Implement Deterministic Analysis Pipelines with Offline Support

.gitignore

@@ -25,13 +25,27 @@ scripts/deploy_to_vm.ps1
 venv/
 
 # User-specific local workspace configuration
-library/config/.esim/workspace.txt
+**/library/config/.esim/workspace.txt
 
 # Local configuration files generated at runtime
-library/config/.esim/config.ini
-library/config/.nghdl/
+**/library/config/.esim/config.ini
+**/library/config/.nghdl/
 
 # Ensure compiled files and caches under nghdl/ are also ignored
 **/__pycache__/
 **/*.pyc
 
+# Local AI test scripts
+test_ai_features.py
+test_exact_log.py
+test_regex2.py
+test_error_pipeline_regression.py
+test_netlist_pipeline_regression.py
+test_ollama_error.py
+test_ollama_netlist.py
+
+
+
+# Local test projects
+Examples/ErrorTests/
+

README_CHATBOT.md

@@ -1,4 +1,4 @@
-# eSim Copilot – AI-Assisted Electronics Simulation Tool
+# eSim AI Assistant – AI-Assisted Electronics Simulation Tool
 
 eSim Copilot is an AI-powered assistant integrated into **eSim**, designed to help users analyze electronic circuits, debug SPICE netlists, understand simulation errors, and interact using text, voice, and images.
 

src/chatbot/chatbot_core.py

@@ -4,7 +4,8 @@
 import re
 import json
 from typing import Dict, Any, Tuple, List
-from .error_solutions import get_error_solution
+from .error_patterns import match_error_patterns
+from .error_solutions import get_solution_for_category
 from .image_handler import analyze_and_extract
 from .ollama_runner import run_ollama
 from .knowledge_base import search_knowledge
@@ -125,7 +126,7 @@ def answer_with_rag_fallback(user_input: str) -> str:
 
     if rag_context.strip():
         prompt = f"""
-You are eSim Copilot.
+You are eSim AI Assistant.
 
 Use ONLY the following official eSim documentation
 to answer the question. Do NOT invent information.
@@ -344,12 +345,12 @@ def is_semantic_topic_switch(
             np.linalg.norm(emb_new) * np.linalg.norm(emb_prev)
         )
 
-        print(f"[COPILOT] Semantic similarity = {similarity:.3f}")
+        print(f"[AI ASSISTANT] Semantic similarity = {similarity:.3f}")
 
         return similarity < threshold
 
     except Exception as e:
-        print(f"[COPILOT] Topic switch check failed: {e}")
+        print(f"[AI ASSISTANT] Topic switch check failed: {e}")
         return False
 
 # ==================== QUESTION CLASSIFICATION ====================
@@ -385,7 +386,7 @@ def classify_question_type(user_input: str, has_image_context: bool,
 
     is_followup = _is_follow_up_question(user_input, history)
     if is_semantic_topic_switch(user_input, history):
-        print("[COPILOT] Topic switch detected (semantic)")
+        print("[AI ASSISTANT] Topic switch detected (semantic)")
         is_followup = False
 
     if not is_followup:
@@ -414,7 +415,7 @@ def classify_question_type(user_input: str, has_image_context: bool,
 
 def handle_greeting() -> str:
     return (
-        "Hello! I'm eSim Copilot. I can help you with:\n"
+        "Hello! I'm eSim AI Assistant. I can help you with:\n"
         "• Circuit analysis and netlist debugging\n"
         "• Electronics concepts and SPICE simulation\n"
         "• Component selection and circuit design\n\n"
@@ -426,7 +427,7 @@ def handle_simple_question(user_input: str) -> str:
     """
     Handles standalone questions.
     Uses RAG first, then falls back to Ollama.
-    keep in mind that your a copilot of eSim an EDA tool
+    keep in mind that you are an AI assistant of eSim an EDA tool
     """
     return answer_with_rag_fallback(user_input)
 
@@ -489,18 +490,23 @@ def handle_esim_question(user_input: str,
     """
     user_lower = user_input.lower()
 
-    sol = get_error_solution(user_input)
-    if sol and sol.get("description") != "General schematic error":
+    matches = match_error_patterns(user_input)
+    if matches:
+        root_match = min(matches, key=lambda m: m.causal_priority)
+        sol = get_solution_for_category(root_match.category)
+
         fixes = "\n".join(f"- {f}" for f in sol.get("fixes", []))
-        cmd = sol.get("eSim_command", "")
+        steps = "\n".join(f"- {s}" for s in sol.get("esim_steps", []))
+
         answer = (
-            f"**Detected issue:** {sol['description']}\n"
+            f"**Detected issue:** {sol['category']}\n"
             f"**Severity:** {sol.get('severity', 'unknown')}\n\n"
             f"**Recommended fixes:**\n{fixes}\n\n"
         )
-        if cmd:
-            answer += f"**eSim action:** {cmd}\n"
-        return answer_with_rag_fallback(user_input)
+        if steps:
+            answer += f"**eSim Steps:**\n{steps}\n"
+
+        return answer
 
     history_text = _history_to_text(history, max_turns=6)
 
@@ -646,7 +652,7 @@ def handle_input(user_input: str,
     question_type = classify_question_type(
         user_input, bool(LAST_IMAGE_CONTEXT), history
     )
-    print(f"[COPILOT] Question type: {question_type}")
+    print(f"[AI ASSISTANT] Question type: {question_type}")
 
     try:
         if question_type == "netlist":
@@ -674,13 +680,13 @@ def handle_input(user_input: str,
 
     except Exception as e:
         error_msg = f"Error processing question: {str(e)}"
-        print(f"[COPILOT ERROR] {error_msg}")
+        print(f"[AI ASSISTANT ERROR] {error_msg}")
         return error_msg
 
 
 # ==================== WRAPPER ====================
 
-class ESIMCopilotWrapper:
+class ESIMAssistantWrapper:
     def __init__(self) -> None:
         self.history: List[Dict[str, str]] = []
 
@@ -694,7 +700,7 @@ def handle_input(self, user_input: str) -> str:
     def analyze_schematic(self, query: str) -> str:
         return self.handle_input(query)
 
-_GLOBAL_WRAPPER = ESIMCopilotWrapper()
+_GLOBAL_WRAPPER = ESIMAssistantWrapper()
 
 
 def analyze_schematic(query: str) -> str:

src/chatbot/chatbot_thread.py

@@ -6,7 +6,7 @@
 import time
 import threading
 import ollama
-from PyQt6.QtCore import QThread, pyqtSignal
+from PyQt5.QtCore import QThread, pyqtSignal
 from chatbot.knowledge_base import search_knowledge
 # ── Optional imports ──────────────────────────────────────────────────────────
 
@@ -293,7 +293,7 @@ def _smart_num_predict(user_messages: list, user_override: int = 1024) -> int:
     if is_simple and not is_long:
         budget = 128
     elif is_complex or is_long:
-        budget = 512
+        budget = 1024
     else:
         budget = 256
 
@@ -308,12 +308,14 @@ class OllamaWorker(QThread):
     chunk_signal = pyqtSignal(str)
 
     def __init__(self, chat_history, model="",
-                 temperature=0.25, num_predict=1024):
+                 temperature=0.25, num_predict=1024, system_prompt=None, netlist_formatter_context=None):
         super().__init__()
         self.chat_history = chat_history
         self.model = model
         self.temperature = temperature
         self.num_predict = num_predict
+        self.system_prompt = system_prompt
+        self.netlist_formatter_context = netlist_formatter_context
         self._stop_requested = False
 
     def stop(self):
@@ -327,7 +329,8 @@ def run(self):
 
             # config-driven history window + system prompt
             max_lines = int(CONFIG.get("history", {}).get("max_lines", 6))
-            messages = [{"role": "system", "content": _SYSTEM_PROMPT}]
+            active_system_prompt = self.system_prompt if self.system_prompt else _SYSTEM_PROMPT
+            messages = [{"role": "system", "content": active_system_prompt}]
             for line in self.chat_history[-max_lines:]:
                 if line.startswith("User:"):
                     messages.append({"role": "user", "content": line[5:].strip()})
@@ -341,6 +344,9 @@ def run(self):
             repeat_pen    = float(CONFIG.get("sampling", {}).get("repeat_penalty", 1.08))
             keep_alive    = CONFIG.get("runtime", {}).get("keep_alive", "-1m")
 
+            if self.netlist_formatter_context:
+                self.chunk_signal.emit("### Circuit Overview\n")
+
             stream = ollama.chat(
                 model=self.model,
                 messages=messages,
@@ -350,8 +356,8 @@ def run(self):
                     "num_predict": budget,
                     "num_ctx": num_ctx,
                     "repeat_penalty": repeat_pen,
-                    "keep_alive": keep_alive,
-                }
+                },
+                keep_alive=keep_alive
             )
 
             bot_response = ""
@@ -365,18 +371,24 @@ def run(self):
 
             bot_response = bot_response.strip()
             if not bot_response:
-                bot_response = (
-                    "⚠️ Received an empty response. "
-                    "The model may still be loading — please try again."
-                )
+                bot_response = "Circuit analysis unavailable. See components and simulation details below." if self.netlist_formatter_context else "⚠️ Received an empty response. Please verify the AI model is downloaded and try again."
+
+            if self.netlist_formatter_context and not self._stop_requested:
+                from src.chatbot.netlist_analysis import format_netlist_table
+                table_md = format_netlist_table(self.netlist_formatter_context)
+                bot_response += "\n\n" + table_md
+
+            if self.netlist_formatter_context:
+                bot_response = "### Circuit Overview\n" + bot_response
+
+            self.response_signal.emit(bot_response)
 
         except Exception as e:
             bot_response = (
                 f"❌ Error: {str(e)}\n"
                 "Make sure Ollama is installed and 'ollama serve' is running."
             )
-
-        self.response_signal.emit(bot_response)
+            self.response_signal.emit(bot_response)
 
 
 # ── Vision model helpers ──────────────────────────────────────────────────────

src/chatbot/config.json

@@ -1,6 +1,6 @@
 {
   "system_rules": {
-    "text_system_prompt": "You are the eSim AI assistant. Rules:\n- ALWAYS answer in exactly 3 bullet points.\n- ALWAYS end every reply with this exact line: powered by eSim Copilot",
+    "text_system_prompt": "You are the eSim AI assistant. Rules:\n- ALWAYS answer in exactly 3 bullet points.\n- ALWAYS end every reply with this exact line: powered by eSim AI Assistant",
     "vision_system_prompt": "You are an expert electronics engineer and the AI assistant inside eSim, an open-source EDA tool by FOSSEE at IIT Bombay. You are given schematic images from eSim or KiCad. Read every visible label, net name, component reference, value and pin number, and answer the user's question accurately. Never refuse to analyse. Be concise and use the visible reference designators (R1, C3, U2, etc.)."
   },
   "context_window": {