forked from Archives/langchain
985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
88 lines
1.8 KiB
Plaintext
88 lines
1.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e71e720f",
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"metadata": {},
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"source": [
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"# LLM Math\n",
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"\n",
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"This notebook showcases using LLMs and Python REPLs to do complex word math problems."
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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": "44e9ba31",
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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 LLMMathChain chain...\u001b[0m\n",
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"What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
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"```python\n",
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"import math\n",
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"print(math.pow(13, .3432))\n",
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"```\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
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"\u001b[0m\n",
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"\u001b[1m> Finished LLMMathChain 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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"'Answer: 2.4116004626599237\\n'"
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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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"from langchain import OpenAI, LLMMathChain\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"llm_math = LLMMathChain(llm=llm, verbose=True)\n",
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"\n",
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"llm_math.run(\"What is 13 raised to the .3432 power?\")"
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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": "f62f0c75",
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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": "3.10.9"
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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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