mirror of
https://github.com/hwchase17/langchain
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247 lines
7.9 KiB
Plaintext
247 lines
7.9 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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"metadata": {},
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"source": [
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"# Google Cloud Platform Vertex AI PaLM \n",
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"\n",
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"Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
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"\n",
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"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
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"\n",
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"To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
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"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
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"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
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"\n",
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"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
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"\n",
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"For more information, see: \n",
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"- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
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"- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\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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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#!pip install google-cloud-aiplatform"
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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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"from langchain.chat_models import ChatVertexAI\n",
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"from langchain.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import HumanMessage, SystemMessage"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"chat = ChatVertexAI()"
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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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{
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"data": {
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"text/plain": [
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"AIMessage(content='Sure, here is the translation of the sentence \"I love programming\" from English to French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 4,
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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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"messages = [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant that translates English to French.\"\n",
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" ),\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" ),\n",
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"]\n",
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"chat(messages)"
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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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"metadata": {},
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"source": [
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"You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
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"\n",
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"For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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"system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
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"human_template = \"{text}\"\n",
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"human_message_prompt = HumanMessagePromptTemplate.from_template(human_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": 7,
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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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"AIMessage(content='Sure, here is the translation of \"I love programming\" in French:\\n\\nJ\\'aime programmer.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 7,
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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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"chat_prompt = ChatPromptTemplate.from_messages(\n",
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" [system_message_prompt, human_message_prompt]\n",
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")\n",
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"\n",
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"# get a chat completion from the formatted messages\n",
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"chat(\n",
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" chat_prompt.format_prompt(\n",
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" input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
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" ).to_messages()\n",
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")"
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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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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:09:25.423568Z",
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"iopub.status.busy": "2023-06-17T21:09:25.423213Z",
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"iopub.status.idle": "2023-06-17T21:09:25.429641Z",
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"shell.execute_reply": "2023-06-17T21:09:25.429060Z",
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"shell.execute_reply.started": "2023-06-17T21:09:25.423546Z"
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},
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"tags": []
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},
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"source": [
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"You can now leverage the Codey API for code chat within Vertex AI. The model name is:\n",
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"- codechat-bison: for code assistance"
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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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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:30:43.974841Z",
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"iopub.status.busy": "2023-06-17T21:30:43.974431Z",
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"iopub.status.idle": "2023-06-17T21:30:44.248119Z",
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"shell.execute_reply": "2023-06-17T21:30:44.247362Z",
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"shell.execute_reply.started": "2023-06-17T21:30:43.974820Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatVertexAI(model_name=\"codechat-bison\")"
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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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"execution": {
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"iopub.execute_input": "2023-06-17T21:30:45.146093Z",
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"iopub.status.busy": "2023-06-17T21:30:45.145752Z",
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"iopub.status.idle": "2023-06-17T21:30:47.449126Z",
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"shell.execute_reply": "2023-06-17T21:30:47.448609Z",
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"shell.execute_reply.started": "2023-06-17T21:30:45.146069Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='The following Python function can be used to identify all prime numbers up to a given integer:\\n\\n```\\ndef is_prime(n):\\n \"\"\"\\n Determines whether the given integer is prime.\\n\\n Args:\\n n: The integer to be tested for primality.\\n\\n Returns:\\n True if n is prime, False otherwise.\\n \"\"\"\\n\\n # Check if n is divisible by 2.\\n if n % 2 == 0:\\n return False\\n\\n # Check if n is divisible by any integer from 3 to the square root', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 4,
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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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"messages = [\n",
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" HumanMessage(\n",
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" content=\"How do I create a python function to identify all prime numbers?\"\n",
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" )\n",
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"]\n",
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"chat(messages)"
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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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"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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"vscode": {
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"interpreter": {
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"hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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