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4_lab4.ipynb

@@ -438,22 +438,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'me/linkedin.pdf'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 163\u001b[39m\n\u001b[32m    159\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m response.choices[\u001b[32m0\u001b[39m].message.content\n\u001b[32m    162\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[34m__name__\u001b[39m == \u001b[33m\"\u001b[39m\u001b[33m__main__\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m     me = \u001b[43mMe\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    164\u001b[39m     gr.ChatInterface(me.chat, \u001b[38;5;28mtype\u001b[39m=\u001b[33m\"\u001b[39m\u001b[33mmessages\u001b[39m\u001b[33m\"\u001b[39m).launch()\n",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 93\u001b[39m, in \u001b[36mMe.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     90\u001b[39m \u001b[38;5;28mself\u001b[39m.openai = gemini     \u001b[38;5;66;03m# REPLACED OpenAI WITH GEMINI\u001b[39;00m\n\u001b[32m     91\u001b[39m \u001b[38;5;28mself\u001b[39m.name = \u001b[33m\"\u001b[39m\u001b[33mEd Donner\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m93\u001b[39m reader = \u001b[43mPdfReader\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mme/linkedin.pdf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     94\u001b[39m \u001b[38;5;28mself\u001b[39m.linkedin = \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     95\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m page \u001b[38;5;129;01min\u001b[39;00m reader.pages:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32md:\\projectsUdemy\\agents\\.venv\\Lib\\site-packages\\pypdf\\_reader.py:131\u001b[39m, in \u001b[36mPdfReader.__init__\u001b[39m\u001b[34m(self, stream, strict, password)\u001b[39m\n\u001b[32m    127\u001b[39m \u001b[38;5;28mself\u001b[39m._page_id2num: Optional[\u001b[38;5;28mdict\u001b[39m[Any, Any]] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    129\u001b[39m \u001b[38;5;28mself\u001b[39m._validated_root: Optional[DictionaryObject] = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_initialize_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    132\u001b[39m \u001b[38;5;28mself\u001b[39m._known_objects: \u001b[38;5;28mset\u001b[39m[\u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mint\u001b[39m]] = \u001b[38;5;28mset\u001b[39m()\n\u001b[32m    134\u001b[39m \u001b[38;5;28mself\u001b[39m._override_encryption = \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32md:\\projectsUdemy\\agents\\.venv\\Lib\\site-packages\\pypdf\\_reader.py:150\u001b[39m, in \u001b[36mPdfReader._initialize_stream\u001b[39m\u001b[34m(self, stream)\u001b[39m\n\u001b[32m    148\u001b[39m \u001b[38;5;28mself\u001b[39m._stream_opened = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m    149\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(stream, (\u001b[38;5;28mstr\u001b[39m, Path)):\n\u001b[32m--> \u001b[39m\u001b[32m150\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrb\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m fh:\n\u001b[32m    151\u001b[39m         stream = BytesIO(fh.read())\n\u001b[32m    152\u001b[39m     \u001b[38;5;28mself\u001b[39m._stream_opened = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'me/linkedin.pdf'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from dotenv import load_dotenv\n",
     "from openai import OpenAI\n",
@@ -618,7 +603,9 @@
     "\n",
     "if __name__ == \"__main__\":\n",
     "    me = Me()\n",
-    "    gr.ChatInterface(me.chat, type=\"messages\").launch()\n"
+    "    gr.ChatInterface(me.chat, type=\"messages\").launch()\n",
+    "    # gr.ChatInterface(me.chat).launch()\n",
+    "\n"
    ]
   },
   {

app.py

@@ -133,6 +133,176 @@
 #     gr.ChatInterface(me.chat, type="messages").launch()
 
 
+
+
+# from dotenv import load_dotenv
+# from openai import OpenAI
+# import json
+# import os
+# import requests
+# from pypdf import PdfReader
+# import gradio as gr
+
+# load_dotenv(override=True)
+
+# GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
+# google_api_key = os.getenv("GOOGLE_API_KEY")
+
+# # Initialize Gemini client
+# gemini = OpenAI(
+#     base_url=GEMINI_BASE_URL,
+#     api_key=google_api_key
+# )
+
+# def push(text):
+#     requests.post(
+#         "https://api.pushover.net/1/messages.json",
+#         data={
+#             "token": os.getenv("PUSHOVER_TOKEN"),
+#             "user": os.getenv("PUSHOVER_USER"),
+#             "message": text,
+#         }
+#     )
+
+
+# def record_user_details(email, name="Name not provided", notes="not provided"):
+#     push(f"Recording {name} with email {email} and notes {notes}")
+#     return {"recorded": "ok"}
+
+
+# def record_unknown_question(question):
+#     push(f"Recording {question}")
+#     return {"recorded": "ok"}
+
+
+# record_user_details_json = {
+#     "name": "record_user_details",
+#     "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
+#     "parameters": {
+#         "type": "object",
+#         "properties": {
+#             "email": {
+#                 "type": "string",
+#                 "description": "The email address of this user"
+#             },
+#             "name": {
+#                 "type": "string",
+#                 "description": "The user's name, if they provided it"
+#             },
+#             "notes": {
+#                 "type": "string",
+#                 "description": "Any additional information about the conversation that's worth recording to give context"
+#             }
+#         },
+#         "required": ["email"],
+#         "additionalProperties": False
+#     }
+# }
+
+# record_unknown_question_json = {
+#     "name": "record_unknown_question",
+#     "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
+#     "parameters": {
+#         "type": "object",
+#         "properties": {
+#             "question": {
+#                 "type": "string",
+#                 "description": "The question that couldn't be answered"
+#             },
+#         },
+#         "required": ["question"],
+#         "additionalProperties": False
+#     }
+# }
+
+# tools = [
+#     {"type": "function", "function": record_user_details_json},
+#     {"type": "function", "function": record_unknown_question_json}
+# ]
+
+
+# class Me:
+
+#     def __init__(self):
+#         self.openai = gemini     # REPLACED OpenAI WITH GEMINI
+#         self.name = "AKASH M J"
+
+#         reader = PdfReader("me/Profile.pdf")
+#         self.linkedin = ""
+#         for page in reader.pages:
+#             text = page.extract_text()
+#             if text:
+#                 self.linkedin += text
+
+#         with open("me/summary.txt", "r", encoding="utf-8") as f:
+#             self.summary = f.read()
+
+#     def handle_tool_call(self, tool_calls):
+#         results = []
+#         for tool_call in tool_calls:
+#             tool_name = tool_call.function.name
+#             arguments = json.loads(tool_call.function.arguments)
+#             print(f"Tool called: {tool_name}", flush=True)
+#             tool = globals().get(tool_name)
+#             result = tool(**arguments) if tool else {}
+#             results.append({
+#                 "role": "tool",
+#                 "content": json.dumps(result),
+#                 "tool_call_id": tool_call.id
+#             })
+#         return results
+
+#     def system_prompt(self):
+#         system_prompt = (
+#             f"You are acting as {self.name}. You are answering questions on {self.name}'s website, "
+#             f"particularly questions related to {self.name}'s career, background, skills and experience. "
+#             f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. "
+#             f"You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. "
+#             f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. "
+#             f"If you don't know the answer to any question, use your record_unknown_question tool to record the question. "
+#             f"If the user is engaging in discussion, try to steer them towards getting in touch via email."
+#         )
+
+#         system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
+#         system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
+#         return system_prompt
+
+#     def chat(self, message, history):
+#         messages = [
+#             {"role": "system", "content": self.system_prompt()}
+#         ] + history + [
+#             {"role": "user", "content": message}
+#         ]
+
+#         done = False
+#         while not done:
+#             # ---- CHANGED TO USE GEMINI ----
+#             response = self.openai.chat.completions.create(
+#                 model="gemini-2.0-flash",
+#                 messages=messages,
+#                 tools=tools
+#             )
+#             # --------------------------------
+
+#             if response.choices[0].finish_reason == "tool_calls":
+#                 message = response.choices[0].message
+#                 tool_calls = message.tool_calls
+#                 results = self.handle_tool_call(tool_calls)
+#                 messages.append(message)
+#                 messages.extend(results)
+#             else:
+#                 done = True
+
+#         return response.choices[0].message.content
+
+
+# if __name__ == "__main__":
+#     me = Me()
+#     gr.ChatInterface(me.chat, type="messages").launch()
+#     # gr.ChatInterface(me.chat).launch()
+
+
+# app.py
 from dotenv import load_dotenv
 from openai import OpenAI
 import json
@@ -140,57 +310,68 @@ import os
 import requests
 from pypdf import PdfReader
 import gradio as gr
+import sqlite3
+import time
 
 load_dotenv(override=True)
 
+# --- CONFIG ---
 GEMINI_BASE_URL = "https://generativelanguage.googleapis.com/v1beta/openai/"
 google_api_key = os.getenv("GOOGLE_API_KEY")
 
-# Initialize Gemini client
-gemini = OpenAI(
-    base_url=GEMINI_BASE_URL,
-    api_key=google_api_key
-)
+# Initialize Gemini client (using OpenAI wrapper you used earlier)
+gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)
 
+# --- Pushover helper ---
 def push(text):
-    requests.post(
-        "https://api.pushover.net/1/messages.json",
-        data={
-            "token": os.getenv("PUSHOVER_TOKEN"),
-            "user": os.getenv("PUSHOVER_USER"),
-            "message": text,
-        }
-    )
-
+    token = os.getenv("PUSHOVER_TOKEN")
+    user = os.getenv("PUSHOVER_USER")
+    if not token or not user:
+        print("Pushover credentials not set. Skipping push.")
+        return
+    try:
+        requests.post(
+            "https://api.pushover.net/1/messages.json",
+            data={"token": token, "user": user, "message": text},
+            timeout=5
+        )
+    except Exception as e:
+        print("Pushover error:", e)
 
+# --- Tools (actual implementations) ---
 def record_user_details(email, name="Name not provided", notes="not provided"):
-    push(f"Recording {name} with email {email} and notes {notes}")
-    return {"recorded": "ok"}
-
+    push(f"Recording contact: {name} <{email}> notes: {notes}")
+    return {"recorded": "ok", "email": email, "name": name}
 
 def record_unknown_question(question):
-    push(f"Recording {question}")
-    return {"recorded": "ok"}
-
-
+    push(f"Unknown question recorded: {question}")
+    # Optionally write to a local file for audits
+    os.makedirs("me/logs", exist_ok=True)
+    with open("me/logs/unknown_questions.txt", "a", encoding="utf-8") as f:
+        f.write(question.strip() + "\n")
+    return {"recorded": "ok", "question": question}
+
+def search_faq(query):
+    db_path = os.path.join("me", "qa.db")
+    if not os.path.exists(db_path):
+        return {"answer": "FAQ database not found."}
+    conn = sqlite3.connect(db_path)
+    cur = conn.cursor()
+    cur.execute("SELECT answer FROM faq WHERE question LIKE ? LIMIT 1", (f"%{query}%",))
+    row = cur.fetchone()
+    conn.close()
+    return {"answer": row[0]} if row else {"answer": "not found"}
+
+# --- Tool JSON metadata (for function-calling style) ---
 record_user_details_json = {
     "name": "record_user_details",
-    "description": "Use this tool to record that a user is interested in being in touch and provided an email address",
+    "description": "Record an interested user's email and optional name/notes.",
     "parameters": {
         "type": "object",
         "properties": {
-            "email": {
-                "type": "string",
-                "description": "The email address of this user"
-            },
-            "name": {
-                "type": "string",
-                "description": "The user's name, if they provided it"
-            },
-            "notes": {
-                "type": "string",
-                "description": "Any additional information about the conversation that's worth recording to give context"
-            }
+            "email": {"type": "string"},
+            "name": {"type": "string"},
+            "notes": {"type": "string"}
         },
         "required": ["email"],
         "additionalProperties": False
@@ -199,48 +380,93 @@ record_user_details_json = {
 
 record_unknown_question_json = {
     "name": "record_unknown_question",
-    "description": "Always use this tool to record any question that couldn't be answered as you didn't know the answer",
+    "description": "Record any question the assistant could not answer.",
     "parameters": {
         "type": "object",
         "properties": {
-            "question": {
-                "type": "string",
-                "description": "The question that couldn't be answered"
-            },
+            "question": {"type": "string"}
         },
         "required": ["question"],
         "additionalProperties": False
     }
 }
 
+search_faq_json = {
+    "name": "search_faq",
+    "description": "Search the FAQ database for a question.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string"}
+        },
+        "required": ["query"],
+        "additionalProperties": False
+    }
+}
+
 tools = [
     {"type": "function", "function": record_user_details_json},
-    {"type": "function", "function": record_unknown_question_json}
+    {"type": "function", "function": record_unknown_question_json},
+    {"type": "function", "function": search_faq_json}
 ]
 
-
+# --- The assistant class ---
 class Me:
-
     def __init__(self):
-        self.openai = gemini     # REPLACED OpenAI WITH GEMINI
+        self.openai = gemini
         self.name = "AKASH M J"
 
-        reader = PdfReader("me/Profile.pdf")
+        # Load profile PDF into self.linkedin
         self.linkedin = ""
-        for page in reader.pages:
-            text = page.extract_text()
-            if text:
-                self.linkedin += text
+        try:
+            reader = PdfReader(os.path.join("me", "Profile.pdf"))
+            for page in reader.pages:
+                text = page.extract_text()
+                if text:
+                    self.linkedin += text + "\n"
+        except Exception as e:
+            print("Could not read Profile.pdf:", e)
+
+        # Load summary
+        try:
+            with open(os.path.join("me", "summary.txt"), "r", encoding="utf-8") as f:
+                self.summary = f.read()
+        except Exception as e:
+            print("Could not read summary.txt:", e)
+            self.summary = ""
+
+        # Load knowledge files (RAG-style simple concatenation)
+        self.knowledge = ""
+        kb_dir = os.path.join("me", "knowledge")
+        if os.path.exists(kb_dir):
+            for fn in sorted(os.listdir(kb_dir)):
+                path = os.path.join(kb_dir, fn)
+                try:
+                    with open(path, "r", encoding="utf-8") as f:
+                        self.knowledge += f"# {fn}\n" + f.read() + "\n\n"
+                except Exception as e:
+                    print("Error reading", path, e)
 
-        with open("me/summary.txt", "r", encoding="utf-8") as f:
-            self.summary = f.read()
+    def system_prompt(self):
+        system_prompt = (
+            f"You are acting as {self.name}. Answer questions about {self.name}'s background "
+            "and experience using the context provided. Be professional and concise. "
+            "If you don't know an answer, use the record_unknown_question tool."
+        )
+        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n"
+        system_prompt += f"## LinkedIn profile (extracted):\n{self.linkedin}\n\n"
+        system_prompt += f"## Knowledge base:\n{self.knowledge}\n\n"
+        return system_prompt
 
     def handle_tool_call(self, tool_calls):
         results = []
         for tool_call in tool_calls:
             tool_name = tool_call.function.name
-            arguments = json.loads(tool_call.function.arguments)
-            print(f"Tool called: {tool_name}", flush=True)
+            try:
+                arguments = json.loads(tool_call.function.arguments)
+            except Exception:
+                arguments = {}
+            print("Tool called:", tool_name, arguments, flush=True)
             tool = globals().get(tool_name)
             result = tool(**arguments) if tool else {}
             results.append({
@@ -250,52 +476,94 @@ class Me:
             })
         return results
 
-    def system_prompt(self):
-        system_prompt = (
-            f"You are acting as {self.name}. You are answering questions on {self.name}'s website, "
-            f"particularly questions related to {self.name}'s career, background, skills and experience. "
-            f"Your responsibility is to represent {self.name} for interactions on the website as faithfully as possible. "
-            f"You are given a summary of {self.name}'s background and LinkedIn profile which you can use to answer questions. "
-            f"Be professional and engaging, as if talking to a potential client or future employer who came across the website. "
-            f"If you don't know the answer to any question, use your record_unknown_question tool to record the question. "
-            f"If the user is engaging in discussion, try to steer them towards getting in touch via email."
-        )
-
-        system_prompt += f"\n\n## Summary:\n{self.summary}\n\n## LinkedIn Profile:\n{self.linkedin}\n\n"
-        system_prompt += f"With this context, please chat with the user, always staying in character as {self.name}."
-        return system_prompt
+    # Simple router/orchestrator: route common queries to the FAQ or to the LLM
+    def route_question(self, question):
+        q = question.lower()
+        # keywords that map to FAQ
+        faq_keywords = ["project", "tech stack", "stack", "skill", "skills", "study", "education", "experience"]
+        if any(k in q for k in faq_keywords):
+            return "search_faq"
+        return None
+
+    def evaluate_answer(self, user_question, ai_answer):
+        # Simple evaluator: ask the LLM to judge the quality
+        eval_prompt = f"""
+You are an evaluator. Judge whether the assistant reply is clear, correct, and complete for the user question.
+Return exactly PASS or FAIL and a one-line reason.
+
+User question:
+{user_question}
+
+Assistant reply:
+{ai_answer}
+"""
+        try:
+            ev = self.openai.chat.completions.create(
+                model="gemini-2.0-flash",
+                messages=[{"role":"system","content":"You are an evaluator."},
+                          {"role":"user","content":eval_prompt}]
+            )
+            text = ev.choices[0].message.content.strip()
+            # very simple parse
+            if text.upper().startswith("PASS"):
+                return {"result":"PASS", "note": text}
+            else:
+                return {"result":"FAIL", "note": text}
+        except Exception as e:
+            print("Evaluator failed:", e)
+            return {"result":"UNKNOWN", "note": str(e)}
 
     def chat(self, message, history):
-        messages = [
-            {"role": "system", "content": self.system_prompt()}
-        ] + history + [
-            {"role": "user", "content": message}
-        ]
+        # build messages with system prompt + history + user
+        messages = [{"role":"system","content":self.system_prompt()}] + history + [{"role":"user","content":message}]
+
+        # 1) Router: check if the question should use the FAQ tool
+        tool_to_use = self.route_question(message)
+        if tool_to_use == "search_faq":
+            # call tool directly and return evaluated answer
+            tool_result = search_faq(message)
+            raw_answer = tool_result.get("answer", "I don't have that in my FAQ.")
+            eval_res = self.evaluate_answer(message, raw_answer)
+            if eval_res["result"] == "PASS":
+                return raw_answer
+            else:
+                # fall back to LLM if FAIL
+                pass
 
+        # 2) Normal LLM flow with tools support (function-calling style)
         done = False
         while not done:
-            # ---- CHANGED TO USE GEMINI ----
             response = self.openai.chat.completions.create(
-                model="gemini-2.5-flash",
+                model="gemini-2.0-flash",
                 messages=messages,
                 tools=tools
             )
-            # --------------------------------
 
-            if response.choices[0].finish_reason == "tool_calls":
-                message = response.choices[0].message
-                tool_calls = message.tool_calls
+            finish = response.choices[0].finish_reason
+            if finish == "tool_calls":
+                # the LLM asked to call a tool
+                message_obj = response.choices[0].message
+                tool_calls = getattr(message_obj, "tool_calls", [])
                 results = self.handle_tool_call(tool_calls)
-                messages.append(message)
+                messages.append(message_obj)
                 messages.extend(results)
+                # loop again so the LLM can consume tool outputs
             else:
                 done = True
 
-        return response.choices[0].message.content
+        ai_answer = response.choices[0].message.content
+        # 3) Evaluate the answer; if FAIL, ask LLM to improve
+        eval_res = self.evaluate_answer(message, ai_answer)
+        if eval_res["result"] == "FAIL":
+            # ask the model to improve using the critique
+            improve_prompt = f"User question:\n{message}\n\nAssistant previous reply:\n{ai_answer}\n\nEvaluator note:\n{eval_res['note']}\n\nPlease produce an improved concise answer."
+            messages.append({"role":"user","content":improve_prompt})
+            improved_resp = self.openai.chat.completions.create(model="gemini-2.0-flash", messages=messages)
+            ai_answer = improved_resp.choices[0].message.content
 
+        return ai_answer
 
+# --- Launch ---
 if __name__ == "__main__":
     me = Me()
-    # gr.ChatInterface(me.chat, type="messages").launch()
-    gr.ChatInterface(me.chat).launch()
-
+    gr.ChatInterface(me.chat, type="messages").launch()

init_db.py

@@ -0,0 +1,32 @@
+# init_db.py
+import sqlite3
+import os
+
+os.makedirs("me", exist_ok=True)
+db_path = os.path.join("me", "qa.db")
+
+conn = sqlite3.connect(db_path)
+cur = conn.cursor()
+
+cur.execute("""
+CREATE TABLE IF NOT EXISTS faq (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    question TEXT,
+    answer TEXT
+)
+""")
+
+# sample seed data (you can edit)
+faq_data = [
+    ("What is Akash's tech stack?", "Akash works with the MERN stack: MongoDB, Express, React, Node.js."),
+    ("What projects has Akash completed?", "Online Marketplace for Local Artisans, Freelance Collaboration Platform, Hotel Reservation System."),
+    ("What is Akash studying?", "MCA at KLN College."),
+]
+
+# clear existing sample rows (optional)
+cur.execute("DELETE FROM faq")
+cur.executemany("INSERT INTO faq (question, answer) VALUES (?, ?)", faq_data)
+
+conn.commit()
+conn.close()
+print("me/qa.db created and seeded.")

me/knowledge/certifications.txt

@@ -0,0 +1,4 @@
+The Complete 2023 Web Development Bootcamp.
+Java Certification: Covered core concepts, OOPS principles, and practical applications.
+Full Stack Web Development: focused on front-end and back-end technologies including HTML, CSS, JavaScript, Node.js, and Express.js.
+Software Testing (Basic): Completed at Besant Technologies, covering Manual testing, Automation testing, SDLC, and STLC.

me/knowledge/education.txt

@@ -0,0 +1,22 @@
+Master of Computer Applications (MCA)
+Institute: K.L.N. College of Engineering, Madurai.
+Duration: 2023 – 2025.
+Performance: 8.6 CGPA.
+
+B.Sc. Computer Science
+Institute: Sourashtra College, Madurai.
+Duration: 2020 – 2023.
+Performance: 8.1 CGPA.
+
+
+Computer Science - HSC
+Institute: The TVS School
+Duration: 2019-2020
+Performance: 67.66%
+
+
+SSLC 
+Institute : KNG Matriculation School
+Duration : Till 2018
+Performance : 91 %
+

me/knowledge/experience.txt

@@ -0,0 +1,15 @@
+Analyst (DevOps Analyst) | KPMG India, Bengaluru
+Duration: July 2025 – Present.
+Department: Advisory Consulting - Technology Transformation - Digital Engineering & Quality Team.
+Focus: Currently as a Trainer
+
+
+
+Web Development Intern | Digizura Technologies Private Limited, Bengaluru
+Duration: January 2025 – June 2025.
+
+Key Contributions:
+Developed core modules (RFQ, Business Development) using Angular and LoopBack for lifecycle automation.
+Implemented Excel parsing logic to auto-generate BOM tables and margin calculations.
+Developed the frontend for the IT Audit module to streamline workflow reviews.
+Collaborated with backend teams for API integration and data flow.

me/knowledge/projects.txt

@@ -0,0 +1,9 @@
+Online Marketplace for Local Artisans
+Description: A full-stack web application designed for local artisans to display, personalize, and sell handmade products.
+Key Features: Product listing, customization, secure Stripe payment integration, live chat between buyer and seller, and role-based access control (artisan, customer, admin).
+Tech Stack: React.js, Node.js, Express.js, MongoDB, Stripe, Tailwind CSS, Socket.io.
+
+Hotel Management System
+Description: A system allowing staff to manage room bookings, customer details, and services.
+Key Features: Integrated MySQL database for efficient data storage and retrieval of booking information.
+Tech Stack: HTML, Tailwind CSS, JavaScript, PHP, MySQL, XAMPP Server.

me/knowledge/skills.txt

@@ -0,0 +1,5 @@
+Programming Languages: Java (Core Java), JavaScript.
+Web Development: HTML, CSS, Bootstrap, Node.js, Express.js.
+Databases: MySQL.
+Testing: Automation Testing, Selenium WebDriver (Basic).
+Languages: English, Tamil, Hindi, Sourashtra

me/logs/unknown_questions.txt

@@ -0,0 +1,2 @@
+Does Akash M J hold any patents?
+Do you have any knowledge on cricket?