diff --git a/.gitignore b/.gitignore
index 168d306..d0a939b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,8 @@
+code/notebook/.ipynb_checkpoints/
+code/__pycache__/
+code/.ipynb_checkpoints/
+.virtual_documents/
+.gpt_config.env
 .DS_Store
 manuscript/
 # lambda-pipeline
diff --git a/code/notebook/.ipynb_checkpoints/pinecone_retrieval-checkpoint.ipynb b/code/notebook/.ipynb_checkpoints/pinecone_retrieval-checkpoint.ipynb
deleted file mode 100644
index 49550d2..0000000
--- a/code/notebook/.ipynb_checkpoints/pinecone_retrieval-checkpoint.ipynb
+++ /dev/null
@@ -1,191 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d9a114f4-c03a-49bf-a519-89db2c5bb3f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from llama_index.core import Settings\n",
-    "from llama_index.embeddings.openai import OpenAIEmbedding\n",
-    "\n",
-    "from pinecone import Pinecone\n",
-    "from dotenv import load_dotenv, find_dotenv\n",
-    "import os\n",
-    "\n",
-    "import sys\n",
-    "sys.path.insert(0, '..')\n",
-    "from utility import get_GPT_response, client\n",
-    "\n",
-    "import json\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2dc22ebd-63d5-4b4c-a2dc-eabb627dae82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "load_dotenv(os.path.join(os.path.expanduser('~'), '.openai_config.env'))\n",
-    "api_key = os.environ.get('HACKATHON_API_KEY')\n",
-    "\n",
-    "load_dotenv(os.path.join(os.path.expanduser('~'), '.pinecone_config.env'))\n",
-    "pinecone_api_key = os.environ.get('ANDREW_API_KEY')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "a1db1e41-63a4-4b80-9e72-ccdef603d73f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embedding_model = \"text-embedding-ada-002\"\n",
-    "\n",
-    "embed_model = OpenAIEmbedding(\n",
-    "    model=embedding_model,\n",
-    "    api_key=api_key,\n",
-    ")\n",
-    "\n",
-    "\n",
-    "Settings.embed_model = embed_model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "cadb5fb8-2ed1-4cbe-a124-c0dfba19594b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pc = Pinecone(api_key=pinecone_api_key)\n",
-    "\n",
-    "index_name = \"eds\"\n",
-    "\n",
-    "pinecone_index = pc.Index(index_name)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "ba3dd1db-8385-4e36-abcc-79aae7cafcd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"what drugs should be avoided by eds patients?\"\n",
-    "top_k = 10\n",
-    "query_embedding = embed_model.get_text_embedding(\n",
-    "    query\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "2988ef50-0906-40f0-9228-ba56e671f22e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retrieved_doc = pinecone_index.query(\n",
-    "    vector=query_embedding, \n",
-    "    top_k=top_k, \n",
-    "    include_metadata=True\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "cd338c5f-bc65-4131-9fa8-82934bec0811",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "extracted_context_summary = list(map(lambda x:json.loads(x.metadata['_node_content'])['metadata']['section_summary'], retrieved_doc.matches))\n",
-    "provenance = list(map(lambda x:x.metadata['c_document_id'], retrieved_doc.matches))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "2088a355-561c-4262-86eb-cdac2fe07978",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "context = ''\n",
-    "for i in range(top_k):\n",
-    "    context += extracted_context_summary[i] + '(Ref: ' + provenance[i] + '. '\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "715753c0-75c9-42ed-8c08-b693d626c45c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system_prompt = '''\n",
-    "You are an expert in the rare disease Ehlers-Danlos syndrome (EDS).\n",
-    "You are supposed to answer the question asked by the user. \n",
-    "For this, you need to check the given context and then answer the question by grounding your response on the provided context.\n",
-    "If you are using the provided context, always make sure to provide the citation. You can find the citation in the context marked as 'Ref' \n",
-    "'''\n",
-    "\n",
-    "prompt = 'Context: ' + context + 'Query: ' + query\n",
-    "\n",
-    "response = get_GPT_response(prompt, system_prompt, os.environ.get('MODEL_NAME'), temperature=0.3)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "92b21cf3-c01b-481a-9f2b-7499223a870c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Based on the provided context, individuals with Ehlers-Danlos Syndrome (EDS) should avoid long-term opioid use for pain management due to the associated risks and complications, such as tolerance, hyperalgesia, potential overdose, and the risks of opioid use disorder (Ref: 10.7759/cureus.45713). It is recommended to opt for alternative pain management strategies, including non-opioid medications like NSAIDs, acetaminophen, anticonvulsants, and antidepressants for long-term relief and improved function (Ref: 10.7759/cureus.45713). Additionally, caution is advised when prescribing opioids to EDS patients due to connective tissue vulnerabilities and potential complications, emphasizing the importance of personalized tapering plans for patients on long-term opioids to ensure their safety and well-being (Ref: 10.7759/cureus.45713).\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(response)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9ef68ce-e3c7-450d-9aa1-453a33af73b9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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/code/notebook/pinecone_retrieval.ipynb b/code/notebook/pinecone_retrieval.ipynb
index 149c7a8..ccebd20 100644
--- a/code/notebook/pinecone_retrieval.ipynb
+++ b/code/notebook/pinecone_retrieval.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 1,
    "id": "d9a114f4-c03a-49bf-a519-89db2c5bb3f1",
    "metadata": {},
    "outputs": [],
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 2,
    "id": "2dc22ebd-63d5-4b4c-a2dc-eabb627dae82",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 3,
    "id": "a1db1e41-63a4-4b80-9e72-ccdef603d73f",
    "metadata": {},
    "outputs": [],
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "id": "cadb5fb8-2ed1-4cbe-a124-c0dfba19594b",
    "metadata": {},
    "outputs": [],
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 5,
    "id": "ba3dd1db-8385-4e36-abcc-79aae7cafcd0",
    "metadata": {},
    "outputs": [],
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 6,
    "id": "2988ef50-0906-40f0-9228-ba56e671f22e",
    "metadata": {},
    "outputs": [],
diff --git a/lambda-pipeline/app.py b/lambda-pipeline/app.py
index f3b3019..7fa643d 100644
--- a/lambda-pipeline/app.py
+++ b/lambda-pipeline/app.py
@@ -32,7 +32,7 @@ def get_model_response(input_text, temperature: float = 0.7):
         logging.error(f"Failed to get model response: {str(e)}")
         return None
 
-def get_rag_context(query, top_k=5):
+def get_rag_context(query, top_k=2):
     try:
         embed_model = OpenAIEmbedding(
             model='text-embedding-ada-002',
@@ -47,7 +47,8 @@ def get_rag_context(query, top_k=5):
             top_k=top_k,
             include_metadata=True
         )
-        extracted_context_summary = list(map(lambda x: json.loads(x.metadata['_node_content'])['metadata']['section_summary'], retrieved_doc.matches))
+        # extracted_context_summary = list(map(lambda x: json.loads(x.metadata['_node_content'])['metadata']['section_summary'], retrieved_doc.matches))
+        extracted_context_summary = list(map(lambda x: json.loads(x.metadata['_node_content'])['metadata']['text'], retrieved_doc.matches))
         provenance = list(map(lambda x: x.metadata['c_document_id'], retrieved_doc.matches))
         context = ''
         for i in range(top_k):