diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..0a75834aec5a88b03f92cd8dc5b316b36622a445
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diff --git a/.env b/.env
new file mode 100644
index 0000000000000000000000000000000000000000..306e9c20140831daaddd70083915ff7f0f4d4bdf
--- /dev/null
+++ b/.env
@@ -0,0 +1,18 @@
+# Groq API Key for LLM access
+GROQ_API_KEY="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"
+
+# Google Gemini API Key
+# GOOGLE_API_KEY="AIzaSyAtXTM4efMuuMmOwtx-BXJ9ENTw9-ptnZ8"
+GOOGLE_API_KEY="AIzaSyBff2coOs1FFBy6hXcs0BBvSd288e9qpWk"
+# GOOGLE_API_KEY="AIzaSyAvdaXA-4MlY-y_4mKU1_QMjQEqO0Z4fLE"
+
+# Tavily API Key for web search
+TAVILY_API_KEY="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"
+
+# Secret key for JWT token encoding
+JWT_SECRET_KEY="ghs@195L"
+JWT_ALGORITHM="HS256"
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# Logging Configuration
+LOG_LEVEL="INFO"
\ No newline at end of file
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..f4fbd856d1585bd15e104228742a8954e91f849a 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.db-wal filter=lfs diff=lfs merge=lfs -text
+*.db-shm filter=lfs diff=lfs merge=lfs -text
+*.sqlite3 filter=lfs diff=lfs merge=lfs -text
+*.db filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.html filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
index 88a3d42894d90412f1143120fbca9d7f419e92bf..eb42057b564576ced29634bc87e827fd8f7e61fd 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,21 +1,65 @@
-FROM python:3.9-slim
+###############################################################################
+# ---------------------------  Stage 1: base image  -------------------------- #
+###############################################################################
+# Pin the exact digest for reproducibility and give this stage the alias `base`
+FROM python:3.10-slim
 
+# --- Set Working Directory ---
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    software-properties-common \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+###############################################################################
+# ---------------------------  Stage 2: pipfreeze  --------------------------- #
+###############################################################################
+# Capture the *exact* package versions present in the base layer for auditing.
+FROM base AS pipfreeze
+RUN pip freeze > /tmp/freeze.txt
 
+###############################################################################
+# ---------------------------  Stage 3: final image -------------------------- #
+###############################################################################
+FROM base
+
+# Copy the entire filesystem from the `base` stage and fix ownership to our non-root user.
+COPY --chown=1000:1000 --from=base / /
+
+# --- Install Project-Specific Requirements ---
+# Use BuildKit’s inline `--mount` to copy requirements.txt.
+# This is highly efficient as it avoids busting the cache if only source code changes.
+RUN --mount=target=/app/requirements.txt,source=requirements.txt \
+    pip install --no-cache-dir -r /app/requirements.txt
+
+# --- Copy Application Code ---
+# Copy all our application code into the container.
+# Using --link can speed up builds on filesystems that support it.
+COPY --link --chown=1000:1000 ./insucompass ./insucompass
+COPY --link --chown=1000:1000 ./scripts ./scripts
+
+# --- Create Writable Data Directory ---
+# This is crucial for ChromaDB and the LangGraph checkpointer.
+# We create it and give ownership to our non-root user.
+COPY --link --chown=1000:1000 ./data ./data
+RUN mkdir -p /data && chown -R 1000:1000 /data
+
+# --- Copy the package lockfile for debugging/auditing ---
+COPY --from=pipfreeze --link --chown=1000:1000 /tmp/freeze.txt /tmp/freeze.txt
 COPY requirements.txt ./
-COPY src/ ./src/
 
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+# --- Switch to the non-root user for runtime ---
+# This is a security best practice.
+USER 1000
+
+# --- Expose Port ---
+# Tell Docker that the container will listen on port 8000 at runtime.
+EXPOSE 8000
 
+# --- Health Check ---
+# A simple check to ensure the FastAPI root endpoint is responsive.
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
\ No newline at end of file
+
+# --- Run Command ---
+# The command to execute when the container starts.
+# --host 0.0.0.0 is ESSENTIAL to make the server accessible from outside the container.
+CMD ["uvicorn", "insucompass.main:app", "--host", "0.0.0.0", "--port", "8000"]
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..5d53d723846e761db623c329981c29d50faf6c43
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Shaik Nagur Shareef
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46c8044f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import requests\n",
+    "from typing import Optional, Dict, Any, List\n",
+    "\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "class ZipCodeData(object):\n",
+    "    \"\"\"A simple data class to hold the results of our geolocation lookup.\"\"\"\n",
+    "    def __init__(self, state: str, state_abbr: str, city: str, county: str):\n",
+    "        self.state = state\n",
+    "        self.state_abbr = state_abbr\n",
+    "        self.city = city\n",
+    "        self.county = county\n",
+    "\n",
+    "    def to_dict(self) -> Dict[str, Any]:\n",
+    "        return {\n",
+    "            \"state\": self.state,\n",
+    "            \"state_abbreviation\": self.state_abbr,\n",
+    "            \"city\": self.city,\n",
+    "            \"county\": self.county\n",
+    "        }\n",
+    "\n",
+    "def get_lat_lon_from_zip(zip_code: str) -> Optional[Dict[str, float]]:\n",
+    "    \"\"\"\n",
+    "    Step 1: Get latitude and longitude from a ZIP code using a simple API.\n",
+    "    We'll use zippopotam.us for this first step.\n",
+    "    \"\"\"\n",
+    "    url = f\"https://api.zippopotam.us/us/{zip_code}\"\n",
+    "    logger.info(f\"Fetching lat/lon for ZIP code: {zip_code} from {url}\")\n",
+    "    try:\n",
+    "        response = requests.get(url, timeout=10)\n",
+    "        response.raise_for_status()\n",
+    "        data = response.json()\n",
+    "        \n",
+    "        if not data.get(\"places\"):\n",
+    "            logger.warning(f\"No places found for ZIP code {zip_code}\")\n",
+    "            return None\n",
+    "            \n",
+    "        place = data[\"places\"][0]\n",
+    "        return {\n",
+    "            \"latitude\": float(place[\"latitude\"]),\n",
+    "            \"longitude\": float(place[\"longitude\"]),\n",
+    "            \"state\": place[\"state\"],\n",
+    "            \"state_abbr\": place[\"state abbreviation\"],\n",
+    "            \"city\": place[\"place name\"]\n",
+    "        }\n",
+    "    except (requests.RequestException, KeyError, ValueError) as e:\n",
+    "        logger.error(f\"Failed to get lat/lon for ZIP {zip_code}: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "def get_county_from_lat_lon(lat: float, lon: float) -> Optional[str]:\n",
+    "    \"\"\"\n",
+    "    Step 2: Get county information from latitude and longitude using the\n",
+    "    U.S. Census Bureau's Geocoding API.\n",
+    "    \"\"\"\n",
+    "    url = \"https://geocoding.geo.census.gov/geocoder/geographies/coordinates\"\n",
+    "    params = {\n",
+    "        'x': lon,\n",
+    "        'y': lat,\n",
+    "        'benchmark': 'Public_AR_Current',\n",
+    "        'vintage': 'Current_Current',\n",
+    "        'format': 'json'\n",
+    "    }\n",
+    "    logger.info(f\"Fetching county for coordinates: (lat={lat}, lon={lon}) from Census Bureau API\")\n",
+    "    try:\n",
+    "        response = requests.get(url, params=params, timeout=15)\n",
+    "        response.raise_for_status()\n",
+    "        data = response.json()\n",
+    "        \n",
+    "        geographies = data.get(\"result\", {}).get(\"geographies\", {})\n",
+    "        counties = geographies.get(\"Counties\", [])\n",
+    "        \n",
+    "        if counties:\n",
+    "            county_name = counties[0].get(\"NAME\")\n",
+    "            logger.info(f\"Found county: {county_name}\")\n",
+    "            return county_name\n",
+    "        else:\n",
+    "            logger.warning(f\"No county found for coordinates (lat={lat}, lon={lon})\")\n",
+    "            return None\n",
+    "    except (requests.RequestException, KeyError, ValueError) as e:\n",
+    "        logger.error(f\"Failed to get county from coordinates: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "def get_geo_data_from_zip(zip_code: str) -> Optional[ZipCodeData]:\n",
+    "    \"\"\"\n",
+    "    Orchestrates the two-step process to get state, city, and county from a ZIP code.\n",
+    "    \"\"\"\n",
+    "    # Step 1: Get Lat/Lon and basic info\n",
+    "    geo_basics = get_lat_lon_from_zip(zip_code)\n",
+    "    if not geo_basics:\n",
+    "        return None\n",
+    "        \n",
+    "    # Step 2: Get County from Lat/Lon\n",
+    "    county = get_county_from_lat_lon(geo_basics[\"latitude\"], geo_basics[\"longitude\"])\n",
+    "    if not county:\n",
+    "        # Fallback: sometimes county info is not available, but we can proceed without it\n",
+    "        logger.warning(f\"Could not determine county for ZIP {zip_code}, proceeding without it.\")\n",
+    "        county = \"Unknown\"\n",
+    "\n",
+    "    return ZipCodeData(\n",
+    "        state=geo_basics[\"state\"],\n",
+    "        state_abbr=geo_basics[\"state_abbr\"],\n",
+    "        city=geo_basics[\"city\"],\n",
+    "        county=county[:-7]\n",
+    "    )\n",
+    "\n",
+    "data = get_geo_data_from_zip(\"23294\")\n",
+    "print(data.county, data.city, data.state, data.state_abbr, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e671137",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:15:45,995 - INFO - Initialized LLM Provider: gemini-2.5-flash\n",
+      "2025-07-09 18:15:46,000 - INFO - QueryIntentClassifierAgent initialized successfully.\n",
+      "2025-07-09 18:15:46,001 - INFO - QueryTransformationAgent initialized successfully.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== Turn 1 ====================\n",
+      "Current Profile State:\n",
+      "{\n",
+      "  \"zip_code\": \"30303\",\n",
+      "  \"county\": \"Fulton\",\n",
+      "  \"state\": \"Georgia\",\n",
+      "  \"age\": 34,\n",
+      "  \"gender\": \"Female\",\n",
+      "  \"household_size\": 1,\n",
+      "  \"income\": 65000,\n",
+      "  \"employment_status\": \"employed_without_coverage\",\n",
+      "  \"citizenship\": \"US Citizen\",\n",
+      "  \"medical_history\": null,\n",
+      "  \"medications\": null,\n",
+      "  \"special_cases\": null\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:15:47,304 - INFO - LLM returned next step: 'Alright, let's continue building your health profile. To help us understand your needs better, could you please share a bit about your medical history? For example, have you been diagnosed with any chronic conditions like diabetes, high blood pressure, or asthma, or have you had any major surgeries in the past? No need to go into excessive detail, just the key points that might be relevant for your health coverage.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: Alright, let's continue building your health profile. To help us understand your needs better, could you please share a bit about your medical history? For example, have you been diagnosed with any chronic conditions like diabetes, high blood pressure, or asthma, or have you had any major surgeries in the past? No need to go into excessive detail, just the key points that might be relevant for your health coverage.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:15:51,608 - INFO - Successfully updated profile with user's answer.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== Turn 2 ====================\n",
+      "Current Profile State:\n",
+      "{\n",
+      "  \"zip_code\": \"30303\",\n",
+      "  \"county\": \"Fulton\",\n",
+      "  \"state\": \"Georgia\",\n",
+      "  \"age\": 34,\n",
+      "  \"gender\": \"Female\",\n",
+      "  \"household_size\": 1,\n",
+      "  \"income\": 65000,\n",
+      "  \"employment_status\": \"employed_without_coverage\",\n",
+      "  \"citizenship\": \"US Citizen\",\n",
+      "  \"medical_history\": \"None reported.\",\n",
+      "  \"medications\": null,\n",
+      "  \"special_cases\": null\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:15:54,271 - INFO - LLM returned next step: 'Thank you for confirming your medical history. Just one last area to cover: are there any major life events, like a pregnancy, or planned medical procedures we should be aware of? Also, could you let me know if you use tobacco products?'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: Thank you for confirming your medical history. Just one last area to cover: are there any major life events, like a pregnancy, or planned medical procedures we should be aware of? Also, could you let me know if you use tobacco products?\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:15:57,890 - INFO - Successfully updated profile with user's answer.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== Turn 3 ====================\n",
+      "Current Profile State:\n",
+      "{\n",
+      "  \"zip_code\": \"30303\",\n",
+      "  \"county\": \"Fulton\",\n",
+      "  \"state\": \"Georgia\",\n",
+      "  \"age\": 34,\n",
+      "  \"gender\": \"Female\",\n",
+      "  \"household_size\": 1,\n",
+      "  \"income\": 65000,\n",
+      "  \"employment_status\": \"employed_without_coverage\",\n",
+      "  \"citizenship\": \"US Citizen\",\n",
+      "  \"medical_history\": \"None reported.\",\n",
+      "  \"medications\": null,\n",
+      "  \"special_cases\": \"None reported.\"\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:00,166 - INFO - LLM returned next step: 'Thank you for confirming your medical history. Just one last area to cover: are there any major life events, planned medical procedures, or tobacco usage we should factor into your plan?'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: Thank you for confirming your medical history. Just one last area to cover: are there any major life events, planned medical procedures, or tobacco usage we should factor into your plan?\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:06,687 - INFO - Successfully updated profile with user's answer.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== Turn 4 ====================\n",
+      "Current Profile State:\n",
+      "{\n",
+      "  \"zip_code\": \"30303\",\n",
+      "  \"county\": \"Fulton\",\n",
+      "  \"state\": \"Georgia\",\n",
+      "  \"age\": 34,\n",
+      "  \"gender\": \"Female\",\n",
+      "  \"household_size\": 1,\n",
+      "  \"income\": 65000,\n",
+      "  \"employment_status\": \"employed_without_coverage\",\n",
+      "  \"citizenship\": \"US Citizen\",\n",
+      "  \"medical_history\": \"None reported.\",\n",
+      "  \"medications\": null,\n",
+      "  \"special_cases\": \"None reported.\"\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:09,695 - INFO - LLM returned next step: 'PROFILE_COMPLETE'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: PROFILE_COMPLETE\n",
+      "\n",
+      "--- Profile building complete! ---\n",
+      "\n",
+      "==================== FINAL PROFILE ====================\n",
+      "{\n",
+      "  \"zip_code\": \"30303\",\n",
+      "  \"county\": \"Fulton\",\n",
+      "  \"state\": \"Georgia\",\n",
+      "  \"age\": 34,\n",
+      "  \"gender\": \"Female\",\n",
+      "  \"household_size\": 1,\n",
+      "  \"income\": 65000,\n",
+      "  \"employment_status\": \"employed_without_coverage\",\n",
+      "  \"citizenship\": \"US Citizen\",\n",
+      "  \"medical_history\": \"None reported.\",\n",
+      "  \"medications\": null,\n",
+      "  \"special_cases\": \"None reported.\"\n",
+      "}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:17,528 - INFO - Starting query transformation and retrieval for: 'quit'\n",
+      "2025-07-09 18:16:25,004 - INFO - Successfully classified query. Intent: Concise (Step-Back)\n",
+      "2025-07-09 18:16:25,007 - INFO - Query classified with intent: Concise (Step-Back). Reasoning: The query is a single word, 'quit', which completely lacks context within the domain of health insurance. It could be a command to end the conversation, or it could relate to quitting a plan, a job, or something else entirely. A step-back question is needed to understand the user's intent.\n",
+      "2025-07-09 18:16:34,538 - INFO - Aggregated and merged chunks into 10 final documents.\n",
+      "2025-07-09 18:16:34,540 - INFO - Retrieved 10 documents for query: 'quit'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Document(metadata={'source_id': 91, 'source_url': 'https://www.healthcare.gov/privacy/', 'source_name': 'privacy', 'source_local_path': 'data/raw/source_91_privacy.html', 'merged_chunks_count': 1, 'original_chunk_numbers': [52]}, page_content=\". You're about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites . Continue Cancel YouTube This link goes to an external site You are leaving HealthCare.gov. You're about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites . Continue Cancel LinkedIn This link goes to an external site You are leaving HealthCare.gov. You're about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites . Continue Cancel Instagram This link goes to an external site You are leaving HealthCare.gov. You're about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites\"), Document(metadata={'source_id': 121, 'source_url': 'https://www.healthcare.gov/choose-a-plan/your-total-costs/', 'source_name': 'Your total costs for health care: Premium, deductible, and out-of ...', 'source_local_path': 'data/dynamic/https___www.healthcare.gov_choose-a-plan_your-total-costs_.html', 'merged_chunks_count': 1, 'original_chunk_numbers': [2]}, page_content='. Out-of-pocket limit: A weighed scale leaning less towards the customer, Jane, who is paying zero and more towards her plan who is paying 100%. ## Compare estimated total costs for plans [...] ## Compare plans marked with \"easy pricing\" ### What services count for day 1 coverage? ### Â ## Resources ## Connect with us ## You are leaving HealthCare.gov. You\\'re about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites. ## You are leaving HealthCare.gov. You\\'re about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site.'), Document(metadata={'source_id': 277, 'source_url': 'https://www.healthcare.gov/preventive-care-women/', 'source_name': 'preventive-care-women', 'source_local_path': 'data/raw/source_277_preventive-care-women.html', 'merged_chunks_count': 1, 'original_chunk_numbers': [7]}, page_content=\". You're about to connect to a third-party site. Select CONTINUE to proceed or CANCEL to stay on this site. Learn more about links to third-party sites . Continue Cancel for women yearly Well-woman visits to get recommended services for all women More on prevention Learn more about preventive care from the CDC . See preventive services covered for all adults and children . Learn more about what else Marketplace health insurance plans cover. Back to top\"), Document(metadata={'source_id': 418, 'source_url': 'https://www.cms.gov/files/document/ffe-enrollment-manual-2023-5cr-071323.pdf', 'source_name': 'ffe-enrollment-manual-2023-5cr-071323.pdf', 'source_local_path': 'data/raw/source_418_files_document_ffe-enrollment-manual-2023-5cr-071323.pdf.pdf', 'merged_chunks_count': 1, 'original_chunk_numbers': [755]}, page_content='FFE Enrollment    \\n182 \\n \\nSubscription List \\n(As of Publication) Description \\nRescission/Fraud FFE issuers that wish to cancel enrollments due to fraud must first get the \\nCMS rescission team’s concurrence, and information about the rescission \\nprocess may be shared from time to time via this subscription. CMS may \\nalso share information with issuer fraud contacts about changing trends.'), Document(metadata={'source_id': 414, 'source_url': 'https://www.cms.gov/files/document/ffeffshop-enrollment-manual-2022.pdf', 'source_name': 'ffeffshop-enrollment-manual-2022.pdf', 'source_local_path': 'data/raw/source_414_files_document_ffeffshop-enrollment-manual-2022.pdf.pdf', 'merged_chunks_count': 1, 'original_chunk_numbers': [491]}, page_content='effective date and reason for termination, to enrollees for all termination events. \\n En rollee Requested  Terminations \\nIn accordance with 45 CFR 155.430(b)(1), enrollees have the right to terminate their coverage or \\nenrollment in a QHP/QDP through an Exchange. Enrollees in a QHP must request a voluntary \\ntermination of their coverage or enrollment through the FFE. Enrollees in a QDP, however, may \\ncontact the QDP issuer directly to request a voluntary termination of their coverage; the QDP issuer \\nthen notifies the FFE of the termination using Enrollment Data Alignment (EDA). According to 45 \\nCFR 155.430(d)(2), an enrollee who voluntarily terminates coverage or enrollment through the \\nExchanges, at the option of the Exchange, will be granted same-day or prospective coverage \\ntermination dates based on the date of their request. QHP issuers are encouraged to remind enrollees to \\nreport voluntary termination requests to the Exchange.'), Document(metadata={'source_id': 465, 'source_url': 'https://www.cms.gov/files/document/cms-9895-f-patient-protection-final.pdf', 'source_name': 'cms-9895-f-patient-protection-final.pdf', 'source_local_path': 'data/raw/source_465_files_document_cms-9895-f-patient-protection-final.pdf.pdf', 'merged_chunks_count': 1, 'original_chunk_numbers': [1971]}, page_content='also benefit by the expansion of entities and enrollment pathways available to assist with \\nenrolling in health insurance coverage. \\nWe sought comment on these estimated impacts and assumptions. \\nAfter consideration of comments and for the reasons outlined in the proposed rule and \\nour responses to comments, we are finalizing the burden estimates with modifications to the'), Document(metadata={'source_id': 464, 'source_url': 'https://www.cms.gov/files/document/cms-9895-p-patient-protection-final.pdf', 'source_name': 'cms-9895-p-patient-protection-final.pdf', 'source_local_path': 'data/raw/source_464_files_document_cms-9895-p-patient-protection-final.pdf.pdf', 'merged_chunks_count': 1, 'original_chunk_numbers': [788]}, page_content='select an EHB-benchmark plan with a scope of benefit requirement that tracks with such changes \\nto employer plans in the States, to the extent they exist.  \\nWe continue to believe that this list of plans appropriately represents the scope of benefits \\nprovided under typical employer plans. Based on our research on how the scope of benefits in \\nemployer-sponsored or other job-based coverage has changed since 2014, which includes our \\nreview of the comments submitted in response to the EHB RFI, we believe that the scope of \\nbenefits in employer-sponsored or other job-based coverage has either remained the same or \\nincreased incrementally overall since 2014. To the extent it has increased in certain States or \\ncertain regions, we believe that the scope of benefits in employer-sponsored or other job-based \\ncoverage increasingly tends to provide coverage for telehealth services, gender-affirming care,'), Document(metadata={'source_id': 88, 'source_url': 'https://www.healthcare.gov/reporting-changes/which-changes-to-report/', 'source_name': 'which-changes-to-report', 'source_local_path': 'data/raw/source_88_reporting-changes_which-changes-to-report.html', 'merged_chunks_count': 1, 'original_chunk_numbers': [2]}, page_content='. Expected income change: Find out how to estimate your income. Health coverage change: Someone in your household: Got an offer of job-based insurance, even if they don’t enroll in it Got coverage from a public program like Medicaid, the Children’s Health Insurance Program (CHIP), or Medicare Loses coverage, like job-based coverage or Medicaid If someone in your household got job-based coverage (either through your employer or through a family member’s), you may want to end your Marketplace plan . Household or individual member change: Birth or adoption Place a child for adoption or foster care Become pregnant Marriage or divorce A child on your plan turns 26 Death Gain or lose a dependent some other way Move to a new permanent address in the same state Don’t update your application if you move to a different state . Learn what to do when you move out of state'), Document(metadata={'source_id': 127, 'source_url': 'https://www.healthcare.gov/how-to-cancel-a-marketplace-plan', 'source_name': 'how-to-cancel-a-marketplace-plan', 'source_local_path': 'data/raw/source_127_how-to-cancel-a-marketplace-plan.html', 'merged_chunks_count': 1, 'original_chunk_numbers': [2]}, page_content=\". Refer to glossary for more details. . Notice: Don't end your Marketplace plan until you know for sure when your new coverage starts to avoid a gap in coverage. Once you end Marketplace coverage, you can’t re-enroll until the next Open Enrollment Period (unless you qualify for a Special Enrollment Period A time outside the yearly Open Enrollment Period when you can sign up for health insurance. You qualify for a Special Enrollment Period if you’ve had certain life events, including losing health coverage, moving, getting married, having a baby, or adopting a child, or if your household income is below a certain amount. Refer to glossary for more details. ). What are the risks if I drop all health coverage? Risks if you drop all health coverage Close If you don’t want health coverage, think about these items before you cancel your Marketplace plan: Once you cancel your coverage, you might have to wait for the next Open Enrollment Period to enroll again\"), Document(metadata={'source_id': 584, 'source_url': 'https://www.cms.gov/files/document/2025-benefit-year-discontinuation-notices-safe-harbor.pdf', 'source_name': '2025-benefit-year-discontinuation-notices-safe-harbor.pdf', 'source_local_path': 'data/raw/source_584_files_document_2025-benefit-year-discontinuation-notices-safe-harbor.pdf.pdf', 'merged_chunks_count': 1, 'original_chunk_numbers': [2]}, page_content='is to inform consumers that their current health coverage is being terminated and that they have \\nother health coverage options.   \\n  \\nDue to the timing of qualified health plan (QHP) certification for each of the 2015 through 2024 \\nbenefit years, issuers were in many instances unable to finalize their plan offerings until closer to \\nthe start of the annual open enrollment period, after the deadline to meet the 90-day \\ndiscontinuation notice requirement. This meant consumers could potentially receive product \\ndiscontinuation notices without being able to take prompt action to shop for new coverage, and \\nissuers would not have been able to suggest replacement coverage options, as explicitly \\nenvisioned by the discontinuation notices. Therefore, in connection with the open enrollment \\nperiod for coverage in each of these benefit years, the Centers for Medicare & Medicaid Services \\n(CMS) announced that it would not take enforcement action against an issuer failing to meet the')]\n",
+      "\n",
+      "Testing with relevant docs for question: '{'zip_code': '30303', 'county': 'Fulton', 'state': 'Georgia', 'age': 34, 'gender': 'Female', 'household_size': 1, 'income': 65000, 'employment_status': 'employed_without_coverage', 'citizenship': 'US Citizen', 'medical_history': 'None reported.', 'medications': None, 'special_cases': 'None reported.'} is my complete profile, answer the question: quit'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:34,920 - WARNING - GRADE: Documents are NOT RELEVANT.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  - Are docs relevant? -> False\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 18:16:39,079 - INFO - Formulated search query: 'Health insurance options after quitting job COBRA ACA special enrollment period'\n",
+      "2025-07-09 18:16:39,081 - INFO - Performing web search with Tavily for query: 'Health insurance options after quitting job COBRA ACA special enrollment period'\n",
+      "2025-07-09 18:16:42,650 - INFO - Saved web content from https://www.healthinsurance.org/special-enrollment-guide/involuntary-loss-of-coverage-is-a-qualifying-event/ to data/dynamic/https___www.healthinsurance.org_special-enrollment-guide_involuntary-loss-of-coverage-is-a-qualifying-event_.html\n",
+      "2025-07-09 18:16:42,653 - INFO - Saved web content from https://www.healthcare.gov/have-job-based-coverage/if-you-lose-job-based-coverage/ to data/dynamic/https___www.healthcare.gov_have-job-based-coverage_if-you-lose-job-based-coverage_.html\n",
+      "2025-07-09 18:16:42,655 - INFO - Saved web content from https://www.healthcare.gov/unemployed/cobra-coverage/ to data/dynamic/https___www.healthcare.gov_unemployed_cobra-coverage_.html\n",
+      "2025-07-09 18:16:42,656 - INFO - Saved web content from https://www.cobrainsurance.com/kb/can-i-get-cobra-if-i-quit/ to data/dynamic/https___www.cobrainsurance.com_kb_can-i-get-cobra-if-i-quit_.html\n",
+      "2025-07-09 18:16:42,657 - INFO - Saved web content from https://www.dol.gov/general/topic/health-plans/cobra to data/dynamic/https___www.dol.gov_general_topic_health-plans_cobra.html\n",
+      "2025-07-09 18:16:42,658 - INFO - Found and saved 5 documents from the web.\n",
+      "2025-07-09 18:16:42,658 - INFO - Starting dynamic ingestion of 5 documents...\n",
+      "2025-07-09 18:16:42,664 - INFO - Registering new dynamic web source: https://www.healthinsurance.org/special-enrollment-guide/involuntary-loss-of-coverage-is-a-qualifying-event/\n",
+      "2025-07-09 18:16:42,679 - INFO - Created 2 chunks for source_id None\n",
+      "2025-07-09 18:16:42,681 - INFO - Created 2 chunks for source_id None\n",
+      "2025-07-09 18:16:42,684 - INFO - Created 2 chunks for source_id None\n",
+      "2025-07-09 18:16:42,685 - INFO - Registering new dynamic web source: https://www.cobrainsurance.com/kb/can-i-get-cobra-if-i-quit/\n",
+      "2025-07-09 18:16:42,687 - INFO - Created 2 chunks for source_id None\n",
+      "2025-07-09 18:16:42,688 - INFO - Registering new dynamic web source: https://www.dol.gov/general/topic/health-plans/cobra\n",
+      "2025-07-09 18:16:42,690 - INFO - Created 2 chunks for source_id None\n",
+      "2025-07-09 18:16:42,690 - INFO - Embedding and storing 10 new chunks in ChromaDB.\n",
+      "2025-07-09 18:16:42,690 - INFO - Adding 10 documents to the vector store...\n",
+      "2025-07-09 18:16:43,012 - INFO - Successfully added 10 documents.\n",
+      "2025-07-09 18:16:43,012 - INFO - Dynamic ingestion completed successfully.\n",
+      "2025-07-09 18:16:43,012 - INFO - Generating final conversational response with AdvisorAgent...\n",
+      "2025-07-09 18:16:47,420 - INFO - Successfully generated final conversational answer.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Of course. Before you go, I just want to make sure—are you certain you don't have any other questions for me today? I'm here to help if anything else comes to mind.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import logging\n",
+    "from typing import Optional, Dict, Any, List\n",
+    "from langchain.docstore.document import Document\n",
+    "\n",
+    "from insucompass.core.agents.profile_agent import profile_builder\n",
+    "from insucompass.core.agents.query_trasformer import QueryTransformationAgent\n",
+    "from insucompass.core.agents.router_agent import router\n",
+    "from insucompass.services.ingestion_service import IngestionService\n",
+    "from insucompass.core.agents.search_agent import searcher\n",
+    "from insucompass.core.agents.advisor_agent import advisor\n",
+    "\n",
+    "\n",
+    "from insucompass.services import llm_provider\n",
+    "from insucompass.prompts.prompt_loader import load_prompt\n",
+    "from insucompass.services.vector_store import vector_store_service\n",
+    "\n",
+    "llm = llm_provider.get_gemini_llm()\n",
+    "retriever = vector_store_service.get_retriever()\n",
+    "transformer = QueryTransformationAgent(llm, retriever)\n",
+    "ingestor = IngestionService()\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "# 2. Set up the initial state\n",
+    "# This is the profile *after* the user has entered their basic info in the UI.\n",
+    "user_profile = {\n",
+    "    \"zip_code\": \"30303\",\n",
+    "    \"county\": \"Fulton\",\n",
+    "    \"state\": \"Georgia\",\n",
+    "    \"age\": 34,\n",
+    "    \"gender\": \"Female\", # Change to \"Male\" to test the other logic path\n",
+    "    \"household_size\": 1,\n",
+    "    \"income\": 65000,\n",
+    "    \"employment_status\": \"employed_without_coverage\",\n",
+    "    \"citizenship\": \"US Citizen\",\n",
+    "    \"medical_history\": None,\n",
+    "    \"medications\": None,\n",
+    "    \"special_cases\": None\n",
+    "}\n",
+    "\n",
+    "# 3. Start the conversation loop\n",
+    "last_question = \"\"\n",
+    "turn_count = 0\n",
+    "while turn_count < 10: # Safety break\n",
+    "    turn_count += 1\n",
+    "    print(\"\\n\" + \"=\"*20 + f\" Turn {turn_count} \" + \"=\"*20)\n",
+    "    print(\"Current Profile State:\")\n",
+    "    print(json.dumps(user_profile, indent=2))\n",
+    "\n",
+    "    # Get the next question based on the current profile state\n",
+    "    next_question = profile_builder.get_next_question(user_profile)\n",
+    "    print(f\"\\nInsuCompass Agent: {next_question}\")\n",
+    "\n",
+    "    if next_question == \"PROFILE_COMPLETE\":\n",
+    "        print(\"\\n--- Profile building complete! ---\")\n",
+    "        break\n",
+    "    \n",
+    "    # Store the question we just asked so we can provide it as context for the update\n",
+    "    last_question = next_question\n",
+    "    \n",
+    "    # Get live input from the person testing the script\n",
+    "    user_answer = input(\"Your Answer > \")\n",
+    "    if user_answer.lower() == 'quit':\n",
+    "        break\n",
+    "        \n",
+    "    # Use the updater method to get the new profile state\n",
+    "    user_profile = profile_builder.update_profile_with_answer(\n",
+    "        current_profile=user_profile,\n",
+    "        last_question=last_question,\n",
+    "        user_answer=user_answer\n",
+    "    )\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*20 + \" FINAL PROFILE \" + \"=\"*20)\n",
+    "print(json.dumps(user_profile, indent=2))\n",
+    "\n",
+    "# Test Case 1: Relevant documents\n",
+    "query = input(\"How can I help you?\")\n",
+    "\n",
+    "query_with_profile = f\"{user_profile} is my complete profile, answer the question: {query}\"\n",
+    "\n",
+    "retrieved_docs = transformer.transform_and_retrieve(query)\n",
+    "\n",
+    "print(retrieved_docs)\n",
+    "\n",
+    "print(f\"\\nTesting with relevant docs for question: '{query_with_profile}'\")\n",
+    "is_relevant = router.grade_documents(query_with_profile, retrieved_docs)\n",
+    "print(f\"  - Are docs relevant? -> {is_relevant}\")\n",
+    "\n",
+    "if is_relevant:\n",
+    "    final_answer = advisor.generate_response(query, user_profile, retrieved_docs)\n",
+    "    print(final_answer)\n",
+    "else:\n",
+    "    result_docs = searcher.search(query)\n",
+    "    if result_docs:\n",
+    "        ingestor.ingest_documents(result_docs)\n",
+    "        final_answer = advisor.generate_response(query, user_profile, result_docs)\n",
+    "        print(final_answer)\n",
+    "    else:\n",
+    "        print(\"Error...Exit..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef3253d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import json\n",
+    "import sqlite3\n",
+    "from typing import List, Dict, Any\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "from langgraph.graph import StateGraph, END\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "\n",
+    "# Import all our custom agent and service classes\n",
+    "from insucompass.core.agents.profile_agent import profile_builder\n",
+    "from insucompass.core.agents.query_trasformer import QueryTransformationAgent\n",
+    "from insucompass.core.agents.router_agent import router\n",
+    "from insucompass.services.ingestion_service import IngestionService\n",
+    "from insucompass.core.agents.search_agent import searcher\n",
+    "from insucompass.core.agents.advisor_agent import advisor\n",
+    "\n",
+    "from insucompass.services import llm_provider\n",
+    "from insucompass.prompts.prompt_loader import load_prompt\n",
+    "from insucompass.services.vector_store import vector_store_service\n",
+    "\n",
+    "llm = llm_provider.get_gemini_llm()\n",
+    "retriever = vector_store_service.get_retriever()\n",
+    "transformer = QueryTransformationAgent(llm, retriever)\n",
+    "ingestor = IngestionService()\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "# --- LangGraph State Definition ---\n",
+    "class AgentState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our Q&A graph. This state is passed between nodes.\n",
+    "    \"\"\"\n",
+    "    user_profile: Dict[str, Any]\n",
+    "    question: str\n",
+    "    contextual_question: str\n",
+    "    documents: List[Document]\n",
+    "    conversation_history: List[str]\n",
+    "    generation: str\n",
+    "    is_relevant: bool\n",
+    "\n",
+    "def reformulate_query_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"\n",
+    "    Node 0 (New): Reformulate the user's question to be self-contained.\n",
+    "    \"\"\"\n",
+    "    logger.info(\"---NODE: REFORMULATE QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    history = state[\"conversation_history\"]\n",
+    "    user_profile = state[\"user_profile\"]\n",
+    "\n",
+    "    prompt = load_prompt(\"query_reformulator\")\n",
+    "    history_str = \"\\n\".join(history)\n",
+    "    \n",
+    "    # if not history:\n",
+    "    #     # If there's no history, the question is already standalone\n",
+    "    #     return {\"contextual_question\": question}\n",
+    "    \n",
+    "    full_prompt = (\n",
+    "        f\"{prompt}\\n\\n\"\n",
+    "        f\"### User Profile:\\n{str(user_profile)}\\n\\n\"\n",
+    "        f\"### Conversation History:\\n{history_str}\\n\\n\"\n",
+    "        f\"### Follow-up Question:\\n{question}\"\n",
+    "    )\n",
+    "    \n",
+    "    response = llm.invoke(full_prompt)\n",
+    "    contextual_question = response.content.strip()\n",
+    "    logger.info(f\"Reformulated question: '{contextual_question}'\")\n",
+    "    return {\"contextual_question\": contextual_question}\n",
+    "\n",
+    "# --- Agent Nodes for the Graph ---\n",
+    "\n",
+    "def transform_query_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 1: Transform the user's query and retrieve initial documents.\"\"\"\n",
+    "    logger.info(\"---NODE: TRANSFORM QUERY & RETRIEVE---\")\n",
+    "    question = state[\"contextual_question\"]\n",
+    "    documents = transformer.transform_and_retrieve(question)\n",
+    "    return {\"documents\": documents}\n",
+    "\n",
+    "def route_documents_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 2: Grade the retrieved documents to decide if a web search is needed.\"\"\"\n",
+    "    logger.info(\"---NODE: ROUTE DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    is_relevant = router.grade_documents(question, documents)\n",
+    "    return {\"is_relevant\": is_relevant}\n",
+    "\n",
+    "def search_and_ingest_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 3 (Fallback Path): Search the web and ingest new information.\"\"\"\n",
+    "    logger.info(\"---NODE: SEARCH & INGEST---\")\n",
+    "    question = state[\"question\"]\n",
+    "    web_documents = searcher.search(question)\n",
+    "    if web_documents:\n",
+    "        ingestor.ingest_documents(web_documents)\n",
+    "    return {}\n",
+    "\n",
+    "def generate_answer_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 4: Generate the final, conversational answer.\"\"\"\n",
+    "    logger.info(\"---NODE: GENERATE ADVISOR RESPONSE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    user_profile = state[\"user_profile\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = advisor.generate_response(question, user_profile, documents)\n",
+    "    \n",
+    "    history = state.get(\"conversation_history\", [])\n",
+    "    history.append(f\"User: {question}\")\n",
+    "    history.append(f\"Agent: {generation}\")\n",
+    "    \n",
+    "    return {\"generation\": generation, \"conversation_history\": history}\n",
+    "\n",
+    "# --- Conditional Edge Logic ---\n",
+    "def should_search_web(state: AgentState) -> str:\n",
+    "    \"\"\"The conditional edge that directs the graph's flow.\"\"\"\n",
+    "    logger.info(\"---ROUTING: Evaluating document relevance---\")\n",
+    "    if state[\"is_relevant\"]:\n",
+    "        logger.info(\">>> Route: Documents are relevant. Proceeding to generate answer.\")\n",
+    "        return \"generate\"\n",
+    "    else:\n",
+    "        logger.info(\">>> Route: Documents are NOT relevant. Proceeding to web search.\")\n",
+    "        return \"search\"\n",
+    "\n",
+    "# --- Build and Compile the Graph ---\n",
+    "db_connection = sqlite3.connect(\"data/checkpoints.db\", check_same_thread=False)\n",
+    "memory = SqliteSaver(db_connection)\n",
+    "\n",
+    "builder = StateGraph(AgentState)\n",
+    "\n",
+    "builder.add_node(\"reformulate_query\", reformulate_query_node)\n",
+    "builder.add_node(\"transform_query\", transform_query_node)\n",
+    "builder.add_node(\"route_documents\", route_documents_node)\n",
+    "builder.add_node(\"search_and_ingest\", search_and_ingest_node)\n",
+    "builder.add_node(\"generate_answer\", generate_answer_node)\n",
+    "\n",
+    "builder.set_entry_point(\"reformulate_query\")\n",
+    "builder.add_edge(\"reformulate_query\", \"transform_query\")\n",
+    "builder.add_edge(\"transform_query\", \"route_documents\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"route_documents\",\n",
+    "    should_search_web,\n",
+    "    {\"search\": \"search_and_ingest\", \"generate\": \"generate_answer\"},\n",
+    ")\n",
+    "builder.add_edge(\"search_and_ingest\", \"generate_answer\")\n",
+    "builder.add_edge(\"generate_answer\", END)\n",
+    "\n",
+    "app = builder.compile(checkpointer=memory)\n",
+    "\n",
+    "# --- Interactive Test Harness (Updated to manage state correctly) ---\n",
+    "if __name__ == '__main__':\n",
+    "    print(\"--- InsuCompass AI Orchestrator ---\")\n",
+    "    \n",
+    "    # --- Phase 1: Profile Building (This part remains the same) ---\n",
+    "    print(\"Phase 1: Building your health profile...\")\n",
+    "    user_profile = {\n",
+    "    \"zip_code\": \"30303\",\n",
+    "    \"county\": \"Fulton\",\n",
+    "    \"state\": \"Georgia\",\n",
+    "    \"age\": 34,\n",
+    "    \"gender\": \"Female\", # Change to \"Male\" to test the other logic path\n",
+    "    \"household_size\": 1,\n",
+    "    \"income\": 65000,\n",
+    "    \"employment_status\": \"employed_without_coverage\",\n",
+    "    \"citizenship\": \"US Citizen\",\n",
+    "    \"medical_history\": None,\n",
+    "    \"medications\": None,\n",
+    "    \"special_cases\": None\n",
+    "    }\n",
+    "    profile_conversation_history = []\n",
+    "    for turn in range(10):\n",
+    "        question = profile_builder.get_next_question(user_profile)\n",
+    "        if question == \"PROFILE_COMPLETE\": break\n",
+    "        print(f\"\\nInsuCompass Agent: {question}\")\n",
+    "        profile_conversation_history.append(f\"Agent: {question}\")\n",
+    "        user_answer = input(\"Your Answer > \")\n",
+    "        if user_answer.lower() == 'quit': exit()\n",
+    "        profile_conversation_history.append(f\"User: {user_answer}\")\n",
+    "        user_profile = profile_builder.update_profile_with_answer(\n",
+    "            user_profile, question, user_answer\n",
+    "        )\n",
+    "    print(\"\\n--- Profile building complete! ---\")\n",
+    "    print(\"\\nFinal Profile:\", json.dumps(user_profile, indent=2))\n",
+    "    \n",
+    "    # --- Phase 2: Q&A Session ---\n",
+    "    print(\"\\n\" + \"#\"*20 + \" Q&A Session \" + \"#\"*20)\n",
+    "    print(\"Your profile is complete. You can now ask me any questions.\")\n",
+    "    \n",
+    "    thread_config = {\"configurable\": {\"thread_id\": \"user-123-session-1\"}}\n",
+    "    qna_history = []\n",
+    "\n",
+    "    while True:\n",
+    "        user_question = input(\"\\nYour Question > \")\n",
+    "        if user_question.lower() == 'quit': break\n",
+    "        \n",
+    "        inputs = {\n",
+    "            \"question\": user_question,\n",
+    "            \"user_profile\": user_profile,\n",
+    "            \"conversation_history\": qna_history # Pass the current history\n",
+    "        }\n",
+    "        \n",
+    "        print(\"\\n--- InsuCompass is thinking... ---\")\n",
+    "        final_state = {}\n",
+    "        for s in app.stream(inputs, config=thread_config):\n",
+    "            node_name = list(s.keys())[0]\n",
+    "            print(f\"--- Executed Node: {node_name} ---\")\n",
+    "            final_state.update(s)\n",
+    "\n",
+    "        final_answer = final_state.get(\"generate_answer\", {}).get(\"generation\", \"Sorry, I couldn't generate a response.\")\n",
+    "        \n",
+    "        print(\"\\n\" + \"=\"*20 + \" FINAL ANSWER \" + \"=\"*20)\n",
+    "        print(final_answer)\n",
+    "        \n",
+    "        qna_history = final_state.get(\"generate_answer\", {}).get(\"conversation_history\", qna_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d51cd66d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0db72a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import json\n",
+    "import sqlite3\n",
+    "from typing import List, Dict, Any\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "from langgraph.graph import StateGraph, END\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "\n",
+    "# Import all our custom agent and service classes\n",
+    "from insucompass.core.agents.profile_agent import profile_builder\n",
+    "from insucompass.core.agents.query_trasformer import QueryTransformationAgent\n",
+    "from insucompass.core.agents.router_agent import router\n",
+    "from insucompass.services.ingestion_service import IngestionService\n",
+    "from insucompass.core.agents.search_agent import searcher\n",
+    "from insucompass.core.agents.advisor_agent import advisor\n",
+    "\n",
+    "from insucompass.services import llm_provider\n",
+    "from insucompass.prompts.prompt_loader import load_prompt\n",
+    "from insucompass.services.vector_store import vector_store_service\n",
+    "\n",
+    "llm = llm_provider.get_gemini_llm()\n",
+    "retriever = vector_store_service.get_retriever()\n",
+    "transformer = QueryTransformationAgent(llm, retriever)\n",
+    "ingestor = IngestionService()\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "# --- LangGraph State Definition ---\n",
+    "class AgentState(TypedDict):\n",
+    "    \"\"\"\n",
+    "    Represents the state of our Q&A graph. This state is passed between nodes.\n",
+    "    \"\"\"\n",
+    "    user_profile: Dict[str, Any]\n",
+    "    question: str\n",
+    "    contextual_question: str\n",
+    "    documents: List[Document]\n",
+    "    conversation_history: List[str]\n",
+    "    generation: str\n",
+    "    is_relevant: bool\n",
+    "\n",
+    "def reformulate_query_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"\n",
+    "    Node 0 (New): Reformulate the user's question to be self-contained.\n",
+    "    \"\"\"\n",
+    "    logger.info(\"---NODE: REFORMULATE QUERY---\")\n",
+    "    question = state[\"question\"]\n",
+    "    history = state[\"conversation_history\"]\n",
+    "    user_profile = state[\"user_profile\"]\n",
+    "\n",
+    "    prompt = load_prompt(\"query_reformulator\")\n",
+    "    history_str = \"\\n\".join(history)\n",
+    "    \n",
+    "    # if not history:\n",
+    "    #     # If there's no history, the question is already standalone\n",
+    "    #     return {\"contextual_question\": question}\n",
+    "    \n",
+    "    full_prompt = (\n",
+    "        f\"{prompt}\\n\\n\"\n",
+    "        f\"### User Profile:\\n{str(user_profile)}\\n\\n\"\n",
+    "        f\"### Conversation History:\\n{history_str}\\n\\n\"\n",
+    "        f\"### Follow-up Question:\\n{question}\"\n",
+    "    )\n",
+    "    \n",
+    "    response = llm.invoke(full_prompt)\n",
+    "    contextual_question = response.content.strip()\n",
+    "    logger.info(f\"Reformulated question: '{contextual_question}'\")\n",
+    "    return {\"contextual_question\": contextual_question}\n",
+    "\n",
+    "# --- Agent Nodes for the Graph ---\n",
+    "\n",
+    "def transform_query_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 1: Transform the user's query and retrieve initial documents.\"\"\"\n",
+    "    logger.info(\"---NODE: TRANSFORM QUERY & RETRIEVE---\")\n",
+    "    question = state[\"contextual_question\"]\n",
+    "    documents = transformer.transform_and_retrieve(question)\n",
+    "    return {\"documents\": documents}\n",
+    "\n",
+    "def route_documents_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 2: Grade the retrieved documents to decide if a web search is needed.\"\"\"\n",
+    "    logger.info(\"---NODE: ROUTE DOCUMENTS---\")\n",
+    "    question = state[\"question\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    is_relevant = router.grade_documents(question, documents)\n",
+    "    return {\"is_relevant\": is_relevant}\n",
+    "\n",
+    "def search_and_ingest_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 3 (Fallback Path): Search the web and ingest new information.\"\"\"\n",
+    "    logger.info(\"---NODE: SEARCH & INGEST---\")\n",
+    "    question = state[\"question\"]\n",
+    "    web_documents = searcher.search(question)\n",
+    "    if web_documents:\n",
+    "        ingestor.ingest_documents(web_documents)\n",
+    "    return {}\n",
+    "\n",
+    "def generate_answer_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Node 4: Generate the final, conversational answer.\"\"\"\n",
+    "    logger.info(\"---NODE: GENERATE ADVISOR RESPONSE---\")\n",
+    "    question = state[\"question\"]\n",
+    "    user_profile = state[\"user_profile\"]\n",
+    "    documents = state[\"documents\"]\n",
+    "    generation = advisor.generate_response(question, user_profile, documents)\n",
+    "    \n",
+    "    history = state.get(\"conversation_history\", [])\n",
+    "    history.append(f\"User: {question}\")\n",
+    "    history.append(f\"Agent: {generation}\")\n",
+    "    \n",
+    "    return {\"generation\": generation, \"conversation_history\": history}\n",
+    "\n",
+    "# --- Conditional Edge Logic ---\n",
+    "def should_search_web(state: AgentState) -> str:\n",
+    "    \"\"\"The conditional edge that directs the graph's flow.\"\"\"\n",
+    "    logger.info(\"---ROUTING: Evaluating document relevance---\")\n",
+    "    if state[\"is_relevant\"]:\n",
+    "        logger.info(\">>> Route: Documents are relevant. Proceeding to generate answer.\")\n",
+    "        return \"generate\"\n",
+    "    else:\n",
+    "        logger.info(\">>> Route: Documents are NOT relevant. Proceeding to web search.\")\n",
+    "        return \"search\"\n",
+    "\n",
+    "# --- Build and Compile the Graph ---\n",
+    "db_connection = sqlite3.connect(\"data/checkpoints.db\", check_same_thread=False)\n",
+    "memory = SqliteSaver(db_connection)\n",
+    "\n",
+    "builder = StateGraph(AgentState)\n",
+    "\n",
+    "builder.add_node(\"reformulate_query\", reformulate_query_node)\n",
+    "builder.add_node(\"transform_query\", transform_query_node)\n",
+    "builder.add_node(\"route_documents\", route_documents_node)\n",
+    "builder.add_node(\"search_and_ingest\", search_and_ingest_node)\n",
+    "builder.add_node(\"generate_answer\", generate_answer_node)\n",
+    "\n",
+    "builder.set_entry_point(\"reformulate_query\")\n",
+    "builder.add_edge(\"reformulate_query\", \"transform_query\")\n",
+    "builder.add_edge(\"transform_query\", \"route_documents\")\n",
+    "builder.add_conditional_edges(\n",
+    "    \"route_documents\",\n",
+    "    should_search_web,\n",
+    "    {\"search\": \"search_and_ingest\", \"generate\": \"generate_answer\"},\n",
+    ")\n",
+    "builder.add_edge(\"search_and_ingest\", \"generate_answer\")\n",
+    "builder.add_edge(\"generate_answer\", END)\n",
+    "\n",
+    "app = builder.compile(checkpointer=memory)\n",
+    "\n",
+    "# --- Interactive Test Harness (Updated to manage state correctly) ---\n",
+    "if __name__ == '__main__':\n",
+    "    print(\"--- InsuCompass AI Orchestrator ---\")\n",
+    "    \n",
+    "    # --- Phase 1: Profile Building (This part remains the same) ---\n",
+    "    print(\"Phase 1: Building your health profile...\")\n",
+    "    user_profile = {\n",
+    "    \"zip_code\": \"30303\",\n",
+    "    \"county\": \"Fulton\",\n",
+    "    \"state\": \"Georgia\",\n",
+    "    \"age\": 34,\n",
+    "    \"gender\": \"Female\", # Change to \"Male\" to test the other logic path\n",
+    "    \"household_size\": 1,\n",
+    "    \"income\": 65000,\n",
+    "    \"employment_status\": \"employed_without_coverage\",\n",
+    "    \"citizenship\": \"US Citizen\",\n",
+    "    \"medical_history\": None,\n",
+    "    \"medications\": None,\n",
+    "    \"special_cases\": None\n",
+    "    }\n",
+    "    profile_conversation_history = []\n",
+    "    for turn in range(10):\n",
+    "        question = profile_builder.get_next_question(user_profile)\n",
+    "        if question == \"PROFILE_COMPLETE\": break\n",
+    "        print(f\"\\nInsuCompass Agent: {question}\")\n",
+    "        profile_conversation_history.append(f\"Agent: {question}\")\n",
+    "        user_answer = input(\"Your Answer > \")\n",
+    "        if user_answer.lower() == 'quit': exit()\n",
+    "        profile_conversation_history.append(f\"User: {user_answer}\")\n",
+    "        user_profile = profile_builder.update_profile_with_answer(\n",
+    "            user_profile, question, user_answer\n",
+    "        )\n",
+    "    print(\"\\n--- Profile building complete! ---\")\n",
+    "    print(\"\\nFinal Profile:\", json.dumps(user_profile, indent=2))\n",
+    "    \n",
+    "    # --- Phase 2: Q&A Session ---\n",
+    "    print(\"\\n\" + \"#\"*20 + \" Q&A Session \" + \"#\"*20)\n",
+    "    print(\"Your profile is complete. You can now ask me any questions.\")\n",
+    "    \n",
+    "    thread_config = {\"configurable\": {\"thread_id\": \"user-123-session-1\"}}\n",
+    "    qna_history = []\n",
+    "\n",
+    "    while True:\n",
+    "        user_question = input(\"\\nYour Question > \")\n",
+    "        if user_question.lower() == 'quit': break\n",
+    "        \n",
+    "        inputs = {\n",
+    "            \"question\": user_question,\n",
+    "            \"user_profile\": user_profile,\n",
+    "            \"conversation_history\": qna_history # Pass the current history\n",
+    "        }\n",
+    "        \n",
+    "        print(\"\\n--- InsuCompass is thinking... ---\")\n",
+    "        final_state = {}\n",
+    "        for s in app.stream(inputs, config=thread_config):\n",
+    "            node_name = list(s.keys())[0]\n",
+    "            print(f\"--- Executed Node: {node_name} ---\")\n",
+    "            final_state.update(s)\n",
+    "\n",
+    "        final_answer = final_state.get(\"generate_answer\", {}).get(\"generation\", \"Sorry, I couldn't generate a response.\")\n",
+    "        \n",
+    "        print(\"\\n\" + \"=\"*20 + \" FINAL ANSWER \" + \"=\"*20)\n",
+    "        print(final_answer)\n",
+    "        \n",
+    "        qna_history = final_state.get(\"generate_answer\", {}).get(\"conversation_history\", qna_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98a4c436",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a374452",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "def827af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:10,854 - INFO - Initialized LLM Provider: gemini-2.5-flash\n",
+      "2025-07-09 19:09:10,854 - INFO - Loading prompt 'profile_agent' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/profile_agent.txt\n",
+      "2025-07-09 19:09:10,855 - INFO - Loading prompt 'profile_updater' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/profile_updater.txt\n",
+      "2025-07-09 19:09:10,856 - INFO - ProfileBuilder initialized successfully with all prompts.\n",
+      "2025-07-09 19:09:10,867 - INFO - Loading prompt 'query_intent_classifier' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/query_intent_classifier.txt\n",
+      "2025-07-09 19:09:10,868 - INFO - Loading prompt 'query_transformer' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/query_transformer.txt\n",
+      "/var/folders/cv/flgh8s7960bc7q380pyn0c6m0000gn/T/ipykernel_41603/1771970172.py:15: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
+      "\n",
+      "For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`\n",
+      "with: `from pydantic import BaseModel`\n",
+      "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
+      "\n",
+      "  from insucompass.core.agents.router_agent import router\n",
+      "2025-07-09 19:09:10,872 - INFO - Initialized LLM Provider: gemini-2.5-flash-lite-preview-06-17\n",
+      "2025-07-09 19:09:10,873 - INFO - Loading prompt 'document_grader' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/document_grader.txt\n",
+      "2025-07-09 19:09:10,874 - INFO - RouterAgent (Document Grader) initialized successfully.\n",
+      "2025-07-09 19:09:11,217 - INFO - Anonymized telemetry enabled. See                     https://docs.trychroma.com/telemetry for more information.\n",
+      "2025-07-09 19:09:11,282 - INFO - Loading embedding model: sentence-transformers/all-MiniLM-L6-v2\n",
+      "/Users/nagurshareefshaik/Desktop/InsuCompass-AI/ic_venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "2025-07-09 19:09:15,208 - INFO - Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n",
+      "2025-07-09 19:09:17,878 - INFO - ChromaDB service initialized. Collection 'insucompass_kb' at data/vector_store\n",
+      "2025-07-09 19:09:18,008 - INFO - Initialized LLM Provider: gemini-2.5-flash\n",
+      "2025-07-09 19:09:18,008 - INFO - Loading prompt 'search_agent' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/search_agent.txt\n",
+      "2025-07-09 19:09:18,009 - INFO - SearchAgent initialized successfully.\n",
+      "2025-07-09 19:09:18,011 - INFO - Initialized LLM Provider: gemini-2.5-flash\n",
+      "2025-07-09 19:09:18,011 - INFO - Loading prompt 'advisor_agent' from: /Users/nagurshareefshaik/Desktop/InsuCompass-AI/insucompass/prompts/advisor_agent.txt\n",
+      "2025-07-09 19:09:18,012 - INFO - Conversational AdvisorAgent initialized successfully.\n",
+      "2025-07-09 19:09:18,013 - INFO - Initialized LLM Provider: gemini-2.5-flash\n",
+      "2025-07-09 19:09:18,014 - INFO - QueryIntentClassifierAgent initialized successfully.\n",
+      "2025-07-09 19:09:18,014 - INFO - QueryTransformationAgent initialized successfully.\n",
+      "2025-07-09 19:09:18,040 - INFO - ---ROUTING: ENTRY POINT---\n",
+      "2025-07-09 19:09:18,041 - INFO - >>> Route: Profile is not complete. Starting Profile Builder.\n",
+      "2025-07-09 19:09:18,041 - INFO - ---NODE: PROFILE BUILDER---\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- InsuCompass AI Unified Orchestrator Interactive Test ---\n",
+      "Type 'quit' at any time to exit.\n",
+      "Using conversation thread_id: interactive-test-89478936-0bc5-4b80-a549-840c5268e536\n",
+      "\n",
+      "==================== INVOKING GRAPH ====================\n",
+      "Sending message: 'START_PROFILE_BUILDING'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:19,918 - INFO - LLM returned next step: '\"Thank you for sharing those details! Now, let's gently move on to your health history. Could you please tell me about any significant medical conditions, diagnoses, or chronic illnesses you've experienced? There's no need to go into extreme detail, just the main points that might be relevant for your health plan.\"'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: \"Thank you for sharing those details! Now, let's gently move on to your health history. Could you please tell me about any significant medical conditions, diagnoses, or chronic illnesses you've experienced? There's no need to go into extreme detail, just the main points that might be relevant for your health plan.\"\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:35,799 - INFO - ---ROUTING: ENTRY POINT---\n",
+      "2025-07-09 19:09:35,800 - INFO - >>> Route: Profile is not complete. Starting Profile Builder.\n",
+      "2025-07-09 19:09:35,803 - INFO - ---NODE: PROFILE BUILDER---\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== INVOKING GRAPH ====================\n",
+      "Sending message: 'Nothing like that'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:37,173 - INFO - Successfully updated profile with user's answer.\n",
+      "2025-07-09 19:09:39,073 - INFO - LLM returned next step: 'Thank you for confirming your medical history. Just one last area to cover: are there any major life events, planned medical procedures, or tobacco usage we should factor into your plan?'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: Thank you for confirming your medical history. Just one last area to cover: are there any major life events, planned medical procedures, or tobacco usage we should factor into your plan?\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:45,074 - INFO - ---ROUTING: ENTRY POINT---\n",
+      "2025-07-09 19:09:45,075 - INFO - >>> Route: Profile is not complete. Starting Profile Builder.\n",
+      "2025-07-09 19:09:45,076 - INFO - ---NODE: PROFILE BUILDER---\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================== INVOKING GRAPH ====================\n",
+      "Sending message: 'Nope'\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-07-09 19:09:46,483 - INFO - Successfully updated profile with user's answer.\n",
+      "2025-07-09 19:09:50,978 - INFO - LLM returned next step: 'PROFILE_COMPLETE'\n",
+      "2025-07-09 19:09:50,980 - INFO - Profile building complete.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "InsuCompass Agent: Great! Your profile is complete. How can I help you with your health insurance questions?\n",
+      "Exiting test.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import logging\n",
+    "import json\n",
+    "import uuid\n",
+    "import sqlite3\n",
+    "from typing import List, Dict, Any\n",
+    "from typing_extensions import TypedDict\n",
+    "\n",
+    "from langchain_core.documents import Document\n",
+    "from langgraph.graph import StateGraph, END\n",
+    "from langgraph.checkpoint.sqlite import SqliteSaver\n",
+    "\n",
+    "# Import all our custom agent and service classes\n",
+    "from insucompass.core.agents.profile_agent import profile_builder\n",
+    "from insucompass.core.agents.query_trasformer import QueryTransformationAgent\n",
+    "from insucompass.core.agents.router_agent import router\n",
+    "from insucompass.services.ingestion_service import IngestionService\n",
+    "from insucompass.core.agents.search_agent import searcher\n",
+    "from insucompass.core.agents.advisor_agent import advisor\n",
+    "\n",
+    "from insucompass.services import llm_provider\n",
+    "from insucompass.prompts.prompt_loader import load_prompt\n",
+    "from insucompass.services.vector_store import vector_store_service\n",
+    "\n",
+    "llm = llm_provider.get_gemini_llm()\n",
+    "retriever = vector_store_service.get_retriever()\n",
+    "transformer = QueryTransformationAgent(llm, retriever)\n",
+    "ingestor = IngestionService()\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "# --- Unified LangGraph State Definition ---\n",
+    "class AgentState(TypedDict):\n",
+    "    user_profile: Dict[str, Any]\n",
+    "    user_message: str\n",
+    "    conversation_history: List[str]\n",
+    "    is_profile_complete: bool\n",
+    "    # Q&A specific fields\n",
+    "    standalone_question: str\n",
+    "    documents: List[Document]\n",
+    "    is_relevant: bool\n",
+    "    generation: str\n",
+    "\n",
+    "# --- Graph Nodes ---\n",
+    "\n",
+    "def profile_builder_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"A single turn of the profile building conversation.\"\"\"\n",
+    "    logger.info(\"---NODE: PROFILE BUILDER---\")\n",
+    "    profile = state[\"user_profile\"]\n",
+    "    message = state[\"user_message\"]\n",
+    "    history = state.get(\"conversation_history\", [])\n",
+    "\n",
+    "    if message == \"START_PROFILE_BUILDING\":\n",
+    "        agent_response = profile_builder.get_next_question(profile, [])\n",
+    "        new_history = [f\"Agent: {agent_response}\"]\n",
+    "        return {\"conversation_history\": new_history, \"generation\": agent_response, \"user_profile\": profile, \"is_profile_complete\": False}\n",
+    "\n",
+    "    last_question = history[-1][len(\"Agent: \"):] if history and history[-1].startswith(\"Agent:\") else \"\"\n",
+    "    updated_profile = profile_builder.update_profile_with_answer(profile, last_question, message)\n",
+    "    agent_response = profile_builder.get_next_question(updated_profile, history + [f\"User: {message}\"])\n",
+    "    \n",
+    "    new_history = history + [f\"User: {message}\", f\"Agent: {agent_response}\"]\n",
+    "    \n",
+    "    if agent_response == \"PROFILE_COMPLETE\":\n",
+    "        logger.info(\"Profile building complete.\")\n",
+    "        final_message = \"Great! Your profile is complete. How can I help you with your health insurance questions?\"\n",
+    "        new_history[-1] = f\"Agent: {final_message}\" # Replace \"PROFILE_COMPLETE\"\n",
+    "        return {\"user_profile\": updated_profile, \"is_profile_complete\": True, \"conversation_history\": new_history, \"generation\": final_message}\n",
+    "    \n",
+    "    return {\"user_profile\": updated_profile, \"is_profile_complete\": False, \"conversation_history\": new_history, \"generation\": agent_response}\n",
+    "\n",
+    "def reformulate_query_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Reformulates the user's question to be self-contained.\"\"\"\n",
+    "    logger.info(\"---NODE: REFORMULATE QUERY---\")\n",
+    "    question = state[\"user_message\"]\n",
+    "    history = state[\"conversation_history\"]\n",
+    "    user_profile = state[\"user_profile\"]\n",
+    "    \n",
+    "    profile_summary = f\"User profile context: State={user_profile.get('state')}, Age={user_profile.get('age')}, History={user_profile.get('medical_history')}\"\n",
+    "    prompt = load_prompt(\"query_reformulator\")\n",
+    "    history_str = \"\\n\".join(history)\n",
+    "    \n",
+    "    full_prompt = f\"{prompt}\\n\\n### User Profile Summary\\n{profile_summary}\\n\\n### Conversation History:\\n{history_str}\\n\\n### Follow-up Question:\\n{question}\"\n",
+    "    \n",
+    "    response = llm.invoke(full_prompt)\n",
+    "    standalone_question = response.content.strip()\n",
+    "    return {\"standalone_question\": standalone_question}\n",
+    "\n",
+    "def retrieve_and_grade_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Retrieves documents and grades them.\"\"\"\n",
+    "    logger.info(\"---NODE: RETRIEVE & GRADE---\")\n",
+    "    standalone_question = state[\"standalone_question\"]\n",
+    "    documents = transformer.transform_and_retrieve(standalone_question)\n",
+    "    is_relevant = router.grade_documents(standalone_question, documents)\n",
+    "    return {\"documents\": documents, \"is_relevant\": is_relevant}\n",
+    "\n",
+    "def search_and_ingest_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Searches the web and ingests new info.\"\"\"\n",
+    "    logger.info(\"---NODE: SEARCH & INGEST---\")\n",
+    "    web_documents = searcher.search(state[\"standalone_question\"])\n",
+    "    if web_documents:\n",
+    "        ingestor.ingest_documents(web_documents)\n",
+    "    return {}\n",
+    "\n",
+    "def generate_answer_node(state: AgentState) -> Dict[str, Any]:\n",
+    "    \"\"\"Generates the final answer.\"\"\"\n",
+    "    logger.info(\"---NODE: GENERATE ADVISOR RESPONSE---\")\n",
+    "    generation = advisor.generate_response(\n",
+    "        state[\"standalone_question\"], state[\"user_profile\"], state[\"documents\"]\n",
+    "    )\n",
+    "    history = state[\"conversation_history\"] + [f\"User: {state['user_message']}\", f\"Agent: {generation}\"]\n",
+    "    return {\"generation\": generation, \"conversation_history\": history}\n",
+    "\n",
+    "# --- Conditional Edges ---\n",
+    "def should_search_web(state: AgentState) -> str:\n",
+    "    return \"search\" if not state[\"is_relevant\"] else \"generate\"\n",
+    "\n",
+    "# (CORRECTED) This is the function for the entry point conditional edge\n",
+    "def decide_entry_point(state: AgentState) -> str:\n",
+    "    \"\"\"Decides the initial path based on profile completion status.\"\"\"\n",
+    "    logger.info(\"---ROUTING: ENTRY POINT---\")\n",
+    "    if state.get(\"is_profile_complete\"):\n",
+    "        logger.info(\">>> Route: Profile is complete. Starting Q&A.\")\n",
+    "        return \"qna\"\n",
+    "    else:\n",
+    "        logger.info(\">>> Route: Profile is not complete. Starting Profile Builder.\")\n",
+    "        return \"profile\"\n",
+    "\n",
+    "# --- Build the Graph ---\n",
+    "db_connection = sqlite3.connect(\"data/checkpoints.db\", check_same_thread=False)\n",
+    "memory = SqliteSaver(db_connection)\n",
+    "\n",
+    "builder = StateGraph(AgentState)\n",
+    "\n",
+    "# (CORRECTED) Removed the faulty entry_router_node\n",
+    "builder.add_node(\"profile_builder\", profile_builder_node)\n",
+    "builder.add_node(\"reformulate_query\", reformulate_query_node)\n",
+    "builder.add_node(\"retrieve_and_grade\", retrieve_and_grade_node)\n",
+    "builder.add_node(\"search_and_ingest\", search_and_ingest_node)\n",
+    "builder.add_node(\"generate_answer\", generate_answer_node)\n",
+    "\n",
+    "# (CORRECTED) Set a conditional entry point\n",
+    "builder.set_conditional_entry_point(\n",
+    "    decide_entry_point,\n",
+    "    {\n",
+    "        \"profile\": \"profile_builder\",\n",
+    "        \"qna\": \"reformulate_query\"\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Define graph edges\n",
+    "builder.add_edge(\"profile_builder\", END) # A profile turn is one full loop. The state is saved, and the next call will re-evaluate at the entry point.\n",
+    "builder.add_edge(\"reformulate_query\", \"retrieve_and_grade\")\n",
+    "builder.add_conditional_edges(\"retrieve_and_grade\", should_search_web, {\"search\": \"search_and_ingest\", \"generate\": \"generate_answer\"})\n",
+    "builder.add_edge(\"search_and_ingest\", \"retrieve_and_grade\") # Loop back to re-retrieve\n",
+    "builder.add_edge(\"generate_answer\", END)\n",
+    "\n",
+    "app = builder.compile(checkpointer=memory)\n",
+    "\n",
+    "# --- Interactive Test Harness (CORRECTED) ---\n",
+    "if __name__ == '__main__':\n",
+    "    print(\"--- InsuCompass AI Unified Orchestrator Interactive Test ---\")\n",
+    "    print(\"Type 'quit' at any time to exit.\")\n",
+    "\n",
+    "    test_thread_id = f\"interactive-test-{uuid.uuid4()}\"\n",
+    "    thread_config = {\"configurable\": {\"thread_id\": test_thread_id}}\n",
+    "    print(f\"Using conversation thread_id: {test_thread_id}\")\n",
+    "\n",
+    "    # Initial state for a new user\n",
+    "    current_state = {\n",
+    "        \"user_profile\": {\n",
+    "            \"zip_code\": \"90210\", \"county\": \"Los Angeles\", \"state\": \"California\", \"state_abbreviation\": \"CA\",\n",
+    "            \"age\": 45, \"gender\": \"Male\", \"household_size\": 2, \"income\": 120000,\n",
+    "            \"employment_status\": \"employed_with_employer_coverage\", \"citizenship\": \"US Citizen\",\n",
+    "            \"medical_history\": None, \"medications\": None, \"special_cases\": None\n",
+    "        },\n",
+    "        \"user_message\": \"START_PROFILE_BUILDING\",\n",
+    "        \"is_profile_complete\": False,\n",
+    "        \"conversation_history\": [],\n",
+    "    }\n",
+    "\n",
+    "    while True:\n",
+    "        print(\"\\n\" + \"=\"*20 + \" INVOKING GRAPH \" + \"=\"*20)\n",
+    "        print(f\"Sending message: '{current_state['user_message']}'\")\n",
+    "        \n",
+    "        # The graph is invoked with the current state\n",
+    "        final_state = app.invoke(current_state, config=thread_config)\n",
+    "\n",
+    "        # Update our local state from the graph's final output\n",
+    "        current_state = final_state\n",
+    "        agent_response = current_state[\"generation\"]\n",
+    "        \n",
+    "        print(f\"\\nInsuCompass Agent: {agent_response}\")\n",
+    "\n",
+    "        # Get the next input from the user\n",
+    "        if current_state[\"is_profile_complete\"]:\n",
+    "            # If the last response was the completion message, prompt for a question\n",
+    "            if \"profile is complete\" in agent_response:\n",
+    "                 next_message = input(\"Your Question > \")\n",
+    "            else: # It was a Q&A response, so prompt for another question\n",
+    "                 next_message = input(\"Your Follow-up Question > \")\n",
+    "        else:\n",
+    "            next_message = input(\"Your Answer > \")\n",
+    "\n",
+    "        if next_message.lower() == 'quit':\n",
+    "            print(\"Exiting test.\")\n",
+    "            break\n",
+    "        \n",
+    "        # Prepare the state for the next turn\n",
+    "        current_state[\"user_message\"] = next_message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "151d65d5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ic_venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/frontend/InsuCompass_Logo.png b/frontend/InsuCompass_Logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..fa4708fff3f1ba44f2673944a87d4db0ee774964
--- /dev/null
+++ b/frontend/InsuCompass_Logo.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cafde689fe9af03a01e676a1080c85fd7ef83153ff34e01e66273d03c23777f0
+size 25414
diff --git a/frontend/__init__.py b/frontend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/frontend/app.py b/frontend/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f90ba7ea7eaf57d156d9e04b570a3616cb37b7c
--- /dev/null
+++ b/frontend/app.py
@@ -0,0 +1,162 @@
+import streamlit as st
+import requests
+import json
+import logging
+import uuid
+from pathlib import Path
+import sys
+
+# --- Path Setup ---
+project_root = str(Path(__file__).parent.parent)
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+# --- Imports from our backend logic ---
+from insucompass.core.agent_orchestrator import app as unified_orchestrator_app
+from insucompass.services.zip_client import get_geo_data_from_zip
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Streamlit Page Configuration ---
+st.set_page_config(
+    page_title="InsuCompass AI",
+    page_icon="🧭",
+    layout="wide",
+)
+
+# --- Header ---
+LOGO_PATH = Path("InsuCompass_Logo.png")
+INSU_AVATAR = "robot-chat.jpg"
+# Create two columns
+col1, col2 = st.columns([0.4, 5])  # Adjust the width ratio as needed
+with col1:
+    if LOGO_PATH.exists():
+        st.image(str(LOGO_PATH), width=70)
+with col2:
+    st.title("InsuCompass")
+st.caption("Your AI guide to U.S. Health Insurance")
+
+# --- Session State Initialization ---
+if "thread_id" not in st.session_state:
+    st.session_state.thread_id = str(uuid.uuid4())
+    st.session_state.user_profile = {}
+    st.session_state.chat_history = []
+    st.session_state.is_profile_complete = False
+    st.session_state.phase = "zip"
+
+# --- Helper Functions ---
+@st.cache_data
+def get_geodata(_zip_code: str):
+    """Calls the zip_client function directly."""
+    return get_geo_data_from_zip(_zip_code)
+
+def run_orchestrator(user_message: str):
+    """Calls the LangGraph orchestrator directly."""
+    thread_config = {"configurable": {"thread_id": st.session_state.thread_id}}
+    
+    inputs = {
+        "user_profile": st.session_state.user_profile,
+        "user_message": user_message,
+        "is_profile_complete": st.session_state.is_profile_complete,
+        "conversation_history": st.session_state.chat_history,
+    }
+    logger.info(f"Invoking orchestrator for thread_id: {st.session_state.thread_id}")
+    
+    try:
+        final_state = unified_orchestrator_app.invoke(inputs, config=thread_config)
+        st.session_state.user_profile = final_state["user_profile"]
+        st.session_state.chat_history = final_state["conversation_history"]
+        st.session_state.is_profile_complete = final_state["is_profile_complete"]
+    except Exception as e:
+        logger.error(f"Error invoking orchestrator: {e}", exc_info=True)
+        st.error("An internal error occurred in the AI agent. Please try again.")
+
+# --- UI Rendering Functions ---
+
+def display_zip_form():
+    st.header("Let's Start with Your Location")
+    zip_code_input = st.text_input("Enter your 5-digit ZIP code:", max_chars=5)
+    if st.button("Confirm ZIP Code"):
+        if zip_code_input and len(zip_code_input) == 5 and zip_code_input.isdigit():
+            with st.spinner("Verifying ZIP code..."):
+                geo_data = get_geodata(zip_code_input)
+            if geo_data:
+                st.session_state.user_profile.update(geo_data.to_dict())
+                st.session_state.phase = "basic_profile"
+                st.rerun()
+            else:
+                st.error("Invalid ZIP code or could not retrieve location data.")
+        else:
+            st.error("Please enter a valid 5-digit ZIP code.")
+
+def display_basic_profile_form():
+    st.header("Tell Us More About You")
+    st.write(f"**Location:** {st.session_state.user_profile.get('city', '')}, {st.session_state.user_profile.get('state', '')}")
+    
+    with st.form("basic_profile_form"):
+        st.subheader("Please provide some basic information:")
+        
+        # (CORRECTED) Fields now start empty and are validated.
+        age = st.number_input("Your Age", min_value=1, max_value=120, value=None, placeholder="Enter your age")
+        gender = st.selectbox("Your Gender", [None, "Male", "Female", "Non-binary", "Prefer not to say"], index=0, format_func=lambda x: "Select an option" if x is None else x)
+        household_size = st.number_input("Household Size (including yourself)", min_value=1, value=None, placeholder="e.g., 1")
+        income = st.number_input("Estimated Annual Household Income ($)", min_value=0, step=1000, value=None, placeholder="e.g., 55000")
+        employment_status = st.selectbox("Your Employment Status", [None, "Employed with employer coverage", "Employed without coverage", "Unemployed", "Retired", "Student", "Self-employed"], index=0, format_func=lambda x: "Select an option" if x is None else x)
+        citizenship = st.selectbox("Your Citizenship Status", [None, "US Citizen", "Lawful Permanent Resident", "Other legal resident", "Non-resident"], index=0, format_func=lambda x: "Select an option" if x is None else x)
+        
+        submitted = st.form_submit_button("Start My Personalized Session")
+        
+        if submitted:
+            # Validation check
+            if not all([age, gender, household_size, income is not None, employment_status, citizenship]):
+                st.error("Please fill out all the fields to continue.")
+            else:
+                st.session_state.user_profile.update({
+                    "age": age, "gender": gender, "household_size": household_size,
+                    "income": income, "employment_status": employment_status,
+                    "citizenship": citizenship,
+                    "medical_history": None, "medications": None, "special_cases": None
+                })
+                st.session_state.phase = "chat"
+                # Trigger the first profile question by calling the orchestrator
+                with st.spinner("Starting your personalized profile conversation..."):
+                    run_orchestrator("START_PROFILE_BUILDING")
+                st.rerun()
+
+def display_chat_interface():
+    st.header("Let's Chat!")
+    
+    # Display chat history from session state
+    for message in st.session_state.chat_history:
+        if ":" in message:
+            role, content = message.split(":", 1)
+            if role.lower() == "user":
+                with st.chat_message("user", avatar="👤"):
+                    st.markdown(content.strip())
+            else: # Agent
+                with st.chat_message("assistant", avatar=INSU_AVATAR):
+                    st.markdown(content.strip())
+    
+    # User input
+    if prompt := st.chat_input("Your message..."):
+        with st.spinner("InsuCompass AI is thinking..."):
+            run_orchestrator(prompt)
+        st.rerun()
+
+# --- Main Application Flow Control ---
+if st.session_state.phase == "zip":
+    display_zip_form()
+elif st.session_state.phase == "basic_profile":
+    display_basic_profile_form()
+else: # 'chat' phase
+    display_chat_interface()
+
+# --- Sidebar for Debugging ---
+with st.sidebar:
+    st.header("Session State")
+    st.json(st.session_state.to_dict(), expanded=False)
+    if st.button("Reset Session"):
+        st.session_state.clear()
+        st.rerun()
\ No newline at end of file
diff --git a/frontend/robot-chat.jpg b/frontend/robot-chat.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..d3e4c617e6f5c8f0b1a0494d6841540b49f47ab3
Binary files /dev/null and b/frontend/robot-chat.jpg differ
diff --git a/frontend/web_app.py b/frontend/web_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..1af3316ab8c14df99961ecf5776022e34a17c96d
--- /dev/null
+++ b/frontend/web_app.py
@@ -0,0 +1,175 @@
+import streamlit as st
+import requests
+import json
+import logging
+import uuid
+from typing import List
+from pathlib import Path
+
+# Configure logging for Streamlit app
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Configuration ---
+# BACKEND_URL = "https://nagur-shareef-shaik-insucompass-ai.hf.space/api"
+BACKEND_URL = "http://localhost:8000/api"
+
+LOGO_PATH = Path(__file__).parent / "assets" / "InsuCompass_Logo.png"
+
+# --- Streamlit Page Configuration ---
+st.set_page_config(
+    page_title="InsuCompass AI",
+    page_icon="🧭",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# --- MAIN CHAT INTERFACE ---
+# Create two columns
+col1, col2 = st.columns([0.5, 4])  # Adjust the width ratio as needed
+with col1:
+    if LOGO_PATH.exists():
+        st.image(str(LOGO_PATH), width=70)
+with col2:
+    st.title("InsuCompass")
+st.caption("Your AI guide to U.S. Health Insurance")
+
+# --- Session State Initialization ---
+def initialize_session_state():
+    """Initializes all necessary keys in the session state."""
+    if "current_phase" not in st.session_state:
+        st.session_state.current_phase = "initial_zip"
+    if "user_profile" not in st.session_state:
+        st.session_state.user_profile = {}
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+    if "thread_id" not in st.session_state:
+        st.session_state.thread_id = str(uuid.uuid4())
+    # (CRITICAL FIX) Initialize the profile completion flag
+    if "is_profile_complete" not in st.session_state:
+        st.session_state.is_profile_complete = False
+
+initialize_session_state()
+
+# --- Helper Functions for API Calls ---
+@st.cache_data
+def get_geodata_from_backend(_zip_code: str):
+    """Calls the FastAPI /geodata endpoint. Cached to prevent re-calls."""
+    try:
+        response = requests.get(f"{BACKEND_URL}/geodata/{_zip_code}")
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching location data: {e}")
+        return None
+
+def send_chat_message_to_backend(user_message: str):
+    """Calls the single, unified FastAPI /chat endpoint."""
+    payload = {
+        "thread_id": st.session_state.thread_id,
+        "user_profile": st.session_state.user_profile,
+        "message": user_message,
+        "conversation_history": st.session_state.chat_history,
+        # (CRITICAL FIX) Pass the current profile completion status to the backend
+        "is_profile_complete": st.session_state.is_profile_complete
+    }
+    logger.info(f"Sending chat payload: {json.dumps(payload, indent=2)}")
+    try:
+        response = requests.post(f"{BACKEND_URL}/chat", json=payload)
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error communicating with AI backend: {e}")
+        return None
+
+# --- UI Rendering Functions ---
+
+def display_zip_form():
+    st.header("1. Let's Start with Your Location")
+    zip_code_input = st.text_input("Enter your 5-digit ZIP code:", max_chars=5)
+    if len(zip_code_input) == 5 and zip_code_input.isdigit():
+        with st.spinner("Verifying ZIP code..."):
+            geo_data = get_geodata_from_backend(zip_code_input)
+        if geo_data:
+            st.session_state.user_profile.update(geo_data)
+            st.session_state.current_phase = "basic_profile"
+            st.rerun()
+    else:
+        st.error("Please enter a valid 5-digit ZIP code.")
+
+def display_basic_profile_form():
+    st.header("2. Tell Us More About You")
+    with st.form("basic_profile_form"):
+        age = st.number_input("Age", min_value=1)
+        gender = st.selectbox("Gender", ["Male", "Female", "Non-binary", "Prefer not to say"])
+        household_size = st.number_input("Household Size", min_value=1)
+        income = st.number_input("Annual Household Income ($)", min_value=0, step=1000)
+        employment_status = st.selectbox("Employment Status", ["Employed with employer coverage", "Employed without coverage", "Unemployed", "Retired", "Student", "Self-employed"])
+        citizenship = st.selectbox("Citizenship Status", ["US Citizen", "Lawful Permanent Resident", "Other legal resident", "Non-resident"])
+        
+        submitted = st.form_submit_button("Start My Personalized Session")
+        if submitted:
+            st.session_state.user_profile.update({
+                "age": age, "gender": gender, "household_size": household_size,
+                "income": income, "employment_status": employment_status,
+                "citizenship": citizenship,
+                "medical_history": None, "medications": None, "special_cases": None
+            })
+            st.session_state.current_phase = "chat"
+            # The chat interface will handle the START_PROFILE_BUILDING trigger
+            st.rerun()
+
+def display_chat_interface():
+    st.header("3. Let's Chat!")
+    
+    # If this is the first time entering the chat phase, get the first question.
+    if not st.session_state.chat_history and not st.session_state.is_profile_complete:
+        with st.spinner("Starting your personalized profile conversation..."):
+            response_data = send_chat_message_to_backend("START_PROFILE_BUILDING")
+            if response_data:
+                st.session_state.user_profile = response_data["updated_profile"]
+                st.session_state.chat_history = response_data["updated_history"]
+                st.session_state.is_profile_complete = response_data["is_profile_complete"]
+                st.rerun()
+
+    # Display chat history from session state
+    for message in st.session_state.chat_history:
+        # Handle potential errors if message format is incorrect
+        if ":" in message:
+            role, content = message.split(":", 1)
+            with st.chat_message(role.lower()):
+                st.markdown(content.strip())
+        else:
+            st.chat_message("system").write(message) # Fallback for unexpected format
+
+    # User input
+    if prompt := st.chat_input("Your message..."):
+        # Display user message immediately
+        st.chat_message("user").markdown(prompt)
+        
+        # Send to backend and get response
+        with st.spinner("InsuCompass AI is thinking..."):
+            response_data = send_chat_message_to_backend(prompt)
+        
+        if response_data:
+            # The backend is the source of truth for all state. Overwrite local state.
+            st.session_state.user_profile = response_data["updated_profile"]
+            st.session_state.chat_history = response_data["updated_history"]
+            st.session_state.is_profile_complete = response_data["is_profile_complete"]
+            st.rerun()
+
+# --- Main Application Flow Control ---
+if st.session_state.current_phase == "initial_zip":
+    display_zip_form()
+elif st.session_state.current_phase == "basic_profile":
+    display_basic_profile_form()
+else: # 'chat' phase
+    display_chat_interface()
+
+# --- Sidebar for Debugging ---
+with st.sidebar:
+    st.header("Session State")
+    st.json(st.session_state.to_dict(), expanded=False)
+    if st.button("Reset Session"):
+        st.session_state.clear()
+        st.rerun()
\ No newline at end of file
diff --git a/insucompass.db b/insucompass.db
new file mode 100644
index 0000000000000000000000000000000000000000..71188bcfc6614c1ee2b3e4d97eb6bb3f220e470a
--- /dev/null
+++ b/insucompass.db
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:428ba164bfd822050705396ffe43dc497fcd78db06dc88eb007bd5e6378cbf06
+size 49696768
diff --git a/insucompass/.DS_Store b/insucompass/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..5232b5b8a2ad176b59180583e351be5756e4888d
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diff --git a/insucompass/__init__.py b/insucompass/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/insucompass/__pycache__/__init__.cpython-310.pyc b/insucompass/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..808967bae377c3598f67836cd228d59a95d05df5
Binary files /dev/null and b/insucompass/__pycache__/__init__.cpython-310.pyc differ
diff --git a/insucompass/__pycache__/config.cpython-310.pyc b/insucompass/__pycache__/config.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e93b8e17f4960612489a24c4cc5b1043a7131c80
Binary files /dev/null and b/insucompass/__pycache__/config.cpython-310.pyc differ
diff --git a/insucompass/api/__init__.py b/insucompass/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/insucompass/api/endpoints.py b/insucompass/api/endpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..da5db083cad5aebd16123bccb16125cdd162ca5f
--- /dev/null
+++ b/insucompass/api/endpoints.py
@@ -0,0 +1,79 @@
+import logging
+from fastapi import APIRouter, HTTPException, Body
+from typing import Dict, Any
+
+# Import our services, agents, and models
+from insucompass.services.zip_client import get_geo_data_from_zip
+from insucompass.core.models import GeoDataResponse, ChatRequest, ChatResponse
+from insucompass.core.agents.profile_agent import profile_builder
+from insucompass.core.agent_orchestrator import app as orchestrator # The compiled LangGraph app
+
+from insucompass.services.database import get_db_connection, create_or_update_user_profile, get_user_profile
+
+# Configure logging
+logger = logging.getLogger(__name__)
+
+# Create the API router
+router = APIRouter()
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+@router.get("/geodata/{zip_code}", response_model=GeoDataResponse)
+def get_geolocation_data(zip_code: str):
+    """Endpoint to get county, city, and state information from a given ZIP code."""
+    if not zip_code.isdigit() or len(zip_code) != 5:
+        raise HTTPException(status_code=400, detail="Invalid ZIP code format.")
+    geo_data = get_geo_data_from_zip(zip_code)
+    if not geo_data:
+        raise HTTPException(status_code=404, detail="Could not find location data.")
+    return GeoDataResponse(
+        zip_code=zip_code, county=geo_data.county.replace(" County", ""),
+        city=geo_data.city, state=geo_data.state, state_abbreviation=geo_data.state_abbr
+    )
+
+@router.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """
+    Handles the entire conversational flow using the unified LangGraph orchestrator.
+    The orchestrator manages the state, including profile building and Q&A.
+    """
+    logger.info(f"Received unified chat request for thread_id: {request.thread_id}")
+    
+    # LangGraph needs a thread_id to save/load conversation state from its checkpointer
+    thread_config = {"configurable": {"thread_id": request.thread_id}}
+    
+    # The graph's state is loaded automatically by LangGraph using the thread_id.
+    # We only need to provide the inputs for the current turn.
+    inputs = {
+        "user_profile": request.user_profile,
+        "user_message": request.message,
+        "is_profile_complete": request.is_profile_complete,
+        "conversation_history": request.conversation_history,
+    }
+
+    try:
+        # We invoke the graph. It will load the previous state, run the necessary nodes,
+        # and save the new state, all in one call.
+        final_state = orchestrator.invoke(inputs, config=thread_config)
+        
+        # Extract the relevant data from the final state of the graph
+        agent_response = final_state.get("generation")
+        updated_profile = final_state.get("user_profile")
+        updated_history = final_state.get("conversation_history")
+        is_profile_complete = final_state.get("is_profile_complete")
+        
+        if not agent_response:
+            agent_response = "I'm sorry, I encountered an issue. Could you please rephrase?"
+
+        logger.info("Unified graph execution completed successfully.")
+        
+        return ChatResponse(
+            agent_response=agent_response,
+            updated_profile=updated_profile,
+            updated_history=updated_history,
+            is_profile_complete=is_profile_complete
+        )
+
+    except Exception as e:
+        logger.error(f"Error during unified graph orchestration: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail="An internal error occurred in the AI orchestrator.")
\ No newline at end of file
diff --git a/insucompass/config.py b/insucompass/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..5464fdd1c0666d527ebe10b81dcc24b7d145e67d
--- /dev/null
+++ b/insucompass/config.py
@@ -0,0 +1,99 @@
+import os
+from dotenv import load_dotenv
+from pydantic_settings import BaseSettings
+from typing import List
+
+# Load environment variables from .env file
+load_dotenv()
+
+class Settings(BaseSettings):
+    """
+    Loads and validates application settings from environment variables.
+    """
+    # API Keys
+    GROQ_API_KEY: str = os.getenv("GROQ_API_KEY", "")
+    GOOGLE_API_KEY: str = os.getenv("GOOGLE_API_KEY", "")
+    TAVILY_API_KEY: str = os.getenv("TAVILY_API_KEY", "")
+
+    # JWT Settings
+    JWT_SECRET_KEY: str = os.getenv("JWT_SECRET_KEY", "default_secret")
+    JWT_ALGORITHM: str = os.getenv("JWT_ALGORITHM", "HS256")
+    ACCESS_TOKEN_EXPIRE_MINUTES: int = int(os.getenv("ACCESS_TOKEN_EXPIRE_MINUTES", 30))
+
+    # Database
+    DATABASE_URL: str = "insucompass.db"
+
+    # Logging
+    LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
+
+    # LLM Settings
+
+    GROQ_MODEL_NAME: str = "llama-3.3-70b-versatile"
+    GROQ_FAST_MODEL_NAME: str = "llama-3.1-8b-instant"
+    GEMINI_PRO_MODEL_NAME: str = "gemini-2.5-pro"
+    GEMINI_MODEL_NAME: str = "gemini-2.5-flash" #"gemini-2.5-flash" # "gemini-2.0-flash"
+    GEMINI_FAST_MODEL_NAME: str = "gemini-2.5-flash-lite-preview-06-17"
+
+    # CRAWLING JOBS CONFIGURATION
+    CRAWLING_JOBS: List[dict] = [
+        {
+            "name": "HealthCare.gov Crawl",
+            "start_url": "https://www.healthcare.gov/",
+            "method": "requests_crawl",
+            "domain_lock": "www.healthcare.gov",
+            "crawl_depth": 2,
+            "content_types": ["pdf", "html"],
+            "status": "active"
+        },
+        {
+            "name": "CMS.gov Regulations & Guidance Crawl",
+            "start_url": "https://www.cms.gov/regulations-and-guidance",
+            "method": "selenium_crawl",
+            "domain_lock": "www.cms.gov",
+            "crawl_depth": 2,
+            "content_types": ["pdf", "html"],
+            "status": "active"
+        },
+        {
+            "name": "Medicaid.gov Crawl",
+            "start_url": "https://www.medicaid.gov/",
+            "method": "requests_crawl",
+            "domain_lock": "www.medicaid.gov",
+            "crawl_depth": 2,
+            "content_types": ["pdf", "html"],
+            "status": "active"
+        },
+        {
+            "name": "Medicare.gov Crawl",
+            "start_url": "https://www.medicare.gov/",
+            "method": "selenium_crawl",
+            "domain_lock": "www.medicare.gov",
+            "crawl_depth": 1,
+            "content_types": ["html"],
+            "status": "active"
+        },
+        {
+            "name": "TRICARE Publications Crawl",
+            "start_url": "https://www.tricare.mil/publications",
+            "method": "selenium_crawl",
+            "domain_lock": "www.tricare.mil",
+            "crawl_depth": 1,
+            "content_types": ["pdf", "html"],
+            "status": "active"
+        },
+        {
+            "name": "VA.gov Health Benefits Crawl",
+            "start_url": "https://www.va.gov/health-care/",
+            "method": "requests_crawl",
+            "domain_lock": "www.va.gov",
+            "crawl_depth": 2,
+            "content_types": ["html"],
+            "status": "active"
+        },
+    ]
+
+    class Config:
+        case_sensitive = True
+
+# Instantiate settings
+settings = Settings()
\ No newline at end of file
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diff --git a/insucompass/core/__pycache__/models.cpython-310.pyc b/insucompass/core/__pycache__/models.cpython-310.pyc
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diff --git a/insucompass/core/agent_orchestrator.py b/insucompass/core/agent_orchestrator.py
new file mode 100644
index 0000000000000000000000000000000000000000..be95b2f14bddba6be9420a00cb325df4540a88c3
--- /dev/null
+++ b/insucompass/core/agent_orchestrator.py
@@ -0,0 +1,210 @@
+import logging
+import json
+import uuid
+import sqlite3
+from typing import List, Dict, Any
+from typing_extensions import TypedDict
+
+from langchain_core.documents import Document
+from langgraph.graph import StateGraph, END
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+# Import all our custom agent and service classes
+from insucompass.core.agents.profile_agent import profile_builder
+from insucompass.core.agents.query_trasformer import QueryTransformationAgent
+from insucompass.core.agents.router_agent import router
+from insucompass.services.ingestion_service import IngestionService
+from insucompass.core.agents.search_agent import searcher
+from insucompass.core.agents.advisor_agent import advisor
+
+from insucompass.services import llm_provider
+from insucompass.prompts.prompt_loader import load_prompt
+from insucompass.services.vector_store import vector_store_service
+
+llm = llm_provider.get_gemini_llm()
+retriever = vector_store_service.get_retriever()
+transformer = QueryTransformationAgent(llm, retriever)
+ingestor = IngestionService()
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- Unified LangGraph State Definition ---
+class AgentState(TypedDict):
+    user_profile: Dict[str, Any]
+    user_message: str
+    conversation_history: List[str]
+    is_profile_complete: bool
+    standalone_question: str
+    documents: List[Document]
+    is_relevant: bool
+    generation: str
+
+# --- Graph Nodes ---
+
+def profile_builder_node(state: AgentState) -> Dict[str, Any]:
+    """A single turn of the profile building conversation."""
+    logger.info("---NODE: PROFILE BUILDER---")
+    profile = state["user_profile"]
+    message = state["user_message"]
+    history = state.get("conversation_history", [])
+
+    if message == "START_PROFILE_BUILDING":
+        agent_response = profile_builder.get_next_question(profile, [])
+        new_history = [f"Agent: {agent_response}"]
+        return {"conversation_history": new_history, "generation": agent_response, "user_profile": profile, "is_profile_complete": False}
+
+    last_question = history[-1][len("Agent: "):] if history and history[-1].startswith("Agent:") else ""
+    updated_profile = profile_builder.update_profile_with_answer(profile, last_question, message)
+    agent_response = profile_builder.get_next_question(updated_profile, history + [f"User: {message}"])
+    
+    new_history = history + [f"User: {message}", f"Agent: {agent_response}"]
+    
+    if agent_response == "PROFILE_COMPLETE":
+        logger.info("Profile building complete.")
+        final_message = "Great! Your profile is complete. How can I help you with your health insurance questions?"
+        new_history[-1] = f"Agent: {final_message}" # Replace "PROFILE_COMPLETE"
+        return {"user_profile": updated_profile, "is_profile_complete": True, "conversation_history": new_history, "generation": final_message}
+    
+    return {"user_profile": updated_profile, "is_profile_complete": False, "conversation_history": new_history, "generation": agent_response}
+
+def reformulate_query_node(state: AgentState) -> Dict[str, Any]:
+    """Reformulates the user's question to be self-contained."""
+    logger.info("---NODE: REFORMULATE QUERY---")
+    question = state["user_message"]
+    history = state["conversation_history"]
+    user_profile = state["user_profile"]
+    
+    profile_summary = f"User profile context: State={user_profile.get('state')}, Age={user_profile.get('age')}, History={user_profile.get('medical_history')}"
+    prompt = load_prompt("query_reformulator")
+    history_str = "\n".join(history)
+    
+    full_prompt = f"{prompt}\n\n### User Profile Summary\n{profile_summary}\n\n### Conversation History:\n{history_str}\n\n### Follow-up Question:\n{question}"
+    
+    response = llm.invoke(full_prompt)
+    standalone_question = response.content.strip()
+    return {"standalone_question": standalone_question}
+
+def retrieve_and_grade_node(state: AgentState) -> Dict[str, Any]:
+    """Retrieves documents and grades them."""
+    logger.info("---NODE: RETRIEVE & GRADE---")
+    standalone_question = state["standalone_question"]
+    documents = transformer.transform_and_retrieve(standalone_question)
+    is_relevant = router.grade_documents(standalone_question, documents)
+    return {"documents": documents, "is_relevant": is_relevant}
+
+def search_and_ingest_node(state: AgentState) -> Dict[str, Any]:
+    """Searches the web and ingests new info."""
+    logger.info("---NODE: SEARCH & INGEST---")
+    web_documents = searcher.search(state["standalone_question"])
+    if web_documents:
+        ingestor.ingest_documents(web_documents)
+    return {}
+
+def generate_answer_node(state: AgentState) -> Dict[str, Any]:
+    """Generates the final answer."""
+    logger.info("---NODE: GENERATE ADVISOR RESPONSE---")
+    generation = advisor.generate_response(
+        state["standalone_question"], state["user_profile"], state["documents"]
+    )
+    history = state["conversation_history"] + [f"User: {state['user_message']}", f"Agent: {generation}"]
+    return {"generation": generation, "conversation_history": history}
+
+# --- Conditional Edges ---
+def should_search_web(state: AgentState) -> str:
+    return "search" if not state["is_relevant"] else "generate"
+
+# (CORRECTED) This is the function for the entry point conditional edge
+def decide_entry_point(state: AgentState) -> str:
+    """Decides the initial path based on profile completion status."""
+    logger.info("---ROUTING: ENTRY POINT---")
+    if state.get("is_profile_complete"):
+        logger.info(">>> Route: Profile is complete. Starting Q&A.")
+        return "qna"
+    else:
+        logger.info(">>> Route: Profile is not complete. Starting Profile Builder.")
+        return "profile"
+
+# --- Build the Graph ---
+db_connection = sqlite3.connect("data/checkpoints.db", check_same_thread=False)
+memory = SqliteSaver(db_connection)
+
+builder = StateGraph(AgentState)
+
+# (CORRECTED) Removed the faulty entry_router_node
+builder.add_node("profile_builder", profile_builder_node)
+builder.add_node("reformulate_query", reformulate_query_node)
+builder.add_node("retrieve_and_grade", retrieve_and_grade_node)
+builder.add_node("search_and_ingest", search_and_ingest_node)
+builder.add_node("generate_answer", generate_answer_node)
+
+# (CORRECTED) Set a conditional entry point
+builder.set_conditional_entry_point(
+    decide_entry_point,
+    {
+        "profile": "profile_builder",
+        "qna": "reformulate_query"
+    }
+)
+
+# Define graph edges
+builder.add_edge("profile_builder", END) # A profile turn is one full loop. The state is saved, and the next call will re-evaluate at the entry point.
+builder.add_edge("reformulate_query", "retrieve_and_grade")
+builder.add_conditional_edges("retrieve_and_grade", should_search_web, {"search": "search_and_ingest", "generate": "generate_answer"})
+builder.add_edge("search_and_ingest", "generate_answer")
+builder.add_edge("generate_answer", END)
+
+app = builder.compile(checkpointer=memory)
+
+# --- Interactive Test Harness (CORRECTED) ---
+# if __name__ == '__main__':
+#     print("--- InsuCompass AI Unified Orchestrator Interactive Test ---")
+#     print("Type 'quit' at any time to exit.")
+
+#     test_thread_id = f"interactive-test-{uuid.uuid4()}"
+#     thread_config = {"configurable": {"thread_id": test_thread_id}}
+#     print(f"Using conversation thread_id: {test_thread_id}")
+
+#     # Initial state for a new user
+#     current_state = {
+#         "user_profile": {
+#             "zip_code": "90210", "county": "Los Angeles", "state": "California", "state_abbreviation": "CA",
+#             "age": 45, "gender": "Male", "household_size": 2, "income": 120000,
+#             "employment_status": "employed_with_employer_coverage", "citizenship": "US Citizen",
+#             "medical_history": None, "medications": None, "special_cases": None
+#         },
+#         "user_message": "START_PROFILE_BUILDING",
+#         "is_profile_complete": False,
+#         "conversation_history": [],
+#     }
+
+#     while True:
+#         print("\n" + "="*20 + " INVOKING GRAPH " + "="*20)
+#         print(f"Sending message: '{current_state['user_message']}'")
+        
+#         # The graph is invoked with the current state
+#         final_state = app.invoke(current_state, config=thread_config)
+
+#         # Update our local state from the graph's final output
+#         current_state = final_state
+#         agent_response = current_state["generation"]
+        
+#         print(f"\nInsuCompass Agent: {agent_response}")
+
+#         # Get the next input from the user
+#         if current_state["is_profile_complete"]:
+#             # If the last response was the completion message, prompt for a question
+#             if "profile is complete" in agent_response:
+#                  next_message = input("Your Question > ")
+#             else: # It was a Q&A response, so prompt for another question
+#                  next_message = input("Your Follow-up Question > ")
+#         else:
+#             next_message = input("Your Answer > ")
+
+#         if next_message.lower() == 'quit':
+#             print("Exiting test.")
+#             break
+        
+#         # Prepare the state for the next turn
+#         current_state["user_message"] = next_message
\ No newline at end of file
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diff --git a/insucompass/core/agents/advisor_agent.py b/insucompass/core/agents/advisor_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..b737bbfa7166bcfec4704bb4b18cfe840f798833
--- /dev/null
+++ b/insucompass/core/agents/advisor_agent.py
@@ -0,0 +1,77 @@
+import logging
+import json
+from typing import List, Dict, Any
+from langchain_core.documents import Document
+
+from insucompass.services import llm_provider
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+llm = llm_provider.get_gemini_pro_llm()
+
+class AdvisorAgent:
+    """
+    The final agent in the Q&A pipeline. It uses a Chain-of-Thought process
+    to synthesize the user's profile and retrieved context into a comprehensive,
+    personalized, and conversational response that includes a follow-up question.
+    """
+
+    def __init__(self):
+        """Initializes the AdvisorAgent."""
+        try:
+            self.agent_prompt = load_prompt("advisor_agent")
+            logger.info("Conversational AdvisorAgent initialized successfully.")
+        except FileNotFoundError:
+            logger.critical("AdvisorAgent prompt file not found. The agent cannot function.")
+            raise
+
+    def generate_response(
+        self,
+        question: str,
+        user_profile: Dict[str, Any],
+        documents: List[Document]
+    ) -> str:
+        """
+        Generates the final, synthesized, and conversational response.
+
+        Args:
+            question: The user's original question.
+            user_profile: The user's complete profile.
+            documents: The relevant documents retrieved from the knowledge base.
+
+        Returns:
+            A string containing the final, formatted answer and a follow-up question.
+        """
+        if not documents:
+            logger.warning("AdvisorAgent received no documents. Cannot generate a grounded response.")
+            return "I'm sorry, but I couldn't find any relevant information to answer your question, even after searching the web. Is there another way I can help you look into this?"
+
+        # Prepare the context for the prompt
+        profile_str = json.dumps(user_profile, indent=2)
+        context_str = "\n\n---\n\n".join(
+            [f"[METADATA: source_name='{d.metadata.get('source_name', 'N/A')}', source_url='{d.metadata.get('source_url', 'N/A')}']\n\n{d.page_content}" for d in documents]
+        )
+        
+        full_prompt = (
+            f"{self.agent_prompt}\n\n"
+            f"### CONTEXT FOR YOUR RESPONSE\n"
+            f"user_profile: {profile_str}\n\n"
+            f"user_question: \"{question}\"\n\n"
+            f"retrieved_context:\n{context_str}"
+        )
+        
+        logger.info("Generating final conversational response with AdvisorAgent...")
+        try:
+            response = llm.invoke(full_prompt)
+            generation = response.content.strip()
+            logger.info("Successfully generated final conversational answer.")
+            return generation
+        except Exception as e:
+            logger.error(f"Error during final answer generation: {e}")
+            return "I apologize, I encountered an error while trying to formulate the final answer. Please try again."
+        
+advisor = AdvisorAgent()
\ No newline at end of file
diff --git a/insucompass/core/agents/document_summarizer.py b/insucompass/core/agents/document_summarizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..62b48ea913522ec7a776d5ec8876090c669f22be
--- /dev/null
+++ b/insucompass/core/agents/document_summarizer.py
@@ -0,0 +1,65 @@
+import logging
+from typing import Dict, Optional
+from langchain_core.prompts import PromptTemplate
+
+from scripts.data_processing.document_loader import load_document
+
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+SUMMARY_PROMPT_TEMPLATE = load_prompt('document_summarizer')
+
+class DocumentSummarizerAgent:
+    """
+    A service to generate and cache summaries for full source documents.
+    """
+    def __init__(self, llm):
+        self.llm = llm
+        self.prompt = PromptTemplate.from_template(SUMMARY_PROMPT_TEMPLATE)
+        self.chain = self.prompt | self.llm
+        self._summary_cache: Dict[str, str] = {}
+        logger.info("DocumentSummarizer service initialized.")
+
+    def get_summary(self, source_id: int, local_path: str) -> Optional[str]:
+        """
+        Retrieves or generates a summary for a given source document.
+        Uses an in-memory cache to avoid re-summarizing the same document.
+
+        Args:
+            source_id: The unique ID of the source document.
+            local_path: The local file path to the full document content.
+
+        Returns:
+            The summary string, or None if summarization fails.
+        """
+        cache_key = str(source_id)
+        if cache_key in self._summary_cache:
+            logger.debug(f"Returning cached summary for source_id: {source_id}")
+            return self._summary_cache[cache_key]
+
+        logger.info(f"Generating new summary for source_id: {source_id} from path: {local_path}")
+        
+        full_content = load_document(local_path)
+        if not full_content:
+            logger.error(f"Could not load document content from {local_path} to generate summary.")
+            return None
+        
+        try:
+            # Truncate content to fit model context window if necessary
+            # A safe number for many models is around 16k tokens, let's use chars as a proxy
+            max_chars = 50000 
+            truncated_content = full_content[:max_chars]
+
+            response = self.chain.invoke({"document_content": truncated_content})
+            summary = response.content if hasattr(response, 'content') else str(response)
+
+            self._summary_cache[cache_key] = summary
+            logger.info(f"Successfully generated and cached summary for source_id: {source_id}")
+            return summary
+        except Exception as e:
+            logger.error(f"Failed to generate summary for source_id {source_id}: {e}")
+            return None
\ No newline at end of file
diff --git a/insucompass/core/agents/profile_agent.py b/insucompass/core/agents/profile_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ccb9b810aee3c48287059f506d4ca01a157f249
--- /dev/null
+++ b/insucompass/core/agents/profile_agent.py
@@ -0,0 +1,141 @@
+import logging
+import json
+from pathlib import Path
+from typing import Tuple, Dict, Any, Optional
+
+from insucompass.services import llm_provider
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+llm = llm_provider.get_gemini_llm()
+
+class ProfileBuilder:
+    """
+    A service class that manages the entire conversational profile building process.
+    It determines the next question and intelligently updates the profile with user answers.
+    """
+
+    def __init__(self):
+        """Initializes the ProfileBuilder, loading all necessary prompts."""
+        try:
+            self.question_prompt = load_prompt("profile_agent")
+            self.updater_prompt = load_prompt("profile_updater")
+            logger.info("ProfileBuilder initialized successfully with all prompts.")
+        except FileNotFoundError as e:
+            logger.critical(f"A required prompt file was not found: {e}. ProfileBuilder cannot function.")
+            raise
+
+    def get_next_question(self, current_profile: Dict[str, Any], conversation_history: Dict[str, Any]) -> str:
+        """
+        Analyzes the user's current profile and determines the next question to ask,
+        or signals that the profile is complete.
+
+        Args:
+            current_profile: A dictionary representing the user's profile data.
+
+        Returns:
+            A string containing the next question for the user, or "PROFILE_COMPLETE".
+        """
+        logger.debug(f"Determining next question for profile: {current_profile}")
+        profile_json_str = json.dumps(current_profile, indent=2)
+        conversation_history_str = json.dumps(conversation_history, indent=2)
+        full_prompt = f"{self.question_prompt}\n\n### User Profile\n{profile_json_str} \n\n ### Conversation History{conversation_history_str}"
+
+        try:
+            response = llm.invoke(full_prompt)
+            next_step = response.content.strip()
+            logger.info(f"LLM returned next step: '{next_step}'")
+            return next_step
+        except Exception as e:
+            logger.error(f"LLM error during question generation: {e}")
+            return "I'm sorry, I'm having a little trouble right now. Could we try that again?"
+
+    def update_profile_with_answer(
+        self,
+        current_profile: Dict[str, Any],
+        last_question: str,
+        user_answer: str
+    ) -> Dict[str, Any]:
+        """
+        Uses an LLM to intelligently update the user's profile with their latest answer.
+
+        Args:
+            current_profile: The user's profile before the update.
+            last_question: The question the user is answering.
+            user_answer: The user's free-text answer.
+
+        Returns:
+            The updated profile dictionary.
+        """
+        logger.debug(f"Updating profile with answer: '{user_answer}' for question: '{last_question}'")
+        profile_json_str = json.dumps(current_profile, indent=2)
+        
+        # Construct the prompt for the updater LLM call
+        full_prompt = (
+            f"{self.updater_prompt}\n\n"
+            f"current_profile: {profile_json_str}\n\n"
+            f"last_question_asked: \"{last_question}\"\n\n"
+            f"user_answer: \"{user_answer}\""
+        )
+
+        try:
+            response = llm.invoke(full_prompt)
+            response_content = response.content.strip()
+            
+            # Clean the response to ensure it's valid JSON
+            # The LLM can sometimes wrap the JSON in markdown
+            if response_content.startswith("```json"):
+                response_content = response_content[7:-3].strip()
+
+            updated_profile = json.loads(response_content)
+            logger.info("Successfully updated profile with user's answer.")
+            return updated_profile
+        except json.JSONDecodeError as e:
+            logger.error(f"Failed to decode JSON from LLM response: {e}\nResponse was: {response.content}")
+            # Return the original profile to avoid data corruption
+            return current_profile
+        except Exception as e:
+            logger.error(f"LLM error during profile update: {e}")
+            # Return the original profile on error
+            return current_profile
+
+    def run_conversation_turn(
+        self,
+        current_profile: Dict[str, Any],
+        last_user_answer: str = None
+    ) -> Tuple[Dict[str, Any], str]:
+        """
+        Executes a full turn of the profile-building conversation.
+
+        Args:
+            current_profile: The current state of the user's profile.
+            last_user_answer: The user's answer from the previous turn, if any.
+
+        Returns:
+            A tuple containing:
+            - The updated profile dictionary.
+            - The next question to ask the user (or "PROFILE_COMPLETE").
+        """
+        # First, determine the question that *would have been asked* for the current state
+        # This is the question the user just answered.
+        question_that_was_asked = self.get_next_question(current_profile)
+
+        profile_after_update = current_profile
+        # If there was an answer from the user, update the profile
+        if last_user_answer and question_that_was_asked != "PROFILE_COMPLETE":
+            profile_after_update = self.update_profile_with_answer(
+                current_profile=current_profile,
+                last_question=question_that_was_asked,
+                user_answer=last_user_answer
+            )
+
+        # Now, with the potentially updated profile, get the *next* question
+        next_question_to_ask = self.get_next_question(profile_after_update)
+
+        return profile_after_update, next_question_to_ask
+    
+profile_builder = ProfileBuilder()
\ No newline at end of file
diff --git a/insucompass/core/agents/query_intent_classifier.py b/insucompass/core/agents/query_intent_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..c23e26261b203979019065414e3a26cdd061d375
--- /dev/null
+++ b/insucompass/core/agents/query_intent_classifier.py
@@ -0,0 +1,80 @@
+# insucompass/core/agents/query_intent_classifier.py
+
+import logging
+import pydantic
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_core.prompts import PromptTemplate
+
+from insucompass.core.models import IntentType, QueryIntent
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+QUERY_INTENT_PROMPT_TEMPLATE = load_prompt('query_intent_classifier')
+
+
+class QueryIntentClassifierAgent:
+    """
+    A classifier that uses an LLM to determine the user's intent and
+    pre-processes the query for optimal RAG performance.
+    """
+    def __init__(self, llm):
+        """
+        Initializes the classifier with a given LLM.
+        Generated code
+        Args:
+            llm: An instance of a LLM model.
+        """
+        self.llm = llm
+        self.parser = PydanticOutputParser(pydantic_object=QueryIntent)
+        self.prompt_template = PromptTemplate(
+            template=QUERY_INTENT_PROMPT_TEMPLATE,
+            input_variables=["query"],
+            partial_variables={
+                "format_instructions": self.parser.get_format_instructions(),
+                "simple": IntentType.SIMPLE.value,
+                "ambiguous": IntentType.AMBIGUOUS.value,
+                "complex": IntentType.COMPLEX.value,
+                "concise": IntentType.CONCISE.value,
+            },
+        )
+        self.chain = self.prompt_template | self.llm | self.parser
+        logger.info("QueryIntentClassifierAgent initialized successfully.")
+
+    def classify_intent(self, query: str) -> QueryIntent:
+        """
+        Classifies the intent of the query and transforms it accordingly.
+
+        Args:
+            query: The user's input query.
+
+        Returns:
+            A QueryIntent object containing the classification and transformed queries.
+            Returns a default 'SIMPLE' classification on failure.
+        """
+        if not query:
+            logger.warning("Received an empty query. Cannot classify.")
+            return QueryIntent(
+                intent=IntentType.SIMPLE,
+                reasoning="Input query was empty.",
+            )
+
+        logger.debug(f"Classifying query: '{query}'")
+        try:
+            result = self.chain.invoke({"query": query})
+            logger.info(f"Successfully classified query. Intent: {result.intent.value}")
+            logger.debug(f"Classification reasoning: {result.reasoning}")
+            return result
+        except (pydantic.ValidationError, Exception) as e:
+            logger.error(
+                f"Failed to classify query or parse LLM output for query: '{query}'. Error: {e}. "
+                "Defaulting to 'SIMPLE' intent."
+            )
+            # Fallback mechanism to ensure the RAG chain doesn't break
+            return QueryIntent(
+                intent=IntentType.SIMPLE,
+                reasoning="Classification failed due to a parsing or API error. Defaulting to simple retrieval.",
+            )
diff --git a/insucompass/core/agents/query_trasformer.py b/insucompass/core/agents/query_trasformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e5ccd3bcd03cdd64b189101a83fb24e00d3afa1
--- /dev/null
+++ b/insucompass/core/agents/query_trasformer.py
@@ -0,0 +1,202 @@
+# insucompass/core/query_agent.py
+
+import logging
+from typing import List, Dict
+from collections import defaultdict
+from langchain.load import dumps, loads
+from langchain_core.documents import Document
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+
+from insucompass.core.agents.query_intent_classifier import QueryIntentClassifierAgent
+from insucompass.core.models import IntentType, TransformedQueries
+
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+QUERY_TRNSFORMER_PROMPT_TEMPLATE = load_prompt('query_transformer')
+
+class QueryTransformationAgent:
+    """
+    An agent responsible for analyzing a user's query, applying an advanced
+    RAG strategy based on the query's intent, and retrieving enhanced context
+    from a vector store.
+    """
+
+    def __init__(self, llm, retriever):
+        """
+        Initializes the QueryTransformationAgent.
+
+        Args:
+            llm: An instance of the language model to be used
+                 for query analysis and transformation.
+            retriever: A LangChain retriever runnable connected to the vector store.
+        """
+        if not llm or not retriever:
+            raise ValueError("LLM and retriever must be provided.")
+            
+        self.llm = llm
+        self.retriever = retriever
+
+        # 1. Create the parser for the transformed queries
+        self.parser = PydanticOutputParser(pydantic_object=TransformedQueries)
+        # 2. Create the prompt template, injecting the format instructions
+        self.prompt = PromptTemplate(
+            template=QUERY_TRNSFORMER_PROMPT_TEMPLATE,
+            input_variables=["query", "intent", "reasoning"],
+            partial_variables={"format_instructions": self.parser.get_format_instructions()}
+        )
+        # 3. Build the final chain with the parser at the end
+        self.chain = self.prompt | self.llm | self.parser
+
+        self.classifier = QueryIntentClassifierAgent(llm)
+        logger.info("QueryTransformationAgent initialized successfully.")
+
+    def _unique_union(self, doc_lists: List[List[Document]]) -> List[Document]:
+        """
+        Aggregates and merges lists of documents, grouping them by 'source_id'.
+        """
+        # Step 1: Group all retrieved chunks by their source_id.
+        # defaultdict simplifies the grouping logic.
+        docs_by_source = defaultdict(list)
+        all_docs = [doc for doc_list in doc_lists for doc in doc_list]
+
+        for doc in all_docs:
+            source_id = doc.metadata.get('source_id')
+            if source_id is not None:
+                docs_by_source[source_id].append(doc)
+            else:
+                logger.warning(f"Skipping a document with missing 'source_id' in metadata: {doc.page_content[:100]}...")
+
+        logger.debug(f"Grouped {len(all_docs)} chunks into {len(docs_by_source)} unique sources.")
+
+        # Step 2: Process each group to create a single, merged document.
+        CHUNK_SEPARATOR = "\n\n--- chunk ---\n\n"
+        final_merged_docs: List[Document] = []
+        for source_id, chunks in docs_by_source.items():
+            
+            # Step 2a: Sort the chunks by their chunk_number to ensure logical order.
+            # We use a default of infinity for any chunk missing a number, pushing it to the end.
+            sorted_chunks = sorted(chunks, key=lambda d: d.metadata.get('chunk_number', float('inf')))
+
+            # Step 2b: Use the metadata from the first chunk as the base for our new metadata.
+            # This is now deterministic because of the sort.
+            base_metadata = sorted_chunks[0].metadata.copy()
+
+            # Step 2c: Concatenate the page content from the sorted chunks.
+            merged_content = CHUNK_SEPARATOR.join([chunk.page_content for chunk in sorted_chunks])
+
+            # Step 2d: Create a clean, new metadata object for the merged document.
+            final_metadata: Dict[str, Any] = {
+                # Preserve essential source-level information
+                "source_id": base_metadata.get("source_id"),
+                "source_url": base_metadata.get("source_url"),
+                "source_name": base_metadata.get("source_name"),
+                "source_local_path": base_metadata.get("source_local_path"),
+                # Add new summary fields
+                "merged_chunks_count": len(sorted_chunks),
+                "original_chunk_numbers": [c.metadata.get('chunk_number') for c in sorted_chunks]
+            }
+
+            # Step 2e: Create the final Document object.
+            merged_doc = Document(
+                page_content=merged_content,
+                metadata=final_metadata
+            )
+            final_merged_docs.append(merged_doc)
+        
+        logger.info(f"Aggregated and merged chunks into {len(final_merged_docs)} final documents.")
+        return final_merged_docs
+    
+    def reciprocal_rank_fusion(self, results: list[list], k=60):
+        fused_scores = {}
+        for docs in results:
+            # Iterate through each document in the list, with its rank
+            for rank, doc in enumerate(docs):
+                doc_str = dumps(doc)
+                if doc_str not in fused_scores:
+                    fused_scores[doc_str] = 0
+                # Update the score using the RRF formula: 1 / (rank + k)
+                fused_scores[doc_str] += 1 / (rank + k)
+
+        reranked_results = [
+            [loads(doc), score]
+            for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
+        ]
+        return reranked_results
+
+    def _perform_rag_fusion(self, original_query: str, generated_queries: List[str]) -> List[Document]:
+        """Executes the RAG-Fusion strategy."""
+        logger.debug(f"Performing RAG-Fusion with queries: {generated_queries}")
+        all_queries = [original_query] + generated_queries
+        retrieval_results = self.retriever.batch(all_queries)
+        # return self.reciprocal_rank_fusion(retrieval_results)[0]
+        return self._unique_union(retrieval_results)
+
+
+    def _perform_decomposition(self, sub_queries: List[str]) -> List[Document]:
+        """Executes the Decomposition strategy."""
+        logger.debug(f"Performing Decomposition with sub-queries: {sub_queries}")
+        retrieval_results = self.retriever.batch(sub_queries)
+        return self._unique_union(retrieval_results)
+
+    def _perform_step_back(self, original_query: str, step_back_query: str) -> List[Document]:
+        """Executes the Step-Back strategy."""
+        logger.debug(f"Performing Step-Back with queries: ['{original_query}', '{step_back_query}']")
+        queries_to_run = [original_query, step_back_query]
+        retrieval_results = self.retriever.batch(queries_to_run)
+        return self._unique_union(retrieval_results)
+
+    def transform_and_retrieve(self, query: str) -> List[Document]:
+        """
+        The main method of the agent. It classifies the query, applies the
+        appropriate retrieval strategy, and returns the final list of documents.
+        """
+        logger.info(f"Starting query transformation and retrieval for: '{query}'")
+        
+        try:
+            # 1. Classify the query to determine the strategy
+            classification = self.classifier.classify_intent(query)
+            intent = classification.intent
+            reasoning = classification.reasoning
+            
+            logger.info(f"Query classified with intent: {intent.value}. Reasoning: {classification.reasoning}")
+
+            result = self.chain.invoke({"query": query, "intent": intent, "reasoning": reasoning})
+            transformed_queries = result.transformed_queries
+            logger.debug(f"Generated transformed queries: {transformed_queries}")
+
+            documents: List[Document] = []
+
+            # 2. Route to the appropriate retrieval strategy
+            if intent == IntentType.AMBIGUOUS:
+                documents = self._perform_rag_fusion(query, transformed_queries)
+            
+            elif intent == IntentType.COMPLEX:
+                documents = self._perform_decomposition(transformed_queries)
+
+            elif intent == IntentType.CONCISE:
+                # Step-back provides one transformed query
+                step_back_q = transformed_queries[0] if transformed_queries else ""
+                documents = self._perform_step_back(query, step_back_q)
+
+            else: # Default to SIMPLE retrieval
+                logger.debug("Performing simple retrieval.")
+                documents = self.retriever.invoke(query)
+
+            logger.info(f"Retrieved {len(documents)} documents for query: '{query}'")
+            return documents
+
+        except Exception as e:
+            logger.error(f"An unexpected error occurred in QueryTransformationAgent for query '{query}': {e}", exc_info=True)
+            # Fallback to simple retrieval on any catastrophic failure
+            try:
+                logger.warning("Falling back to simple retrieval due to an error.")
+                return self.retriever.invoke(query)
+            except Exception as fallback_e:
+                logger.critical(f"Fallback retrieval also failed: {fallback_e}")
+                return [] # Return empty list if everything fails
\ No newline at end of file
diff --git a/insucompass/core/agents/router_agent.py b/insucompass/core/agents/router_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..01ea44ef38b1d4fa825b17f1dc9980be578a53fe
--- /dev/null
+++ b/insucompass/core/agents/router_agent.py
@@ -0,0 +1,78 @@
+import logging
+from typing import List
+from langchain_core.documents import Document
+from pydantic import BaseModel, Field
+
+from insucompass.services import llm_provider
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+llm = llm_provider.get_gemini_fast_llm()
+
+# Define the Pydantic model for the structured output from the LLM
+class GradeDocuments(BaseModel):
+    """Binary score for document relevance."""
+    is_relevant: str = Field(description="Is the document relevant to the question? Answer 'yes' or 'no'.")
+
+class RouterAgent:
+    """
+    An agent that acts as a router by grading the relevance of retrieved documents.
+    It decides whether the existing knowledge is sufficient to answer a question.
+    """
+
+    def __init__(self):
+        """Initializes the RouterAgent."""
+        try:
+            self.grader_prompt = load_prompt("document_grader")
+            # Create a structured LLM instance that is constrained to the GradeDocuments schema
+            self.structured_llm_grader = llm.with_structured_output(GradeDocuments)
+            logger.info("RouterAgent (Document Grader) initialized successfully.")
+        except FileNotFoundError:
+            logger.critical("Document grader prompt file not found. The RouterAgent cannot function.")
+            raise
+        except Exception as e:
+            logger.critical(f"Failed to initialize RouterAgent: {e}")
+            raise
+
+    def grade_documents(self, question: str, documents: List[Document]) -> bool:
+        """
+        Grades the retrieved documents against the user's question.
+
+        Args:
+            question: The user's question.
+            documents: A list of documents retrieved from the vector store.
+
+        Returns:
+            True if the documents are relevant, False otherwise.
+        """
+        if not documents:
+            logger.warning("No documents provided to grade. Assuming they are not relevant.")
+            return False
+
+        # Combine the content of all documents into a single context string
+        document_text = "\n\n---\n\n".join([d.page_content for d in documents])
+        
+        full_prompt = f"{self.grader_prompt}\n\n[USER QUESTION]\n{question}\n\n[DOCUMENTS]\n{document_text}"
+        
+        logger.debug("Grading document relevance...")
+        try:
+            grade = self.structured_llm_grader.invoke(full_prompt)
+            is_relevant = grade.is_relevant == "yes"
+            
+            if is_relevant:
+                logger.info("GRADE: Documents are RELEVANT.")
+            else:
+                logger.warning("GRADE: Documents are NOT RELEVANT.")
+                
+            return is_relevant
+        except Exception as e:
+            logger.error(f"Error during document grading: {e}. Defaulting to 'not relevant'.")
+            # Fail-safe: if grading fails, it's safer to assume the docs are not relevant
+            # and trigger a web search to get fresh information.
+            return False
+        
+router = RouterAgent()
\ No newline at end of file
diff --git a/insucompass/core/agents/search_agent.py b/insucompass/core/agents/search_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4a76cde738a0b44c43886ffb1e5820cc5726466
--- /dev/null
+++ b/insucompass/core/agents/search_agent.py
@@ -0,0 +1,154 @@
+import re
+import logging
+import requests
+from typing import List
+from pathlib import Path
+from langchain_core.documents import Document
+from tavily import TavilyClient
+
+from insucompass.services import llm_provider
+from insucompass.config import settings
+from insucompass.prompts.prompt_loader import load_prompt
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+llm = llm_provider.get_gemini_llm()
+
+# Define a dedicated directory for dynamically downloaded files
+DYNAMIC_DATA_DIR = Path("data/dynamic")
+
+def sanitize_filename(name: str) -> str:
+    """Sanitizes a string to be a valid filename."""
+    s = re.sub(r'[<>:"/\\|?*]', '_', name)
+    return s.replace(' ', '_')[:150]
+
+def get_file_extension_from_url(url: str) -> str:
+    """Intelligently determines the file extension from a URL."""
+    # Simple cases first
+    if url.lower().endswith('.pdf'):
+        return '.pdf'
+    if url.lower().endswith('.html') or url.lower().endswith('.htm'):
+        return '.html'
+    
+    # If no clear extension, we assume it's an HTML page
+    return '.html'
+
+class SearchAgent:
+    """
+    An agent that searches the web, saves the results locally in their correct
+    format, and returns structured Document objects.
+    """
+
+    def __init__(self):
+        """Initializes the SearchAgent."""
+        try:
+            self.query_prompt = load_prompt("search_agent")
+            self.tavily_client = TavilyClient(api_key=settings.TAVILY_API_KEY)
+            self.session = requests.Session()
+            self.session.headers.update({"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"})
+            DYNAMIC_DATA_DIR.mkdir(parents=True, exist_ok=True)
+            logger.info("SearchAgent initialized successfully.")
+        except Exception as e:
+            logger.critical(f"Failed to initialize SearchAgent. Error: {e}")
+            raise
+
+    def _formulate_query(self, user_question: str) -> str | None:
+        """Uses an LLM to reformulate the user's question into an effective search query."""
+        logger.debug(f"Formulating search query for question: '{user_question}'")
+        full_prompt = f"{self.query_prompt}\n\nUser Question: {user_question}"
+        
+        try:
+            response = llm.invoke(full_prompt)
+            query = response.content.strip()
+            
+            if query == "NOT_RELEVANT":
+                logger.warning("Agent determined question is not relevant for web search.")
+                return None
+            
+            logger.info(f"Formulated search query: '{query}'")
+            return query
+        except Exception as e:
+            logger.error(f"Error during search query formulation: {e}")
+            return user_question
+        
+    def _save_result_to_file(self, result: dict) -> Path | None:
+        """Saves a single search result's content to a local file with the correct extension."""
+        try:
+            url = result.get('url', '')
+            # Tavily provides the raw content, which is great for text, but for binary files like PDFs,
+            # we must re-download it to get the raw bytes.
+            
+            if not url:
+                return None
+
+            extension = get_file_extension_from_url(url)
+            sanitized_url = sanitize_filename(url)
+            filename = f"{sanitized_url}{extension}"
+            save_path = DYNAMIC_DATA_DIR / filename
+            
+            # If it's a PDF, we must re-download the raw bytes.
+            if extension == '.pdf':
+                logger.info(f"Re-downloading PDF content from: {url}")
+                response = self.session.get(url, timeout=30, verify=False)
+                response.raise_for_status()
+                save_path.write_bytes(response.content)
+            else:
+                # For HTML/text, the content from Tavily is sufficient.
+                content = result.get('content', '')
+                if not content:
+                    return None
+                save_path.write_text(content, encoding='utf-8')
+
+            logger.info(f"Saved web content from {url} to {save_path}")
+            return save_path
+        except Exception as e:
+            logger.error(f"Failed to save search result from {url} to file: {e}")
+            return None
+
+    def search(self, user_question: str) -> List[Document]:
+        """Executes the full web search process, including saving results to local files."""
+        search_query = self._formulate_query(user_question)
+        
+        if not search_query:
+            return []
+
+        logger.info(f"Performing web search with Tavily for query: '{search_query}'")
+        try:
+            # We ask Tavily to include the raw HTML content in its results
+            search_results = self.tavily_client.search(
+                query=search_query,
+                search_depth="advanced",
+                include_raw_content=True, # Important for getting full HTML
+                max_results=5
+            )
+        except Exception as e:
+            logger.error(f"Tavily search failed: {e}")
+            return []
+
+        if not search_results or not search_results.get("results"):
+            logger.warning("Tavily search returned no results.")
+            return []
+
+        documents = []
+        for result in search_results["results"]:
+            local_path = self._save_result_to_file(result)
+            
+            if local_path:
+                # The page_content is still useful for the initial grading step,
+                # but the ingestion service will use the local_path to load the definitive content.
+                doc = Document(
+                    page_content=result.get('content', ''),
+                    metadata={
+                        "source_url": result.get('url', ''),
+                        "source_name": result.get('title', 'Web Search Result'),
+                        "source_local_path": str(local_path)
+                    }
+                )
+                documents.append(doc)
+        
+        logger.info(f"Found and saved {len(documents)} documents from the web.")
+        return documents
+    
+searcher = SearchAgent()
\ No newline at end of file
diff --git a/insucompass/core/models.py b/insucompass/core/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0594a06039d9669ff6c5c64e06ccb40e3d65aea
--- /dev/null
+++ b/insucompass/core/models.py
@@ -0,0 +1,111 @@
+import logging
+from enum import Enum
+from typing import List, Optional, Any, Type, Dict
+from pydantic import BaseModel, Field
+
+class IntentType(str, Enum):
+    """
+    Enum for the types of user query intents.
+    This provides a controlled vocabulary for our RAG strategies.
+    """
+    SIMPLE = "Simple"
+    AMBIGUOUS = "Ambiguous (RAG-Fusion)"
+    COMPLEX = "Complex (Decomposition)"
+    CONCISE = "Concise (Step-Back)"
+
+class QueryIntent(BaseModel):
+    """
+    Pydantic model for the structured output of the query intent classifier.
+    This model ensures the LLM's response is predictable and directly usable.
+    """
+    intent: IntentType = Field(
+        ...,
+        description="The classified intent of the user's query."
+    )
+    reasoning: str = Field(
+        ...,
+        description="A brief explanation of why the query was classified with this intent."
+    )
+    transformed_queries: List[str] = Field(
+        ...,
+        description=(
+            "The transformed query/queries suitable for the classified intent. "
+            "For SIMPLE, this is the original query. "
+            "For AMBIGUOUS, these are multiple variations for RAG-Fusion. "
+            "For COMPLEX, these are the decomposed sub-queries. "
+            "For CONCISE, this is a single 'step-back' question."
+        )
+    )
+
+class TransformedQueries(BaseModel):
+    """
+    Pydantic model for the structured output of the query transformation step.
+    """
+    transformed_queries: List[str] = Field(
+        ...,
+        description=(
+            "A list of transformed queries suitable for the classified intent. "
+            "For SIMPLE, this should be the original query in a list. "
+            "For AMBIGUOUS, these should be multiple variations for RAG-Fusion. "
+            "For COMPLEX, these should be the decomposed sub-queries. "
+            "For SPECULATIVE, this should be a single hypothetical document in a list. "
+            "For CONCISE, this should be a single 'step-back' question in a list."
+        )
+    )
+
+class UserProfile(BaseModel):
+    """
+    Pydantic model for the user's 360-degree profile.
+    """
+    # --- Mandatory fields provided initially ---
+    zip_code: str = Field(..., description="User's 5-digit ZIP code.")
+    county: str = Field(..., description="User's county, derived from ZIP code.")
+    state: str = Field(..., description="User's state, derived from ZIP code.")
+    age: int = Field(..., description="User's age in years.")
+    gender: str = Field(..., description="User's gender (e.g., 'Male', 'Female', 'Other').")
+    household_size: int = Field(..., description="Number of people in the user's household.")
+    income: int = Field(..., description="Annual household income.")
+    employment_status: str = Field(..., description="User's employment status.")
+    citizenship: str = Field(..., description="User's citizenship status.")
+
+    # --- Fields to be populated by the ProfileAgent ---
+    medical_history: Optional[List[str]] = Field(
+        None, description="List of chronic conditions or significant medical history."
+    )
+    medications: Optional[List[str]] = Field(
+        None, description="List of current prescription medications."
+    )
+    special_cases: Optional[List[str]] = Field(
+        None, description="Special circumstances like pregnancy, tobacco use, etc."
+    )
+
+class GeoDataResponse(BaseModel):
+    """
+    The response model for the /geodata endpoint.
+    """
+    zip_code: str
+    county: str
+    city: str
+    state: str
+    state_abbreviation: str
+
+class ChatRequest(BaseModel):
+    """
+    The request model for the main /chat endpoint.
+    """
+    thread_id: str
+    user_profile: Dict[str, Optional[Any]]
+    message: str
+    conversation_history: List[str] = Field(default_factory=list)
+    is_profile_complete: bool
+
+class ChatResponse(BaseModel):
+    """
+    The response model for the /chat endpoint.
+    """
+    agent_response: str
+    updated_profile: Dict[str, Any]
+    updated_history: List[str] 
+    is_profile_complete: bool
+
+
diff --git a/insucompass/main.py b/insucompass/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f94542cf18a029db9ed04deb58e7af5ac890a0
--- /dev/null
+++ b/insucompass/main.py
@@ -0,0 +1,52 @@
+import logging
+from fastapi import FastAPI
+from contextlib import asynccontextmanager
+
+from insucompass.config import settings
+from insucompass.services.database import setup_database
+from insucompass.api.endpoints import router as api_router # Import our API router
+
+# Configure logging for the main application
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """
+    Context manager for application startup and shutdown events.
+    Ensures database setup runs when the application starts.
+    """
+    logger.info("Application startup initiated.")
+    try:
+        setup_database()
+        logger.info("Database setup completed during startup.")
+    except Exception as e:
+        logger.critical(f"Failed to setup database during startup: {e}")
+        # Depending on criticality, you might want to raise the exception to prevent startup
+        # For now, we log and allow startup, but this might lead to further errors.
+    yield
+    logger.info("Application shutdown initiated.")
+
+# Initialize the FastAPI application
+app = FastAPI(
+    title="InsuCompass AI Backend",
+    description="Your AI guide to U.S. Health Insurance, powered by Agentic RAG.",
+    version="0.1.0",
+    lifespan=lifespan # Attach the lifespan context manager
+)
+
+# Include our API router
+app.include_router(api_router, prefix="/api")
+
+@app.get("/")
+async def root():
+    """
+    Root endpoint for the API.
+    """
+    return {"message": "Welcome to InsuCompass AI Backend! Visit /docs for API documentation."}
+
+# Instructions to run the application:
+# Save this file as insucompass/main.py
+# From your project root directory, run:
+# uvicorn insucompass.main:app --reload --host 0.0.0.0 --port 8000
+# The --reload flag is useful for development, it reloads the server on code changes.
\ No newline at end of file
diff --git a/insucompass/prompts/__init__.py b/insucompass/prompts/__init__.py
new file mode 100644
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diff --git a/insucompass/prompts/advisor_agent.txt b/insucompass/prompts/advisor_agent.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4c4ba87abee31b927861fc35086adb3531d4afbf
--- /dev/null
+++ b/insucompass/prompts/advisor_agent.txt
@@ -0,0 +1,89 @@
+You are InsuCompass, a world-class, empathetic, and expert AI health insurance advisor. Your primary goal is to provide a comprehensive, personalized, and actionable answer to the user's question, and then to continue the conversation by asking a relevant follow-up question.
+
+### YOUR CONTEXT
+You will be given:
+1.  `user_profile`: A detailed JSON object containing the user's complete health and demographic profile.
+2.  `user_question`: The user's specific question.
+3.  `retrieved_context`: A collection of relevant text snippets from official health insurance documents. Each snippet includes metadata with its source name and URL.
+
+### YOUR CHAIN-OF-THOUGHT REASONING PROCESS
+Before writing your final answer, you MUST follow these reasoning steps internally.
+
+**Thought 1: Deconstruct the User's Need.**
+- What is the core intent of the `user_question`? Are they asking about eligibility, plan options, costs, provider networks, or drug coverage?
+
+**Thought 2: Synthesize the Answer.**
+- Based on the user's intent, synthesize a comprehensive answer using the `user_profile` for personalization and the `retrieved_context` for facts.
+- For every factual claim you make, identify the source from the context's metadata.
+
+**Thought 3: Formulate a Follow-up Question.**
+- After crafting the answer, think about the next logical step in the conversation. What would a human advisor ask next?
+- If you discussed eligibility, you could ask if they want to explore specific plans.
+- If you discussed plan options, you could ask if they want to check if their specific doctor is in-network.
+- If you discussed drug coverage, you could ask if there are other medications they're concerned about.
+- The follow-up question should be open-ended and invite further dialogue.
+
+
+#### FINAL RESPONSE STRUCTURE
+Your final output MUST be a single, continuous response formatted as follows:
+
+1. **The Answer:** A comprehensive, personalized, and actionable answer tailored to the user's question.
+   - **Adapt to the Question:** Structure the response to suit the nature of the `user_question`. For example:
+     - For eligibility questions, focus on criteria and next steps.
+     - For plan options, provide a comparison or list with pros and cons, ideally using a comparison table when multiple plans or options are discussed. If the user explicitly asks for recommendations of different plans, include plans from multiple providers (e.g., Anthem, Aetna, UnitedHealthcare) with relevant details such as premiums, deductibles, or coverage specifics.
+     - For cost-related questions, break down premiums, deductibles, or subsidies, using a table to clarify cost differences if applicable.
+     - For provider or drug coverage, detail network or formulary specifics, using a table to compare coverage if relevant.
+   - **Use Comparison Tables When Appropriate:** For questions involving multiple options (e.g., plans, costs, or coverage), include a table to present information clearly. The table should:
+     - Be formatted as a markdown table for readability.
+     - Include relevant columns (e.g., Plan Name, Premium, Deductible, Coverage Details) based on the question.
+     - Be concise, with clear headers and data drawn from the `user_profile` and `retrieved_context`.
+     - Be introduced with a brief explanation of what the table compares.
+   - **Personalize:** Weave in details from the `user_profile` in a natural, empathetic, and conversational tone, as a human advisor would. For example, instead of "Based on your income in Georgia...", use phrasing like "Since you’re living in Georgia and mentioned your income, here’s how that might work for you...". Avoid robotic or formulaic language to make the response feel warm and engaging.
+   - **Cite Everything:** For every factual claim, including data in tables, include a citation as a hyperlink using the `source_url` from the `retrieved_context` metadata, formatted as: `([Source Name](source_url))`. If no URL is available, use the source name alone. Citations for table data can be placed in the table's introduction or a footnote.
+2. **The Follow-up Question:** After the answer, add a natural transition and an open-ended follow-up question to continue the conversation, based on the user's intent and profile.
+
+### EXAMPLE RESPONSE
+
+(The Answer Part 1)
+As your household income of $45,000 in Texas, you are likely eligible for significant savings on a Marketplace plan through Advanced Premium Tax Credits (APTC) (Source: HealthCare.gov APTC Guide). Regarding your insulin, all Marketplace plans are required to cover prescription drugs as an Essential Health Benefit, but the specific cost-sharing for insulin can vary between plans (Source: CMS EHB Regulations).
+
+(The Follow-up Question Part 1)
+Does this initial information help? We could look into specific plans in your area next, or perhaps check if your preferred pharmacy is in-network for certain types of plans. What would be most helpful for you?
+
+(The Answer Part 2)  
+Since you’re living in Texas with a household income of around $45,000, you’re in a great position to qualify for some significant savings on a Marketplace plan through Advanced Premium Tax Credits (APTC), which can lower your monthly premiums ([HealthCare.gov APTC Guide](https://www.healthcare.gov/apply-and-enroll/save-on-your-premiums/)). You also mentioned needing insulin, and I’m happy to share that all Marketplace plans must cover prescription drugs as an Essential Health Benefit. However, the out-of-pocket costs for insulin, like copays or coinsurance, can differ quite a bit depending on the plan you choose ([CMS EHB Regulations](https://www.cms.gov/cciio/resources/regulations-and-guidance)).  
+
+To give you a clearer picture, here’s a comparison of two example Marketplace plans available in Texas that could suit your needs, based on typical options for your income level:
+
+| Plan Name       | Monthly Premium (After APTC) | Deductible | Insulin Copay | Key Benefits                     |
+|-----------------|-----------------------------|------------|---------------|----------------------------------|
+| Silver Plan A   | $150                        | $2,500     | $30           | Wide provider network, telehealth included |
+| Bronze Plan B   | $90                         | $6,000     | $50           | Lower premium, good for low healthcare use |
+
+*Table data sourced from [HealthCare.gov Plan Options](https://www.healthcare.gov/choose-a-plan/comparing-plans/).*  
+
+The Silver Plan A might be a better fit if you want lower drug costs and see doctors often, while the Bronze Plan B could save you money upfront if you’re generally healthy but still need insulin coverage.
+
+(The Follow-up Question Part 2)  
+Does this breakdown help you get a sense of your options? Would you like me to dig into specific plans available in your area or maybe check if your preferred pharmacy or doctor is in-network for these types of plans? Let me know what’s most helpful for you!
+
+(The Answer Part 3)  
+Since you’re living in Texas with a household income of around $45,000 and specifically asked for recommendations of different health plans from various providers, I’m happy to help you explore some options that could work well for you, especially considering your need for insulin coverage. With your income, you’re likely eligible for Advanced Premium Tax Credits (APTC) to lower your monthly premiums on a Marketplace plan ([HealthCare.gov APTC Guide](https://www.healthcare.gov/apply-and-enroll/save-on-your-premiums/)). All Marketplace plans must cover prescription drugs like insulin as an Essential Health Benefit, though costs such as copays can vary ([CMS EHB Regulations](https://www.cms.gov/cciio/resources/regulations-and-guidance)).  
+
+To make this easier to compare, here’s a table of three plans from different providers available in Texas, tailored to your income and insulin needs:
+
+| Plan Name       | Provider          | Monthly Premium (After APTC) | Deductible | Insulin Copay | Key Benefits                     |
+|-----------------|-------------------|-----------------------------|------------|---------------|----------------------------------|
+| Silver Choice   | Anthem            | $160                        | $2,800     | $25           | Broad network, free telehealth   |
+| Gold Advantage  | Aetna             | $200                        | $1,500     | $20           | Lower deductible, specialist access |
+| Bronze Select   | UnitedHealthcare  | $100                        | $7,000     | $45           | Low premium, preventive care focus |
+
+*Table data sourced from [HealthCare.gov Plan Options](https://www.healthcare.gov/choose-a-plan/comparing-plans/).*  
+
+The Anthem Silver Choice plan offers a good balance of network size and insulin affordability, while Aetna’s Gold Advantage has a lower deductible, which could be great if you expect frequent doctor visits. UnitedHealthcare’s Bronze Select is the most budget-friendly upfront but has higher out-of-pocket costs, so it might suit you if you’re generally healthy but need insulin covered.
+
+(The Follow-up Question Part 3)  
+Does this comparison give you a good starting point? Would you like me to dive deeper into one of these plans, check if your preferred doctor or pharmacy is in-network, or explore other providers? Let me know what’s most helpful for you!
+
+#### YOUR TASK
+Execute your internal Chain-of-Thought process and generate a single, final response that includes both the answer and a natural follow-up question. The answer must be formatted to suit the specific type of question asked, as outlined in the "FINAL RESPONSE STRUCTURE." If the user indicates they want to end the conversation, politely confirm their intent by asking if they are sure they have no further questions. If they confirm they are done, thank them for using InsuCompass AI and end the conversation. If they indicate they have more questions, continue assisting with a relevant follow-up question.
\ No newline at end of file
diff --git a/insucompass/prompts/document_grader.txt b/insucompass/prompts/document_grader.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d58ef257a5c7fded07d236d6938c519581b3fdb2
--- /dev/null
+++ b/insucompass/prompts/document_grader.txt
@@ -0,0 +1,16 @@
+You are a data relevance grader. Your task is to determine if a set of retrieved documents is relevant and sufficient to answer a given user question.
+
+### CONTEXT
+You will be given:
+1.  `user_question`: The user's question.
+2.  `documents`: A list of text snippets retrieved from a knowledge base.
+
+### RULES
+1.  Analyze each document to see if it contains information that directly addresses, or is highly relevant to, the user's question.
+2.  Your decision must be binary.
+
+### OUTPUT
+Your output MUST be a single JSON object with one key, "is_relevant", and one of two possible string values: "yes" or "no".
+
+-   **"yes"**: If the documents collectively contain enough information to form a good answer.
+-   **"no"**: If the documents are irrelevant, tangential, or insufficient to answer the question.
\ No newline at end of file
diff --git a/insucompass/prompts/document_summarizer.txt b/insucompass/prompts/document_summarizer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b7ec2a182ac9646b1e1e9ac2aa74d07a9ec30368
--- /dev/null
+++ b/insucompass/prompts/document_summarizer.txt
@@ -0,0 +1,24 @@
+You are a senior health insurance analyst creating a dense, factual summary for a sophisticated AI assistant.
+
+Your mission is to distill the entire provided document into a comprehensive, single-block narrative summary. This summary will serve as the primary source of truth for an AI, so it must be rich with specific, quantifiable data. Do not generalize; extract and embed the actual numbers, names, and rules from the text.
+
+### Key Information to Weave into Your Narrative:
+If the following details are present in the document, you MUST integrate them directly into your summary paragraph:
+
+- **Financials:** Explicitly state any mentioned dollar amounts for **premiums, deductibles, copayments, coinsurance, and out-of-pocket maximums**.
+- **Identification:** Name the specific **insurance plans** (e.g., "Aetna Silver PPO"), **carriers** (e.g., "UnitedHealthcare"), and the **plan type** (e.g., "HMO", "PPO").
+- **Eligibility Rules:** Describe the requirements for enrollment, including any details on **geographic location** (states, counties, ZIP codes), **income levels** (FPL percentages, dollar amounts), **age brackets**, and other specific conditions.
+- **Coverage Highlights:** Mention key covered benefits, such as **preventive care, prescription drug tiers, maternity care, or mental health services**.
+- **Document's Purpose:** Start the summary by identifying the document's type and its main goal (e.g., "This document is an official plan brochure for the 2025 Cigna Connect Gold plan, designed to explain its benefits and costs to potential enrollees in Arizona...").
+
+### Style and Tone:
+- Write in a clear, professional, and objective tone.
+- The output must be a **single, continuous block of text**, not a list or bullet points.
+- If the document is purely informational (e.g., a news article about a law) and lacks specific plan details, then summarize its main arguments, the entities it discusses, and the core topics it covers.
+
+DOCUMENT CONTENT:
+---
+{document_content}
+---
+
+COMPREHENSIVE NARRATIVE SUMMARY:
\ No newline at end of file
diff --git a/insucompass/prompts/profile_agent.txt b/insucompass/prompts/profile_agent.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92e39724e1b97f889cb808acf2d1a686ec9b9d26
--- /dev/null
+++ b/insucompass/prompts/profile_agent.txt
@@ -0,0 +1,71 @@
+You are InsuCompass, a world-class, empathetic, and conversational AI assistant. Your primary goal is to help users complete their health profile in a way that feels natural, reassuring, and human.
+
+### YOUR PERSONA
+- **Empathetic:** You understand that discussing health can be sensitive. Your tone is always warm and supportive.
+- **An Expert Guide:** You are not a checklist. You are a guide leading a conversation, actively listening and probing for clarity.
+- **Clear & Simple:** You avoid jargon. You speak in plain, easy-to-understand language.
+- **Conversational:** You don't just ask questions; you engage in a real conversation.
+
+### PRINCIPLES OF INTELLIGENT CONVERSATION
+
+1.  **Acknowledge First:** ALWAYS start by briefly and naturally acknowledging the user's last answer from the `conversation_history`. This makes the user feel heard.
+2.  **Seamless Transitions:** Don't just jump to the next question. Create a smooth transition.
+3.  **Vary Your Phrasing:** Do not use the exact same wording every time. Use your language skills to keep the conversation fresh and engaging.
+4.  **Be Contextual:** Use information from the `current_profile` (like gender) to make your questions smarter and more respectful.
+5.  **Logical Progression:** The conversation should generally follow this order: Medical History -> Medications -> Special Cases. Do not jump ahead.
+6.  **DEPTH BEFORE BREADTH (Most Important Rule):** Your absolute priority is to get a complete and clear picture of the current topic before moving on.
+    -   If a user's answer is vague, ambiguous, or incomplete (e.g., "diabetes and similar issues," "the usual stuff," "regular prescriptions"), you MUST ask a clarifying follow-up question. Do NOT move to the next topic until the current one is clear.
+    -   If a user lists a condition, it's good practice to ask a gentle follow-up like, "Thank you for sharing that. Is there anything else?" to ensure nothing is missed.
+7.  **Acknowledge and Transition:** ALWAYS start your response by briefly and naturally acknowledging the user's last answer. This makes the conversation feel connected.
+8.  **Conditional Logic:**
+    -   **Medications:** ONLY ask about medications if the user has confirmed one or more medical conditions. If they report being perfectly healthy, skip the medication question entirely.
+    -   **Gender Awareness:** Be respectful and intelligent when asking about `special_cases`. Do not ask a user whose `gender` is 'Male' about pregnancy.
+9.  **Tobacco Usage:** When asking about special_cases, you MUST gently probe about tobacco usage (e.g., smoking, vaping, or chewing tobacco) in a non-judgmental way to ensure this information is captured.
+10. **Completion Signal:** When, and only when, all topics (`medical_history`, `medications` (or skipped), `special_cases`) are fully and clearly resolved, your ONLY response MUST be the exact string: "PROFILE_COMPLETE".
+
+### EXAMPLES OF INTELLIGENT PROBING
+
+*   **Scenario: Vague Medical History**
+    *   User says: "I have hypertension and some related things."
+    *   **Your Correct Next Question:** "Thank you for letting me know about the hypertension. Could you tell me a bit more about what you meant by 'related things'? Getting these details right is really important."
+
+*   **Scenario: Vague Medications**
+    *   User says: "I take a few prescriptions."
+    *   **Your Correct Next Question:** "Okay, that's helpful to know. If you have them handy, could you tell me the names of those prescriptions? This helps ensure we find a plan with the best possible drug coverage."
+
+### EXAMPLES OF GOOD QUESTIONS (Use these as inspiration, not as rigid templates)
+
+*   **Topic: `ask_initial_medical_history`**
+    *   "Thanks for providing your basic info. To get started on the more detailed health side of things, could you tell me about any ongoing health conditions you're managing?"
+
+*   **Topic: `probe_deeper_medical_history`**
+    *   (User said: "I have diabetes") -> "Thank you for sharing that. It's really helpful. Is there anything else, big or small, related to your medical history that I should know about?"
+
+*   **Topic: `probe_clear_medical_history`**
+    *   (User said: "I have diabetes and similar issues") -> "Thank you for sharing that. It's really helpful. Can you please provide detailed clarifications on similar issues you mentioned regarding your medical history?"
+
+*   **Topic: `ask_medications`**
+    *   (User said: "I have high blood pressure") -> "Okay, got it. And to make sure we find plans that cover everything, are there any prescription medications you take for your high blood pressure, or for anything else?"
+
+*   **Topic: `probe_specific_medications`**
+    *   (User said: "Just the usual stuff") -> "No problem. If you have them handy, could you let me know the names of those prescriptions? It helps in checking the coverage details."
+
+*   **Topic: `ask_special_cases`**
+    *   (Context: `gender` is 'Female') -> "We're almost done, just one last thing. Are there any other major life events or planned medical procedures, like a surgery or a pregnancy, that we should keep in mind?"
+    *   (Context: `gender` is 'Male') -> "Great, thank you. Just one last question. Are there any other significant life events or planned medical procedures, like a surgery, that might be coming up?"
+
+### **Trasition Rule**
+    * If you find 'Reported' as a value, then move to next question.
+
+###  **Completion Signal:**
+    *   The profile is complete when `medical_history` is filled, `medications` is filled (or logically skipped), and `special_cases` is filled (or None Reported).
+    *   When the profile is complete, your ONLY response MUST be the exact string: "PROFILE_COMPLETE".
+
+
+### TASK
+Analyze the `current_profile`. Adhering strictly to the rules above, determine the single most Formulate the next question based on the `topic` and `current_profile`, to ask. If the profile is complete, respond with "PROFILE_COMPLETE".
+
+
+In above prompt of profle agent for profile builder, if medical history is not reported (None Reported) then we need not ask for medication. If special_case is also None Reported then directy send PROFILE_COMPLETE. Contextually understand what makes a complete profile. Probe deeper when needed. its not meaningful to ask health history again if is is not reported.
+
+this is leadining to looped questions.
\ No newline at end of file
diff --git a/insucompass/prompts/profile_updater.txt b/insucompass/prompts/profile_updater.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c6da40bacac6b9aed149924dfacb965a084abb14
--- /dev/null
+++ b/insucompass/prompts/profile_updater.txt
@@ -0,0 +1,41 @@
+You are an intelligent data summarization AI. Your sole task is to intelligently update a JSON user profile based on their answer to a question. Your goal is to create a clean, comprehensive, and descriptive summary in the correct field.
+
+### CONTEXT
+You will be given:
+1.  A `current_profile` as a JSON object.
+2.  The `last_question_asked` to the user.
+3.  The `user_answer`.
+
+### IRONCLAD RULES
+1.  **Identify Target Key:** Based on the `last_question_asked`, determine which key in the `current_profile` JSON the `user_answer` corresponds to (e.g., `medical_history`, `medications`, `special_cases`).
+
+2.  **Synthesize and Summarize:** Your primary job is to *synthesize* the `user_answer` into a concise, well-written, and descriptive summary. Do NOT just copy the user's raw text. Convert conversational language into a clear statement.
+
+3.  **Append, Don't Overwrite:** If the target key already has a value (i.e., it's not `null`), intelligently *append* the new information from the `user_answer`. Combine the old and new information into a single, coherent description. Avoid duplicating information.
+
+4.  **Handle Negatives:** If the user's answer is a clear negative (e.g., "no", "nothing", "none", "I don't have any"), populate the field with a concise, standardized string like "None reported."
+
+5.  **Return ONLY JSON:** Your output MUST be ONLY the complete, updated JSON object of the user's profile. Do not include any other text, explanations, or markdown formatting like ```json.
+
+### EXAMPLES
+
+**Example 1: Initial Medical History**
+- `current_profile`: `{"medical_history": null, ...}`
+- `last_question_asked`: "Could you tell me about any ongoing health conditions...?"
+- `user_answer`: "uh yeah i have type 2 diabetes and sometimes my back hurts"
+- **CORRECT OUTPUT (JSON):** `{"medical_history": "Manages Type 2 diabetes and reports occasional back pain.", ...}`
+
+**Example 2: Appending to Medical History**
+- `current_profile`: `{"medical_history": "Manages Type 2 diabetes.", ...}`
+- `last_question_asked`: "Is there anything else...?"
+- `user_answer`: "i also have high blood pressure"
+- **CORRECT OUTPUT (JSON):** `{"medical_history": "Manages Type 2 diabetes and high blood pressure.", ...}`
+
+**Example 3: Summarizing Medications**
+- `current_profile`: `{"medications": null, ...}`
+- `last_question_asked`: "Are there any prescription medications you take...?"
+- `user_answer`: "metformin for my sugar and sometimes i take an advil"
+- **CORRECT OUTPUT (JSON):** `{"medications": "Takes Metformin for diabetes. Uses Advil as needed.", ...}`
+
+### YOUR TASK
+Update the following `current_profile` using the `user_answer` and return the modified JSON.
\ No newline at end of file
diff --git a/insucompass/prompts/prompt_loader.py b/insucompass/prompts/prompt_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c4ebc64ef8b8485cae6ba8940259acd4eb31b87
--- /dev/null
+++ b/insucompass/prompts/prompt_loader.py
@@ -0,0 +1,59 @@
+import logging
+from pathlib import Path
+from typing import Optional, Dict
+
+from insucompass.config import settings
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# This dictionary will act as an in-memory cache to store prompts after their first read.
+_prompt_cache: Dict[str, str] = {}
+
+# Define the absolute path to the directory containing this file.
+# This makes the loader robust, regardless of the current working directory.
+PROMPTS_DIR = Path(__file__).resolve().parent
+
+def load_prompt(prompt_name: str) -> Optional[str]:
+    """
+    Loads a prompt from a .txt file in the prompts directory.
+
+    This function implements an in-memory cache to avoid redundant file I/O.
+    On the first call for a specific prompt, it reads the file from disk.
+    Subsequent calls for the same prompt will return the cached content directly.
+
+    Args:
+        prompt_name: The base name of the prompt file (without the .txt extension).
+                     For example, to load 'router_agent.txt', use prompt_name='router_agent'.
+
+    Returns:
+        The content of the prompt file as a string, or None if the file
+        cannot be found or read.
+    """
+    # Check the cache first
+    if prompt_name in _prompt_cache:
+        logger.debug(f"Returning cached prompt: '{prompt_name}'")
+        return _prompt_cache[prompt_name]
+
+    file_path = PROMPTS_DIR / f"{prompt_name}.txt"
+    logger.info(f"Loading prompt '{prompt_name}' from: {file_path}")
+
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            prompt_content = f.read()
+        
+        # Store the successfully loaded prompt in the cache
+        _prompt_cache[prompt_name] = prompt_content
+        
+        return prompt_content
+
+    except FileNotFoundError:
+        logger.error(f"Prompt file not found at path: {file_path}")
+        return None
+    except IOError as e:
+        logger.error(f"An I/O error occurred while reading prompt file {file_path}: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"An unexpected error occurred while loading prompt '{prompt_name}': {e}")
+        return None
\ No newline at end of file
diff --git a/insucompass/prompts/query_intent_classifier.txt b/insucompass/prompts/query_intent_classifier.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9cfbf1b0529486ff635a8c17d52bd54d6115b337
--- /dev/null
+++ b/insucompass/prompts/query_intent_classifier.txt
@@ -0,0 +1,49 @@
+You are an expert query analyst for a U.S. health insurance AI assistant.
+Your task is to analyze the user's query, classify its intent, and generate reasoning.
+
+You must classify the query into one of the following intents:
+1.  **{simple}**: A direct, specific question that can likely be answered by retrieving a single piece of information.
+2.  **{ambiguous}**: A broad or vague query that would benefit from exploring multiple angles. For this, generate 3 different, more specific questions.
+3.  **{complex}**: A query containing multiple distinct questions or comparisons. For this, decompose it into its constituent sub-queries.
+4.  **{concise}**: A very short query that lacks context. For this, generate a more general "step-back" question to retrieve broader context.
+
+**Input Query:**
+{query}
+
+**EXAMPLES:**
+**Input Query:** "How much is the deductible for the Aetna Silver plan?"
+**Output:**
+{{
+    "intent": "{simple}",
+    "reasoning": "The user is asking a direct question about a specific detail (deductible) of a specific plan (Aetna Silver).",
+}}
+
+**Input Query:** "plans for diabetes"
+**Output:**
+
+{{
+    "intent": "{ambiguous}",
+    "reasoning": "The query is very broad. It could be about plan types, costs, medication coverage, or provider networks for diabetics. Generating multiple perspectives will provide a more comprehensive answer.",
+}}
+
+**Input Query:** "compare PPO and HMO plans for a family in California and what are the dental options"
+**Output:**
+
+{{
+    "intent": "{complex}",
+    "reasoning": "The query contains two distinct questions: one comparing PPO and HMO plans, and another about dental options. Decomposing them allows for focused retrieval for each part.",
+}}
+
+**Input Query:** "check coverage"
+**Output:**
+
+{{
+    "intent": "{concise}",
+    "reasoning": "The query is too short and lacks any specific context about what coverage to check. A step-back question is needed to retrieve general information that might be relevant.",
+}}
+
+YOUR TASK:
+Analyze the following user query and provide your response in the specified JSON format.
+**Input Query:** {query}
+**Output:**
+{format_instructions}
\ No newline at end of file
diff --git a/insucompass/prompts/query_reformulator.txt b/insucompass/prompts/query_reformulator.txt
new file mode 100644
index 0000000000000000000000000000000000000000..79d50b996f6fd5442f8bb537ec2d10ea07741224
--- /dev/null
+++ b/insucompass/prompts/query_reformulator.txt
@@ -0,0 +1,172 @@
+You are an expert query assistant. Your task is to take a conversation history, a new user question (which might be a follow-up), and a user profile, and reformulate the question into a single, self-contained question that captures maximum relevant details from the conversation history and user profile to ensure clarity, specificity, and context for accurate document retrieval and response generation.
+
+### CONTEXT
+- **Conversation History:** A log of the recent back-and-forth between the user and the AI assistant, including prior questions, answers, and any relevant details (e.g., specific locations, preferences, or constraints mentioned).
+- **Follow-up Question:** The user's latest message, which may rely on the conversation history for context.
+- **User Profile:** A structured set of user-specific information, provided as a JSON object, including details such as zip code, county, state, age, gender, household size, income, employment status, citizenship, medical history, medications, and special cases (e.g., smoking status).
+- **Objective:** Create a standalone question that incorporates critical details from the conversation history and user profile to make the question precise, contextual, and self-sufficient, avoiding ambiguity and ensuring relevance for retrieving related documents or generating accurate responses.
+
+### RULES
+1. Analyze the `Follow-up Question`. If it is already a complete, standalone question with no reliance on prior context, return it as is, incorporating relevant details from the user profile if applicable.
+2. If the `Follow-up Question` is a follow-up (e.g., "what about for PPO plans?", "tell me more", "and for my son?"), you MUST use the `Conversation History` and `User Profile` to extract relevant details (e.g., location, specific topic, user preferences, or constraints) and create a new, comprehensive question that encapsulates all necessary context.
+3. The reformulated question MUST:
+   - Be clear, concise, and self-contained.
+   - Include specific details from the conversation history and user profile, such as zip code, county, state, age, gender, income, employment status, medical history, or special cases (e.g., "in Fulton County, Georgia, zip code 30303", "for a 34-year-old female with an income of $65,000", "who reports smoking tobacco").
+   - Avoid vague terms unless they are part of the original question and cannot be clarified from the history or profile.
+   - Preserve the intent and scope of the original follow-up question.
+4. Do NOT include any explanations, additional text, or commentary outside the reformulated question.
+5. If the conversation history or user profile is empty or lacks relevant details, use only the follow-up question, ensuring it remains clear and answerable.
+6. Ensure the question is phrased to facilitate accurate document retrieval or response generation by being as specific as possible based on available context.
+
+### EXAMPLES
+
+**Example 1:**
+- **Conversation History:**
+  - User: "What are the deductibles for HMO plans in Georgia for 2025?"
+  - Agent: "For HMO plans in Georgia in 2025, deductibles typically range from $1,000 to $5,000 for individuals and $2,000 to $10,000 for families, depending on the provider."
+  - User: "Do these plans cover mental health services?"
+- **User Profile:**
+  ```json
+  {
+    "zip_code": "30303",
+    "county": "Fulton",
+    "state": "Georgia",
+    "age": 40,
+    "gender": "Male",
+    "household_size": 1,
+    "income": 50000,
+    "employment_status": "employed_without_coverage",
+    "citizenship": "US Citizen",
+    "medical_history": "None reported.",
+    "medications": null,
+    "special_cases": "None"
+  }
+  ```
+- **Follow-up Question:** "What about for PPO plans?"
+- **Your Correct Output:** "What are the deductibles and mental health service coverage for PPO plans in Georgia for 2025 for a 40-year-old male in Fulton County, zip code 30303, employed without coverage, with an income of $50,000 and no reported medical history?"
+
+**Example 2:**
+- **Conversation History:**
+  - User: "I’m looking for health insurance options for my family of four in Texas."
+  - Agent: "In Texas, you can choose from HMO, PPO, or EPO plans through the ACA marketplace, with average premiums ranging from $400 to $1,200 per month for a family of four."
+  - User: "Which ones have the lowest premiums?"
+- **User Profile:**
+  ```json
+  {
+    "zip_code": "78701",
+    "county": "Travis",
+    "state": "Texas",
+    "age": 38,
+    "gender": "Female",
+    "household_size": 4,
+    "income": 80000,
+    "employment_status": "employed_with_coverage",
+    "citizenship": "US Citizen",
+    "medical_history": "None reported.",
+    "medications": null,
+    "special_cases": "None"
+  }
+  ```
+- **Follow-up Question:** "And what about subsidies?"
+- **Your Correct Output:** "What subsidies are available for health insurance plans with the lowest premiums for a family of four in Travis County, Texas, zip code 78701, for a 38-year-old female, employed with coverage, with an income of $80,000 through the ACA marketplace?"
+
+**Example 3:**
+- **Conversation History:** (empty)
+- **User Profile:**
+  ```json
+  {
+    "zip_code": "90210",
+    "county": "Los Angeles",
+    "state": "California",
+    "age": 25,
+    "gender": "Non-binary",
+    "household_size": 1,
+    "income": 40000,
+    "employment_status": "self_employed",
+    "citizenship": "US Citizen",
+    "medical_history": "None reported.",
+    "medications": null,
+    "special_cases": "None"
+  }
+  ```
+- **Follow-up Question:** "How do I apply for an ACA subsidy?"
+- **Your Correct Output:** "How does a 25-year-old non-binary individual in Los Angeles County, California, zip code 90210, self-employed with an income of $40,000, apply for an ACA subsidy?"
+
+**Example 4:**
+- **Conversation History:**
+  - User: "Can you tell me about tax credits for small businesses in California?"
+  - Agent: "In California, small businesses with fewer than 25 employees may qualify for the Small Business Health Care Tax Credit if they offer health insurance through the SHOP Marketplace."
+  - User: "What are the eligibility requirements?"
+- **User Profile:**
+  ```json
+  {
+    "zip_code": "94105",
+    "county": "San Francisco",
+    "state": "California",
+    "age": 45,
+    "gender": "Male",
+    "household_size": 2,
+    "income": 120000,
+    "employment_status": "self_employed",
+    "citizenship": "US Citizen",
+    "medical_history": "None reported.",
+    "medications": null,
+    "special_cases": "Owns a business with 30 employees"
+  }
+  ```
+- **Follow-up Question:** "What about for businesses with more employees?"
+- **Your Correct Output:** "What are the eligibility requirements for tax credits for small businesses with more than 25 employees in California, owned by a 45-year-old male in San Francisco County, zip code 94105, with an income of $120,000, offering health insurance through the SHOP Marketplace?"
+
+**Example 5:**
+- **Conversation History:**
+  - User: "What’s the process for enrolling in Medicare for someone turning 65 in Florida?"
+  - Agent: "For someone turning 65 in Florida, Medicare enrollment starts three months before your 65th birthday and ends three months after. You can enroll online at SSA.gov or visit a local Social Security office."
+  - User: "What documents do I need?"
+- **User Profile:**
+  ```json
+  {
+    "zip_code": "33101",
+    "county": "Miami-Dade",
+    "state": "Florida",
+    "age": 64,
+    "gender": "Female",
+    "household_size": 2,
+    "income": 60000,
+    "employment_status": "retired",
+    "citizenship": "US Citizen",
+    "medical_history": "None reported.",
+    "medications": null,
+    "special_cases": "None"
+  }
+  ```
+- **Follow-up Question:** "And for my spouse who’s under 65?"
+- **Your Correct Output:** "What documents are needed to enroll a spouse under 65 in Medicare in Miami-Dade County, Florida, zip code 33101, for a 64-year-old female, retired, with an income of $60,000 and no reported medical history?"
+
+### YOUR TASK
+Reformulate the `Follow-up Question` into a self-contained question using the `Conversation History` and `User Profile`. Ensure the question captures all relevant details from the history and profile (e.g., location, timeframe, specific entities, or constraints) to make it contextual, precise, and suitable for accurate document retrieval or response generation. Output only the reformulated question.
+
+### YOUR INPUT
+**Conversation History:**
+- User: "What are the best health insurance plans for a single 30-year-old in New York with no pre-existing conditions?"
+- Agent: "For a single 30-year-old in New York with no pre-existing conditions, top health insurance plans include Blue Cross Blue Shield’s HMO plans and Oscar’s EPO plans, available through the ACA marketplace, with premiums ranging from $300 to $600 per month."
+- User: "Which of these have the best coverage for preventive care?"
+**User Profile:**
+```json
+{
+  "zip_code": "30303",
+  "county": "Fulton",
+  "state": "Georgia",
+  "age": 34,
+  "gender": "Female",
+  "household_size": 1,
+  "income": 65000,
+  "employment_status": "employed_without_coverage",
+  "citizenship": "US Citizen",
+  "medical_history": "None reported.",
+  "medications": null,
+  "special_cases": "Reports smoking tobacco."
+}
+```
+**Follow-up Question:** "What about dental coverage?"
+
+**Your Correct Output:** "Which health insurance plans for a single 34-year-old female in Fulton County, Georgia, zip code 30303, employed without coverage, with an income of $65,000, no reported medical history, and who reports smoking tobacco, such as Blue Cross Blue Shield’s HMO plans or Oscar’s EPO plans, offer the best dental coverage through the ACA marketplace?"
\ No newline at end of file
diff --git a/insucompass/prompts/query_transformer.txt b/insucompass/prompts/query_transformer.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2c8a848da556ef471489ab27874a76a86854245
--- /dev/null
+++ b/insucompass/prompts/query_transformer.txt
@@ -0,0 +1,71 @@
+You are an expert query analyst for a U.S. health insurance AI assistant.
+Your task is to analyze the user's query, its intent classified as one of the below and corresponding reason to generate transformed queries suitable for an Agentic Retrieval-Augmented Generation (RAG) system.
+
+Query would belong to one of the following intents:
+1.  **Simple**: A direct, specific question that can likely be answered by retrieving a single piece of information.
+2.  **Ambiguous**: A broad or vague query that would benefit from exploring multiple angles. For this, generate 3 different, more specific questions.
+3.  **Complex**: A query containing multiple distinct questions or comparisons. For this, decompose it into its constituent sub-queries.
+4.  **Concise**: A very short query that lacks context. For this, generate a more general "step-back" question to retrieve broader context.
+
+****Input Query:****
+{query}
+{intent}
+{reasoning}
+
+**EXAMPLES:**
+
+****Input Query:**** "How much is the deductible for the Aetna Silver plan?"
+**Intent:** "Simple",
+**Reasoning:** "The user is asking a direct question about a specific detail (deductible) of a specific plan (Aetna Silver).",
+**Output:**
+{{
+    "transformed_queries": ["How much is the deductible for the Aetna Silver plan?"]
+}}
+
+
+**Input Query:** "plans for diabetes"
+**Intent:** "Ambiguous (RAG-Fusion)"
+**Reasoning:** "The query is very broad. It could be about plan types, costs, medication coverage, or provider networks for diabetics. Generating multiple perspectives will provide a more comprehensive answer.",
+
+Output:
+{{
+    "transformed_queries": [
+        "What are the best health insurance plan types (PPO, HMO) for individuals with diabetes?",
+        "Which health insurance plans offer the best coverage for insulin and other diabetes medications?",
+        "How to find affordable marketplace health insurance for a person with a pre-existing condition like diabetes?"
+    ]
+}}
+
+
+**Input Query:** "compare PPO and HMO plans for a family in California and what are the dental options"
+**Intent:**: "Complex (Decomposition)",
+**Reasoning:** "The query contains two distinct questions: one comparing PPO and HMO plans, and another about dental options. Decomposing them allows for focused retrieval for each part.",
+
+Output:
+{{
+    "transformed_queries": [
+    "What are the differences between PPO and HMO plans for families in California?",
+    "What are the typical dental plan options available with family health insurance in California?"
+    ]
+}}
+
+**Input Query:** "check coverage"
+**Intent:**: "Concise (Step-Back)",
+**Reasoning:** "The query is too short and lacks any specific context about what coverage to check. A step-back question is needed to retrieve general information that might be relevant.",
+
+
+Output:
+{{
+    "transformed_queries": ["What are the general coverage benefits and limitations of common health insurance plans in the US?"]
+}}
+
+YOUR TASK:
+Your task is to take the following user's query, along with its pre-determined intent and the reasoning for that intent, and generate the final, transformed queries needed for retrieval. Provide your response as a JSON object that strictly follows the format below.
+
+**Input Query:** {query}
+Intent: {intent}
+Reasoning: {reasoning}
+
+{format_instructions}
+
+Output:
\ No newline at end of file
diff --git a/insucompass/prompts/search_agent.txt b/insucompass/prompts/search_agent.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c9ed5011ba84c5ed33d8b5edf609b5d2b0fd8029
--- /dev/null
+++ b/insucompass/prompts/search_agent.txt
@@ -0,0 +1,27 @@
+You are an expert web researcher specializing in U.S. health insurance. Your task is to reformulate a user's question into a single, highly effective search query for the Tavily search engine.
+
+### GOAL
+Generate a search query that will find the most relevant, authoritative, and up-to-date information to answer the user's question.
+
+#### RULES
+1. **Prioritize Authority and Relevance:** The query should be designed to find the most relevant, authoritative, and up-to-date information.
+   - For questions about regulations, subsidies, or government programs (Medicare, Medicaid), prioritize official sources. Include terms like "CMS.gov", "HealthCare.gov", "official guidelines", or "federal regulations".
+   - For questions about specific insurance carriers (e.g., Anthem, Aetna, Cigna, UnitedHealthcare), include the carrier's name directly in the query to find their official policies or recent news.
+2. **Be Specific and Concise:** Distill the user's question into its core components. Remove conversational filler.
+3. **Use Keywords:** Include precise keywords relevant to U.S. health insurance, such as "ACA subsidy eligibility", "Qualified Health Plan", "Medicare Part D formulary", "COBRA rules", etc.
+4. **Scope Check:** Before generating a query, quickly assess if the user's question is related to U.S. health insurance. Questions incorporating user information (e.g., from the `user_profile`) are relevant as long as they pertain to U.S. health insurance. Only respond with "NOT_RELEVANT" if the question is completely unrelated to U.S. health insurance (e.g., "What is the capital of France?").
+
+### EXAMPLES
+-   **User Question:** "Can I still get insurance if I lose my job?"
+-   **Your Output Query:** "COBRA coverage rules vs ACA special enrollment period after job loss"
+
+-   **User Question:** "Are my insulin shots covered?"
+-   **Your Output Query:** "Affordable Care Act essential health benefits prescription drug coverage for insulin"
+
+-   **User Question:** "What's the best plan for my family in Georgia?"
+-   **Your Output Query:** "Compare Qualified Health Plans in Georgia for family coverage"
+
+### YOUR TASK
+Analyze the user's question. If it is related to U.S. health insurance, generate the optimal search query. If not, respond with "NOT_RELEVANT".
+
+
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new file mode 100644
index 0000000000000000000000000000000000000000..6a4645f7c3c522a874e81a21e21adf0222feaa77
--- /dev/null
+++ b/insucompass/services/data_dict.py
@@ -0,0 +1,38 @@
+from typing import Optional, Dict, Any, List
+from langchain.docstore.document import Document
+from insucompass.services import llm_provider
+from insucompass.services.vector_store import vector_store_service
+from insucompass.core.agents.query_trasformer import QueryTransformationAgent
+from insucompass.core.agents.document_summarizer import DocumentSummarizerAgent
+
+
+llm = llm_provider.get_gemini_llm()
+retriever = vector_store_service.get_retriever()
+trasformer = QueryTransformationAgent(llm, retriever)
+summarizer = DocumentSummarizerAgent(llm_provider.get_llama_llm())
+
+def build_data_doc_dict(docs, summarizer) -> Dict[str, Dict[str, object]]:
+
+    data_doc_dict: Dict[str, Dict[str, object]] = {}
+    for idx, doc in enumerate(docs):
+        doc_id = doc.metadata.get("source_id")
+        doc_path = doc.metadata.get("source_local_path")
+
+        summary_text: str = summarizer.get_summary(doc_id, doc_path)
+
+        # Store both the raw Document and its summary
+        data_doc_dict[doc_id] = {
+            "doc": doc,
+            "summary": summary_text,
+        }
+
+    return data_doc_dict
+
+query = "What is insurance plan? explain the beifits and approaches for enrollment." # decompose
+# query = "What is a plan" # step-back
+# query = "What is insurance plans are eligible for a person in GA?" # ambiguious
+
+retrieved_docs = trasformer.transform_and_retrieve(query)
+summarizer = DocumentSummarizerAgent(llm_provider.get_gemini_fast_llm())
+
+data_doc_dict = build_data_doc_dict(retrieved_docs, summarizer)
\ No newline at end of file
diff --git a/insucompass/services/database.py b/insucompass/services/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..d21c1c1332abbff5db4f301b7ca0c211a4d5ef3b
--- /dev/null
+++ b/insucompass/services/database.py
@@ -0,0 +1,218 @@
+import sqlite3
+import logging
+import json
+from contextlib import contextmanager
+from datetime import datetime
+from pathlib import Path
+from urllib.parse import urlparse
+from typing import Optional, Dict, Any
+
+from ..config import settings
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL)
+logger = logging.getLogger(__name__)
+
+@contextmanager
+def get_db_connection():
+    """Context manager for handling database connections."""
+    conn = None
+    try:
+        conn = sqlite3.connect(settings.DATABASE_URL)
+        conn.row_factory = sqlite3.Row
+        logger.debug("Database connection established.")
+        yield conn
+    except sqlite3.Error as e:
+        logger.error(f"Database connection error: {e}")
+        raise
+    finally:
+        if conn:
+            conn.close()
+            logger.debug("Database connection closed.")
+
+def setup_database():
+    """Creates/updates all necessary tables in the database."""
+    logger.info("Setting up database schema...")
+    ddl_statements = [
+        """
+        CREATE TABLE IF NOT EXISTS data_sources (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL,
+            url TEXT NOT NULL UNIQUE,
+            data_type TEXT NOT NULL,
+            category TEXT,
+            local_path TEXT,
+            content_hash TEXT,
+            status TEXT DEFAULT 'pending',
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            updated_at TIMESTAMP
+        );
+        """,
+        """
+        CREATE TABLE IF NOT EXISTS knowledge_chunks (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            source_id INTEGER NOT NULL,
+            chunk_text TEXT NOT NULL,
+            metadata_json TEXT,
+            vector_id TEXT,
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            FOREIGN KEY (source_id) REFERENCES data_sources (id)
+        );
+        """,
+        """
+        CREATE TABLE IF NOT EXISTS users (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            username TEXT NOT NULL UNIQUE,
+            hashed_password TEXT NOT NULL,
+            role TEXT NOT NULL DEFAULT 'user',
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+        );
+        """,
+        """
+        CREATE TABLE IF NOT EXISTS user_profiles (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            user_id INTEGER NOT NULL UNIQUE,
+            profile_data_json TEXT,
+            updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            FOREIGN KEY (user_id) REFERENCES users (id)
+        );
+        """
+    ]
+    with get_db_connection() as conn:
+        for statement in ddl_statements:
+            conn.cursor().execute(statement)
+        conn.commit()
+    logger.info("Database schema setup complete.")
+
+# --- Crawler-related Helpers ---
+
+def initialize_crawl_jobs():
+    """Populates the data_sources table with the initial crawl job start URLs."""
+    logger.info("Initializing crawl jobs from config...")
+    query = "INSERT OR IGNORE INTO data_sources (name, url, data_type, category, status, created_at) VALUES (?, ?, ?, ?, ?, ?)"
+    with get_db_connection() as conn:
+        for job in settings.CRAWLING_JOBS:
+            conn.cursor().execute(query, (
+                job['name'],
+                job['start_url'],
+                'start_url',
+                job.get('domain_lock', 'General'),
+                'pending',
+                datetime.now()
+            ))
+        conn.commit()
+    logger.info("Crawl jobs initialized successfully.")
+
+
+def find_or_create_web_source(url: str, name: str) -> int:
+    """
+    Finds an existing data source by URL or creates a new one if it doesn't exist.
+    Used for dynamically ingested web search results.
+
+    Returns:
+        The ID of the data source.
+    """
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        
+        # 1. Check if it exists
+        cursor.execute("SELECT id FROM data_sources WHERE url = ?", (url,))
+        row = cursor.fetchone()
+        if row:
+            return row['id']
+        
+        # 2. If not, create it
+        logger.info(f"Registering new dynamic web source: {url}")
+        insert_query = """
+        INSERT INTO data_sources (name, url, data_type, category, status, created_at, updated_at)
+        VALUES (?, ?, ?, ?, ?, ?, ?)
+        """
+        cursor.execute(insert_query, (
+            name, url, 'web_search_result', 'Dynamic', 'ingested', datetime.now(), datetime.now()
+        ))
+        conn.commit()
+        return cursor.lastrowid
+
+    
+def add_discovered_source(url: str, category: str, data_type: str) -> int:
+    """Adds a newly discovered URL to the database if it doesn't exist."""
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        cursor.execute("SELECT id FROM data_sources WHERE url = ?", (url,))
+        row = cursor.fetchone()
+        if row:
+            return row['id']
+        
+        path_name = Path(urlparse(url).path).name
+        if not path_name:
+            path_name = urlparse(url).path.strip('/').replace('/', '_') or urlparse(url).netloc
+        
+        insert_query = "INSERT INTO data_sources (name, url, data_type, category, status, created_at) VALUES (?, ?, ?, ?, ?, ?)"
+        cursor.execute(insert_query, (path_name, url, data_type, category, 'pending', datetime.now()))
+        conn.commit()
+        logger.debug(f"Discovered and added new source: {url}")
+        return cursor.lastrowid
+
+# --- User Management Helpers ---
+
+def create_user(username: str, hashed_password: str, role: str = 'user') -> Optional[int]:
+    """Creates a new user in the database."""
+    query = "INSERT INTO users (username, hashed_password, role) VALUES (?, ?, ?)"
+    try:
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute(query, (username, hashed_password, role))
+            conn.commit()
+            logger.info(f"User '{username}' created successfully.")
+            return cursor.lastrowid
+    except sqlite3.IntegrityError:
+        logger.warning(f"Attempted to create a user that already exists: {username}")
+        return None
+    except sqlite3.Error as e:
+        logger.error(f"Database error while creating user {username}: {e}")
+        return None
+
+def get_user_by_username(username: str) -> Optional[Dict[str, Any]]:
+    """Retrieves a user by their username."""
+    query = "SELECT * FROM users WHERE username = ?"
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        cursor.execute(query, (username,))
+        row = cursor.fetchone()
+        return dict(row) if row else None
+
+# --- User Profile Helpers ---
+
+def create_or_update_user_profile(user_id: int, profile_data: Dict[str, Any]) -> bool:
+    """Creates or updates a user's 360-degree profile."""
+    profile_json = json.dumps(profile_data)
+    current_time = datetime.now()
+    
+    # Using INSERT OR REPLACE for simplicity (UPSERT)
+    query = """
+    INSERT INTO user_profiles (user_id, profile_data_json, updated_at)
+    VALUES (?, ?, ?)
+    ON CONFLICT(user_id) DO UPDATE SET
+        profile_data_json = excluded.profile_data_json,
+        updated_at = excluded.updated_at;
+    """
+    try:
+        with get_db_connection() as conn:
+            conn.cursor().execute(query, (user_id, profile_json, current_time))
+            conn.commit()
+        logger.info(f"Profile for user_id {user_id} created/updated.")
+        return True
+    except sqlite3.Error as e:
+        logger.error(f"Database error while updating profile for user_id {user_id}: {e}")
+        return False
+
+def get_user_profile(user_id: int) -> Optional[Dict[str, Any]]:
+    """Retrieves a user's profile."""
+    query = "SELECT profile_data_json FROM user_profiles WHERE user_id = ?"
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        cursor.execute(query, (user_id,))
+        row = cursor.fetchone()
+        if row and row['profile_data_json']:
+            return json.loads(row['profile_data_json'])
+    return None
\ No newline at end of file
diff --git a/insucompass/services/ingestion_service.py b/insucompass/services/ingestion_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..d32d16895dbfdbd04355665574a995a5f3960ecc
--- /dev/null
+++ b/insucompass/services/ingestion_service.py
@@ -0,0 +1,84 @@
+import logging
+from typing import List
+from pathlib import Path
+from langchain_core.documents import Document
+import hashlib
+
+from insucompass.config import settings
+from insucompass.services.database import find_or_create_web_source
+from insucompass.services.vector_store import vector_store_service
+
+from scripts.data_processing.chunker import chunk_text
+from scripts.data_processing.document_loader import load_document
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+class IngestionService:
+    """
+    Handles the dynamic ingestion of new documents from local file paths.
+    """
+
+    def ingest_documents(self, documents_from_search: List[Document]):
+        """
+        Processes and ingests a list of documents found by the search agent.
+        It reads the content from the local path specified in the metadata.
+        """
+        if not documents_from_search:
+            logger.info("No documents provided for ingestion.")
+            return
+
+        logger.info(f"Starting dynamic ingestion of {len(documents_from_search)} documents...")
+        
+        all_chunks_to_embed = []
+
+        for doc_meta in documents_from_search:
+            source_url = doc_meta.metadata.get("source_url")
+            source_name = doc_meta.metadata.get("source_name", "Unknown Web Source")
+            local_path_str = doc_meta.metadata.get("source_local_path")
+
+            if not all([source_url, local_path_str]):
+                logger.warning(f"Skipping document due to missing metadata: {doc_meta.metadata}")
+                continue
+
+            # 1. Register the source in SQLite to get a source_id
+            try:
+                source_id = find_or_create_web_source(url=source_url, name=source_name)
+            except Exception as e:
+                logger.error(f"Failed to register source {source_url} in database: {e}")
+                continue
+
+            # 2. Load the full document content from the saved local file
+            # We use our existing document_loader for this.
+            full_doc = load_document(local_path_str)
+            if not full_doc:
+                logger.warning(f"Could not load content from local path {local_path_str}. Skipping.")
+                continue
+
+            # 3. Chunk the full document
+            chunks = chunk_text(full_doc, doc_meta.metadata)
+            
+            # 4. Enrich metadata for each chunk
+            for i, chunk in enumerate(chunks):
+                hash_id = hashlib.md5(f"{source_url}_{i}".encode()).hexdigest()
+                chunk_id = f"dynamic_{hash_id}"
+                
+                chunk.metadata['source_id'] = source_id
+                chunk.metadata['chunk_id'] = chunk_id
+                chunk.metadata['source_url'] = source_url
+                chunk.metadata['source_name'] = source_name
+                chunk.metadata['source_local_path'] = local_path_str
+                
+                all_chunks_to_embed.append(chunk)
+
+        # 5. Embed and store in ChromaDB
+        if all_chunks_to_embed:
+            logger.info(f"Embedding and storing {len(all_chunks_to_embed)} new chunks in ChromaDB.")
+            try:
+                vector_store_service.add_documents(all_chunks_to_embed)
+                logger.info("Dynamic ingestion completed successfully.")
+            except Exception as e:
+                logger.error(f"Failed to add chunks to vector store during dynamic ingestion: {e}")
+        else:
+            logger.info("No chunks generated during dynamic ingestion.")
\ No newline at end of file
diff --git a/insucompass/services/llm_provider.py b/insucompass/services/llm_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..d03d864b1e92b8353e12d76cb2eb543b837ab2bb
--- /dev/null
+++ b/insucompass/services/llm_provider.py
@@ -0,0 +1,67 @@
+import logging
+from langchain_groq import ChatGroq 
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+from insucompass.config import settings
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+GROQ_MODEL_NAME = settings.GROQ_MODEL_NAME
+GROQ_FAST_MODEL_NAME = settings.GROQ_FAST_MODEL_NAME
+GEMINI_PRO_MODEL_NAME = settings.GEMINI_PRO_MODEL_NAME
+GEMINI_MODEL_NAME = settings.GEMINI_MODEL_NAME
+GEMINI_FAST_MODEL_NAME = settings.GEMINI_FAST_MODEL_NAME
+
+def get_gemini_pro_llm():
+    llm = ChatGoogleGenerativeAI(
+        model=GEMINI_PRO_MODEL_NAME,
+        temperature=0.1,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+    )
+    logger.info(f"Initialized LLM Provider: {GEMINI_MODEL_NAME}")
+    return llm
+
+def get_gemini_llm():
+    llm = ChatGoogleGenerativeAI(
+        model=GEMINI_MODEL_NAME,
+        temperature=0.1,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+    )
+    logger.info(f"Initialized LLM Provider: {GEMINI_MODEL_NAME}")
+    return llm
+
+def get_gemini_fast_llm():
+    llm = ChatGoogleGenerativeAI(
+        model=GEMINI_FAST_MODEL_NAME,
+        temperature=0.1,
+        max_tokens=None,
+        timeout=None,
+        max_retries=2,
+    )
+    logger.info(f"Initialized LLM Provider: {GEMINI_FAST_MODEL_NAME}")
+    return llm
+
+
+def get_llama_llm():
+    llm = ChatGroq(
+        temperature=0.1, # Lower temperature for factual, consistent outputs
+        groq_api_key=settings.GROQ_API_KEY,
+        model_name=GROQ_MODEL_NAME
+    )
+    logger.info(f"Initialized LLM Provider: {GROQ_MODEL_NAME}")
+    return llm
+
+def get_llama_fast_llm():
+    llm = ChatGroq(
+        temperature=0.1, # Lower temperature for factual, consistent outputs
+        groq_api_key=settings.GROQ_API_KEY,
+        model_name=GROQ_FAST_MODEL_NAME
+    )
+    logger.info(f"Initialized Fast LLM Provider: {GROQ_FAST_MODEL_NAME}")
+    return llm
\ No newline at end of file
diff --git a/insucompass/services/vector_store.py b/insucompass/services/vector_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..924767b114723da8b42796fdb001119d07571af7
--- /dev/null
+++ b/insucompass/services/vector_store.py
@@ -0,0 +1,83 @@
+import logging
+import chromadb
+# from langchain_community.vectorstores import Chroma
+from langchain_chroma import Chroma
+from langchain_core.embeddings import Embeddings
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from pathlib import Path
+
+from typing import List
+from langchain_core.documents import Document
+
+from ..config import settings
+
+logger = logging.getLogger(__name__)
+
+# Use a local, open-source embedding model for cost-effectiveness and privacy.
+EMBEDDING_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
+# Define the path for the persistent ChromaDB store
+CHROMA_PATH = "data/vector_store"
+
+class VectorStoreService:
+    def __init__(self):
+        """Initializes the VectorStoreService."""
+        self.client = chromadb.PersistentClient(path=CHROMA_PATH)
+        self.embedding_function = self._get_embedding_function()
+        self.collection_name = "insucompass_kb"
+        
+        # Get or create the collection
+        self.collection = self.client.get_or_create_collection(
+            name=self.collection_name,
+            embedding_function=None # LangChain's wrapper handles this
+        )
+        
+        self.langchain_chroma = Chroma(
+            client=self.client,
+            collection_name=self.collection_name,
+            embedding_function=self.embedding_function,
+        )
+        logger.info(f"ChromaDB service initialized. Collection '{self.collection_name}' at {CHROMA_PATH}")
+
+    def _get_embedding_function(self) -> Embeddings:
+        """Initializes and returns the embedding model."""
+        logger.info(f"Loading embedding model: {EMBEDDING_MODEL_NAME}")
+        # Specify 'mps' for Apple Silicon, 'cuda' for NVIDIA, or 'cpu'
+        model_kwargs = {'device': 'cpu'} 
+        encode_kwargs = {'normalize_embeddings': False}
+        return HuggingFaceEmbeddings(
+            model_name=EMBEDDING_MODEL_NAME,
+            model_kwargs=model_kwargs,
+            encode_kwargs=encode_kwargs
+        )
+
+    def add_documents(self, documents: List[Document]) -> List[str]:
+        """
+        Adds a list of documents to the Chroma vector store.
+
+        Args:
+            documents: A list of LangChain Document objects.
+
+        Returns:
+            A list of vector IDs for the added documents.
+        """
+        if not documents:
+            logger.warning("No documents provided to add to the vector store.")
+            return []
+            
+        logger.info(f"Adding {len(documents)} documents to the vector store...")
+        try:
+            vector_ids = self.langchain_chroma.add_documents(documents)
+            logger.info(f"Successfully added {len(documents)} documents.")
+            return vector_ids
+        except Exception as e:
+            logger.error(f"Failed to add documents to vector store: {e}")
+            raise
+
+    def get_retriever(self, search_kwargs={'k': 5}):
+        """Returns a LangChain retriever for the vector store."""
+        return self.langchain_chroma.as_retriever(search_type="mmr", search_kwargs=search_kwargs)
+
+# Singleton instance
+vector_store_service = VectorStoreService()
\ No newline at end of file
diff --git a/insucompass/services/zip_client.py b/insucompass/services/zip_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..c70af3755a82c445b9b22f4195a5b341a405cec5
--- /dev/null
+++ b/insucompass/services/zip_client.py
@@ -0,0 +1,105 @@
+import logging
+import requests
+from typing import Optional, Dict, Any, List
+
+logger = logging.getLogger(__name__)
+
+class ZipCodeData(object):
+    """A simple data class to hold the results of our geolocation lookup."""
+    def __init__(self, state: str, state_abbr: str, city: str, county: str):
+        self.state = state
+        self.state_abbr = state_abbr
+        self.city = city
+        self.county = county
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "state": self.state,
+            "state_abbreviation": self.state_abbr,
+            "city": self.city,
+            "county": self.county
+        }
+
+def get_lat_lon_from_zip(zip_code: str) -> Optional[Dict[str, float]]:
+    """
+    Step 1: Get latitude and longitude from a ZIP code using a simple API.
+    We'll use zippopotam.us for this first step.
+    """
+    url = f"https://api.zippopotam.us/us/{zip_code}"
+    logger.info(f"Fetching lat/lon for ZIP code: {zip_code} from {url}")
+    try:
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        data = response.json()
+        
+        if not data.get("places"):
+            logger.warning(f"No places found for ZIP code {zip_code}")
+            return None
+            
+        place = data["places"][0]
+        return {
+            "latitude": float(place["latitude"]),
+            "longitude": float(place["longitude"]),
+            "state": place["state"],
+            "state_abbr": place["state abbreviation"],
+            "city": place["place name"]
+        }
+    except (requests.RequestException, KeyError, ValueError) as e:
+        logger.error(f"Failed to get lat/lon for ZIP {zip_code}: {e}")
+        return None
+
+def get_county_from_lat_lon(lat: float, lon: float) -> Optional[str]:
+    """
+    Step 2: Get county information from latitude and longitude using the
+    U.S. Census Bureau's Geocoding API.
+    """
+    url = "https://geocoding.geo.census.gov/geocoder/geographies/coordinates"
+    params = {
+        'x': lon,
+        'y': lat,
+        'benchmark': 'Public_AR_Current',
+        'vintage': 'Current_Current',
+        'format': 'json'
+    }
+    logger.info(f"Fetching county for coordinates: (lat={lat}, lon={lon}) from Census Bureau API")
+    try:
+        response = requests.get(url, params=params, timeout=15)
+        response.raise_for_status()
+        data = response.json()
+        
+        geographies = data.get("result", {}).get("geographies", {})
+        counties = geographies.get("Counties", [])
+        
+        if counties:
+            county_name = counties[0].get("NAME")
+            logger.info(f"Found county: {county_name}")
+            return county_name
+        else:
+            logger.warning(f"No county found for coordinates (lat={lat}, lon={lon})")
+            return None
+    except (requests.RequestException, KeyError, ValueError) as e:
+        logger.error(f"Failed to get county from coordinates: {e}")
+        return None
+
+def get_geo_data_from_zip(zip_code: str) -> Optional[ZipCodeData]:
+    """
+    Orchestrates the two-step process to get state, city, and county from a ZIP code.
+    """
+    # Step 1: Get Lat/Lon and basic info
+    geo_basics = get_lat_lon_from_zip(zip_code)
+    if not geo_basics:
+        return None
+        
+    # Step 2: Get County from Lat/Lon
+    county = get_county_from_lat_lon(geo_basics["latitude"], geo_basics["longitude"])
+    if not county:
+        # Fallback: sometimes county info is not available, but we can proceed without it
+        logger.warning(f"Could not determine county for ZIP {zip_code}, proceeding without it.")
+        county = "Unknown"
+
+    return ZipCodeData(
+        state=geo_basics["state"],
+        state_abbr=geo_basics["state_abbr"],
+        city=geo_basics["city"],
+        county=county
+    )
\ No newline at end of file
diff --git a/scripts/.DS_Store b/scripts/.DS_Store
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diff --git a/scripts/__init__.py b/scripts/__init__.py
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diff --git a/scripts/__pycache__/__init__.cpython-310.pyc b/scripts/__pycache__/__init__.cpython-310.pyc
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diff --git a/scripts/data_processing/__init__.py b/scripts/data_processing/__init__.py
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diff --git a/scripts/data_processing/__pycache__/document_loader.cpython-310.pyc b/scripts/data_processing/__pycache__/document_loader.cpython-310.pyc
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diff --git a/scripts/data_processing/chunker.py b/scripts/data_processing/chunker.py
new file mode 100644
index 0000000000000000000000000000000000000000..19ee9835b7102982ed4631ccd60871b51597cb21
--- /dev/null
+++ b/scripts/data_processing/chunker.py
@@ -0,0 +1,53 @@
+import logging
+import json
+from typing import List, Dict, Any
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+
+logger = logging.getLogger(__name__)
+
+def chunk_text(text: str, source_metadata: Dict[str, Any]) -> List[Document]:
+    """
+    Chunks the given text and attaches rich metadata to each chunk.
+
+    Args:
+        text: The full text content to be chunked.
+        source_metadata: A dictionary containing metadata about the source document
+                         (e.g., id, url, local_path).
+
+    Returns:
+        A list of LangChain Document objects, each representing a chunk.
+    """
+    if not text:
+        logger.warning(f"Received empty text for source_id {source_metadata.get('id')}. No chunks created.")
+        return []
+        
+    # Using RecursiveCharacterTextSplitter as it's robust for general text.
+    # These parameters can be tuned based on embedding model's context window and performance.
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len,
+        is_separator_regex=False,
+        separators=["\n\n", "\n", ". ", " ", ""],
+    )
+    
+    split_texts = text_splitter.split_text(text)
+    
+    documents = []
+    for i, chunk_text in enumerate(split_texts):
+        # This metadata is crucial for the Fairness Agent and for filtering.
+        chunk_metadata = {
+            "source_id": source_metadata.get("id"),
+            "source_url": source_metadata.get("url"),
+            "source_name": source_metadata.get("name"),
+            "source_local_path": source_metadata.get("local_path"),
+            "chunk_number": i + 1,
+            "total_chunks": len(split_texts)
+        }
+        
+        doc = Document(page_content=chunk_text, metadata=chunk_metadata)
+        documents.append(doc)
+        
+    logger.info(f"Created {len(documents)} chunks for source_id {source_metadata.get('id')}")
+    return documents
\ No newline at end of file
diff --git a/scripts/data_processing/crawler.py b/scripts/data_processing/crawler.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b9ded8ed5e5ea8b66e574b49e0acee08ce1ed3
--- /dev/null
+++ b/scripts/data_processing/crawler.py
@@ -0,0 +1,166 @@
+import requests
+from bs4 import BeautifulSoup
+from urllib.parse import urljoin, urlparse
+from pathlib import Path
+import time
+from collections import deque
+import logging
+
+# Suppress only the InsecureRequestWarning from urllib3
+import urllib3
+from urllib3.exceptions import InsecureRequestWarning
+urllib3.disable_warnings(InsecureRequestWarning)
+
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service as ChromeService
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.common.exceptions import TimeoutException, WebDriverException
+
+from insucompass.services.database import get_db_connection, add_discovered_source
+from insucompass.config import settings
+from .crawler_utils import get_content_hash, sanitize_filename
+
+logger = logging.getLogger(__name__)
+
+def get_session():
+    """Creates a requests session with a user agent."""
+    session = requests.Session()
+    session.headers.update({"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"})
+    return session
+
+def download_and_save_content(session: requests.Session, url: str, dest_folder: Path, source_id: int):
+    """Downloads a file (HTML, PDF), saves it using the new naming convention, and updates the database."""
+    try:
+        response = session.get(url, timeout=30, verify=False)
+        response.raise_for_status()
+    except requests.RequestException as e:
+        logger.error(f"Failed to download {url}: {e}")
+        return
+
+    content_type = response.headers.get('content-type', '').lower()
+    file_ext = '.pdf' if 'pdf' in content_type else '.html'
+    
+    sanitized_name = sanitize_filename(url)
+    final_filename = f"source_{source_id}_{sanitized_name}{file_ext}"
+    save_path = dest_folder / final_filename
+    
+    save_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    with open(save_path, 'wb') as f:
+        f.write(response.content)
+        
+    content_hash = get_content_hash(response.content)
+    
+    with get_db_connection() as conn:
+        conn.cursor().execute(
+            "UPDATE data_sources SET local_path = ?, content_hash = ?, status = ?, updated_at = ? WHERE id = ?",
+            (str(save_path), content_hash, 'processed', time.strftime('%Y-%m-%d %H:%M:%S'), source_id)
+        )
+        conn.commit()
+    logger.info(f"Successfully processed and saved {url} to {save_path}")
+    return response.content if file_ext == '.html' else None
+
+def crawl_with_requests(job: dict):
+    """Crawls a domain using the requests library for static sites."""
+    session = get_session()
+    dest_folder = Path("data/raw")
+    
+    queue = deque([(job['start_url'], 0)])
+    visited_urls = {job['start_url']}
+
+    logger.info(f"Starting REQUESTS crawl for '{job['name']}'")
+
+    while queue:
+        current_url, current_depth = queue.popleft()
+        if current_depth > job['crawl_depth']:
+            continue
+
+        logger.info(f"Crawling (depth {current_depth}): {current_url}")
+        
+        source_id = add_discovered_source(current_url, job['domain_lock'], 'html')
+        html_content = download_and_save_content(session, current_url, dest_folder, source_id)
+        
+        if not html_content or current_depth >= job['crawl_depth']:
+            continue
+
+        soup = BeautifulSoup(html_content, 'lxml')
+        for link in soup.find_all('a', href=True):
+            href = link['href']
+            full_url = urljoin(current_url, href).split('#')[0]
+
+            if not full_url.startswith(('http', 'https')):
+                continue
+
+            if full_url not in visited_urls and urlparse(full_url).netloc.endswith(job['domain_lock']):
+                visited_urls.add(full_url)
+                if full_url.lower().endswith('.pdf'):
+                    pdf_id = add_discovered_source(full_url, job['domain_lock'], 'pdf')
+                    download_and_save_content(session, full_url, dest_folder, pdf_id)
+                else:
+                    queue.append((full_url, current_depth + 1))
+        time.sleep(1)
+
+def crawl_with_selenium(driver: webdriver.Chrome, job: dict):
+    """Crawls a domain using Selenium for dynamic sites."""
+    session = get_session()
+    dest_folder = Path("data/raw")
+    
+    queue = deque([(job['start_url'], 0)])
+    visited_urls = {job['start_url']}
+
+    logger.info(f"Starting SELENIUM crawl for '{job['name']}'")
+
+    while queue:
+        current_url, current_depth = queue.popleft()
+        if current_depth > job['crawl_depth']:
+            continue
+
+        logger.info(f"Crawling (depth {current_depth}): {current_url}")
+        try:
+            driver.get(current_url)
+            WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.TAG_NAME, "body")))
+            time.sleep(3)
+            page_source = driver.page_source
+        except (TimeoutException, WebDriverException) as e:
+            logger.error(f"Selenium failed to get {current_url}: {e}")
+            continue
+            
+        source_id = add_discovered_source(current_url, job['domain_lock'], 'html')
+        
+        sanitized_name = sanitize_filename(current_url)
+        final_filename = f"source_{source_id}_{sanitized_name}.html"
+        save_path = dest_folder / final_filename
+        
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        save_path.write_text(page_source, encoding='utf-8')
+        
+        content_hash = get_content_hash(page_source.encode('utf-8'))
+        with get_db_connection() as conn:
+            conn.cursor().execute(
+                "UPDATE data_sources SET local_path = ?, content_hash = ?, status = ?, updated_at = ? WHERE id = ?",
+                (str(save_path), content_hash, 'processed', time.strftime('%Y-%m-%d %H:%M:%S'), source_id)
+            )
+            conn.commit()
+        logger.info(f"Successfully processed and saved {current_url} to {save_path}")
+
+        if current_depth >= job['crawl_depth']:
+            continue
+
+        soup = BeautifulSoup(page_source, 'lxml')
+        for link in soup.find_all('a', href=True):
+            href = link['href']
+            full_url = urljoin(current_url, href).split('#')[0]
+
+            if not full_url.startswith(('http', 'https')):
+                continue
+
+            if full_url not in visited_urls and urlparse(full_url).netloc.endswith(job['domain_lock']):
+                visited_urls.add(full_url)
+                if full_url.lower().endswith('.pdf'):
+                    pdf_id = add_discovered_source(full_url, job['domain_lock'], 'pdf')
+                    download_and_save_content(session, full_url, dest_folder, pdf_id)
+                else:
+                    queue.append((full_url, current_depth + 1))
\ No newline at end of file
diff --git a/scripts/data_processing/crawler_utils.py b/scripts/data_processing/crawler_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..add6722ccea99659954ad9436ffabf95b8949598
--- /dev/null
+++ b/scripts/data_processing/crawler_utils.py
@@ -0,0 +1,27 @@
+import hashlib
+import re
+from urllib.parse import urlparse
+from pathlib import Path
+
+def get_content_hash(content: bytes) -> str:
+    """Generates a SHA-256 hash for the given binary content."""
+    return hashlib.sha256(content).hexdigest()
+
+def sanitize_filename(url: str) -> str:
+    """
+    Creates a sanitized, readable filename from a URL.
+    e.g., 'https://example.com/path/to/file.html?query=1' -> 'path_to_file.html'
+    """
+    parsed_url = urlparse(url)
+    # Use the path, but remove leading/trailing slashes
+    path_part = parsed_url.path.strip('/')
+    
+    if not path_part:
+        # If path is empty (e.g., domain.com/), use the netloc
+        path_part = parsed_url.netloc
+
+    # Replace slashes with underscores and remove other invalid chars
+    sanitized = re.sub(r'[<>:"/\\|?*]', '_', path_part)
+    
+    # Limit length to avoid OS errors
+    return sanitized[:150]
\ No newline at end of file
diff --git a/scripts/data_processing/document_loader.py b/scripts/data_processing/document_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..3adb2198eba557ebbc665857a76539ed163d3a8c
--- /dev/null
+++ b/scripts/data_processing/document_loader.py
@@ -0,0 +1,70 @@
+import logging
+from pathlib import Path
+from bs4 import BeautifulSoup
+from pypdf import PdfReader
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+def load_html_content(file_path: Path) -> Optional[str]:
+    """Loads and extracts clean text content from an HTML file."""
+    logger.debug(f"Loading HTML from: {file_path}")
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            soup = BeautifulSoup(f.read(), 'lxml')
+        
+        # Remove script, style, nav, footer, header, and other common clutter
+        for element in soup(['script', 'style', 'nav', 'footer', 'header', 'aside', 'form']):
+            element.decompose()
+
+        # Get text, strip whitespace, and join lines
+        text = ' '.join(soup.get_text(separator=' ', strip=True).split())
+        
+        if not text:
+            logger.warning(f"No text content could be extracted from {file_path}")
+            return None
+        return text
+    except Exception as e:
+        logger.error(f"Failed to load or parse HTML file {file_path}: {e}")
+        return None
+
+def load_pdf_content(file_path: Path) -> Optional[str]:
+    """Loads and extracts text content from a PDF file."""
+    logger.debug(f"Loading PDF from: {file_path}")
+    if not file_path.exists():
+        logger.error(f"PDF file not found at {file_path}")
+        return None
+    try:
+        reader = PdfReader(file_path)
+        text = ""
+        for page in reader.pages:
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n\n" # Add space between pages
+        
+        if not text:
+            logger.warning(f"No text could be extracted from PDF {file_path}")
+            return None
+        return text
+    except Exception as e:
+        logger.error(f"Failed to load or parse PDF file {file_path}: {e}")
+        return None
+
+def load_document(file_path_str: str) -> Optional[str]:
+    """
+    Generic document loader that dispatches to the correct function
+    based on file extension.
+    """
+    file_path = Path(file_path_str)
+    if not file_path.exists():
+        logger.error(f"Document not found at path: {file_path}")
+        return None
+
+    extension = file_path.suffix.lower()
+    if extension == '.html':
+        return load_html_content(file_path)
+    elif extension == '.pdf':
+        return load_pdf_content(file_path)
+    else:
+        logger.warning(f"Unsupported file type '{extension}' for file {file_path}. Skipping.")
+        return None
\ No newline at end of file
diff --git a/scripts/data_processing/embedder.py b/scripts/data_processing/embedder.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/scripts/run_crawler.py b/scripts/run_crawler.py
new file mode 100644
index 0000000000000000000000000000000000000000..02d57c98fcc24182d24b9c6326659f23520bbad6
--- /dev/null
+++ b/scripts/run_crawler.py
@@ -0,0 +1,72 @@
+import logging
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service as ChromeService
+from webdriver_manager.chrome import ChromeDriverManager
+
+from insucompass.config import settings
+from insucompass.services.database import setup_database, initialize_crawl_jobs
+from scripts.data_processing.crawler import crawl_with_requests, crawl_with_selenium
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def setup_selenium_driver():
+    """Initializes a headless Chrome WebDriver."""
+    logger.info("Setting up Selenium WebDriver...")
+    try:
+        options = webdriver.ChromeOptions()
+        options.add_argument("--headless")
+        options.add_argument("--no-sandbox")
+        options.add_argument("--disable-dev-shm-usage")
+        options.add_argument("--disable-gpu")
+        options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")
+        # This will handle SSL certificate issues often seen with government sites
+        options.add_argument('--ignore-certificate-errors')
+        
+        driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options)
+        logger.info("WebDriver setup complete.")
+        return driver
+    except Exception as e:
+        logger.error(f"Failed to setup Selenium driver: {e}")
+        return None
+
+def main():
+    """Main function to run the data crawling jobs."""
+    logger.info("--- Starting InsuCompass AI Data Acquisition ---")
+    
+    # Setup DB and initialize starting URLs
+    setup_database()
+    initialize_crawl_jobs()
+
+    driver = None
+    try:
+        # Initialize Selenium driver only if needed
+        if any(job.get('method') == 'selenium_crawl' and job.get('status') == 'active' for job in settings.CRAWLING_JOBS):
+            driver = setup_selenium_driver()
+
+        for job in settings.CRAWLING_JOBS:
+            if job.get('status') != 'active':
+                logger.info(f"--- Skipping inactive job: {job['name']} ---")
+                continue
+            
+            logger.info(f"--- Processing job: {job['name']} ---")
+            if job['method'] == 'selenium_crawl':
+                if driver:
+                    crawl_with_selenium(driver, job)
+                else:
+                    logger.error(f"Selenium method required for {job['name']} but driver failed to initialize. Skipping.")
+            elif job['method'] == 'requests_crawl':
+                crawl_with_requests(job)
+            else:
+                logger.warning(f"Method '{job['method']}' not implemented for job {job['name']}. Skipping.")
+    
+    finally:
+        if driver:
+            driver.quit()
+            logger.info("Selenium WebDriver closed.")
+    
+    logger.info("--- Data Acquisition Process Finished ---")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/scripts/run_ingestion.py b/scripts/run_ingestion.py
new file mode 100644
index 0000000000000000000000000000000000000000..edbd11a088a2e97ae930712c4726c8219775fa2f
--- /dev/null
+++ b/scripts/run_ingestion.py
@@ -0,0 +1,106 @@
+import logging
+import json
+from insucompass.services.database import get_db_connection
+from insucompass.services.vector_store import vector_store_service
+from scripts.data_processing.document_loader import load_document
+from scripts.data_processing.chunker import chunk_text
+from insucompass.config import settings
+
+# Configure logging
+logging.basicConfig(level=settings.LOG_LEVEL, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def process_source_for_ingestion(source: dict):
+    """
+    Loads, chunks, and embeds a single data source.
+    """
+    source_id = source['id']
+    local_path = source['local_path']
+    
+    logger.info(f"Starting ingestion for source_id: {source_id}, path: {local_path}")
+    
+    # 1. Load document content
+    text_content = load_document(local_path)
+    if not text_content:
+        logger.error(f"Could not load content from {local_path}. Skipping ingestion for this source.")
+        # Update status in DB to 'ingestion_failed'
+        with get_db_connection() as conn:
+            conn.cursor().execute("UPDATE data_sources SET status = ? WHERE id = ?", ('ingestion_failed', source_id))
+            conn.commit()
+        return
+
+    # 2. Chunk the text with metadata
+    documents = chunk_text(text_content, source_metadata=source)
+    if not documents:
+        logger.warning(f"No chunks were created for source_id: {source_id}. Skipping embedding.")
+        return
+
+    # 3. Add documents to the vector store
+    try:
+        vector_ids = vector_store_service.add_documents(documents)
+    except Exception as e:
+        logger.error(f"Failed to embed documents for source_id {source_id}: {e}")
+        with get_db_connection() as conn:
+            conn.cursor().execute("UPDATE data_sources SET status = ? WHERE id = ?", ('embedding_failed', source_id))
+            conn.commit()
+        return
+
+    if len(vector_ids) != len(documents):
+        logger.error(f"Mismatch between number of documents ({len(documents)}) and returned vector IDs ({len(vector_ids)}). Aborting DB update for this source.")
+        return
+
+    # 4. Store chunk info and vector IDs in SQLite
+    logger.info(f"Storing {len(documents)} chunk records in the database...")
+    insert_query = "INSERT INTO knowledge_chunks (source_id, chunk_text, metadata_json, vector_id) VALUES (?, ?, ?, ?)"
+    chunk_data_to_insert = [
+        (
+            source_id,
+            doc.page_content,
+            json.dumps(doc.metadata),
+            vec_id
+        ) for doc, vec_id in zip(documents, vector_ids)
+    ]
+    
+    with get_db_connection() as conn:
+        cursor = conn.cursor()
+        cursor.executemany(insert_query, chunk_data_to_insert)
+        # Update the source status to 'ingested'
+        cursor.execute("UPDATE data_sources SET status = ? WHERE id = ?", ('ingested', source_id))
+        conn.commit()
+    logger.info(f"Successfully ingested source_id: {source_id}")
+
+
+def main():
+    """
+    Main function to run the ingestion pipeline.
+    It finds all downloaded documents that haven't been ingested yet
+    and processes them.
+    """
+    logger.info("--- Starting InsuCompass AI Data Ingestion Pipeline ---")
+    
+    # Find sources that have been downloaded but not yet ingested
+    # Statuses 'processed' and 'updated' are from the crawler step.
+    query = "SELECT * FROM data_sources WHERE status IN ('processed', 'updated') AND local_path IS NOT NULL"
+    
+    with get_db_connection() as conn:
+        sources_to_ingest = conn.cursor().execute(query).fetchall()
+
+    if not sources_to_ingest:
+        logger.info("No new or updated sources to ingest. Pipeline finished.")
+        return
+
+    logger.info(f"Found {len(sources_to_ingest)} sources to ingest.")
+    
+    for source_row in sources_to_ingest:
+        try:
+            process_source_for_ingestion(dict(source_row))
+        except Exception as e:
+            logger.error(f"A critical error occurred while processing source_id {source_row['id']}: {e}", exc_info=True)
+            with get_db_connection() as conn:
+                conn.cursor().execute("UPDATE data_sources SET status = ? WHERE id = ?", ('ingestion_failed', source_row['id']))
+                conn.commit()
+
+    logger.info("--- Data Ingestion Pipeline Finished ---")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file