forked from Archives/langchain
Add Azure OpenAI LLM (#395)
Hi! This PR adds support for the Azure OpenAI service to LangChain.
I've tried to follow the contributing guidelines.
Co-authored-by: Keiji Kanazawa <{ID}+{username}@users.noreply.github.com>
fork-chains
Keiji Kanazawa
1 year ago
committed by
GitHub
parent
bb76440bfa
commit
543db9c2df
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{
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"cells": [
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{
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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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"# Azure OpenAI LLM Example\n",
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"\n",
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"This notebook goes over how to use Langchain with [Azure OpenAI](https://aka.ms/azure-openai).\n",
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"\n",
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"The Azure OpenAI API is compatible with OpenAI's API. The `openai` Python package makes it easy to use both OpenAI and Azure OpenAI. You can call Azure OpenAI the same way you call OpenAI with the exceptions noted below.\n",
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"\n",
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"### API configuration\n",
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"You can configure the `openai` package to use Azure OpenAI using environment variables. The following is for `bash`:\n",
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"\n",
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"```bash\n",
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"# Set this to `azure`\n",
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"export OPENAI_API_TYPE=azure\n",
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"# The API version you want to use: set this to `2022-12-01` for the released version.\n",
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"export OPENAI_API_VERSION=2022-12-01\n",
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"# The base URL for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource.\n",
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"export OPENAI_API_BASE_URL=https://your-resource-name.openai.azure.com\n",
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"# The API key for your Azure OpenAI resource. You can find this in the Azure portal under your Azure OpenAI resource.\n",
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"export OPENAI_API_KEY=<your Azure OpenAI API key>\n",
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"```\n",
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"\n",
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"Alternatively, you can configure the API right within your running Python environment:\n",
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"\n",
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"```python\n",
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"import os\n",
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"os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
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"...\n",
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"```\n",
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"\n",
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"### Deployments\n",
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"With Azure OpenAI, you set up your own deployments of the common GPT-3 and Codex models. When calling the API, you need to specify the deployment you want to use.\n",
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"\n",
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"Let's say your deployment name is `text-davinci-002-prod`. In the `openai` Python API, you can specify this deployment with the `engine` parameter. For example:\n",
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"\n",
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"```python\n",
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"import openai\n",
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"\n",
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"response = openai.Completion.create(\n",
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" engine=\"text-davinci-002-prod\",\n",
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" prompt=\"This is a test\",\n",
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" max_tokens=5\n",
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")\n",
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"```\n"
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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": "8fad2a6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import Azure OpenAI\n",
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"from langchain.llms import AzureOpenAI"
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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": "8c80213a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create an instance of Azure OpenAI\n",
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"# Replace the deployment name with your own\n",
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"llm = AzureOpenAI(deployment_name=\"text-davinci-002-prod\", model_name=\"text-davinci-002\")"
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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": "592dc404",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
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]
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},
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"execution_count": 3,
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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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"# Run the LLM\n",
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"llm(\"Tell me a joke\")"
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]
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},
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{
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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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"We can also print the LLM and see its custom print."
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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": "9c33fa19",
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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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"\u001b[1mAzureOpenAI\u001b[0m\n",
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"Params: {'deployment_name': 'text-davinci-002', 'model_name': 'text-davinci-002', 'temperature': 0.7, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n"
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]
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}
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],
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"source": [
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"print(llm)"
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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": "5a8b5917",
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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": "3.10.8"
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},
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"vscode": {
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"interpreter": {
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"hash": "3bae61d45a4f4d73ecea8149862d4bfbae7d4d4a2f71b6e609a1be8f6c8d4298"
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}
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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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