mirror of
https://github.com/BerriAI/litellm.git
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310 lines
9.1 KiB
Text
Vendored
310 lines
9.1 KiB
Text
Vendored
{
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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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"id": "fNkMBurtxawJ"
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},
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"source": [
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"# LiteLLM Bedrock Usage\n",
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"Important Note: For Bedrock Requests you need to ensure you have `pip install boto3>=1.28.57`, boto3 supports bedrock from `boto3>=1.28.57` and higher "
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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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"id": "htAufI28xeSy"
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},
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"source": [
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"## Pre-Requisites"
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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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"id": "jT5GbPjAuDTp"
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},
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"outputs": [],
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"source": [
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"!pip install litellm\n",
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"!pip install boto3>=1.28.57 # this version onwards has bedrock support"
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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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"id": "H4Vu4er2xnfI"
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},
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"source": [
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"## Set Bedrock/AWS Credentials"
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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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"id": "CtTrBthWxp-t"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"AWS_ACCESS_KEY_ID\"] = \"\" # Access key\n",
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"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"\" # Secret access key\n",
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"os.environ[\"AWS_REGION_NAME\"] = \"\""
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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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"id": "ycRK9NUdx1EI"
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},
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"source": [
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"## Anthropic Requests"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "tgkuoHa5uLOy",
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"outputId": "27a78e86-c6a7-4bcc-8559-0813cb978426"
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},
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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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"Claude instant 1, response\n",
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"{\n",
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" \"object\": \"chat.completion\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"finish_reason\": \"stop\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"content\": \" I'm doing well, thanks for asking!\",\n",
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" \"role\": \"assistant\",\n",
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" \"logprobs\": null\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"id\": \"chatcmpl-4f2e64a1-56d2-43f2-90d3-60ffd6f5086d\",\n",
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" \"created\": 1696256761.3265705,\n",
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" \"model\": \"anthropic.claude-instant-v1\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 11,\n",
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" \"completion_tokens\": 9,\n",
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" \"total_tokens\": 20\n",
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" },\n",
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" \"finish_reason\": \"stop_sequence\"\n",
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"}\n",
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"Claude v2, response\n",
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"{\n",
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" \"object\": \"chat.completion\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"finish_reason\": \"stop\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"content\": \" I'm doing well, thanks for asking!\",\n",
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" \"role\": \"assistant\",\n",
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" \"logprobs\": null\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"id\": \"chatcmpl-34f59b33-f94e-40c2-8bdb-f4af0813405e\",\n",
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" \"created\": 1696256762.2137017,\n",
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" \"model\": \"anthropic.claude-v2\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 11,\n",
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" \"completion_tokens\": 9,\n",
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" \"total_tokens\": 20\n",
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" },\n",
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" \"finish_reason\": \"stop_sequence\"\n",
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"}\n"
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]
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}
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],
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"source": [
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"from litellm import completion\n",
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"\n",
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"response = completion(\n",
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" model=\"bedrock/anthropic.claude-instant-v1\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
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")\n",
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"print(\"Claude instant 1, response\")\n",
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"print(response)\n",
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"\n",
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"\n",
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"response = completion(\n",
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" model=\"bedrock/anthropic.claude-v2\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
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")\n",
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"print(\"Claude v2, response\")\n",
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"print(response)"
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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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"id": "HnM-HtM3yFMT"
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},
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"source": [
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"## Anthropic Requests - With Streaming"
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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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"id": "_JZvg2yovRsU"
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},
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"outputs": [],
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"source": [
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"from litellm import completion\n",
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"\n",
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"response = completion(\n",
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" model=\"bedrock/anthropic.claude-instant-v1\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}],\n",
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" stream=True,\n",
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")\n",
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"print(\"Claude instant 1, response\")\n",
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"for chunk in response:\n",
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" print(chunk)\n",
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"\n",
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"\n",
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"response = completion(\n",
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" model=\"bedrock/anthropic.claude-v2\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}],\n",
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" stream=True\n",
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")\n",
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"print(\"Claude v2, response\")\n",
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"print(response)\n",
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"for chunk in response:\n",
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" print(chunk)"
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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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"id": "zj1U1mh9zEhP"
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},
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"source": [
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"## A121 Requests"
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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": 19,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "6wK6MZLovU7r",
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"outputId": "4cf80c04-f15d-4066-b4c7-113b551538de"
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},
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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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"J2 ultra response\n",
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"{\n",
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" \"object\": \"chat.completion\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"finish_reason\": \"stop\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"content\": \"\\nHi, I'm doing well, thanks for asking! How about you?\",\n",
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" \"role\": \"assistant\",\n",
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" \"logprobs\": null\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"id\": \"chatcmpl-f2de678f-0e70-4e36-a01f-8b184c2e4d50\",\n",
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" \"created\": 1696257116.044311,\n",
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" \"model\": \"ai21.j2-ultra\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 6,\n",
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" \"completion_tokens\": 16,\n",
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" \"total_tokens\": 22\n",
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" }\n",
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"}\n",
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"J2 mid response\n",
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"{\n",
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" \"object\": \"chat.completion\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"finish_reason\": \"stop\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"content\": \"\\nGood. And you?\",\n",
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" \"role\": \"assistant\",\n",
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" \"logprobs\": null\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"id\": \"chatcmpl-420d6bf9-36d8-484b-93b4-4c9e00f7ce2e\",\n",
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" \"created\": 1696257116.5756805,\n",
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" \"model\": \"ai21.j2-mid\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 6,\n",
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" \"completion_tokens\": 6,\n",
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" \"total_tokens\": 12\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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"source": [
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"response = completion(\n",
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" model=\"bedrock/ai21.j2-ultra\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}],\n",
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")\n",
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"print(\"J2 ultra response\")\n",
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"print(response)\n",
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"\n",
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"response = completion(\n",
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" model=\"bedrock/ai21.j2-mid\",\n",
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" messages=[{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}],\n",
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")\n",
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"print(\"J2 mid response\")\n",
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"print(response)"
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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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"id": "Y5gGZIwzzSON"
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},
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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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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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