mirror of
https://github.com/BerriAI/litellm.git
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(code quality) run ruff rule to ban unused imports (#7313)
* remove unused imports * fix AmazonConverseConfig * fix test * fix import * ruff check fixes * test fixes * fix testing * fix imports
This commit is contained in:
Ishaan Jaff
GitHub
parent
5e344497ce
commit
c7f14e936a
347 changed files with 5473 additions and 7207 deletions
315
cookbook/LiteLLM_batch_completion.ipynb
vendored
315
cookbook/LiteLLM_batch_completion.ipynb
vendored
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@ -1,166 +1,163 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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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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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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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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"id": "MbLbs1tbISk-"
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},
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"source": [
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"# LiteLLM Batch Completions Example\n",
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"\n",
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"* This tutorial walks through using `batch_completion`\n",
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"* Docs: https://docs.litellm.ai/docs/completion/batching"
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]
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# LiteLLM Batch Completions Example\n",
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"\n",
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"* This tutorial walks through using `batch_completion`\n",
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"* Docs: https://docs.litellm.ai/docs/completion/batching"
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],
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"metadata": {
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"id": "MbLbs1tbISk-"
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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": "Ty6-ko_aDlPF"
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},
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"outputs": [],
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"source": [
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"!pip install litellm"
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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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"id": "KGhNJRUCIh1j"
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},
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"source": [
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"## Import Batch 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": 7,
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"metadata": {
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"id": "LOtI43snDrSK"
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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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"from litellm import batch_completion\n",
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"\n",
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"# set your API_KEY\n",
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"os.environ['ANTHROPIC_API_KEY'] = \"\""
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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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"id": "Xhv92NBaIpaw"
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},
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"source": [
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"## Calling `litellm.batch_completion`\n",
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"\n",
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"In the batch_completion method, you provide a list of messages where each sub-list of messages is passed to litellm.completion(), allowing you to process multiple prompts efficiently in a single API call."
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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": 11,
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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": "yY7GIRLsDywu",
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"outputId": "009ea67f-95d5-462b-947f-b0d21e60c5bb"
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},
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"outputs": [
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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": "Ty6-ko_aDlPF"
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},
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"outputs": [],
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"source": [
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"!pip install litellm"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Import Batch Completion"
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],
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"metadata": {
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"id": "KGhNJRUCIh1j"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import litellm\n",
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"import os\n",
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"from litellm import batch_completion\n",
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"\n",
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"# set your API_KEY\n",
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"os.environ['ANTHROPIC_API_KEY'] = \"\""
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],
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"metadata": {
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"id": "LOtI43snDrSK"
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},
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Calling `litellm.batch_completion`\n",
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"\n",
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"In the batch_completion method, you provide a list of messages where each sub-list of messages is passed to litellm.completion(), allowing you to process multiple prompts efficiently in a single API call."
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],
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"metadata": {
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"id": "Xhv92NBaIpaw"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import litellm\n",
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"import os\n",
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"from litellm import batch_completion\n",
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"\n",
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"os.environ['ANTHROPIC_API_KEY'] = \"\"\n",
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"\n",
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"\n",
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"responses = batch_completion(\n",
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" model=\"claude-2\",\n",
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" messages = [\n",
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" [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"good morning? \"\n",
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" }\n",
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" ],\n",
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" [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"what's the time? \"\n",
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" }\n",
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" ]\n",
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" ]\n",
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")\n",
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"responses"
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],
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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": "yY7GIRLsDywu",
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"outputId": "009ea67f-95d5-462b-947f-b0d21e60c5bb"
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},
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[<ModelResponse at 0x7a164eed4450> JSON: {\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\": \" Good morning!\",\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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" \"created\": 1694030351.309254,\n",
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" \"model\": \"claude-2\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 11,\n",
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" \"completion_tokens\": 3,\n",
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" \"total_tokens\": 14\n",
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" }\n",
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" },\n",
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" <ModelResponse at 0x7a164eed5800> JSON: {\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 an AI assistant created by Anthropic. I don't actually have a concept of the current time.\",\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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" \"created\": 1694030352.1215081,\n",
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" \"model\": \"claude-2\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 13,\n",
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" \"completion_tokens\": 22,\n",
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" \"total_tokens\": 35\n",
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" }\n",
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" }]"
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]
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},
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"metadata": {},
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"execution_count": 11
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}
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"data": {
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"text/plain": [
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"[<ModelResponse at 0x7a164eed4450> JSON: {\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\": \" Good morning!\",\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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" \"created\": 1694030351.309254,\n",
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" \"model\": \"claude-2\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 11,\n",
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" \"completion_tokens\": 3,\n",
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" \"total_tokens\": 14\n",
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" }\n",
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" },\n",
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" <ModelResponse at 0x7a164eed5800> JSON: {\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 an AI assistant created by Anthropic. I don't actually have a concept of the current time.\",\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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" \"created\": 1694030352.1215081,\n",
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" \"model\": \"claude-2\",\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 13,\n",
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" \"completion_tokens\": 22,\n",
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" \"total_tokens\": 35\n",
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" }\n",
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" }]"
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]
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},
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"execution_count": 11,
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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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],
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"source": [
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"import os\n",
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"\n",
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"os.environ['ANTHROPIC_API_KEY'] = \"\"\n",
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"\n",
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"\n",
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"responses = batch_completion(\n",
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" model=\"claude-2\",\n",
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" messages = [\n",
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" [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"good morning? \"\n",
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" }\n",
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" ],\n",
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" [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": \"what's the time? \"\n",
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" }\n",
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" ]\n",
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" ]\n",
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")\n",
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"responses"
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]
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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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