mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-14 19:42:45 +00:00
4d85485e71
Changed import of `PromptTemplate` from `langchain` to `langchain_core` in all examples (notebooks)
135 lines
3.7 KiB
Plaintext
135 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# OpenLM\n",
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"[OpenLM](https://github.com/r2d4/openlm) is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP. \n",
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"\n",
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"\n",
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"It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code.\n",
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"\n",
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"This examples goes over how to use LangChain to interact with both OpenAI and HuggingFace. You'll need API keys from both."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Setup\n",
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"Install dependencies and set API keys."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Uncomment to install openlm and openai if you haven't already\n",
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"\n",
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"%pip install --upgrade --quiet openlm\n",
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"%pip install --upgrade --quiet langchain-openai"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"# Check if OPENAI_API_KEY environment variable is set\n",
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"if \"OPENAI_API_KEY\" not in os.environ:\n",
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" print(\"Enter your OpenAI API key:\")\n",
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" os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
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"\n",
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"# Check if HF_API_TOKEN environment variable is set\n",
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"if \"HF_API_TOKEN\" not in os.environ:\n",
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" print(\"Enter your HuggingFace Hub API key:\")\n",
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" os.environ[\"HF_API_TOKEN\"] = getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Using LangChain with OpenLM\n",
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"\n",
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"Here we're going to call two models in an LLMChain, `text-davinci-003` from OpenAI and `gpt2` on HuggingFace."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import LLMChain\n",
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"from langchain_community.llms import OpenLM\n",
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"from langchain_core.prompts import PromptTemplate"
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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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"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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"Model: text-davinci-003\n",
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"Result: France is a country in Europe. The capital of France is Paris.\n",
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"Model: huggingface.co/gpt2\n",
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"Result: Question: What is the capital of France?\n",
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"\n",
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"Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more\n"
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]
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}
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],
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"source": [
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"question = \"What is the capital of France?\"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate.from_template(template)\n",
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"\n",
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"for model in [\"text-davinci-003\", \"huggingface.co/gpt2\"]:\n",
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" llm = OpenLM(model=model)\n",
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" llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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" result = llm_chain.run(question)\n",
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" print(\n",
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" \"\"\"Model: {}\n",
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"Result: {}\"\"\".format(model, result)\n",
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" )"
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]
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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