mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
a2779738aa
This patch updates function "run" to "invoke" in smart_llm.ipynb.
Without this patch, you see following warning.
LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
282 lines
11 KiB
Plaintext
282 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "9e9b7651",
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"metadata": {},
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"source": [
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"# How to use a SmartLLMChain\n",
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"\n",
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"A SmartLLMChain is a form of self-critique chain that can help you if have particularly complex questions to answer. Instead of doing a single LLM pass, it instead performs these 3 steps:\n",
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"1. Ideation: Pass the user prompt n times through the LLM to get n output proposals (called \"ideas\"), where n is a parameter you can set \n",
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"2. Critique: The LLM critiques all ideas to find possible flaws and picks the best one \n",
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"3. Resolve: The LLM tries to improve upon the best idea (as chosen in the critique step) and outputs it. This is then the final output.\n",
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"\n",
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"SmartLLMChains are based on the SmartGPT workflow proposed in https://youtu.be/wVzuvf9D9BU.\n",
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"\n",
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"Note that SmartLLMChains\n",
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"- use more LLM passes (ie n+2 instead of just 1)\n",
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"- only work then the underlying LLM has the capability for reflection, which smaller models often don't\n",
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"- only work with underlying models that return exactly 1 output, not multiple\n",
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"\n",
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"This notebook demonstrates how to use a SmartLLMChain."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "714dede0",
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"metadata": {},
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"source": [
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"##### Same LLM for all steps"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "d3f7fb22",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"...\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "10e5ece6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate\n",
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"from langchain_experimental.smart_llm import SmartLLMChain\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1780da51",
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"metadata": {},
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"source": [
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"As example question, we will use \"I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\". This is an example from the original SmartGPT video (https://youtu.be/wVzuvf9D9BU?t=384). While this seems like a very easy question, LLMs struggle do these kinds of questions that involve numbers and physical reasoning.\n",
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"\n",
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"As we will see, all 3 initial ideas are completely wrong - even though we're using GPT4! Only when using self-reflection do we get a correct answer. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "054af6b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"hard_question = \"I have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "8049cecd",
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"metadata": {},
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"source": [
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"So, we first create an LLM and prompt template"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "811ea8e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = PromptTemplate.from_template(hard_question)\n",
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"llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "50b602e4",
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"metadata": {},
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"source": [
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"Now we can create a SmartLLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8cd49199",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = SmartLLMChain(llm=llm, prompt=prompt, n_ideas=3, verbose=True)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "6a72f276",
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"metadata": {},
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"source": [
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"Now we can use the SmartLLM as a drop-in replacement for our LLM. E.g.:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "074e5e75",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new SmartLLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3mI have a 12 liter jug and a 6 liter jug. I want to measure 6 liters. How do I do it?\u001b[0m\n",
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"Idea 1:\n",
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"\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
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"3. Fill the 6-liter jug again.\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
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"5. The amount of water left in the 6-liter jug will be exactly 6 liters.\u001b[0m\n",
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"Idea 2:\n",
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"\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
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"3. Fill the 6-liter jug again.\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
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"5. Since the 12-liter jug is now full, there will be 2 liters of water left in the 6-liter jug.\n",
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"6. Empty the 12-liter jug.\n",
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"7. Pour the 2 liters of water from the 6-liter jug into the 12-liter jug.\n",
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"8. Fill the 6-liter jug completely again.\n",
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"9. Pour the water from the 6-liter jug into the 12-liter jug, which already has 2 liters in it.\n",
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"10. Now, the 12-liter jug will have exactly 6 liters of water (2 liters from before + 4 liters from the 6-liter jug).\u001b[0m\n",
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"Idea 3:\n",
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"\u001b[36;1m\u001b[1;3m1. Fill the 6-liter jug completely.\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug.\n",
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"3. Fill the 6-liter jug again.\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full.\n",
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"5. The amount of water left in the 6-liter jug will be exactly 6 liters.\u001b[0m\n",
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"Critique:\n",
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"\u001b[33;1m\u001b[1;3mIdea 1:\n",
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"1. Fill the 6-liter jug completely. (No flaw)\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
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"3. Fill the 6-liter jug again. (No flaw)\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
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"5. The amount of water left in the 6-liter jug will be exactly 6 liters. (Flaw: This statement is incorrect, as there will be no water left in the 6-liter jug after pouring it into the 12-liter jug.)\n",
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"\n",
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"Idea 2:\n",
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"1. Fill the 6-liter jug completely. (No flaw)\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
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"3. Fill the 6-liter jug again. (No flaw)\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
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"5. Since the 12-liter jug is now full, there will be 2 liters of water left in the 6-liter jug. (Flaw: This statement is incorrect, as the 12-liter jug will not be full and there will be no water left in the 6-liter jug.)\n",
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"6. Empty the 12-liter jug. (No flaw)\n",
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"7. Pour the 2 liters of water from the 6-liter jug into the 12-liter jug. (Flaw: This step is based on the incorrect assumption that there are 2 liters of water left in the 6-liter jug.)\n",
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"8. Fill the 6-liter jug completely again. (No flaw)\n",
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"9. Pour the water from the 6-liter jug into the 12-liter jug, which already has 2 liters in it. (Flaw: This step is based on the incorrect assumption that there are 2 liters of water in the 12-liter jug.)\n",
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"10. Now, the 12-liter jug will have exactly 6 liters of water (2 liters from before + 4 liters from the 6-liter jug). (Flaw: This conclusion is based on the incorrect assumptions made in the previous steps.)\n",
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"\n",
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"Idea 3:\n",
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"1. Fill the 6-liter jug completely. (No flaw)\n",
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"2. Pour the water from the 6-liter jug into the 12-liter jug. (No flaw)\n",
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"3. Fill the 6-liter jug again. (No flaw)\n",
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"4. Carefully pour the water from the 6-liter jug into the 12-liter jug until the 12-liter jug is full. (Flaw: The 12-liter jug will never be full in this step, as it can hold 12 liters and we are only pouring 6 liters into it.)\n",
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"5. The amount of water left in the 6-liter jug will be exactly 6 liters. (Flaw: This statement is incorrect, as there will be no water left in the 6-liter jug after pouring it into the 12-liter jug.)\u001b[0m\n",
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"Resolution:\n",
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"\u001b[32;1m\u001b[1;3m1. Fill the 12-liter jug completely.\n",
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"2. Pour the water from the 12-liter jug into the 6-liter jug until the 6-liter jug is full.\n",
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"3. The amount of water left in the 12-liter jug will be exactly 6 liters.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'1. Fill the 12-liter jug completely.\\n2. Pour the water from the 12-liter jug into the 6-liter jug until the 6-liter jug is full.\\n3. The amount of water left in the 12-liter jug will be exactly 6 liters.'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke({})"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bbfebea1",
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"metadata": {},
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"source": [
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"##### Different LLM for different steps"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "5be6ec08",
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"metadata": {},
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"source": [
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"You can also use different LLMs for the different steps by passing `ideation_llm`, `critique_llm` and `resolve_llm`. You might want to do this to use a more creative (i.e., high-temperature) model for ideation and a more strict (i.e., low-temperature) model for critique and resolution."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "9c33fa19",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = SmartLLMChain(\n",
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" ideation_llm=ChatOpenAI(temperature=0.9, model_name=\"gpt-4\"),\n",
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" llm=ChatOpenAI(\n",
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" temperature=0, model_name=\"gpt-4\"\n",
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" ), # will be used for critique and resolution as no specific llms are given\n",
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" prompt=prompt,\n",
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" n_ideas=3,\n",
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" verbose=True,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "886c1cc1",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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