{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting streamlit\n",
      "  Obtaining dependency information for streamlit from https://files.pythonhosted.org/packages/9a/14/857d0734989f3d26f2f965b2e3f67568ea7a6e8a60cb9c1ed7f774b6d606/streamlit-1.40.1-py2.py3-none-any.whl.metadata\n",
      "  Downloading streamlit-1.40.1-py2.py3-none-any.whl.metadata (8.5 kB)\n",
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      "  Downloading altair-5.5.0-py3-none-any.whl.metadata (11 kB)\n",
      "Collecting blinker<2,>=1.0.0 (from streamlit)\n",
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      "  Obtaining dependency information for gitpython!=3.1.19,<4,>=3.0.7 from https://files.pythonhosted.org/packages/e9/bd/cc3a402a6439c15c3d4294333e13042b915bbeab54edc457c723931fed3f/GitPython-3.1.43-py3-none-any.whl.metadata\n",
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      "Installing collected packages: smmap, narwhals, blinker, pydeck, gitdb, altair, gitpython, streamlit\n",
      "Successfully installed altair-5.5.0 blinker-1.9.0 gitdb-4.0.11 gitpython-3.1.43 narwhals-1.14.2 pydeck-0.9.1 smmap-5.0.1 streamlit-1.40.1\n"
     ]
    }
   ],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Qvv4R8yqCgJq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install langchain langchain-community langchain-core\n",
    "!pip install -U langchain-ollama\n",
    "!pip install -U sentence-transformers\n",
    "!pip install aift\n",
    "!pip install pypdf\n",
    "!pip install tiktoken\n",
    "!pip install chromadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pypdf2\n",
      "  Obtaining dependency information for pypdf2 from https://files.pythonhosted.org/packages/8e/5e/c86a5643653825d3c913719e788e41386bee415c2b87b4f955432f2de6b2/pypdf2-3.0.1-py3-none-any.whl.metadata\n",
      "  Downloading pypdf2-3.0.1-py3-none-any.whl.metadata (6.8 kB)\n",
      "Downloading pypdf2-3.0.1-py3-none-any.whl (232 kB)\n",
      "   ---------------------------------------- 0.0/232.6 kB ? eta -:--:--\n",
      "   - -------------------------------------- 10.2/232.6 kB ? eta -:--:--\n",
      "   ------ -------------------------------- 41.0/232.6 kB 653.6 kB/s eta 0:00:01\n",
      "   ----------------------------- ---------- 174.1/232.6 kB 1.7 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 232.6/232.6 kB 2.0 MB/s eta 0:00:00\n",
      "Installing collected packages: pypdf2\n",
      "Successfully installed pypdf2-3.0.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pypdf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Chroma\n",
      "  Downloading Chroma-0.2.0.tar.gz (5.8 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Building wheels for collected packages: Chroma\n",
      "  Building wheel for Chroma (setup.py): started\n",
      "  Building wheel for Chroma (setup.py): finished with status 'done'\n",
      "  Created wheel for Chroma: filename=Chroma-0.2.0-py3-none-any.whl size=7113 sha256=f86877f2ef866b8201e44bd448f9a1be60fe4a64d3ff86b7545ff39e70495350\n",
      "  Stored in directory: c:\\users\\santi\\appdata\\local\\pip\\cache\\wheels\\d9\\fb\\ff\\59d3b021a3434588b65b15f1a785b4aebca8f21ea516319194\n",
      "Successfully built Chroma\n",
      "Installing collected packages: Chroma\n",
      "Successfully installed Chroma-0.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install Chroma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CFgyN3awCgJs"
   },
   "source": [
    "# mistal model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEkFhrYlCgJt"
   },
   "source": [
    "# PATHUMMA LLM + simcse-model-m-bert-thai-cased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "bAOMF5BIE0_F"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "USER_AGENT environment variable not set, consider setting it to identify your requests.\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "from langchain_community.document_loaders import WebBaseLoader\n",
    "from langchain_community.vectorstores import Chroma\n",
    "from langchain_ollama import embeddings\n",
    "from langchain_ollama import ChatOllama\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain.output_parsers import PydanticOutputParser\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "6iYfpSR4CgJt"
   },
   "outputs": [],
   "source": [
    "class CustomEmbeddings:\n",
    "    def __init__(self, model_name=\"mrp/simcse-model-m-bert-thai-cased\"):\n",
    "        \"\"\"\n",
    "        Initialize the embedding model using SentenceTransformer.\n",
    "        :param model_name: Name of the pre-trained model\n",
    "        \"\"\"\n",
    "        self.model = SentenceTransformer(model_name)\n",
    "\n",
    "    def embed_query(self, text):\n",
    "        \"\"\"\n",
    "        Generate embeddings for a single query.\n",
    "        :param text: Input text to embed\n",
    "        :return: Embedding vector as a Python list\n",
    "        \"\"\"\n",
    "        embedding = self.model.encode([text])\n",
    "        return embedding[0].tolist()  # Convert NumPy array to list\n",
    "\n",
    "    async def aembed_query(self, text):\n",
    "        \"\"\"\n",
    "        Asynchronous version of `embed_query`.\n",
    "        :param text: Input text to embed\n",
    "        :return: Embedding vector as a Python list\n",
    "        \"\"\"\n",
    "        return self.embed_query(text)\n",
    "\n",
    "    def embed_documents(self, texts):\n",
    "        \"\"\"\n",
    "        Generate embeddings for multiple documents.\n",
    "        :param texts: List of input texts to embed\n",
    "        :return: List of embedding vectors as Python lists\n",
    "        \"\"\"\n",
    "        embeddings = self.model.encode(texts)\n",
    "        return [embedding.tolist() for embedding in embeddings]\n",
    "\n",
    "    async def aembed_documents(self, texts):\n",
    "        \"\"\"\n",
    "        Asynchronous version of `embed_documents`.\n",
    "        :param texts: List of input texts to embed\n",
    "        :return: List of embedding vectors as Python lists\n",
    "        \"\"\"\n",
    "        return self.embed_documents(texts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5JpbR1zhCgJu"
   },
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "from aift.multimodal import textqa\n",
    "from aift import setting\n",
    "\n",
    "# Set Pathumma API Key\n",
    "setting.set_api_key('T69FqnYgOdreO5G0nZaM8gHcjo1sifyU')\n",
    "\n",
    "# Define a simple wrapper for Pathumma\n",
    "class PathummaModel:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def generate(self, instruction: str, return_json: bool = False):\n",
    "        response = textqa.generate(instruction=instruction, return_json=return_json)\n",
    "        if return_json:\n",
    "            return response.get(\"content\", \"\")\n",
    "        return response\n",
    "\n",
    "    def __call__(self, input: str):\n",
    "        return self.generate(input, return_json=False)\n",
    "\n",
    "# Initialize Pathumma Model\n",
    "model_local = PathummaModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RYc94UgoCgJt",
    "outputId": "2941c5b0-4628-41a9-f830-9918781c3216"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\santi\\anaconda3\\Lib\\site-packages\\torch\\_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return self.fget.__get__(instance, owner)()\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "\n",
    "# Load the document, split it into chunks, embed each chunk and load it into the vector store.\n",
    "raw_documents = TextLoader('mainn.txt').load()\n",
    "text_splitter = CharacterTextSplitter(chunk_size=7500, chunk_overlap=0)\n",
    "documents = text_splitter.split_documents(raw_documents)\n",
    "\n",
    "# 2. Convert documents to Embeddings and store them\n",
    "vectorstore = Chroma.from_documents(\n",
    "    documents=documents,\n",
    "    collection_name=\"rag-chroma\",\n",
    "    embedding=CustomEmbeddings(model_name=\"mrp/simcse-model-m-bert-thai-cased\"),\n",
    ")\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BVQBgRmhCgJu",
    "outputId": "61c4afe8-57ca-42da-8832-6de27719667e"
   },
   "outputs": [],
   "source": [
    "# 3. Before RAG\n",
    "print(\"Before RAG\\n\")\n",
    "before_rag_template = \"ใครเป็นผู้จัดทำ {topic} \"\n",
    "before_rag_prompt = ChatPromptTemplate.from_template(before_rag_template)\n",
    "before_rag_chain = before_rag_prompt | model_local | StrOutputParser()\n",
    "print(before_rag_chain.invoke({\"topic\": \"การวิเคราะห์และจำแนกระยะอาการเบาหวานขึ้นตาโดยใช้ Classification Analysis and Classification of Diabetic Retinopathy Stages using Classification\"}))\n",
    "\n",
    "# 4. After RAG\n",
    "print(\"\\n########\\nAfter RAG\\n\")\n",
    "after_rag_template = \"\"\"ตอบคำถามโดยพิจารณาจากบริบทต่อไปนี้เท่านั้น:\n",
    "{context}\n",
    "คำถาม: {question}\n",
    "\"\"\"\n",
    "after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)\n",
    "\n",
    "# Query retriever for context and pass to Pathumma\n",
    "question = \"ใครเป็นผู้จัดทำการวิเคราะห์และจำแนกระยะอาการเบาหวานขึ้นตาโดยใช้ Classification Analysis and Classification of Diabetic Retinopathy Stages using Classification\"\n",
    "retrieved_context = retriever.invoke(question)\n",
    "context = \"\\n\".join([doc.page_content for doc in retrieved_context])\n",
    "\n",
    "after_rag_chain = after_rag_prompt.invoke({\n",
    "    \"context\": context,\n",
    "    \"question\": question,\n",
    "})\n",
    "response = model_local(after_rag_chain)\n",
    "print(\"response\")\n",
    "print(response)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "language": "python",
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