": text, "node": node})
+    return node
+
+def base_resonance(text):
+    r = _res.simulate(text)
+    _mem.add({"type":"resonance_query", "query": text, "result": r["summary"]})
+    return r
+
+def music_from_text(text):
+    seq = _music.adapt_text_to_music(text)
+    _mem.add({"type":"music", "query": text, "seq_len": len(seq)})
+    return seq
+
+def chat_refine(user_text, base_model_output, self_improver=None):
+    """
+    Refinador simbiótico: toma output bruto y aplica transformaciones
+    basadas en current self_improver params (if provided).
+    Es una función determinista/simbolic toy but extensible.
+    """ # Added closing triple quotes
+    refined = base_model_output
+    if self_improver:
+        params = self_improver.params
+        if params.get("resonance_scale",1.0) > 1.05:
+            # toy: append resonance note
+            refined = refined + " [SAVANT-RES: emphasis applied]"
+        if params.get("music_influence",0.0) > 0.6:
+            refined = refined + " [SAVANT-MUSIC: melodic hint]"
+    # store
+    _mem.add({"type":"refine", "input": user_text, "before": base_model_output, "after": refined})
+    return refined

core/SAVANT_CORE/SAVANT_CORE/core/engine.py

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+
+"""
+engine.py
+Orquestador principal del motor Savant Simbiótico RRF.
+Expone:
+- handle_query(text): detecta intención (map/resonance/music/chat) y responde
+- access to SimpleTrainer, SelfImprover, MemoryStore for external control
+"""
+import time # Import time
+from .mappings import IcosaMap, DodecaMap
+from .resonance import ResonanceSimulator
+from .music import MusicAdapter
+from .memory import MemoryStore
+from .self_improvement import SelfImprover
+# from .trainer import SimpleTrainer # Avoid circular import, trainer can be instantiated externally
+from .api_helpers import chat_refine
+import os # Import os
+import pandas as pd # Import pandas
+import json # Import json
+import pickle # Import pickle
+
+class SavantEngine:
+    def __init__(self, structured_data_paths=None):
+        self.memory = MemoryStore("SAVANT_memory.jsonl")
+        # Load structured data if paths are provided
+        self.structured_data = {}
+        if structured_data_paths:
+             print("Engine: Loading structured data...")
+             try:
+                 self.structured_data['equations'] = self._load_json_data(structured_data_paths.get('equations'))
+                 nodes_raw = self._load_json_data(structured_data_paths.get('icosahedron_nodes'))
+                 self.structured_data['icosahedron_nodes'] = nodes_raw.get('nodes', []) if isinstance(nodes_raw, dict) else []
+                 self.structured_data['frequencies'] = self._load_csv_data(structured_data_paths.get('frequencies'))
+                 self.structured_data['constants'] = self._load_csv_data(structured_data_paths.get('constants'))
+
+                 print("Engine loaded structured data: Equations={}, Nodes={}, Frequencies={}, Constants={}".format(
+                     len(self.structured_data['equations']) if self.structured_data['equations'] else 0,
+                     len(self.structured_data['icosahedron_nodes']),
+                     len(self.structured_data['frequencies']),
+                     len(self.structured_data['constants'])
+                 ))
+             except Exception as e:
+                 print(f"Engine: Error loading structured data: {e}")
+                 self.structured_data = {} # Reset if loading fails
+
+
+        # Instantiate components, passing relevant structured data
+        self.icosa = IcosaMap(node_data=self.structured_data.get('icosahedron_nodes')) # Pass node data
+        self.dodeca = DodecaMap() # No dodecahedron data provided in list
+        self.resonator = ResonanceSimulator(frequencies_data=self.structured_data.get('frequencies'), constants_data=self.structured_data.get('constants')) # Pass freq/const data
+        self.music = MusicAdapter(frequencies_data=self.structured_data.get('frequencies')) # Pass frequencies data
+        self.self_improver = SelfImprover(self.memory, structured_data=self.structured_data) # Pass structured data to SelfImprover
+
+
+        self._interaction_count = 0 # Initialize interaction count for self-improvement trigger
+
+
+    # Helper methods for loading data within the Engine (copied from Trainer for self-containment)
+    def _load_json_data(self, file_path):
+        """Loads data from a JSON file."""
+        if not file_path or not os.path.exists(file_path):
+            # print(f"JSON file not found or path not provided: {file_path}") # Suppress not found for optional files
+            return None
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            # print(f"Successfully loaded JSON data from {file_path}") # Suppress success for cleaner output
+            return data
+        except json.JSONDecodeError as e:
+            print(f"Error decoding JSON from {file_path}: {e}")
+            return None
+        except Exception as e:
+            print(f"An unexpected error occurred while loading JSON data: {e}")
+            return None
+
+    def _load_csv_data(self, file_path):
+        """Loads data from a CSV file using pandas."""
+        if not file_path or not os.path.exists(file_path):
+             # print(f"CSV file not found or path not provided: {file_path}") # Suppress not found for optional files
+             return []
+        try:
+            df = pd.read_csv(file_path)
+            # print(f"Successfully loaded CSV data from {file_path}") # Suppress success for cleaner output
+            return df.to_dict(orient='records')
+        except Exception as e:
+            print(f"An error occurred while loading CSV data from {file_path}: {e}")
+            return []
+
+
+    def _classify(self, text):
+        t = text.lower()
+        # Enhanced classification based on structured data keywords and patterns
+        if any(k in t for k in ("equation", "ecuacion", "hamiltoniano", "dirac", "formula", "formulae", "formulas")): # Added formula variations
+             return "equation_query"
+        if any(k in t for k in ("node", "nodo", "icosahedron", "dodecahedron", "poly", "vertex", "point", "map")): # Added map keyword to node query
+             # Check for patterns like "node X" where X is a number
+             words = t.split()
+             if len(words) > 1 and words[-1].isdigit() and words[-2] in ("node", "nodo"):
+                  return "node_query"
+             return "node_query"
+        if any(k in t for k in ("frecuen", "freq", "music", "nota", "melod", "tono", "pitch", "scale", "musical", "sound", "audio")): # Added sound, audio
+             return "music_resonance" # Combine music and resonance intent for simplicity here
+        if any(k in t for k in ("constant", "constante", "valor", "unidad", "define", "what is the value of")): # Added "what is the value of"
+             return "constant_query"
+        if any(k in t for k in ("resonance", "resonar", "resonant", "vibration", "oscilla")): # Specific keywords for resonance without music
+             return "resonance_only"
+
+        # Existing classifications (kept as fallbacks or for broader terms)
+        # Removed redundant 'reson' and 'sinton' mapping to music_resonance as specific resonance_only added
+        if any(k in t for k in ("chat", "hola", "qué", "como", "explica", "tell me", "what is", "describe", "info", "information")): # Added info, information
+            return "chat"
+        return "chat" # Default to chat
+
+
+    def handle_query(self, text, base_model_output=None):
+        kind = self._classify(text)
+
+        # Handle query types based on structured data
+        if kind == "equation_query":
+             relevant_eqs = []
+             if self.structured_data.get('equations'):
+                  # Find equations related to the query (more robust keyword matching)
+                  query_words = text.lower().split()
+                  relevant_eqs = [eq for eq in self.structured_data['equations'] if any(word in eq.get('nombre', '').lower() or word in eq.get('descripcion', '').lower() or any(comp.lower() in word for comp in eq.get('componentes', [])) for word in query_words)]
+
+             if relevant_eqs:
+                  # Provide information about found equations
+                  response_parts = ["Based on the RRF Equations data, I found the following relevant equations:"]
+                  for eq in relevant_eqs[:3]: # Limit to first 3 for brevity
+                      response_parts.append(f"- '{eq.get('nombre', 'N/A')}' ({eq.get('tipo', 'Equation')}): {eq.get('ecuacion', 'N/A')} (Components: {', '.join(eq.get('componentes', []))})")
+                  if len(relevant_eqs) > 3:
+                       response_parts.append("...")
+                  response = "\n".join(response_parts)
+                  self._log_interaction(text, base_model_output, response, type="equation_query")
+                  return {"type": "equation_query", "query": text, "result": relevant_eqs, "response": response}
+             else:
+                  response = "I couldn't find any relevant equations in the loaded data for that query."
+                  self._log_interaction(text, base_model_output, response, type="equation_query_not_found")
+                  return {"type": "equation_query", "query": text, "result": [], "response": response}
+
+
+        if kind == "node_query":
+             relevant_nodes = []
+             if self.structured_data.get('icosahedron_nodes'):
+                  query_words = text.lower().split()
+                  # Try to find by ID first if query contains a number
+                  try:
+                       node_id = int(query_words[-1]) if query_words and query_words[-1].isdigit() else None
+                       if node_id is not None:
+                            relevant_nodes = [node for node in self.structured_data['icosahedron_nodes'] if node.get('id') == node_id]
+                  except (ValueError, IndexError):
+                        pass # Not a number query
+
+                  # If not found by ID or not a number query, search by keyword in description/name
+                  if not relevant_nodes:
+                       relevant_nodes = [node for node in self.structured_data['icosahedron_nodes'] if any(word in node.get('description', '').lower() or word in node.get('name', '').lower() for word in query_words)]
+
+             if relevant_nodes:
+                  response_parts = ["Based on the Icosahedron Nodes data, I found the following relevant nodes:"]
+                  for node in relevant_nodes[:3]: # Limit to first 3
+                       response_parts.append(f"- Node {node.get('id', 'N/A')}: {node.get('description', node.get('name', 'No description'))} (Coords: ({node.get('x', 'N/A')}, {node.get('y', 'N/A')}, {node.get('z', 'N/A')}))") # Added N/A checks
+                  if len(relevant_nodes) > 3:
+                       response_parts.append("...")
+                  response = "\n".join(response_parts)
+                  self._log_interaction(text, base_model_output, response, type="node_query")
+                  return {"type": "node_query", "query": text, "result": relevant_nodes, "response": response}
+             else:
+                  response = "I couldn't find any relevant nodes in the loaded data for that query."
+                  self._log_interaction(text, base_model_output, response, type="node_query_not_found")
+                  return {"type": "node_query", "query": text, "result": [], "response": response}
+
+        if kind == "music_resonance":
+            # Can still trigger resonance simulation and music adaptation
+            # Enhance response with information from frequencies/constants if relevant keywords are used
+            response_parts = []
+            if self.structured_data.get('frequencies') and any(k in text.lower() for k in ("frecuen", "freq", "nota", "pitch", "scale", "musical", "sound", "audio")):
+                 query_words = text.lower().split()
+                 relevant_freqs = [f for f in self.structured_data['frequencies'] if any(word in f.get('note', '').lower() or word in f.get('role', '').lower() for word in query_words)]
+                 if relevant_freqs:
+                      response_parts.append("Based on the Frequencies data, I found:")
+                      for freq in relevant_freqs[:3]:
+                           response_parts.append(f"- Note: {freq.get('note', 'N/A')}, Frequency: {freq.get('frequency', 'N/A')} Hz, Role: {freq.get('role', 'N/A')}") # Added N/A checks
+                      if len(relevant_freqs) > 3: response_parts.append("...")
+
+            if self.structured_data.get('constants') and any(k in text.lower() for k in ("constant", "constante")):
+                 query_words = text.lower().split()
+                 relevant_constants = [c for c in self.structured_data['constants'] if any(word in c.get('name', '').lower() for word in query_words)]
+                 if relevant_constants:
+                      response_parts.append("Based on the Constants data, I found:")
+                      for const in relevant_constants[:3]:
+                           response_parts.append(f"- Constant: {const.get('name', 'N/A')}, Value: {const.get('value', 'N/A')}, Units: {const.get('units', 'N/A')}") # Added N/A checks
+                      if len(relevant_constants) > 3: response_parts.append("...")
+
+            # Always run resonance simulation and music adaptation for this type
+            r = self.resonator.simulate(text)
+            seq = self.music.adapt_text_to_music(text)
+
+            response_parts.append(f"Resonance simulation summary: Dominant Frequency={r['summary'].get('dom_freq', 0.0):.4f} Hz, Max Power={r['summary'].get('max_power', 0.0):.4f}.") # Added default values
+            response_parts.append(f"Adapted to music sequence (first 5 notes: pitch, duration): {seq[:5]}...")
+
+            response = "\n".join(response_parts) if response_parts else "Processing music and resonance query..."
+            self._log_interaction(text, base_model_output, response, type="music_resonance")
+            return {"type":"music_resonance","query":text,"resonance_result":r,"music_result":seq, "response": response}
+
+        if kind == "resonance_only": # New handler for resonance-only queries
+             # Can still trigger resonance simulation
+            response_parts = []
+            if self.structured_data.get('constants') and any(k in text.lower() for k in ("constant", "constante")):
+                 query_words = text.lower().split()
+                 relevant_constants = [c for c in self.structured_data['constants'] if any(word in c.get('name', '').lower() for word in query_words)]
+                 if relevant_constants:
+                      response_parts.append("Based on the Constants data, I found:")
+                      for const in relevant_constants[:3]:
+                           response_parts.append(f"- Constant: {const.get('name', 'N/A')}, Value: {const.get('value', 'N/A')}, Units: {const.get('units', 'N/A')}") # Added N/A checks
+                      if len(relevant_constants) > 3: response_parts.append("...")
+
+            r = self.resonator.simulate(text)
+            response_parts.append(f"Resonance simulation summary: Dominant Frequency={r['summary'].get('dom_freq', 0.0):.4f} Hz, Max Power={r['summary'].get('max_power', 0.0):.4f}.") # Added default values
+
+            response = "\n".join(response_parts) if response_parts else "Processing resonance query..."
+            self._log_interaction(text, base_model_output, response, type="resonance_only")
+            return {"type":"resonance_only","query":text,"resonance_result":r, "response": response}
+
+
+        if kind == "constant_query":
+             relevant_constants = []
+             if self.structured_data.get('constants'):
+                  query_words = text.lower().split()
+                  relevant_constants = [c for c in self.structured_data['constants'] if any(word in c.get('name', '').lower() or word in c.get('units', '').lower() for word in query_words)]
+
+             if relevant_constants:
+                  response_parts = ["Based on the RRF Constants data, I found the following relevant constants:"]
+                  for const in relevant_constants[:3]:
+                       response_parts.append(f"- Name: {const.get('name', 'N/A')}, Value: {const.get('value', 'N/A')}, Units: {const.get('units', 'N/A')}") # Added N/A checks
+                  if len(relevant_constants) > 3: response_parts.append("...")
+                  response = "\n".join(response_parts)
+                  self._log_interaction(text, base_model_output, response, type="constant_query")
+                  return {"type": "constant_query", "query": text, "result": relevant_constants, "response": response}
+             else:
+                  response = "I couldn't find any relevant constants in the loaded data for that query."
+                  self._log_interaction(text, base_model_output, response, type="constant_query_not_found")
+                  return {"type": "constant_query", "query": text, "result": [], "response": response}
+
+
+        if kind == "map":
+             # Use icosahedron_nodes data in mapping (already done in IcosaMap)
+            node_label = self.icosa.closest_node(text)
+            response = f"Mapping query '{text}' to closest node: {node_label}"
+            # If we have node data, try to find details about the mapped node
+            if self.structured_data.get('icosahedron_nodes'):
+                 # Assuming node_label is the description or name from node_data used for embedding
+                 # A more robust mapping is needed here to link label back to original node dict by ID
+                 # For now, let's just find the node with a matching description/name if possible
+                 mapped_node_data = next((node for node in self.structured_data['icosahedron_nodes'] if node.get('description', '').lower() == node_label.lower() or node.get('name', '').lower() == node_label.lower()), None)
+                 if mapped_node_data:
+                      response += f" (ID: {mapped_node_data.get('id', 'N/A')}, Coords: ({mapped_node_data.get('x', 'N/A')}, {mapped_node_data.get('y', 'N/A')}, {mapped_node_data.get('z', 'N/A')}))" # Added N/A checks
+
+
+            self._log_interaction(text, base_model_output, response, type="map")
+            return {"type":"map","query":text,"node":node_label, "response": response}
+
+        # chat fallback: if base_model_output provided, refine it using self_improver
+        if kind == "chat":
+            if base_model_output is None:
+                # default echo
+                base = "Echo: " + text
+            else:
+                base = base_model_output
+
+            refined = chat_refine(text, base, self_improver=self.self_improver)
+            response = refined # Use refined output as the main response for chat
+            self._log_interaction(text, base_model_output, refined, type="chat_interaction") # Log chat interaction
+
+            return {"type":"chat","query":text,"base":base,"refined":refined, "response": response}
+
+        # Fallback for unhandled types (shouldn't be reached with current classify)
+        response = "I'm not sure how to handle that query based on the available data and functions."
+        self._log_interaction(text, base_model_output, response, type="unhandled_query")
+        return {"type": "unhandled", "query": text, "response": response}
+
+
+    def _log_interaction(self, user_input, base_output, final_output, type="interaction"):
+         """Logs interaction details to memory and triggers self-improvement if needed."""
+         interaction_record = {
+             "type": type, # Use the specified type (e.g., chat_interaction, equation_query)
+             "user_input": user_input,
+             "base_model_output": base_output, # Might be None for non-chat types
+             "final_output": final_output, # The response generated by handle_query
+             "_ts": time.time() # Add timestamp
+         }
+         self.memory.add(interaction_record)
+
+         # Periodically trigger self-improvement (e.g., every 10 interactions)
+         self._interaction_count = getattr(self, '_interaction_count', 0) + 1
+         if self._interaction_count % 10 == 0:
+             print("SAVANT: Triggering self-improvement cycle...")
+             try:
+                 proposal = self.self_improver.propose()
+                 accepted, metric = self.self_improver.evaluate_and_apply(proposal)
+                 print(f"SAVANT: Self-improvement proposal accepted: {accepted}, New metric: {metric}")
+                 self.memory.add({
+                     "type": "self_improvement_triggered",
+                     "proposal": proposal,
+                     "accepted": accepted,
+                     "metric": metric,
+                     "_ts": time.time()
+                 })
+             except Exception as si_error:
+                     # Log the error and continue
+                     error_message = f"Error during self-improvement: {si_error}"
+                     print(f"SAVANT: {error_message}")
+                     self.memory.add({
+                         "type": "self_improvement_error",
+                         "error": error_message,
+                         "_ts": time.time()
+                     })
+
+
+    # trainer helpers (these are now called externally via SimpleTrainer instance)
+    # def run_training_epochs(self, stimuli, epochs=3):
+    #     return self.trainer.run_epochs(stimuli, epochs)
+
+    def propose_improvement(self):
+        return self.self_improver.propose()
+
+    def apply_improvement(self, proposal):
+        return self.self_improver.evaluate_and_apply(proposal)

core/SAVANT_CORE/SAVANT_CORE/core/mappings.py

@@ -0,0 +1,90 @@
+
+"""
+mappings.py
+Mapeo semántico a nodos icosa/dodeca usando embeddings ligeros.
+Provee: IcosaMap, DodecaMap con función closest_node(text).
+Can potentially use loaded icosahedron node data.
+""" # Added closing triple quotes
+import math, os
+try:
+    from sentence_transformers import SentenceTransformer, util
+    _EMBED_AVAILABLE = True
+except Exception as e:
+    _EMBED_AVAILABLE = False
+    # fallback naive embed
+    import hashlib
+    def _hash_embed(text, dim=128):
+        h = hashlib.sha256(text.encode('utf-8')).digest()
+        vec = [b for b in h]
+        # pad/truncate
+        vec = (vec * (dim//len(vec)+1))[:dim]
+        return [float(v)/255.0 for v in vec]
+
+class BasePolyMap:
+    def __init__(self, graph_name="icosa", node_count=12, embed_model_name="all-MiniLM-L6-v2", node_data=None):
+        self.graph_name = graph_name
+        self.node_count = node_count
+        self.node_labels = [f"{graph_name}_node_{i}" for i in range(node_count)]
+        self.node_data = node_data # Store loaded node data
+
+        if _EMBED_AVAILABLE:
+            try:
+                self.embed = SentenceTransformer(embed_model_name)
+            except Exception:
+                self.embed = None
+        else:
+            self.embed = None
+        # precompute embeddings (lazy)
+        self._emb_cache = {}
+        self._label_to_node_id = {} # Map embedded label back to node ID
+
+        # If node_data is available, prioritize descriptions/names for embedding
+        labels_to_embed_map = {} # Map label string to node ID or original label
+        if self.node_data and isinstance(self.node_data, list):
+             for i, node in enumerate(self.node_data):
+                 if isinstance(node, dict):
+                      label_string = node.get('description', node.get('name', f"{graph_name}_node_{node.get('id', i)}"))
+                      node_id = node.get('id', i)
+                      labels_to_embed_map[label_string] = node_id # Store mapping
+                 # Ensure we have enough default labels if node_data is sparse
+             while len(labels_to_embed_map) < self.node_count:
+                 default_label = f"{graph_name}_node_{len(labels_to_embed_map)}"
+                 labels_to_embed_map[default_label] = len(labels_to_embed_map) # Map default label to its index/ID
+
+             # Ensure we don't exceed node_count
+             labels_to_embed = list(labels_to_embed_map.keys())[:self.node_count]
+             self._label_to_node_id = {label: labels_to_embed_map[label] for label in labels_to_embed} # Store final mapping
+        else:
+            # If no node data, use default labels and map label to its index
+            labels_to_embed = self.node_labels
+            self._label_to_node_id = {label: i for i, label in enumerate(self.node_labels)}
+
+
+        for label in labels_to_embed:
+            self._emb_cache[label] = self._compute_emb(label)
+
+
+    def nodes(self):
+        # Return node labels, potentially augmented with info from node_data if available
+        if self.node_data and isinstance(self.node_data, list):
+             return [
+                 {**node, 'label': f"{self.graph_name}_node_{node.get('id', i)}"} # Combine data and add a label
+                 for i, node in enumerate(self.node_data)
+                 if isinstance(node, dict)
+             ]
+        return [{"label": label} for label in self.node_labels]
+
+
+    def closest_node(self, text):
+        q = self._compute_emb(text)
+        best_label, best_sim = None, -1e9
+
+        # Iterate through the embedded labels
+        for label, v in self._emb_cache.items():
+            # simple dot product for similarity
+            sim = sum(a*b for a,b in zip(q, v))
+            if sim > best_sim:
+                best_label, best_sim = label, sim
+
+        # Return the label string that had the highest similarity
+        return best_label

core/SAVANT_CORE/SAVANT_CORE/core/memory.py

@@ -0,0 +1,38 @@
+
+"""
+MemoryStore: append-only JSONL memory for events, queries and reflections.
+Also supports simple retrieval by type and tail.
+""" # Added closing triple quotes
+import json, os, threading, time
+
+class MemoryStore:
+    def __init__(self, path="SAVANT_memory.jsonl", autosave=True):
+        self.path = path
+        os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
+        self.lock = threading.Lock()
+        self.autosave = autosave
+        # ensure file exists
+        if not os.path.exists(path):
+            open(path, "w").close()
+
+    def add(self, record: dict):
+        record['_ts'] = time.time()
+        with self.lock:
+            with open(self.path, "a", encoding="utf-8") as f:
+                f.write(json.dumps(record, ensure_ascii=False) + "\n")
+
+    def tail(self, n=20):
+        with self.lock:
+            with open(self.path, "r", encoding="utf-8") as f:
+                lines = f.read().strip().splitlines()
+        lines = lines[-n:]
+        return [json.loads(l) for l in lines] if lines else []
+
+    def query_by_type(self, t, n=50):
+        out = []
+        for item in self.tail(1000):
+            if item.get("type")==t:
+                out.append(item)
+            if len(out)>=n:
+                break
+        return out

core/SAVANT_CORE/SAVANT_CORE/core/music.py

@@ -0,0 +1,76 @@
+
+"""
+music.py
+Mapea texto o activaciones nodales a secuencias musicales (MIDI-like tuples).
+Can potentially use loaded frequencies data.
+""" # Added closing triple quotes
+import numpy as np
+import math # Import math for log2
+
+class MusicAdapter:
+    def __init__(self, scale='major', frequencies_data=None):
+        self.scale = scale
+        self.base = 60
+        self.scale_intervals = [0,2,4,5,7,9,11]
+        self.frequencies_data = frequencies_data # Store loaded frequencies data
+
+        # Example: Create a mapping from note names in data to MIDI pitches
+        self.note_to_midi = {}
+        if self.frequencies_data:
+             try:
+                  # Simple mapping assuming 'note' and 'frequency' columns exist
+                  # This is a very basic mapping; frequency to MIDI is more complex
+                  # Let's create a simple mapping based on note names if available
+                  midi_base = 60 # Middle C
+                  # Example: Map C4, D4, E4... to midi_base, midi_base+2, midi_base+4...
+                  # This requires a more sophisticated note parsing/mapping logic
+                  # For simplicity, let's just use the frequencies directly or map a few known notes
+                  known_notes_midi = {'C4': 60, 'D4': 62, 'E4': 64, 'F4': 65, 'G4': 67, 'A4': 69, 'B4': 71} # Example mapping
+                  for entry in self.frequencies_data:
+                       note_name = entry.get('note', '').upper()
+                       if note_name in known_notes_midi:
+                            self.note_to_midi[note_name] = known_notes_midi[note_name]
+
+
+             except Exception as e:
+                  print(f"MusicAdapter: Error processing frequencies data: {e}")
+                  self.note_to_midi = {} # Reset if error
+
+
+    def adapt_text_to_music(self, text, length=16):
+        h = abs(hash(text))
+        rng = np.random.RandomState(h % (2**32))
+        seq = []
+        for i in range(length):
+            pitch = self.base # Default pitch
+
+            # Example: Select a pitch based on frequencies data if available
+            if self.frequencies_data and rng.rand() > 0.5: # Occasionally use a frequency from data
+                 try:
+                      random_freq_entry = rng.choice(self.frequencies_data)
+                      if 'note' in random_freq_entry and random_freq_entry['note'].upper() in self.note_to_midi:
+                           pitch = self.note_to_midi[random_freq_entry['note'].upper()]
+                      elif 'frequency' in random_freq_entry:
+                           # Very rough frequency to MIDI conversion (logarithmic)
+                           # MIDI = 69 + 12 * log2(frequency / 440 Hz)
+                           try:
+                                freq = float(random_freq_entry['frequency'])
+                                if freq > 0:
+                                     pitch = int(round(69 + 12 * math.log2(freq / 440.0)))
+                                     pitch = max(0, min(127, pitch)) # Clamp to MIDI range
+                           except (ValueError, TypeError):
+                                pass # Ignore if frequency is invalid
+
+                 except Exception as e:
+                      # print(f"Error using loaded frequency data in music adaptation: {e}") # Suppress error for demo
+                      pass
+
+            # Fallback to scale intervals if no specific pitch was chosen from data
+            if pitch == self.base: # If still default pitch
+                 step = rng.randint(0, len(self.scale_intervals)) # Corrected rng.randint call
+                 pitch = self.base + self.scale_intervals[step] + rng.randint(-2,2)
+
+
+            dur = float(rng.choice([0.25,0.5,1.0]))
+            seq.append((int(pitch), dur))
+        return seq

core/SAVANT_CORE/SAVANT_CORE/core/resonance.py

@@ -0,0 +1,59 @@
+
+"""
+resonance.py
+Simulador de resonancia: red de osciladores acoplados, cálculo de espectro y resumen.
+Designed to be fast and run on CPU lento.
+Can potentially use loaded frequencies and constants data.
+""" # Added closing triple quotes
+import numpy as np
+from scipy.signal import periodogram
+
+class ResonanceSimulator:
+    def __init__(self, base_freq=1.0, frequencies_data=None, constants_data=None):
+        self.base_freq = base_freq
+        self.frequencies_data = frequencies_data # Store loaded frequencies data
+        self.constants_data = constants_data # Store loaded constants data
+
+        # Example: Adjust base_freq based on constants if available
+        if self.constants_data:
+             # Find a relevant constant, e.g., related to 'frequency' or a base unit
+             # This is a simple example; a real implementation would be more complex
+             freq_constant = next((c for c in self.constants_data if c.get('name', '').lower() == 'base_frequency'), None) # Look for a specific constant
+             if freq_constant and 'value' in freq_constant:
+                  try:
+                       self.base_freq = float(freq_constant['value'])
+                       print(f"ResonanceSimulator: Adjusted base_freq using constant '{freq_constant['name']}': {self.base_freq}")
+                  except ValueError:
+                       print(f"ResonanceSimulator: Could not convert constant value to float: {freq_constant.get('value')}")
+
+
+    def simulate(self, seed_text, n_nodes=12, steps=256, damping=0.04):
+        # determinista por seed_text
+        rng = np.random.RandomState(abs(hash(seed_text)) % (2**32))
+        A = rng.randn(n_nodes, n_nodes) * 0.08
+        A = (A + A.T) * 0.5  # sym
+        state = rng.randn(n_nodes) * 0.01
+        X = np.zeros((steps, n_nodes), dtype=float)
+        for t in range(steps):
+            # Example: Influence input signal based on frequencies data
+            input_signal = np.sin(2*np.pi*(self.base_freq + 0.05*rng.randn()) * (t/steps))
+            if self.frequencies_data and rng.rand() > 0.8: # Occasionally inject a frequency from data
+                 try:
+                      # Pick a random frequency from the loaded data
+                      random_freq_entry = rng.choice(self.frequencies_data)
+                      if 'frequency' in random_freq_entry:
+                           input_signal += np.sin(2*np.pi*float(random_freq_entry['frequency']) * (t/steps)) * 0.1 # Add as a harmonic
+                 except Exception as e:
+                      # print(f"Error using loaded frequency data in simulation: {e}") # Suppress error for demo
+                      pass
+
+
+            state = state + 0.12*(A.dot(state) + input_signal) - damping*state
+            X[t] = state
+        freqs, P = periodogram(X, fs=1.0, axis=0)
+        power = P.sum(axis=1)
+        dom_idx = int(np.argmax(power))
+        dom_freq = float(freqs[dom_idx])
+        summary = {"dom_freq": dom_freq, "max_power": float(power[dom_idx])}
+        # compress small arrays for saving
+        return {"summary": summary, "freqs_len": len(freqs), "power_sample": power[:10].tolist()}

core/SAVANT_CORE/SAVANT_CORE/core/self_improvement.py

@@ -0,0 +1,143 @@
+
+"""
+self_improvement.py
+Módulo de automejora simbiótica:
+- analiza últimas entradas de memoria
+- propone pequeñas modificaciones heurísticas (weights) para el motor
+- salva un registro de propuestas y aplica las que mejoran una métrica simple
+Can potentially use structured data for more informed heuristics.
+""" # Added closing triple quotes
+import time, json, os, math
+import numpy as np # Import numpy for random number generation
+
+class SelfImprover:
+    def __init__(self, memory_store, state_path="savant_state.json", structured_data=None):
+        self.mem = memory_store
+        self.state_path = state_path
+        self.structured_data = structured_data # Store loaded structured data
+
+        # params controlling simple heuristics (toy)
+        self.params = {"resonance_scale": 1.0, "music_influence": 0.5, "mapping_weight": 1.0, "chat_influence": 0.0} # Added chat_influence init
+        self.history = []
+        if os.path.exists(self.state_path):
+            try:
+                with open(self.state_path, "r", encoding="utf-8") as f:
+                    st = json.load(f)
+                    self.params.update(st.get("params", {}))
+                    self.history = st.get("history", [])
+            except Exception:
+                pass
+
+    def propose(self):
+        # produce a small proposal delta based on recent memory and potentially structured data
+        tail = self.mem.tail(50)
+        counts = {}
+        for e in tail:
+            counts[e.get("type","unknown")] = counts.get(e.get("type","unknown"), 0) + 1
+        # heuristic: if many resonance records, increase resonance_scale slightly
+        delta = {}
+        delta["resonance_scale"] = 0.01 * counts.get("resonance", 0) + 0.01 * counts.get("music_resonance", 0) + 0.01 * counts.get("resonance_only", 0) # Include resonance_only
+        delta["music_influence"] = 0.005 * counts.get("music", 0) + 0.005 * counts.get("music_resonance", 0) # Include music_resonance
+        delta["mapping_weight"] = 0.002 * counts.get("map", 0) + 0.002 * counts.get("node_query", 0) # Include node_query
+        delta["chat_influence"] = 0.001 * counts.get("chat_interaction", 0) + 0.0005 * counts.get("equation_query", 0) + 0.0005 * counts.get("constant_query", 0) # Include structured data queries
+
+        # Example: Influence proposal based on structured data availability/size
+        if self.structured_data:
+             if self.structured_data.get('equations'):
+                  # If equations data is loaded, slightly increase mapping weight to encourage use of structured knowledge
+                  delta["mapping_weight"] += len(self.structured_data['equations']) * 0.0001 # Small increase based on number of equations
+             if self.structured_data.get('icosahedron_nodes'):
+                  # If node data is loaded, slightly increase mapping weight
+                   delta["mapping_weight"] += len(self.structured_data['icosahedron_nodes']) * 0.00005 # Smaller increase based on number of nodes
+             if self.structured_data.get('frequencies'):
+                   # If frequencies data is loaded, slightly increase music influence
+                   delta["music_influence"] += len(self.structured_data['frequencies']) * 0.0002
+             if self.structured_data.get('constants'):
+                   # If constants data is loaded, slightly increase resonance or chat influence
+                   delta["resonance_scale"] += len(self.structured_data['constants']) * 0.0001
+                   delta["chat_influence"] += len(self.structured_data['constants']) * 0.00005
+
+        # Example: Add a heuristic based on the ratio of structured data queries to chat interactions
+        total_structured_queries = counts.get("equation_query", 0) + counts.get("node_query", 0) + counts.get("constant_query", 0) + counts.get("music_resonance", 0) + counts.get("resonance_only", 0) + counts.get("map", 0)
+        chat_interactions = counts.get("chat_interaction", 0)
+        if chat_interactions > 0:
+            structured_query_ratio = total_structured_queries / chat_interactions
+            # If structured queries are frequent, slightly increase parameters related to structured data usage
+            delta["mapping_weight"] += structured_query_ratio * 0.001
+            delta["resonance_scale"] += structured_query_ratio * 0.0005
+            delta["music_influence"] += structured_query_ratio * 0.0003
+
+
+        proposal = {"ts": time.time(), "delta": delta}
+        return proposal
+
+    def evaluate_and_apply(self, proposal):
+        # apply temporary, simulate metric (toy), keep if metric improves
+        old_params = self.params.copy()
+        # apply delta
+        for k,v in proposal["delta"].items():
+            self.params[k] = self.params.get(k, 0.0) + v
+        # toy metric: resonance_scale increases score, large music_influence gives bonus,
+        # mapping_weight influences, and chat_influence gives a small bonus.
+        score = self._metric(self.params)
+        old_score = self._metric(old_params) # Corrected variable name
+        accepted = False
+        # More sophisticated acceptance criteria: accept if metric improves significantly or sometimes accept small improvements
+        if score >= old_score * 1.01: # Accept if metric improves by at least 1%
+             accepted = True
+        elif score > old_score and np.random.rand() < 0.3: # Sometimes accept smaller improvements (30% chance)
+             accepted = True
+        elif score < old_score * 0.99 and np.random.rand() < 0.1: # Occasionally accept small regressions (10% chance)
+             accepted = True
+        # Otherwise, if not accepted and metric didn't improve significantly, revert parameters
+
+        if accepted:
+            self.history.append({"proposal": proposal, "result_metric": score, "accepted": True, "params_after": self.params.copy()}) # Store params after
+        else:
+            self.params = old_params # Revert parameters if not accepted
+            self.history.append({"proposal": proposal, "result_metric": score, "accepted": False, "params_after": self.params.copy()}) # Store params after
+
+        self._save_state()
+        return accepted, score # Corrected variable name
+
+    def _metric(self, params):
+        # toy metric: resonance_scale increases score, large music_influence gives bonus,
+        # mapping_weight influences, and chat_influence gives a small bonus.
+        score = params.get("resonance_scale",1.0) * 1.0 + 0.5*params.get("music_influence",0.0) + 0.2*params.get("chat_influence",0.0) # Include chat_influence
+
+        # Example: Add a bonus to the metric based on the presence and size of structured data
+        structured_data_bonus = 0
+        if self.structured_data:
+            if self.structured_data.get('equations'):
+                structured_data_bonus += len(self.structured_data['equations']) * 0.01
+            if self.structured_data.get('icosahedron_nodes'):
+                structured_data_bonus += len(self.structured_data['icosahedron_nodes']) * 0.005
+            if self.structured_data.get('frequencies'):
+                structured_data_bonus += len(self.structured_data['frequencies']) * 0.008
+            if self.structured_data.get('constants'):
+                structured_data_bonus += len(self.structured_data['constants']) * 0.003
+        score += structured_data_bonus
+
+        # Example: Penalty for parameters being too high (encouraging balance)
+        parameter_magnitude_penalty = (abs(params.get("resonance_scale", 1.0) - 1.0) * 0.1 +
+                                        abs(params.get("music_influence", 0.5) - 0.5) * 0.1 +
+                                        abs(params.get("mapping_weight", 1.0) - 1.0) * 0.1 +
+                                        abs(params.get("chat_influence", 0.0) - 0.0) * 0.1)
+        score -= parameter_magnitude_penalty
+
+
+        mw = params.get("mapping_weight",1.0)
+        if mw > 3.0: # Increased penalty threshold
+            score -= (mw-3.0)*1.0 # Increased penalty factor
+        if params.get("resonance_scale", 1.0) > 3.0: # Add penalty for high resonance scale
+             score -= (params.get("resonance_scale", 1.0) - 3.0) * 0.8
+
+
+        return score
+
+    def _save_state(self, file_path="savant_state.json"): # Added default file_path
+        try:
+            with open(self.state_path, "w", encoding="utf-8") as f:
+                json.dump({"params": self.params, "history": self.history[-200:]}, f, indent=2)
+        except Exception as e:
+             print(f"Error saving self-improver state to {self.state_path}: {e}") # Added error handling

core/SAVANT_CORE/SAVANT_CORE/core/trainer.py

@@ -0,0 +1,186 @@
+
+"""
+trainer.py
+Interfaz ligera para "entrenar" el motor Savant:
+- ejecuta ciclos: simular, almacenar en memoria, proponer mejoras, aplicar
+- guarda checkpoints en disco (JSON)
+- Incluye functions for loading various types of data.
+"""
+import os
+import json
+import time
+import pickle
+import pandas as pd # Import pandas for CSV
+
+from .memory import MemoryStore
+from .resonance import ResonanceSimulator
+from .self_improvement import SelfImprover
+
+class SimpleTrainer:
+    def __init__(self, mem_path="SAVANT_memory.jsonl", checkpoint_dir="checkpoints"):
+        self.mem = MemoryStore(mem_path)
+        self.res = ResonanceSimulator()
+        self.si = SelfImprover(self.mem)
+        self.checkpoint_dir = checkpoint_dir
+        os.makedirs(self.checkpoint_dir, exist_ok=True)
+
+    def run_cycle(self, stimulus, epoch=1):
+        # simulate resonance
+        r = self.res.simulate(stimulus)
+        self.mem.add({"type":"resonance", "query": stimulus, "result": r["summary"], "epoch": epoch})
+        # propose improvement
+        proposal = self.si.propose()
+        accepted, metric = self.si.evaluate_and_apply(proposal)
+        self.mem.add({"type":"improvement", "proposal": proposal, "accepted": accepted, "metric": metric, "epoch": epoch})
+        # save checkpoint
+        ckpt = {"epoch": epoch, "stimulus": stimulus, "res": r["summary"], "si_params": self.si.params}
+        p = os.path.join(self.checkpoint_dir, f"ckpt_epoch_{epoch}.json")
+        with open(p, "w", encoding="utf-8") as f:
+            json.dump(ckpt, f, indent=2)
+        return ckpt
+
+    def run_epochs(self, conversations, epochs=3):
+        """
+        Runs training cycles using conversation entries as stimuli.
+
+        Args:
+            conversations (list): A list of conversation entries (dictionaries).
+            epochs (int): Number of epochs to run.
+        """
+        results = []
+        if not conversations:
+            print("No conversations provided for training.")
+            return results
+
+        # Determine how to extract stimulus from conversation entries
+        # Assuming each entry is a dict like {"role": "user", "content": "...", ...}
+        # We will use the 'content' of each entry as a stimulus.
+        # You might need to add logic to handle cases where a conversation entry doesn't have a "user" role or "content".
+        # For simplicity, extract content from any entry that has a 'content' key and is not empty.
+        stimuli_list = [entry.get("content", "") for entry in conversations if entry.get("content")]
+
+        if not stimuli_list:
+            print("No valid stimuli extracted from conversations.")
+            return results
+
+        print(f"Starting training for {epochs} epochs using {len(stimuli_list)} conversation entries as stimuli.")
+
+        for e in range(1, epochs + 1):
+            # Cycle through the stimuli list for each epoch
+            stimulus_for_this_epoch = stimuli_list[(e - 1) % len(stimuli_list)]
+            print(f"Epoch {e}/{epochs}: Using stimulus '{stimulus_for_this_epoch[:50]}...'")
+            res = self.run_cycle(stimulus_for_this_epoch, epoch=e)
+            results.append(res)
+            # Optional: Add a small delay
+            # time.sleep(0.1)
+
+        return results
+
+    def load_pkl_data(self, file_path):
+        """Loads data from a pickle file."""
+        try:
+            with open(file_path, "rb") as f:
+                data = pickle.load(f)
+            print(f"Successfully loaded pickle data from {file_path}")
+            return data
+        except FileNotFoundError:
+            print(f"Error: Pickle file not found at {file_path}")
+            return None
+        except pickle.UnpicklingError:
+            print(f"Error: Could not unpickle data from {file_path}. File might be corrupted.")
+            return None
+        except Exception as e:
+            print(f"An unexpected error occurred while loading pickle data: {e}")
+            return None
+
+    def load_json_data(self, file_path):
+        """Loads data from a JSON file."""
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            print(f"Successfully loaded JSON data from {file_path}")
+            return data
+        except FileNotFoundError:
+            print(f"Error: JSON file not found at {file_path}")
+            return None
+        except json.JSONDecodeError as e:
+            print(f"Error decoding JSON from {file_path}: {e}")
+            return None
+        except Exception as e:
+            print(f"An unexpected error occurred while loading JSON data: {e}")
+            return None
+
+    def load_jsonl_data(self, file_path):
+        """Loads data from a JSON Lines file."""
+        data_list = []
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                for line in f:
+                    try:
+                        data_list.append(json.loads(line))
+                    except json.JSONDecodeError as e:
+                        # print(f"Error decoding JSON on line in {file_path}: {line.strip()} - {e}") # Suppress line error for demo
+                        continue
+            print(f"Successfully loaded {len(data_list)} entries from JSONL file {file_path}")
+            return data_list
+        except FileNotFoundError:
+            print(f"Error: JSONL file not found at {file_path}")
+            return []
+        except Exception as e:
+            print(f"An unexpected error occurred while loading JSONL data: {e}")
+            return []
+
+    def combine_conversational_data(self, file_paths):
+        """Loads and combines conversational data from a list of file paths (JSONL, JSON, PKL)."""
+        all_conversations = []
+        for file_path in file_paths:
+            if file_path.lower().endswith('.jsonl'):
+                print(f"Attempting to load conversations from {file_path} (JSONL)...")
+                data = self.load_jsonl_data(file_path)
+                if isinstance(data, list):
+                     all_conversations.extend(data)
+                else:
+                     print(f"Warning: Data from {file_path} was not a list as expected for conversational data.")
+            elif file_path.lower().endswith('.json'):
+                 print(f"Attempting to load conversations from {file_path} (JSON)...")
+                 data = self.load_json_data(file_path)
+                 # Assuming JSON file might contain a list of conversations under a key or is a list itself
+                 if isinstance(data, dict) and "conversations" in data and isinstance(data["conversations"], list):
+                      all_conversations.extend(data["conversations"])
+                 elif isinstance(data, list):
+                      all_conversations.extend(data)
+                 else:
+                       print(f"Warning: Data from {file_path} was not in expected JSON conversational format (list or dict with 'conversations' key).")
+            elif file_path.lower().endswith('.pkl'):
+                 print(f"Attempting to load conversations from {file_path} (Pickle)...")
+                 data = self.load_pkl_data(file_path)
+                 if isinstance(data, dict) and "conversations" in data and isinstance(data["conversations"], list):
+                      all_conversations.extend(data["conversations"])
+                 elif isinstance(data, list):
+                      all_conversations.extend(data)
+                 else:
+                       print(f"Warning: Data from {file_path} was not in expected Pickle conversational format (list or dict with 'conversations' key).")
+
+            else:
+                print(f"Skipping unsupported file type for conversational data: {file_path}")
+
+        print(f"Combined total {len(all_conversations)} conversation entries from provided files.")
+        return all_conversations
+
+    def load_structured_data(self, equations_path, nodes_path, frequencies_path, constants_path):
+        """Loads structured data from specified files."""
+        structured_data = {}
+        structured_data['equations'] = self.load_json_data(equations_path)
+        # Assuming nodes_path is a JSON file with a 'nodes' key
+        nodes_raw = self.load_json_data(nodes_path)
+        structured_data['icosahedron_nodes'] = nodes_raw.get('nodes', []) if isinstance(nodes_raw, dict) else []
+        structured_data['frequencies'] = pd.read_csv(frequencies_path).to_dict(orient='records') if os.path.exists(frequencies_path) else [] # Handle CSV directly
+        structured_data['constants'] = pd.read_csv(constants_path).to_dict(orient='records') if os.path.exists(constants_path) else [] # Handle CSV directly
+
+        print("Loaded structured data: Equations={}, Nodes={}, Frequencies={}, Constants={}".format(
+            len(structured_data['equations']) if structured_data['equations'] else 0,
+            len(structured_data['icosahedron_nodes']),
+            len(structured_data['frequencies']),
+            len(structured_data['constants'])
+        ))
+        return structured_data

core/SAVANT_CORE/SAVANT_CORE/requirements.txt

@@ -0,0 +1,10 @@
+
+# lightweight requirements for SAVANT_CORE
+sentence-transformers
+transformers
+gradio
+faiss-cpu
+scipy
+numpy
+gitpython
+pandas # Added pandas for CSV loading

core/SAVANT_CORE/SAVANT_CORE/run_demo.py

@@ -0,0 +1,104 @@
+
+"""
+run_demo.py
+Demo para probar el core SAVANT en consola.
+"""
+import os
+import json
+import time
+import pickle # Import pickle
+from transformers import pipeline
+from core.engine import SavantEngine
+from core.trainer import SimpleTrainer # Import SimpleTrainer
+
+
+def main():
+    engine = SavantEngine()
+    trainer = SimpleTrainer() # Instantiate SimpleTrainer
+    print("SAVANT Simbiótico RRF - demo")
+    print("Escribe 'exit' para salir. Commands: propose, apply, tail, train_memory") # Removed 'train' command from list
+    # small base model for text generation (light)
+    try:
+        gen = pipeline("text-generation", model="distilgpt2")
+    except Exception:
+        gen = None
+
+    # Define the list of data file paths to load from
+    # This list is embedded as a string literal
+    data_files_str = r"""[
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/colab/Savantaut_dataset.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_backup/backup_20250823_220832/savant_full_history.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/new_nodes_data.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/lang_9.json",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/dialog_multinode_full.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_2025.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_multinode_final.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_prompts_master.json",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_dialogo_simbiotico.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_pipeline_advanced.jsonl",
+    "/content/drive/MyDrive/Savant-RRF/savant_memory.pkl"
+]"""
+
+    while True:
+        q = input("You> ").strip()
+        if not q:
+            continue
+        if q.lower() in ("exit","quit","salir"):
+            break
+        if q.lower().startswith("propose"):
+            print(engine.propose_improvement())
+            continue
+        if q.lower().startswith("apply"):
+            p = engine.propose_improvement()
+            print("Applying:", p)
+            print(engine.apply_improvement(p))
+            continue
+        if q.lower().startswith("tail"):
+            print(engine.memory.tail(10))
+            continue
+        # New command handler for training with memory data
+        if q.lower().startswith("train_memory"):
+            print(f"Loading and combining conversational data from specified files...")
+            conversations = [] # Initialize conversations
+            try:
+                # Load the file list from the string literal
+                file_list = json.loads(data_files_str)
+                print(f"Successfully loaded file list: {file_list}")
+                conversations = trainer.combine_conversational_data(file_list) # Load data
+            except Exception as e:
+                print(f"Error loading file list or combining data: {e}")
+                # conversations will remain an empty list
+                pass # Continue to the check if conversations is empty
+
+            if conversations: # Check if the loaded list is not empty
+                print(f"Loaded and combined {len(conversations)} conversation entries.")
+                print("Starting training using loaded memory data for 5 epochs...")
+                # Assuming run_epochs can handle the list of conversation dictionaries
+                training_results = trainer.run_epochs(conversations, epochs=5)
+                print("Training done using memory data.")
+                # Optional: Print a summary of results if run_epochs returns them
+                # print("Training results summary:", training_results)
+            else:
+                print("No conversational data loaded or combined. Training skipped.")
+            continue
+
+        # normal chat: produce base output from gen if available
+        if gen:
+            try:
+                base_out = gen(q, max_length=80, do_sample=True)[0].get("generated_text")
+            except Exception as ex: # Capture exception here with a local variable
+                # Correctly format the f-string inside the string literal
+                # Use repr(ex) to get a string representation of the exception
+                base_out = f"Error generating text: {{repr(ex)}}"
+        else:
+            base_out = "Echo: " + q
+
+        out = engine.handle_query(q, base_model_output=base_out)
+        if out["type"] == "chat":
+            print("SAVANT (base):", out["base"])
+            print("SAVANT (refined):", out["refined"])
+        else:
+            print("SAVANT:", out)
+
+if __name__ == "__main__":
+    main()

core/SAVANT_CORE/SAVANT_CORE/run_savant.py

@@ -0,0 +1,216 @@
+
+"""
+run_savant.py
+New entry point script for SAVANT-RRF, demonstrating data loading and interaction.
+"""
+import os
+import sys
+import json
+import time
+import pickle # Needed for loading some data types
+import pandas as pd # Needed for CSV loading
+
+# Add the directory containing SAVANT_CORE to the Python path if not already there
+# This assumes SAVANT_CORE is the current working directory or in the parent directory
+# If running this script directly from the SAVANT_CORE directory, the first path is fine.
+# If running from a parent directory, you might need to adjust SAVANT_CORE_PARENT_PATH
+SAVANT_CORE_PARENT_PATH = os.path.dirname(os.path.abspath(__file__)) # Assumes script is in SAVANT_CORE/
+SAVANT_CORE_PATH = SAVANT_CORE_PARENT_PATH # If running from SAVANT_CORE/
+
+# Alternative if running from a directory *containing* SAVANT_CORE:
+# SAVANT_CORE_PARENT_PATH = os.path.dirname(os.path.abspath(__file__))
+# SAVANT_CORE_PATH = os.path.join(SAVANT_CORE_PARENT_PATH, "SAVANT_CORE") # Adjust if your structure is different
+
+if SAVANT_CORE_PATH not in sys.path:
+    sys.path.insert(0, SAVANT_CORE_PATH) # Insert at the beginning to ensure local modules are found first
+
+
+# Import classes from the core modules
+try:
+    from core.engine import SavantEngine
+    from core.trainer import SimpleTrainer # Import SimpleTrainer for data loading
+except ImportError as e:
+    print("Error importing SAVANT_CORE modules: {}".format(e))
+    print("Please ensure the directory '{}' is correctly structured".format(SAVANT_CORE_PATH))
+    print("and that it contains the 'core' subfolder with the SAVANT_CORE modules.")
+    # Attempt to list directory contents for debugging
+    print(f"Contents of {SAVANT_CORE_PATH}: {os.listdir(SAVANT_CORE_PATH) if os.path.exists(SAVANT_CORE_PATH) else 'Not found'}")
+    print(f"Contents of {os.path.join(SAVANT_CORE_PATH, 'core')}: {os.listdir(os.path.join(SAVANT_CORE_PATH, 'core')) if os.path.exists(os.path.join(SAVANT_CORE_PATH, 'core')) else 'Not found'}")
+
+    sys.exit(1) # Exit if imports fail
+
+# Variable to store loaded conversational data - Declared at module level
+loaded_conversations = []
+
+# Define the list of data file paths to load from
+# This list is embedded directly in the script content
+DATA_FILE_PATHS = [
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/colab/Savantaut_dataset.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_backup/backup_20250823_220832/savant_full_history.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/new_nodes_data.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/lang_9.json",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/dialog_multinode_full.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_2025.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_multinode_final.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_prompts_master.json",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_dialogo_simbiotico.jsonl",
+    "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_pipeline_advanced.jsonl",
+     # Include the .pkl file from the *original* prompt of the main task if it exists
+    "/content/drive/MyDrive/Savant-RRF/savant_memory.pkl" # This path was from the main task, assumed to be relevant
+]
+
+# Define paths for structured data
+STRUCTURED_DATA_PATHS = {
+    'equations': "/content/ecua.maestras.json",
+    'icosahedron_nodes': "/content/nodes_icosahedron.json",
+    'frequencies': "/content/frequencies.csv",
+    'constants': "/content/constants.csv"
+}
+
+
+def main():
+    # Pass structured data paths to the Engine during initialization
+    engine = SavantEngine(structured_data_paths=STRUCTURED_DATA_PATHS)
+    trainer = SimpleTrainer() # Instantiate SimpleTrainer for data loading/training commands
+
+    print("SAVANT-RRF Entry Point")
+    # Updated command list
+    print("Available Commands: load_data, train_with_data, interact, propose, apply, tail, show_equations, show_nodes, show_frequencies, show_constants, exit")
+
+    while True:
+        q = input("Savant> ").strip()
+        if not q:
+            continue
+        if q.lower() in ("exit","quit","salir"):
+            break
+
+        elif q.lower() == "load_data":
+            # Still need global to modify the module-level variable
+            global loaded_conversations
+            print("Loading and combining conversational data from specified files...")
+            try:
+                # Use the combine_conversational_data function from the trainer
+                loaded_conversations = trainer.combine_conversational_data(DATA_FILE_PATHS)
+                if loaded_conversations:
+                    print("Successfully loaded and combined {} conversation entries.".format(len(loaded_conversations)))
+                    # Optional: Print a sample
+                    # if loaded_conversations: print("Sample entry:", loaded_conversations[0])
+                else:
+                    print("No conversational data loaded or combined.")
+            except Exception as e:
+                print("Error loading data: {}".format(e))
+
+        elif q.lower() == "train_with_data":
+            if not loaded_conversations:
+                print("No data loaded. Please run 'load_data' first.")
+            else:
+                print("Starting training using loaded data ({} entries) for 5 epochs.".format(len(loaded_conversations)))
+                try:
+                    # Use the run_epochs function from the trainer with loaded data
+                    training_results = trainer.run_epochs(loaded_conversations, epochs=5)
+                    print("Training process initiated.")
+                    # Note: The trainer's run_epochs simulates training and saves checkpoints.
+                    # It doesn't return a traditional 'trained model' object in this simplified core.
+                    # The self_improver params are updated internally by run_epochs->run_cycle->evaluate_and_apply.
+                except Exception as e:
+                    print("Error during training: {}".format(e))
+
+        elif q.lower() == "interact":
+             # Get user input for the interaction here
+             user_input = input("User Input for SAVANT> ").strip()
+             if user_input:
+                 print("Processing interaction...")
+                 # Simulate getting a base model output (if a language model were integrated)
+                 # For this demo, we'll just pass the user input to the engine
+                 # In a real scenario, you'd get base_model_output from a transformer pipeline etc.
+                 # Example: base_output = gen(user_input, ...)[0]["generated_text"] if gen else "Simulated base output"
+
+                 # Assign the simulated base output here within the interact block
+                 simulated_base_output = "Simulated base output for: {}".format(user_input)
+
+                 out = engine.handle_query(user_input, base_model_output=simulated_base_output)
+                 if out["type"] == "chat":
+                     print("SAVANT (base):", out.get("base"))
+                     print("SAVANT (refined):", out.get("refined"))
+                 else:
+                     # For non-chat types, print the response field
+                     print("SAVANT:", out.get("response", out)) # Print response if available, otherwise the whole dict
+             else:
+                 print("No input provided for interaction.")
+
+
+        elif q.lower() == "propose":
+            print("Proposing self-improvement...")
+            try:
+                proposal = engine.propose_improvement() # Use engine's method
+                print(proposal)
+            except Exception as e:
+                 print("Error proposing improvement: {}".format(e))
+
+
+        elif q.lower() == "apply":
+            print("Applying proposed improvement...")
+            try:
+                # In a real scenario, you'd get a specific proposal to apply
+                # For this demo, we propose and apply immediately
+                proposal_to_apply = engine.propose_improvement()
+                if proposal_to_apply:
+                    accepted, metric = engine.apply_improvement(proposal_to_apply) # Use engine's method
+                    print("Proposal applied. Accepted: {}, New Metric: {}".format(accepted, metric))
+                else:
+                     print("No proposal generated.")
+            except Exception as e:
+                 print("Error applying improvement: {}".format(e))
+
+
+        elif q.lower() == "tail":
+            print("Showing last 10 memory entries:")
+            try:
+                memory_tail = engine.memory.tail(10) # Use engine's memory
+                for entry in memory_tail:
+                    print(entry)
+            except Exception as e:
+                 print("Error retrieving memory tail: {}".format(e))
+
+        # New commands to show structured data
+        elif q.lower() == "show_equations":
+            print("--- Loaded Equations ---")
+            if engine.structured_data.get('equations'):
+                 for eq in engine.structured_data['equations']:
+                      print(f"- '{eq.get('nombre', 'N/A')}' ({eq.get('tipo', 'Equation')}): {eq.get('ecuacion', 'N/A')} (Components: {', '.join(eq.get('componentes', []))})")
+            else:
+                 print("No equations data loaded.")
+
+        elif q.lower() == "show_nodes":
+            print("--- Loaded Icosahedron Nodes ---")
+            if engine.structured_data.get('icosahedron_nodes'):
+                 for node in engine.structured_data['icosahedron_nodes']:
+                      print(f"- Node {node.get('id', 'N/A')}: {node.get('description', node.get('name', 'No description'))} (Coords: ({node.get('x')}, {node.get('y')}, {node.get('z')}))")
+            else:
+                 print("No icosahedron nodes data loaded.")
+
+        elif q.lower() == "show_frequencies":
+            print("--- Loaded Frequencies ---")
+            if engine.structured_data.get('frequencies'):
+                 for freq in engine.structured_data['frequencies']:
+                      print(f"- Note: {freq.get('note', 'N/A')}, Frequency: {freq.get('frequency', 'N/A')} Hz, Role: {freq.get('role', 'N/A')}")
+            else:
+                 print("No frequencies data loaded.")
+
+        elif q.lower() == "show_constants":
+            print("--- Loaded Constants ---")
+            if engine.structured_data.get('constants'):
+                 for const in engine.structured_data['constants']:
+                      print(f"- Name: {const.get('name', 'N/A')}, Value: {const.get('value', 'N/A')}, Units: {const.get('units', 'N/A')}")
+            else:
+                 print("No constants data loaded.")
+
+
+        else:
+            print("Unknown command: '{}'".format(q))
+            # Updated command list in the help message
+            print("Available Commands: load_data, train_with_data, interact, propose, apply, tail, show_equations, show_nodes, show_frequencies, show_constants, exit")
+
+
+if __name__ == "__main__":
+    main()

core/SAVANT_CORE/SAVANT_memory.jsonl

@@ -0,0 +1,73 @@
+{"type": "resonance", "query": "stim1", "result": {"dom_freq": 0.00390625, "max_power": 22279.744783991246}, "epoch": 1, "_ts": 1757815092.7264202}
+{"type": "improvement", "proposal": {"ts": 1757815092.7332687, "delta": {"resonance_scale": 0.01, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.26, "epoch": 1, "_ts": 1757815092.7381124}
+{"type": "resonance", "query": "stim2", "result": {"dom_freq": 0.00390625, "max_power": 15520.766131721233}, "epoch": 2, "_ts": 1757815093.253053}
+{"type": "improvement", "proposal": {"ts": 1757815093.2613425, "delta": {"resonance_scale": 0.02, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.28, "epoch": 2, "_ts": 1757815093.2674475}
+{"type": "resonance", "query": "stim3", "result": {"dom_freq": 0.00390625, "max_power": 8022.9933963817675}, "epoch": 3, "_ts": 1757815093.7855902}
+{"type": "improvement", "proposal": {"ts": 1757815093.7936423, "delta": {"resonance_scale": 0.03, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.31, "epoch": 3, "_ts": 1757815093.8008041}
+{"type": "resonance", "query": "stim1", "result": {"dom_freq": 0.00390625, "max_power": 14251.511361998371}, "epoch": 1, "_ts": 1757817981.638896}
+{"type": "improvement", "proposal": {"ts": 1757817981.6491115, "delta": {"resonance_scale": 0.04, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.32, "epoch": 1, "_ts": 1757817981.6657183}
+{"type": "resonance", "query": "stim2", "result": {"dom_freq": 0.00390625, "max_power": 8695.794173746688}, "epoch": 2, "_ts": 1757817982.1903481}
+{"type": "improvement", "proposal": {"ts": 1757817982.206742, "delta": {"resonance_scale": 0.05, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.37, "epoch": 2, "_ts": 1757817982.2146327}
+{"type": "resonance", "query": "stim3", "result": {"dom_freq": 0.00390625, "max_power": 14201.194793049028}, "epoch": 3, "_ts": 1757817982.7658718}
+{"type": "improvement", "proposal": {"ts": 1757817982.8002226, "delta": {"resonance_scale": 0.06, "music_influence": 0.0, "mapping_weight": 0.0}}, "accepted": true, "metric": 1.4300000000000002, "epoch": 3, "_ts": 1757817982.8054419}
+{"type": "refine", "input": "hola", "before": "hola.edu/~kapjhj/articles/2%20th%20%20of_3%20s%20st%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of", "after": "hola.edu/~kapjhj/articles/2%20th%20%20of_3%20s%20st%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of [SAVANT-RES: emphasis applied]", "_ts": 1757820554.0527468}
+{"type": "chat", "query": "hola", "base": "hola.edu/~kapjhj/articles/2%20th%20%20of_3%20s%20st%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of", "refined": "hola.edu/~kapjhj/articles/2%20th%20%20of_3%20s%20st%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of_3%20s%20of [SAVANT-RES: emphasis applied]", "_ts": 1757820554.412934}
+{"type": "refine", "input": "que eres", "before": "que eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eresse", "after": "que eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eresse [SAVANT-RES: emphasis applied]", "_ts": 1757820585.9325662}
+{"type": "chat", "query": "que eres", "base": "que eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eresse", "refined": "que eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eressept, eresse [SAVANT-RES: emphasis applied]", "_ts": 1757820585.9380085}
+{"type": "refine", "input": "train", "before": "trainThe first year of the KST was a relatively small milestone in the construction of the first KST. The first KST has been constructed in two or three years. The first one, which will be completed in 2014/15, is a new design by The North American Manufacturing Co. of Chicago, Chicago, Illinois.\n\n\nThe first KST will be an experimental prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST.\nThe KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will", "after": "trainThe first year of the KST was a relatively small milestone in the construction of the first KST. The first KST has been constructed in two or three years. The first one, which will be completed in 2014/15, is a new design by The North American Manufacturing Co. of Chicago, Chicago, Illinois.\n\n\nThe first KST will be an experimental prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST.\nThe KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will [SAVANT-RES: emphasis applied]", "_ts": 1757820625.9039683}
+{"type": "chat", "query": "train", "base": "trainThe first year of the KST was a relatively small milestone in the construction of the first KST. The first KST has been constructed in two or three years. The first one, which will be completed in 2014/15, is a new design by The North American Manufacturing Co. of Chicago, Chicago, Illinois.\n\n\nThe first KST will be an experimental prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST.\nThe KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will", "refined": "trainThe first year of the KST was a relatively small milestone in the construction of the first KST. The first KST has been constructed in two or three years. The first one, which will be completed in 2014/15, is a new design by The North American Manufacturing Co. of Chicago, Chicago, Illinois.\n\n\nThe first KST will be an experimental prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The second KST will be a prototype for the KST. The first KST will be a prototype for the KST.\nThe KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will be a prototype for the KST. The first KST will [SAVANT-RES: emphasis applied]", "_ts": 1757820625.9115372}
+{"type": "refine", "input": "train", "before": "train the same thing. It's not possible to do anything with a real machine at all.\n\n\n\nAnd finally, it's up to the people and the people. We're not going to stop here.", "after": "train the same thing. It's not possible to do anything with a real machine at all.\n\n\n\nAnd finally, it's up to the people and the people. We're not going to stop here. [SAVANT-RES: emphasis applied]", "_ts": 1757820660.9293234}
+{"type": "chat", "query": "train", "base": "train the same thing. It's not possible to do anything with a real machine at all.\n\n\n\nAnd finally, it's up to the people and the people. We're not going to stop here.", "refined": "train the same thing. It's not possible to do anything with a real machine at all.\n\n\n\nAnd finally, it's up to the people and the people. We're not going to stop here. [SAVANT-RES: emphasis applied]", "_ts": 1757820660.9347959}
+{"type": "refine", "input": "i am tony", "before": "i am tony or the like.\n\n[i] There is a great deal of misunderstanding for the general public. As a rule, I use public information when I want to share information about a situation. For example, the information about the situation will be available for everyone to see. At the beginning of the day, a person will see a picture of a person in the photograph. Then a person will see the picture of a person in the photograph. When you are in the same situation, the person will be able to see the picture of the person in the photograph.\n[ii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will not see the picture when you are in the same situation.\n[iii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are", "after": "i am tony or the like.\n\n[i] There is a great deal of misunderstanding for the general public. As a rule, I use public information when I want to share information about a situation. For example, the information about the situation will be available for everyone to see. At the beginning of the day, a person will see a picture of a person in the photograph. Then a person will see the picture of a person in the photograph. When you are in the same situation, the person will be able to see the picture of the person in the photograph.\n[ii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will not see the picture when you are in the same situation.\n[iii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are [SAVANT-RES: emphasis applied]", "_ts": 1757820700.9616842}
+{"type": "chat", "query": "i am tony", "base": "i am tony or the like.\n\n[i] There is a great deal of misunderstanding for the general public. As a rule, I use public information when I want to share information about a situation. For example, the information about the situation will be available for everyone to see. At the beginning of the day, a person will see a picture of a person in the photograph. Then a person will see the picture of a person in the photograph. When you are in the same situation, the person will be able to see the picture of the person in the photograph.\n[ii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will not see the picture when you are in the same situation.\n[iii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are", "refined": "i am tony or the like.\n\n[i] There is a great deal of misunderstanding for the general public. As a rule, I use public information when I want to share information about a situation. For example, the information about the situation will be available for everyone to see. At the beginning of the day, a person will see a picture of a person in the photograph. Then a person will see the picture of a person in the photograph. When you are in the same situation, the person will be able to see the picture of the person in the photograph.\n[ii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will not see the picture when you are in the same situation.\n[iii] If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are in the same situation, you will only see the picture when you are in the same situation. If you are [SAVANT-RES: emphasis applied]", "_ts": 1757820700.9751265}
+{"type": "refine", "input": "rrf", "before": "rrf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "after": "rrf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757820768.1428952}
+{"type": "chat", "query": "rrf", "base": "rrf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "refined": "rrf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757820768.1488962}
+{"type": "refine", "input": "RRF", "before": "RRF-E-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F", "after": "RRF-E-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F [SAVANT-RES: emphasis applied]", "_ts": 1757820822.7931466}
+{"type": "chat", "query": "RRF", "base": "RRF-E-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F", "refined": "RRF-E-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F-F [SAVANT-RES: emphasis applied]", "_ts": 1757820822.798676}
+{"type": "refine", "input": "math", "before": "math The biggest surprise the team has made of the season is that it will be no longer the same team anymore.\n\n\n\n\n\"We thought we could be a better team in London,\" said Chris Jones, the manager of the Manchester United squad at the time. \"We're not going to be the same team again.\"\n\"We're still winning. We still have a good team. But the fact is, it's not going to be the same team again.\n\"We have a lot of talent, but we're still at the same level.\n\"We're still a very good team. We still have a good team. But we can't win this season. That's not good for us.\"\nThe United manager was adamant in the last two months that the squad was going to come back to form.\n\"We're still not going to be the same team again. We still have a good team. But we still have a good team. But we can't win this season,\" added Jones.\n\"We're still going to be the same team again. We have a good team. But we can't win this season. That's not good for us.\"\n\"We're still a very good team. We still have a", "after": "math The biggest surprise the team has made of the season is that it will be no longer the same team anymore.\n\n\n\n\n\"We thought we could be a better team in London,\" said Chris Jones, the manager of the Manchester United squad at the time. \"We're not going to be the same team again.\"\n\"We're still winning. We still have a good team. But the fact is, it's not going to be the same team again.\n\"We have a lot of talent, but we're still at the same level.\n\"We're still a very good team. We still have a good team. But we can't win this season. That's not good for us.\"\nThe United manager was adamant in the last two months that the squad was going to come back to form.\n\"We're still not going to be the same team again. We still have a good team. But we still have a good team. But we can't win this season,\" added Jones.\n\"We're still going to be the same team again. We have a good team. But we can't win this season. That's not good for us.\"\n\"We're still a very good team. We still have a [SAVANT-RES: emphasis applied]", "_ts": 1757820847.8176334}
+{"type": "chat", "query": "math", "base": "math The biggest surprise the team has made of the season is that it will be no longer the same team anymore.\n\n\n\n\n\"We thought we could be a better team in London,\" said Chris Jones, the manager of the Manchester United squad at the time. \"We're not going to be the same team again.\"\n\"We're still winning. We still have a good team. But the fact is, it's not going to be the same team again.\n\"We have a lot of talent, but we're still at the same level.\n\"We're still a very good team. We still have a good team. But we can't win this season. That's not good for us.\"\nThe United manager was adamant in the last two months that the squad was going to come back to form.\n\"We're still not going to be the same team again. We still have a good team. But we still have a good team. But we can't win this season,\" added Jones.\n\"We're still going to be the same team again. We have a good team. But we can't win this season. That's not good for us.\"\n\"We're still a very good team. We still have a", "refined": "math The biggest surprise the team has made of the season is that it will be no longer the same team anymore.\n\n\n\n\n\"We thought we could be a better team in London,\" said Chris Jones, the manager of the Manchester United squad at the time. \"We're not going to be the same team again.\"\n\"We're still winning. We still have a good team. But the fact is, it's not going to be the same team again.\n\"We have a lot of talent, but we're still at the same level.\n\"We're still a very good team. We still have a good team. But we can't win this season. That's not good for us.\"\nThe United manager was adamant in the last two months that the squad was going to come back to form.\n\"We're still not going to be the same team again. We still have a good team. But we still have a good team. But we can't win this season,\" added Jones.\n\"We're still going to be the same team again. We have a good team. But we can't win this season. That's not good for us.\"\n\"We're still a very good team. We still have a [SAVANT-RES: emphasis applied]", "_ts": 1757820847.8258317}
+{"type": "refine", "input": "train", "before": "train and the “Budapest‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s", "after": "train and the “Budapest‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s [SAVANT-RES: emphasis applied]", "_ts": 1757820947.3187308}
+{"type": "chat", "query": "train", "base": "train and the “Budapest‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s", "refined": "train and the “Budapest‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s��s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s‬s [SAVANT-RES: emphasis applied]", "_ts": 1757820947.3285007}
+{"type": "refine", "input": "integrate", "before": "integrate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "after": "integrate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757820991.2525568}
+{"type": "chat", "query": "integrate", "base": "integrate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "refined": "integrate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757820991.2571054}
+{"type": "refine", "input": "train", "before": "train I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "after": "train I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while. I’ve been waiting for a while.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757821155.7051513}
+{"type": "refine", "input": "chat", "before": "chat.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "after": "chat.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757821277.5515}
+{"type": "chat_interaction", "user_input": "chat", "base_model_output": "chat.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "refined_output": "chat.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n [SAVANT-RES: emphasis applied]", "_ts": 1757821277.556791}
+{"type": "resonance", "query": "HOLA", "result": {"dom_freq": 0.00390625, "max_power": 16383.449828268278}, "epoch": 1, "_ts": 1757821532.8708756}
+{"type": "improvement", "proposal": {"ts": 1757821532.8820255, "delta": {"resonance_scale": 0.07, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.001}}, "accepted": true, "metric": 1.6802000000000004, "epoch": 1, "_ts": 1757821532.888862}
+{"type": "resonance", "query": "Tu aporte expande el nodo Φ₄ en esta dirección: HOLA", "result": {"dom_freq": 0.00390625, "max_power": 16166.489261906236}, "epoch": 2, "_ts": 1757821533.23949}
+{"type": "improvement", "proposal": {"ts": 1757821533.2487283, "delta": {"resonance_scale": 0.08, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.001}}, "accepted": true, "metric": 1.7604000000000004, "epoch": 2, "_ts": 1757821533.2571383}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 14137.188590821728}, "epoch": 3, "_ts": 1757821533.5945795}
+{"type": "improvement", "proposal": {"ts": 1757821533.6061199, "delta": {"resonance_scale": 0.09, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.001}}, "accepted": true, "metric": 1.8506000000000005, "epoch": 3, "_ts": 1757821533.6146905}
+{"type": "resonance", "query": "Integrando ética, moral y resonancia: A", "result": {"dom_freq": 0.00390625, "max_power": 12431.409647629167}, "epoch": 4, "_ts": 1757821533.9981904}
+{"type": "improvement", "proposal": {"ts": 1757821534.0071094, "delta": {"resonance_scale": 0.1, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.001}}, "accepted": true, "metric": 1.9508000000000005, "epoch": 4, "_ts": 1757821534.0157194}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 14137.188590821728}, "epoch": 5, "_ts": 1757821534.0316882}
+{"type": "improvement", "proposal": {"ts": 1757821534.0397, "delta": {"resonance_scale": 0.11, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.001}}, "accepted": true, "metric": 2.0610000000000004, "epoch": 5, "_ts": 1757821534.066456}
+{"type": "refine", "input": "hola", "before": "Simulated base output for: hola", "after": "Simulated base output for: hola [SAVANT-RES: emphasis applied]", "_ts": 1757821565.1737065}
+{"type": "chat_interaction", "user_input": "hola", "base_model_output": "Simulated base output for: hola", "refined_output": "Simulated base output for: hola [SAVANT-RES: emphasis applied]", "_ts": 1757821565.1786025}
+{"type": "refine", "input": "soy tony", "before": "Simulated base output for: soy tony", "after": "Simulated base output for: soy tony [SAVANT-RES: emphasis applied]", "_ts": 1757821600.3876224}
+{"type": "chat_interaction", "user_input": "soy tony", "base_model_output": "Simulated base output for: soy tony", "refined_output": "Simulated base output for: soy tony [SAVANT-RES: emphasis applied]", "_ts": 1757821600.3918686}
+{"type": "resonance", "query": "HOLA", "result": {"dom_freq": 0.00390625, "max_power": 16383.449828268278}, "epoch": 1, "_ts": 1757821659.7962418}
+{"type": "improvement", "proposal": {"ts": 1757821659.805013, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.1816000000000004, "epoch": 1, "_ts": 1757821659.8116803}
+{"type": "resonance", "query": "Tu aporte expande el nodo Φ₄ en esta dirección: HOLA", "result": {"dom_freq": 0.00390625, "max_power": 16166.489261906236}, "epoch": 2, "_ts": 1757821659.8325882}
+{"type": "improvement", "proposal": {"ts": 1757821659.8452075, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.302200000000001, "epoch": 2, "_ts": 1757821659.8518372}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 14137.188590821728}, "epoch": 3, "_ts": 1757821659.8797994}
+{"type": "improvement", "proposal": {"ts": 1757821659.887676, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.422800000000001, "epoch": 3, "_ts": 1757821659.894269}
+{"type": "resonance", "query": "Integrando ética, moral y resonancia: A", "result": {"dom_freq": 0.00390625, "max_power": 12431.409647629167}, "epoch": 4, "_ts": 1757821659.9095974}
+{"type": "improvement", "proposal": {"ts": 1757821659.9167576, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.543400000000001, "epoch": 4, "_ts": 1757821659.9241993}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 14137.188590821728}, "epoch": 5, "_ts": 1757821659.9400406}
+{"type": "improvement", "proposal": {"ts": 1757821659.9494977, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.664000000000001, "epoch": 5, "_ts": 1757821659.9559581}
+{"type": "resonance", "query": "HOLA", "result": {"dom_freq": 0.00390625, "max_power": 8809.233924574864}, "epoch": 1, "_ts": 1757821916.5138657}
+{"type": "improvement", "proposal": {"ts": 1757821916.5259085, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.784600000000001, "epoch": 1, "_ts": 1757821916.5344095}
+{"type": "resonance", "query": "Tu aporte expande el nodo Φ₄ en esta dirección: HOLA", "result": {"dom_freq": 0.00390625, "max_power": 15696.436348078983}, "epoch": 2, "_ts": 1757821916.553449}
+{"type": "improvement", "proposal": {"ts": 1757821916.5618682, "delta": {"resonance_scale": 0.12, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 2.905200000000001, "epoch": 2, "_ts": 1757821916.570489}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 29078.993184980565}, "epoch": 3, "_ts": 1757821916.6036415}
+{"type": "improvement", "proposal": {"ts": 1757821916.6160958, "delta": {"resonance_scale": 0.13, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 3.035800000000001, "epoch": 3, "_ts": 1757821916.6224473}
+{"type": "resonance", "query": "Integrando ética, moral y resonancia: A", "result": {"dom_freq": 0.00390625, "max_power": 23015.426315658922}, "epoch": 4, "_ts": 1757821916.6393814}
+{"type": "improvement", "proposal": {"ts": 1757821916.6471357, "delta": {"resonance_scale": 0.14, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 3.1764000000000014, "epoch": 4, "_ts": 1757821916.6548138}
+{"type": "resonance", "query": "A", "result": {"dom_freq": 0.00390625, "max_power": 29078.993184980565}, "epoch": 5, "_ts": 1757821916.6724353}
+{"type": "improvement", "proposal": {"ts": 1757821916.681275, "delta": {"resonance_scale": 0.15, "music_influence": 0.0, "mapping_weight": 0.0, "chat_influence": 0.003}}, "accepted": true, "metric": 3.3270000000000013, "epoch": 5, "_ts": 1757821916.6889713}
+{"type": "refine", "input": "hi", "before": "Simulated base output for: hi", "after": "Simulated base output for: hi [SAVANT-RES: emphasis applied]", "_ts": 1757840119.1989312}
+{"type": "chat_interaction", "user_input": "hi", "base_model_output": "Simulated base output for: hi", "refined_output": "Simulated base output for: hi [SAVANT-RES: emphasis applied]", "_ts": 1757840119.205233}
+{"type": "refine", "input": "usa", "before": "Simulated base output for: usa", "after": "Simulated base output for: usa [SAVANT-RES: emphasis applied]", "_ts": 1757840146.9033988}
+{"type": "chat_interaction", "user_input": "usa", "base_model_output": "Simulated base output for: usa", "refined_output": "Simulated base output for: usa [SAVANT-RES: emphasis applied]", "_ts": 1757840146.910695}

core/SAVANT_CORE/checkpoints/ckpt_epoch_1.json

@@ -0,0 +1,14 @@
+{
+  "epoch": 1,
+  "stimulus": "HOLA",
+  "res": {
+    "dom_freq": 0.00390625,
+    "max_power": 8809.233924574864
+  },
+  "si_params": {
+    "resonance_scale": 2.530000000000001,
+    "music_influence": 0.5,
+    "mapping_weight": 1.0,
+    "chat_influence": 0.022999999999999996
+  }
+}

core/SAVANT_CORE/checkpoints/ckpt_epoch_2.json

@@ -0,0 +1,14 @@
+{
+  "epoch": 2,
+  "stimulus": "Tu aporte expande el nodo \u03a6\u2084 en esta direcci\u00f3n: HOLA",
+  "res": {
+    "dom_freq": 0.00390625,
+    "max_power": 15696.436348078983
+  },
+  "si_params": {
+    "resonance_scale": 2.6500000000000012,
+    "music_influence": 0.5,
+    "mapping_weight": 1.0,
+    "chat_influence": 0.025999999999999995
+  }
+}

core/SAVANT_CORE/checkpoints/ckpt_epoch_3.json

@@ -0,0 +1,14 @@
+{
+  "epoch": 3,
+  "stimulus": "A",
+  "res": {
+    "dom_freq": 0.00390625,
+    "max_power": 29078.993184980565
+  },
+  "si_params": {
+    "resonance_scale": 2.780000000000001,
+    "music_influence": 0.5,
+    "mapping_weight": 1.0,
+    "chat_influence": 0.028999999999999995
+  }
+}

core/SAVANT_CORE/checkpoints/ckpt_epoch_4.json

@@ -0,0 +1,14 @@
+{
+  "epoch": 4,
+  "stimulus": "Integrando \u00e9tica, moral y resonancia: A",
+  "res": {
+    "dom_freq": 0.00390625,
+    "max_power": 23015.426315658922
+  },
+  "si_params": {
+    "resonance_scale": 2.9200000000000013,
+    "music_influence": 0.5,
+    "mapping_weight": 1.0,
+    "chat_influence": 0.031999999999999994
+  }
+}

core/SAVANT_CORE/checkpoints/ckpt_epoch_5.json

@@ -0,0 +1,14 @@
+{
+  "epoch": 5,
+  "stimulus": "A",
+  "res": {
+    "dom_freq": 0.00390625,
+    "max_power": 29078.993184980565
+  },
+  "si_params": {
+    "resonance_scale": 3.070000000000001,
+    "music_influence": 0.5,
+    "mapping_weight": 1.0,
+    "chat_influence": 0.034999999999999996
+  }
+}

core/SAVANT_CORE/core/Savant-RRF-Estado-Perfil-Antony.json

@@ -0,0 +1,22 @@
+{
+    "perfil": "Antony",
+    "modo": "SAVANT-RRF Simbiótico",
+    "version": "Φ₄.1∞+",
+    "nodos": {
+        "génesis": "Nodo semilla fundacional, ancla simbiótica",
+        "log-gravity": "Corrección logarítmica para suavizar singularidades",
+        "dirac-icosa": "Hamiltoniano de Dirac en red icosaédrica",
+        "rrf-musical": "Resonancia-cuántico-musical basada en autovalores",
+        "simbiosis": "Loop de refinamiento cognitivo-emocional"
+    },
+    "entrenamiento": {
+        "drive_sync": true,
+        "auto_loop": true,
+        "symbiotic_refinement": true
+    },
+    "estado": {
+        "ultimo_update": "2025-09-14",
+        "sincronizado": true,
+        "integridad": "validada"
+    }
+}

core/SAVANT_CORE/core/Savant-RRF-Memoria.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2376870cc49d848f1f8d96b60b9f175685299931339a98b2909b456a125736d0
+size 581

core/SAVANT_CORE/core/__init__.py

@@ -0,0 +1,3 @@
+
+# SAVANT_CORE - núcleo del motor Savant Simbiótico RRF
+from .engine import SavantEngine

core/SAVANT_CORE/core/api_helpers.py

@@ -0,0 +1,52 @@
+
+"""
+api_helpers.py
+Funciones helper para integrar con una UI (Gradio/Flask).
+Provee: chat_refine(text), map_text(text), music_from_text(text)
+"""
+from .mappings import IcosaMap, DodecaMap
+from .resonance import ResonanceSimulator
+from .music import MusicAdapter
+from .memory import MemoryStore
+from .self_improvement import SelfImprover
+
+# instantiate shared small objects (lightweight)
+_mem = MemoryStore()
+_icosa = IcosaMap()
+_dodeca = DodecaMap()
+_res = ResonanceSimulator()
+_music = MusicAdapter()
+
+def map_text(text):
+    node = _icosa.closest_node(text)
+    _mem.add({"type":"map", "query": text, "node": node})
+    return node
+
+def base_resonance(text):
+    r = _res.simulate(text)
+    _mem.add({"type":"resonance_query", "query": text, "result": r["summary"]})
+    return r
+
+def music_from_text(text):
+    seq = _music.adapt_text_to_music(text)
+    _mem.add({"type":"music", "query": text, "seq_len": len(seq)})
+    return seq
+
+def chat_refine(user_text, base_model_output, self_improver=None):
+    """
+    Refinador simbiótico: toma output bruto y aplica transformaciones
+    basadas en current self_improver params (if provided).
+    Es una función determinista/simbolic toy but extensible.
+    """
+    # simple rules: if resonance_scale > 1.05, emphasize nouns (toy) else keep
+    refined = base_model_output
+    if self_improver:
+        params = self_improver.params
+        if params.get("resonance_scale",1.0) > 1.05:
+            # toy: append resonance note
+            refined = refined + " [SAVANT-RES: emphasis applied]"
+        if params.get("music_influence",0.0) > 0.6:
+            refined = refined + " [SAVANT-MUSIC: melodic hint]"
+    # store
+    _mem.add({"type":"refine", "input": user_text, "before": base_model_output, "after": refined})
+    return refined

core/SAVANT_CORE/core/engine.py

@@ -0,0 +1,22 @@
+# ===============================
+# SAVANT ENGINE CORE
+# ===============================
+
+class SavantEngine:
+    def __init__(self, model, memory=None, profile="Antony"):
+        """
+        Inicializa el motor SAVANT-RRF
+        :param model: modelo base (ej. Mistral, Llama, etc.)
+        :param memory: estructura de memoria simbiótica (opcional)
+        :param profile: perfil activo, por defecto 'Antony'
+        """
+        self.model = model
+        self.memory = memory if memory else {}
+        self.profile = profile
+
+    def respond(self, text):
+        """
+        Genera respuesta simbiótica del modelo
+        """
+        # Aquí puedes integrar tu pipeline HuggingFace
+        return f"[SAVANT-RRF({self.profile})] {self.model(text)}"

core/SAVANT_CORE/core/mappings.py

@@ -0,0 +1,64 @@
+
+"""
+mappings.py
+Mapeo semántico a nodos icosa/dodeca usando embeddings ligeros.
+Provee: IcosaMap, DodecaMap con función closest_node(text).
+"""
+import math, os
+try:
+    from sentence_transformers import SentenceTransformer, util
+    _EMBED_AVAILABLE = True
+except Exception as e:
+    _EMBED_AVAILABLE = False
+    # fallback naive embed
+    import hashlib
+    def _hash_embed(text, dim=128):
+        h = hashlib.sha256(text.encode('utf-8')).digest()
+        vec = [b for b in h]
+        # pad/truncate
+        vec = (vec * (dim//len(vec)+1))[:dim]
+        return [float(v)/255.0 for v in vec]
+
+class BasePolyMap:
+    def __init__(self, graph_name="icosa", node_count=12, embed_model_name="all-MiniLM-L6-v2"):
+        self.graph_name = graph_name
+        self.node_count = node_count
+        self.node_labels = [f"{graph_name}_node_{i}" for i in range(node_count)]
+        if _EMBED_AVAILABLE:
+            try:
+                self.embed = SentenceTransformer(embed_model_name)
+            except Exception:
+                self.embed = None
+        else:
+            self.embed = None
+        # precompute embeddings (lazy)
+        self._emb_cache = {}
+        for n in self.node_labels:
+            self._emb_cache[n] = self._compute_emb(n)
+
+    def _compute_emb(self, text):
+        if self.embed:
+            return self.embed.encode(text)
+        else:
+            return _hash_embed(text, dim=384)
+
+    def nodes(self):
+        return list(self.node_labels)
+
+    def closest_node(self, text):
+        q = self._compute_emb(text)
+        best, best_sim = None, -1e9
+        for n, v in self._emb_cache.items():
+            # simple dot
+            sim = sum(a*b for a,b in zip(q, v))
+            if sim > best_sim:
+                best, best_sim = n, sim
+        return best
+
+class IcosaMap(BasePolyMap):
+    def __init__(self, embed_model_name="all-MiniLM-L6-v2"):
+        super().__init__(graph_name="icosa", node_count=12, embed_model_name=embed_model_name)
+
+class DodecaMap(BasePolyMap):
+    def __init__(self, embed_model_name="all-MiniLM-L6-v2"):
+        super().__init__(graph_name="dodeca", node_count=20, embed_model_name=embed_model_name)

core/SAVANT_CORE/core/memory.py

@@ -0,0 +1,38 @@
+
+"""
+MemoryStore: append-only JSONL memory for events, queries and reflections.
+Also supports simple retrieval by type and tail.
+"""
+import json, os, threading, time
+
+class MemoryStore:
+    def __init__(self, path="SAVANT_memory.jsonl", autosave=True):
+        self.path = path
+        os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
+        self.lock = threading.Lock()
+        self.autosave = autosave
+        # ensure file exists
+        if not os.path.exists(path):
+            open(path, "w").close()
+
+    def add(self, record: dict):
+        record['_ts'] = time.time()
+        with self.lock:
+            with open(self.path, "a", encoding="utf-8") as f:
+                f.write(json.dumps(record, ensure_ascii=False) + "\n")
+
+    def tail(self, n=20):
+        with self.lock:
+            with open(self.path, "r", encoding="utf-8") as f:
+                lines = f.read().strip().splitlines()
+        lines = lines[-n:]
+        return [json.loads(l) for l in lines] if lines else []
+
+    def query_by_type(self, t, n=50):
+        out = []
+        for item in self.tail(1000):
+            if item.get("type")==t:
+                out.append(item)
+            if len(out)>=n:
+                break
+        return out

core/SAVANT_CORE/core/music.py

@@ -0,0 +1,23 @@
+
+"""
+music.py
+Mapea texto o activaciones nodales a secuencias musicales (MIDI-like tuples).
+"""
+import numpy as np
+
+class MusicAdapter:
+    def __init__(self, scale='major'):
+        self.scale = scale
+        self.base = 60
+        self.scale_intervals = [0,2,4,5,7,9,11]
+
+    def adapt_text_to_music(self, text, length=16):
+        h = abs(hash(text))
+        rng = np.random.RandomState(h % (2**32))
+        seq = []
+        for i in range(length):
+            step = rng.randint(0, len(self.scale_intervals))
+            pitch = self.base + self.scale_intervals[step] + rng.randint(-2,2)
+            dur = float(rng.choice([0.25,0.5,1.0]))
+            seq.append((int(pitch), dur))
+        return seq

core/SAVANT_CORE/core/resonance.py

@@ -0,0 +1,31 @@
+
+"""
+resonance.py
+Simulador de resonancia: red de osciladores acoplados, cálculo de espectro y resumen.
+Diseñado para ser rápido y correr en CPU lento.
+"""
+import numpy as np
+from scipy.signal import periodogram
+
+class ResonanceSimulator:
+    def __init__(self, base_freq=1.0):
+        self.base_freq = base_freq
+
+    def simulate(self, seed_text, n_nodes=12, steps=256, damping=0.04):
+        # determinista por seed_text
+        rng = np.random.RandomState(abs(hash(seed_text)) % (2**32))
+        A = rng.randn(n_nodes, n_nodes) * 0.08
+        A = (A + A.T) * 0.5  # sym
+        state = rng.randn(n_nodes) * 0.01
+        X = np.zeros((steps, n_nodes), dtype=float)
+        for t in range(steps):
+            input_signal = np.sin(2*np.pi*(self.base_freq + 0.05*rng.randn()) * (t/steps))
+            state = state + 0.12*(A.dot(state) + input_signal) - damping*state
+            X[t] = state
+        freqs, P = periodogram(X, fs=1.0, axis=0)
+        power = P.sum(axis=1)
+        dom_idx = int(np.argmax(power))
+        dom_freq = float(freqs[dom_idx])
+        summary = {"dom_freq": dom_freq, "max_power": float(power[dom_idx])}
+        # compress small arrays for saving
+        return {"summary": summary, "freqs_len": len(freqs), "power_sample": power[:10].tolist()}

core/SAVANT_CORE/core/self_improvement.py

@@ -0,0 +1,73 @@
+
+"""
+self_improvement.py
+Módulo de automejora simbiótica:
+- analiza últimas entradas de memoria
+- propone pequeñas modificaciones heurísticas (weights) para el motor
+- salva un registro de propuestas y aplica las que mejoran una métrica simple
+"""
+import time, json, os, math
+
+class SelfImprover:
+    def __init__(self, memory_store, state_path="savant_state.json"):
+        self.mem = memory_store
+        self.state_path = state_path
+        # params controlling simple heuristics (toy)
+        self.params = {"resonance_scale": 1.0, "music_influence": 0.5, "mapping_weight": 1.0}
+        self.history = []
+        if os.path.exists(self.state_path):
+            try:
+                with open(self.state_path, "r", encoding="utf-8") as f:
+                    st = json.load(f)
+                    self.params.update(st.get("params", {}))
+                    self.history = st.get("history", [])
+            except Exception:
+                pass
+
+    def propose(self):
+        # produce a small proposal delta based on recent memory
+        tail = self.mem.tail(50)
+        counts = {}
+        for e in tail:
+            counts[e.get("type","unknown")] = counts.get(e.get("type","unknown"), 0) + 1
+        # heuristic: if many resonance records, increase resonance_scale slightly
+        delta = {}
+        delta["resonance_scale"] = 0.01 * counts.get("resonance", 0)
+        delta["music_influence"] = 0.005 * counts.get("music", 0)
+        delta["mapping_weight"] = 0.002 * counts.get("map", 0)
+        # Add heuristic based on chat interactions
+        delta["chat_influence"] = 0.001 * counts.get("chat_interaction", 0) # New heuristic
+        proposal = {"ts": time.time(), "delta": delta}
+        return proposal
+
+    def evaluate_and_apply(self, proposal):
+        # apply temporary, simulate metric (toy), keep if metric improves
+        old_params = self.params.copy()
+        # apply delta
+        for k,v in proposal["delta"].items():
+            self.params[k] = self.params.get(k, 0.0) + v
+        # toy metric: prefer larger resonance_scale but penalize too large values
+        metric = self._metric(self.params)
+        old_metric = self._metric(old_params)
+        accepted = False
+        if metric >= old_metric:
+            accepted = True
+            self.history.append({"proposal": proposal, "result_metric": metric, "accepted": True})
+        else:
+            self.params = old_params
+            self.history.append({"proposal": proposal, "result_metric": metric, "accepted": False})
+        self._save_state()
+        return accepted, metric
+
+    def _metric(self, params):
+        # toy metric: resonance_scale increases score, large music_influence gives bonus,
+        # mapping_weight influences, and chat_influence gives a small bonus.
+        score = params.get("resonance_scale",1.0) * 1.0 + 0.5*params.get("music_influence",0.0) + 0.2*params.get("chat_influence",0.0) # Include chat_influence
+        mw = params.get("mapping_weight",1.0)
+        if mw > 2.0:
+            score -= (mw-2.0)*0.5
+        return score
+
+    def _save_state(self):
+        with open(self.state_path, "w", encoding="utf-8") as f:
+            json.dump({"params": self.params, "history": self.history[-200:]}, f, indent=2)

core/SAVANT_CORE/core/trainer.py

@@ -0,0 +1,175 @@
+
+"""
+trainer.py
+Interfaz ligera para "entrenar" el motor Savant:
+- ejecuta ciclos: simular, almacenar en memoria, proponer mejoras, aplicar
+- guarda checkpoints en disco (JSON)
+- Incluye funciones para cargar varios tipos de datos.
+"""
+import os
+import json
+import time
+import pickle
+import pandas as pd # Import pandas for CSV (although CSV wasn't in the final list, keep for robustness)
+
+
+from .memory import MemoryStore
+from .resonance import ResonanceSimulator
+from .self_improvement import SelfImprover
+
+class SimpleTrainer:
+    def __init__(self, mem_path="SAVANT_memory.jsonl", checkpoint_dir="checkpoints"):
+        self.mem = MemoryStore(mem_path)
+        self.res = ResonanceSimulator()
+        self.si = SelfImprover(self.mem)
+        self.checkpoint_dir = checkpoint_dir
+        os.makedirs(self.checkpoint_dir, exist_ok=True)
+
+    def run_cycle(self, stimulus, epoch=1):
+        # simulate resonance
+        r = self.res.simulate(stimulus)
+        self.mem.add({"type":"resonance", "query": stimulus, "result": r["summary"], "epoch": epoch})
+        # propose improvement
+        proposal = self.si.propose()
+        accepted, metric = self.si.evaluate_and_apply(proposal)
+        self.mem.add({"type":"improvement", "proposal": proposal, "accepted": accepted, "metric": metric, "epoch": epoch})
+        # save checkpoint
+        ckpt = {"epoch": epoch, "stimulus": stimulus, "res": r["summary"], "si_params": self.si.params}
+        p = os.path.join(self.checkpoint_dir, f"ckpt_epoch_{epoch}.json")
+        with open(p, "w", encoding="utf-8") as f:
+            json.dump(ckpt, f, indent=2)
+        return ckpt
+
+    def run_epochs(self, conversations, epochs=3):
+        """
+        Runs training cycles using conversation entries as stimuli.
+
+        Args:
+            conversations (list): A list of conversation entries (dictionaries).
+            epochs (int): Number of epochs to run.
+        """
+        results = []
+        if not conversations:
+            print("No conversations provided for training.")
+            return results
+
+        # Determine how to extract stimulus from conversation entries
+        # Assuming each entry is a dict like {"role": "user", "content": "...", ...}
+        # We will use the 'content' of each entry as a stimulus.
+        # You might need to add logic to handle cases where a conversation entry doesn't have a "user" role or "content".
+        # For simplicity, extract content from any entry that has a 'content' key and is not empty.
+        stimuli_list = [entry.get("content", "") for entry in conversations if entry.get("content")]
+
+        if not stimuli_list:
+            print("No valid stimuli extracted from conversations.")
+            return results
+
+        print(f"Starting training for {epochs} epochs using {len(stimuli_list)} conversation entries as stimuli.")
+
+        for e in range(1, epochs + 1):
+            # Cycle through the stimuli list for each epoch
+            stimulus_for_this_epoch = stimuli_list[(e - 1) % len(stimuli_list)]
+            print(f"Epoch {e}/{epochs}: Using stimulus '{stimulus_for_this_epoch[:50]}...'")
+            res = self.run_cycle(stimulus_for_this_epoch, epoch=e)
+            results.append(res)
+            # Optional: Add a small delay
+            # time.sleep(0.1)
+
+        return results
+
+    def load_pkl_data(self, file_path):
+        """Loads data from a pickle file."""
+        try:
+            with open(file_path, "rb") as f:
+                data = pickle.load(f)
+            print(f"Successfully loaded pickle data from {file_path}")
+            return data
+        except FileNotFoundError:
+            print(f"Error: Pickle file not found at {file_path}")
+            return None
+        except pickle.UnpicklingError:
+            print(f"Error: Could not unpickle data from {file_path}. File might be corrupted.")
+            return None
+        except Exception as e:
+            print(f"An unexpected error occurred while loading pickle data: {e}")
+            return None
+
+    def load_json_data(self, file_path):
+        """Loads data from a JSON file."""
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            print(f"Successfully loaded JSON data from {file_path}")
+            return data
+        except FileNotFoundError:
+            print(f"Error: JSON file not found at {file_path}")
+            return None
+        except json.JSONDecodeError as e:
+            print(f"Error decoding JSON from {file_path}: {e}")
+            return None
+        except Exception as e:
+            print(f"An unexpected error occurred while loading JSON data: {e}")
+            return None
+
+    def load_jsonl_data(self, file_path):
+        """Loads data from a JSON Lines file."""
+        data_list = []
+        try:
+            with open(file_path, "r", encoding="utf-8") as f:
+                for line in f:
+                    try:
+                        data_list.append(json.loads(line))
+                    except json.JSONDecodeError as e:
+                        print(f"Error decoding JSON on line in {file_path}: {line.strip()} - {e}")
+                        continue
+            print(f"Successfully loaded {len(data_list)} entries from JSONL file {file_path}")
+            return data_list
+        except FileNotFoundError:
+            print(f"Error: JSONL file not found at {file_path}")
+            return []
+        except Exception as e:
+            print(f"An unexpected error occurred while loading JSONL data: {e}")
+            return []
+
+    def combine_conversational_data(self, file_paths):
+        """Loads and combines conversational data from a list of JSONL files."""
+        all_conversations = []
+        for file_path in file_paths:
+            if file_path.lower().endswith('.jsonl'):
+                print(f"Attempting to load conversations from {file_path}...")
+                data = self.load_jsonl_data(file_path)
+                # Assuming JSONL files contain lists of entries, or each line is an entry
+                # Need to inspect the actual file contents to confirm format
+                # For now, assume each line is an entry dictionary
+                if isinstance(data, list):
+                     # Assuming data is a list of entry dictionaries
+                     all_conversations.extend(data)
+                else:
+                     print(f"Warning: Data from {file_path} was not a list as expected for conversational data.")
+            elif file_path.lower().endswith('.json'):
+                 print(f"Attempting to load conversations from JSON file {file_path}...")
+                 data = self.load_json_data(file_path)
+                 # Assuming JSON file might contain a list of conversations under a key
+                 if isinstance(data, dict) and "conversations" in data and isinstance(data["conversations"], list):
+                      all_conversations.extend(data["conversations"])
+                 elif isinstance(data, list):
+                      all_conversations.extend(data)
+                 else:
+                       print(f"Warning: Data from {file_path} was not in expected JSON conversational format (list or dict with 'conversations' key).")
+            elif file_path.lower().endswith('.pkl'):
+                 print(f"Attempting to load conversations from Pickle file {file_path}...")
+                 data = self.load_pkl_data(file_path)
+                 if isinstance(data, dict) and "conversations" in data and isinstance(data["conversations"], list):
+                      all_conversations.extend(data["conversations"])
+                 elif isinstance(data, list):
+                      all_conversations.extend(data)
+                 else:
+                       print(f"Warning: Data from {file_path} was not in expected Pickle conversational format (list or dict with 'conversations' key).")
+
+            else:
+                print(f"Skipping unsupported file type for conversational data: {file_path}")
+
+        print(f"Combined total {len(all_conversations)} conversation entries from provided files.")
+        return all_conversations
+
+

core/SAVANT_CORE/requirements.txt

@@ -0,0 +1,9 @@
+
+# lightweight requirements for SAVANT_CORE
+sentence-transformers
+transformers
+gradio
+faiss-cpu
+scipy
+numpy
+gitpython

core/SAVANT_CORE/run_demo.py

@@ -0,0 +1,91 @@
+
+"""
+run_demo.py
+Demo para probar el core SAVANT en consola.
+"""
+import os
+import json
+import time
+import pickle # Import pickle
+from transformers import pipeline
+from core.engine import SavantEngine
+from core.trainer import SimpleTrainer # Import SimpleTrainer
+
+
+def main():
+    engine = SavantEngine()
+    trainer = SimpleTrainer() # Instantiate SimpleTrainer
+    print("SAVANT Simbiótico RRF - demo")
+    print("Escribe 'exit' para salir. Commands: propose, apply, tail, train_memory") # Removed 'train' command from list
+    # small base model for text generation (light)
+    try:
+        gen = pipeline("text-generation", model="distilgpt2")
+    except Exception:
+        gen = None
+
+    # Define the list of data file paths to load from - MOVED INSIDE main()
+    data_files_str = r"""["/content/drive/MyDrive/ColabNotebooks/Takeout/AI/colab/Savantaut_dataset.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_backup/backup_20250823_220832/savant_full_history.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/new_nodes_data.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/lang_9.json", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/dialog_multinode_full.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_2025.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_multinode_final.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_prompts_master.json", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_dialogo_simbiotico.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_pipeline_advanced.jsonl"]""" # Store file paths as a string literal containing a JSON array
+
+    while True:
+        q = input("You> ").strip()
+        if not q:
+            continue
+        if q.lower() in ("exit","quit","salir"):
+            break
+        if q.lower().startswith("propose"):
+            print(engine.propose_improvement())
+            continue
+        if q.lower().startswith("apply"):
+            p = engine.propose_improvement()
+            print("Applying:", p)
+            print(engine.apply_improvement(p))
+            continue
+        if q.lower().startswith("tail"):
+            print(engine.memory.tail(10))
+            continue
+        # New command handler for training with memory data
+        if q.lower().startswith("train_memory"):
+            print(f"Loading and combining conversational data from specified files...")
+            conversations = [] # Initialize conversations
+            try:
+                print(f"Attempting to load file list from string: {data_files_str}") # Use double braces for f-string literal
+                file_list = json.loads(data_files_str)
+                print(f"Successfully loaded file list: {file_list}") # Use double braces for f-string literal
+                conversations = trainer.combine_conversational_data(file_list) # Load data
+            except Exception as e:
+                print(f"Error loading file list or combining data: {e}") # Use double braces for f-string literal
+                # conversations will remain an empty list
+                pass # Continue to the check if conversations is empty
+
+            if conversations: # Check if the loaded list is not empty
+                print(f"Loaded and combined {len(conversations)} conversation entries.") # Use double braces for f-string literal
+                print("Starting training using loaded memory data for 5 epochs...")
+                # Assuming run_epochs can handle the list of conversation dictionaries
+                training_results = trainer.run_epochs(conversations, epochs=5)
+                print("Training done using memory data.")
+                # Optional: Print a summary of results if run_epochs returns them
+                # print("Training results summary:", training_results)
+            else:
+                print("No conversational data loaded or combined. Training skipped.")
+            continue
+
+        # normal chat: produce base output from gen if available
+        if gen:
+            try:
+                base_out = gen(q, max_length=80, do_sample=True)[0].get("generated_text")
+            except Exception as ex: # Capture exception here with a local variable
+                # Correctly format the f-string inside the string literal
+                # Use repr(ex) to get a string representation of the exception
+                base_out = f"Error generating text: {repr(ex)}"
+        else:
+            base_out = "Echo: " + q
+
+        out = engine.handle_query(q, base_model_output=base_out)
+        if out["type"] == "chat":
+            print("SAVANT (base):", out["base"])
+            print("SAVANT (refined):", out["refined"])
+        else:
+            print("SAVANT:", out)
+
+if __name__ == "__main__":
+    main()

core/SAVANT_CORE/run_savant.py

@@ -0,0 +1,143 @@
+
+"""
+run_savant.py
+New entry point script for SAVANT-RRF, demonstrating data loading and interaction.
+"""
+import os
+import sys
+import json
+import time
+import pickle # Needed for loading some data types
+
+# Add the SAVANT_CORE directory to the Python path if not already there
+# This assumes SAVANT_CORE is in the parent directory or explicitly added
+SAVANT_CORE_PATH = os.path.dirname(os.path.abspath(__file__)) # Get path of the script
+if SAVANT_CORE_PATH not in sys.path:
+    sys.path.append(SAVANT_CORE_PATH)
+
+# Import classes from the core modules
+try:
+    from core.engine import SavantEngine
+    from core.trainer import SimpleTrainer
+except ImportError as e:
+    print(f"Error importing SAVANT_CORE modules: {e}")
+    print(f"Please ensure the directory '{SAVANT_CORE_PATH}' is correctly structured")
+    print("and that it contains the 'core' subfolder with the SAVANT_CORE modules.")
+    sys.exit(1) # Exit if imports fail
+
+# Variable to store loaded data - Declared at module level
+loaded_conversations = []
+
+def main():
+    engine = SavantEngine()
+    trainer = SimpleTrainer() # Instantiate SimpleTrainer
+
+    print("SAVANT-RRF Entry Point")
+    print("Available Commands: load_data, train_with_data, interact, propose, apply, tail, exit")
+
+    # Define the list of data file paths to load from - MOVED INSIDE main()
+    data_files_list = ["/content/drive/MyDrive/ColabNotebooks/Takeout/AI/colab/Savantaut_dataset.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_backup/backup_20250823_220832/savant_full_history.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/new_nodes_data.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/data/lang_9.json", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/dialog_multinode_full.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_2025.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_multinode_final.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_prompts_master.json", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_dialogo_simbiotico.jsonl", "/content/drive/MyDrive/ColabNotebooks/Takeout/AI/savant_datasets/datasets/savant_rrf_pipeline_advanced.jsonl", "/content/drive/MyDrive/Savant-RRF/savant_memory.pkl"] # Embed the list as a JSON string
+
+
+    while True:
+        q = input("Savant> ").strip()
+        if not q:
+            continue
+        if q.lower() in ("exit","quit","salir"):
+            break
+
+        elif q.lower() == "load_data":
+            # No need for global here as loaded_conversations is now module level
+            global loaded_conversations # Still need global to modify the module-level variable
+            print("Loading and combining conversational data from specified files...")
+            try:
+                # Use the combine_conversational_data function from the trainer
+                loaded_conversations = trainer.combine_conversational_data(data_files_list)
+                if loaded_conversations:
+                    print(f"Successfully loaded and combined {len(loaded_conversations)} conversation entries.")
+                    # Optional: Print a sample
+                    # print("Sample entry:", loaded_conversations[0])
+                else:
+                    print("No conversational data loaded or combined.")
+            except Exception as e:
+                print(f"Error loading data: {e}")
+
+        elif q.lower() == "train_with_data":
+            if not loaded_conversations:
+                print("No data loaded. Please run 'load_data' first.")
+            else:
+                print(f"Starting training using loaded data ({len(loaded_conversations)} entries) for 5 epochs...")
+                try:
+                    # Use the run_epochs function from the trainer with loaded data
+                    training_results = trainer.run_epochs(loaded_conversations, epochs=5)
+                    print("Training process initiated.")
+                    # Note: The trainer's run_epochs simulates training and saves checkpoints.
+                    # It doesn't return a traditional 'trained model' object in this simplified core.
+                    # The self_improver params are updated internally by run_epochs->run_cycle->evaluate_and_apply.
+                except Exception as e:
+                    print(f"Error during training: {e}")
+
+        elif q.lower() == "interact":
+             # Get user input for the interaction here
+             user_input = input("User Input for SAVANT> ").strip()
+             if user_input:
+                 print("Processing interaction...")
+                 # Simulate getting a base model output (if a language model were integrated)
+                 # For this demo, we'll just pass the user input to the engine
+                 # In a real scenario, you'd get base_model_output from a transformer pipeline etc.
+                 # Example: base_output = gen(user_input, ...)[0]["generated_text"] if gen else "Simulated base output"
+
+                 # Assign the simulated base output here within the interact block
+                 # Correctly format the f-string inside the string literal
+                 simulated_base_output = f"Simulated base output for: {user_input}"
+
+                 out = engine.handle_query(user_input, base_model_output=simulated_base_output)
+                 if out["type"] == "chat":
+                     print("SAVANT (base):", out.get("base"))
+                     print("SAVANT (refined):", out.get("refined"))
+                 else:
+                     print("SAVANT:", out)
+             else:
+                 print("No input provided for interaction.")
+
+
+        elif q.lower() == "propose":
+            print("Proposing self-improvement...")
+            try:
+                proposal = engine.propose_improvement() # Use engine's method
+                print(proposal)
+            except Exception as e:
+                 print(f"Error proposing improvement: {e}")
+
+
+        elif q.lower() == "apply":
+            print("Applying proposed improvement...")
+            try:
+                # In a real scenario, you'd get a specific proposal to apply
+                # For this demo, we propose and apply immediately
+                proposal_to_apply = engine.propose_improvement()
+                if proposal_to_apply:
+                    accepted, metric = engine.apply_improvement(proposal_to_apply) # Use engine's method
+                    print(f"Proposal applied. Accepted: {accepted}, New Metric: {metric}")
+                else:
+                     print("No proposal generated.")
+            except Exception as e:
+                 print(f"Error applying improvement: {e}")
+
+
+        elif q.lower() == "tail":
+            print("Showing last 10 memory entries:")
+            try:
+                memory_tail = engine.memory.tail(10) # Use engine's memory
+                for entry in memory_tail:
+                    print(entry)
+            except Exception as e:
+                 print(f"Error retrieving memory tail: {e}")
+
+        else:
+            print(f"Unknown command: '{q}'")
+            print("Available Commands: load_data, train_with_data, interact, propose, apply, tail, exit")
+
+
+if __name__ == "__main__":
+    main()

core/SAVANT_CORE/savant_finetuned_model/checkpoint-3/config.json

@@ -0,0 +1,45 @@
+{
+  "_num_labels": 1,
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "dtype": "float32",
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 6,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.56.1",
+  "use_cache": true,
+  "vocab_size": 50257
+}

core/SAVANT_CORE/savant_finetuned_model/checkpoint-3/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.56.1"
+}

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