langchain/docs/extras/use_cases/graph/tot.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Tree of Thought (ToT) example\n",
    "\n",
    "The Tree of Thought (ToT) is a chain that allows you to query a Large Language Model (LLM) using the Tree of Thought technique. This is based on the paper [\"Large Language Model Guided Tree-of-Thought\"](https://arxiv.org/pdf/2305.08291.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.13) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from langchain.llms import OpenAI\n",
    "\n",
    "llm = OpenAI(temperature=1, max_tokens=512, model=\"text-davinci-003\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,*,*,2|1,*,3,*|*,1,*,3|4,*,*,1\n",
      "\n",
      "- This is a 4x4 Sudoku puzzle.\n",
      "- The * represents a cell to be filled.\n",
      "- The | character separates rows.\n",
      "- At each step, replace one or more * with digits 1-4.\n",
      "- There must be no duplicate digits in any row, column or 2x2 subgrid.\n",
      "- Keep the known digits from previous valid thoughts in place.\n",
      "- Each thought can be a partial or the final solution.\n"
     ]
    }
   ],
   "source": [
    "sudoku_puzzle =   \"3,*,*,2|1,*,3,*|*,1,*,3|4,*,*,1\"\n",
    "sudoku_solution = \"3,4,1,2|1,2,3,4|2,1,4,3|4,3,2,1\"\n",
    "problem_description = f\"\"\"\n",
    "{sudoku_puzzle}\n",
    "\n",
    "- This is a 4x4 Sudoku puzzle.\n",
    "- The * represents a cell to be filled.\n",
    "- The | character separates rows.\n",
    "- At each step, replace one or more * with digits 1-4.\n",
    "- There must be no duplicate digits in any row, column or 2x2 subgrid.\n",
    "- Keep the known digits from previous valid thoughts in place.\n",
    "- Each thought can be a partial or the final solution.\n",
    "\"\"\".strip()\n",
    "print(problem_description)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rules Based Checker\n",
    "\n",
    "Each thought is evaluated by the thought checker and is given a validity type: valid, invalid or partial. A simple checker can be rule based. For example, in the case of a sudoku puzzle, the checker can check if the puzzle is valid, invalid or partial.\n",
    "\n",
    "In the following code we implement a simple rule based checker for a specific 4x4 sudoku puzzle.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple\n",
    "from langchain_experimental.tot.checker import ToTChecker\n",
    "from langchain_experimental.tot.thought import ThoughtValidity\n",
    "import re\n",
    "\n",
    "class MyChecker(ToTChecker):\n",
    "    def evaluate(self, problem_description: str, thoughts: Tuple[str, ...] = ()) -> ThoughtValidity:\n",
    "        last_thought = thoughts[-1]\n",
    "        clean_solution = last_thought.replace(\" \", \"\").replace('\"', \"\")\n",
    "        regex_solution = clean_solution.replace(\"*\", \".\").replace(\"|\", \"\\\\|\")\n",
    "        if sudoku_solution in clean_solution:\n",
    "            return ThoughtValidity.VALID_FINAL\n",
    "        elif re.search(regex_solution, sudoku_solution):\n",
    "            return ThoughtValidity.VALID_INTERMEDIATE\n",
    "        else:\n",
    "            return ThoughtValidity.INVALID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just testing the MyChecker class above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "checker = MyChecker()\n",
    "assert checker.evaluate(\"\", (\"3,*,*,2|1,*,3,*|*,1,*,3|4,*,*,1\",)) == ThoughtValidity.VALID_INTERMEDIATE\n",
    "assert checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,3,2,1\",)) == ThoughtValidity.VALID_FINAL\n",
    "assert checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,3,*,1\",)) == ThoughtValidity.VALID_INTERMEDIATE\n",
    "assert checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,*,3,1\",)) == ThoughtValidity.INVALID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree of Thought Chain\n",
    "\n",
    "Initialize and run the ToT chain, with maximum number of interactions `k` set to `30` and the maximum number child thoughts `c` set to `8`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ToTChain chain...\u001b[0m\n",
      "Starting the ToT solve procedure.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harrisonchase/workplace/langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31;1m\u001b[1;3mThought: 3*,*,2|1*,3,*|*,1,*,3|4,*,*,1\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3*,1,2|1*,3,*|*,1,*,3|4,*,*,1\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3*,1,2|1*,3,4|*,1,*,3|4,*,*,1\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3*,1,2|1*,3,4|*,1,2,3|4,*,*,1\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3*,1,2|1*,3,4|2,1,*,3|4,*,*,1\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Type <enum 'ThoughtValidity'> not serializable\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31;1m\u001b[1;3mThought: 3,*,*,2|1,*,3,*|*,1,*,3|4,1,*,*\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3,*,*,2|*,3,2,*|*,1,*,3|4,1,*,*\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3,2,*,2|1,*,3,*|*,1,*,3|4,1,*,*\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3,2,*,2|1,*,3,*|1,1,*,3|4,1,*,*\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3mThought: 3,2,*,2|1,1,3,*|1,1,*,3|4,1,*,*\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mThought: 3,*,*,2|1,2,3,*|*,1,*,3|4,*,*,1\n",
      "\u001b[0m\u001b[31;1m\u001b[1;3m    Thought: 3,1,4,2|1,2,3,4|2,1,4,3|4,3,2,1\n",
      "\u001b[0m\u001b[32;1m\u001b[1;3m    Thought: 3,4,1,2|1,2,3,4|2,1,4,3|4,3,2,1\n",
      "\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3,4,1,2|1,2,3,4|2,1,4,3|4,3,2,1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_experimental.tot.base import ToTChain\n",
    "\n",
    "tot_chain = ToTChain(llm=llm, checker=MyChecker(), k=30, c=5, verbose=True, verbose_llm=False)\n",
    "tot_chain.run(problem_description=problem_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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