mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-04 02:03:32 +00:00
b1fa726377
Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI Embeddings from `langchain_openai` There are likely more --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
258 lines
8.0 KiB
Plaintext
258 lines
8.0 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"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_openai import OpenAI\n",
|
|
"\n",
|
|
"llm = OpenAI(temperature=1, max_tokens=512, model=\"gpt-3.5-turbo-instruct\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"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)"
|
|
]
|
|
},
|
|
{
|
|
"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": [
|
|
"import re\n",
|
|
"from typing import Tuple\n",
|
|
"\n",
|
|
"from langchain_experimental.tot.checker import ToTChecker\n",
|
|
"from langchain_experimental.tot.thought import ThoughtValidity\n",
|
|
"\n",
|
|
"\n",
|
|
"class MyChecker(ToTChecker):\n",
|
|
" def evaluate(\n",
|
|
" self, problem_description: str, thoughts: Tuple[str, ...] = ()\n",
|
|
" ) -> 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 (\n",
|
|
" checker.evaluate(\"\", (\"3,*,*,2|1,*,3,*|*,1,*,3|4,*,*,1\",))\n",
|
|
" == ThoughtValidity.VALID_INTERMEDIATE\n",
|
|
")\n",
|
|
"assert (\n",
|
|
" checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,3,2,1\",))\n",
|
|
" == ThoughtValidity.VALID_FINAL\n",
|
|
")\n",
|
|
"assert (\n",
|
|
" checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,3,*,1\",))\n",
|
|
" == ThoughtValidity.VALID_INTERMEDIATE\n",
|
|
")\n",
|
|
"assert (\n",
|
|
" checker.evaluate(\"\", (\"3,4,1,2|1,2,3,4|2,1,4,3|4,*,3,1\",))\n",
|
|
" == ThoughtValidity.INVALID\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"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(\n",
|
|
" llm=llm, checker=MyChecker(), k=30, c=5, verbose=True, verbose_llm=False\n",
|
|
")\n",
|
|
"tot_chain.run(problem_description=problem_description)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": "3.9.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|