mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
311 lines
8.2 KiB
Plaintext
311 lines
8.2 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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"# Google Vertex AI PaLM \n",
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"\n",
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"**Note:** This is separate from the `Google PaLM` integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`. \n"
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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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"## Setting up"
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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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"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"
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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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"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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import VertexAI"
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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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"## Question-answering example"
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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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"from langchain.prompts import PromptTemplate\nfrom langchain.chains import LLMChain"
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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 = \"\"\"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(template=template, input_variables=[\"question\"])"
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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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"llm = VertexAI()"
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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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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=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": 8,
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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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"'Justin Bieber was born on March 1, 1994. The Super Bowl in 1994 was won by the San Francisco 49ers.\\nThe final answer: San Francisco 49ers.'"
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]
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},
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"execution_count": 8,
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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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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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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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"## Code generation example"
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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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"You can now leverage the `Codey API` for code generation within `Vertex AI`. \n",
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"\n",
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"The model names are:\n",
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"- `code-bison`: for code suggestion\n",
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"- `code-gecko`: for code completion"
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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": 9,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:16:53.149438Z",
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"iopub.status.busy": "2023-06-17T21:16:53.149065Z",
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"iopub.status.idle": "2023-06-17T21:16:53.421824Z",
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"shell.execute_reply": "2023-06-17T21:16:53.421136Z",
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"shell.execute_reply.started": "2023-06-17T21:16:53.149415Z"
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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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"llm = VertexAI(model_name=\"code-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": 12,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:17:11.179077Z",
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"iopub.status.busy": "2023-06-17T21:17:11.178686Z",
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"iopub.status.idle": "2023-06-17T21:17:11.182499Z",
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"shell.execute_reply": "2023-06-17T21:17:11.181895Z",
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"shell.execute_reply.started": "2023-06-17T21:17:11.179052Z"
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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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"llm_chain = LLMChain(prompt=prompt, llm=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": 15,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:18:47.024785Z",
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"iopub.status.busy": "2023-06-17T21:18:47.024230Z",
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"iopub.status.idle": "2023-06-17T21:18:49.352249Z",
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"shell.execute_reply": "2023-06-17T21:18:49.351695Z",
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"shell.execute_reply.started": "2023-06-17T21:18:47.024762Z"
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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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"'```python\\ndef is_prime(n):\\n \"\"\"\\n Determines if a number is prime.\\n\\n Args:\\n n: The number to be tested.\\n\\n Returns:\\n True if the number is prime, False otherwise.\\n \"\"\"\\n\\n # Check if the number is 1.\\n if n == 1:\\n return False\\n\\n # Check if the number is 2.\\n if n == 2:\\n return True\\n\\n'"
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]
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},
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"execution_count": 15,
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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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"question = \"Write a python function that identifies if the number is a prime number?\"\n",
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"\n",
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"llm_chain.run(question)"
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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 models deployed on Vertex Model Garden"
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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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"Vertex Model Garden [exposes](https://cloud.google.com/vertex-ai/docs/start/explore-models) open-sourced models that can be deployed and served on Vertex AI. If you have successfully deployed a model from Vertex Model Garden, you can find a corresponding Vertex AI [endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#what_happens_when_you_deploy_a_model) in the console or via API."
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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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"from langchain.llms import VertexAIModelGarden"
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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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"llm = VertexAIModelGarden(\n",
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" project=\"YOUR PROJECT\",\n",
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" endpoint_id=\"YOUR ENDPOINT_ID\"\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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"metadata": {},
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"outputs": [],
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"source": [
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"llm(\"What is the meaning of life?\")"
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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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"Like all LLMs, we can then compose it with other components:"
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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.prompts import PromptTemplate\n",
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"\n",
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"prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")"
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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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"llm_oss_chain = prompt | llm\n",
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"\n",
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"llm_oss_chain.invoke({\"thing\": \"life\"})"
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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": "poetry-venv",
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"language": "python",
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"name": "poetry-venv"
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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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