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feat: Add chat completion example
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‎examples/assistant/conversation.ipynb‎ renamed to ‎examples/assistant/chat_with_assistant.ipynb‎

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@@ -21,7 +21,7 @@
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{
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"cell_type": "markdown",
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"source": [
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"# Chat with Assistant\n",
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"# TaskingAI: Chat with Assistant Example\n",
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"\n",
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"In this example, we will first create an assistant who knows the meaning of various numbers and will explain it in certain language.\n",
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"Then we will start a chat with the assistant."

‎examples/crud/assistant_crud.ipynb‎

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{
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"cell_type": "markdown",
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"source": [
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"# TasingAI Assistant Module CRUD"
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"# TasingAI Assistant Module CRUD Example"
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],
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"metadata": {
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"collapsed": false

‎examples/crud/retrieval_crud.ipynb‎

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@@ -21,7 +21,7 @@
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{
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"cell_type": "markdown",
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"source": [
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"# TasingAI Retrieval Module CRUD"
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"# TasingAI Retrieval Module CRUD Example"
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],
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"metadata": {
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"collapsed": false

‎examples/crud/tool_crud.ipynb‎

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{
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"cell_type": "markdown",
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"source": [
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"# TasingAI Tool Module CRUD"
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"# TasingAI Tool Module CRUD Exampple"
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],
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"metadata": {
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"collapsed": false

‎examples/inference/chat_completion.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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"source": [
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"import taskingai\n",
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"# Load TaskingAI API Key from environment variable"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:45.292065Z",
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"start_time": "2023-11-28T12:07:45.248068Z"
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}
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},
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"id": "1a6bfd1682fcb23f"
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},
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{
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"cell_type": "markdown",
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"source": [
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"# TasingAI Model Inference: Chat Completion"
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],
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"metadata": {
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"collapsed": false
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},
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"id": "657463bd357a3c3"
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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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"outputs": [],
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"source": [
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"from taskingai.inference import *\n",
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"import json\n",
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"# choose an available chat_completion model from your project\n",
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"model_id = \"Gk1145Bl\""
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:46.170766Z",
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"start_time": "2023-11-28T12:07:46.157613Z"
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}
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},
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"id": "49abde692940b09e"
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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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"outputs": [
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{
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"data": {
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"text/plain": "{'created_timestamp': 1701173269243,\n 'finish_reason': 'stop',\n 'message': {'content': 'Hello! How can I assist you today?',\n 'function_call': None,\n 'role': 'assistant'},\n 'object': 'ChatCompletion'}"
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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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"# normal \n",
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"chat_completion = taskingai.inference.chat_completion(\n",
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" model_id=model_id,\n",
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" messages=[\n",
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" SystemMessage(\"You are a professional assistant.\"),\n",
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" UserMessage(\"Hi\"),\n",
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" ]\n",
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")\n",
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"chat_completion"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:49.355234Z",
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"start_time": "2023-11-28T12:07:46.700962Z"
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}
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},
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"id": "43dcc632665f0de4"
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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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"outputs": [
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{
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"data": {
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"text/plain": "{'created_timestamp': 1701173272255,\n 'finish_reason': 'stop',\n 'message': {'content': \"Of course! Here's another joke for you: Why don't \"\n \"skeletons fight each other? They don't have the guts!\",\n 'function_call': None,\n 'role': 'assistant'},\n 'object': 'ChatCompletion'}"
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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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"# multi round chat completion\n",
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"chat_completion = taskingai.inference.chat_completion(\n",
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" model_id=model_id,\n",
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" messages=[\n",
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" SystemMessage(\"You are a professional assistant.\"),\n",
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" UserMessage(\"Hi\"),\n",
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" AssistantMessage(\"Hello! How can I assist you today?\"),\n",
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" UserMessage(\"Can you tell me a joke?\"),\n",
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" AssistantMessage(\"Sure, here is a joke for you: Why don't scientists trust atoms? Because they make up everything!\"),\n",
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" UserMessage(\"That's funny. Can you tell me another one?\"),\n",
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" ]\n",
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")\n",
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"chat_completion"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:52.367618Z",
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"start_time": "2023-11-28T12:07:50.109888Z"
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}
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},
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"id": "e8933bc07f4b3228"
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "{'created_timestamp': 1701173274744,\n 'finish_reason': 'length',\n 'message': {'content': \"Of course! Here's\",\n 'function_call': None,\n 'role': 'assistant'},\n 'object': 'ChatCompletion'}"
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},
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"execution_count": 5,
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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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"# config max tokens\n",
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"chat_completion = taskingai.inference.chat_completion(\n",
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" model_id=model_id,\n",
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" messages=[\n",
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" SystemMessage(\"You are a professional assistant.\"),\n",
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" UserMessage(\"Hi\"),\n",
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" AssistantMessage(\"Hello! How can I assist you today?\"),\n",
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" UserMessage(\"Can you tell me a joke?\"),\n",
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" AssistantMessage(\"Sure, here is a joke for you: Why don't scientists trust atoms? Because they make up everything!\"),\n",
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" UserMessage(\"That's funny. Can you tell me another one?\"),\n",
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" ],\n",
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" configs={\n",
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" \"max_tokens\": 5\n",
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" }\n",
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")\n",
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"chat_completion"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:54.817719Z",
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"start_time": "2023-11-28T12:07:53.137411Z"
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}
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},
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"id": "f7c1b8be2579d9e0"
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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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"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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"chat_completion = {'created_timestamp': 1701173277776,\n",
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" 'finish_reason': 'function_call',\n",
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" 'message': {'content': None,\n",
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" 'function_call': {'arguments': {'a': 112, 'b': 22},\n",
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" 'name': 'plus_a_and_b'},\n",
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" 'role': 'assistant'},\n",
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" 'object': 'ChatCompletion'}\n",
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"function name: plus_a_and_b, argument content: {\"a\": 112, \"b\": 22}\n"
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]
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}
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],
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"source": [
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"# function call\n",
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"function = Function(\n",
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" name=\"plus_a_and_b\",\n",
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" description=\"Sum up a and b and return the result\",\n",
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" parameters={\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"a\": {\n",
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" \"type\": \"integer\",\n",
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" \"description\": \"The first number\"\n",
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" },\n",
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" \"b\": {\n",
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" \"type\": \"integer\",\n",
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" \"description\": \"The second number\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"a\", \"b\"]\n",
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" },\n",
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")\n",
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"chat_completion = taskingai.inference.chat_completion(\n",
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" model_id=model_id,\n",
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" messages=[\n",
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" SystemMessage(\"You are a professional assistant.\"),\n",
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" UserMessage(\"What is the result of 112 plus 22?\"),\n",
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" ],\n",
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" functions=[function]\n",
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")\n",
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"print(f\"chat_completion = {chat_completion}\")\n",
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"\n",
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"assistant_function_call_message = chat_completion.message\n",
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"fucntion_name = assistant_function_call_message.function_call.name\n",
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"argument_content = json.dumps(assistant_function_call_message.function_call.arguments)\n",
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"print(f\"function name: {fucntion_name}, argument content: {argument_content}\")"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:07:57.823570Z",
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"start_time": "2023-11-28T12:07:55.601317Z"
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}
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},
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"id": "2645bdc3df011e7d"
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"collapsed": false
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},
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"id": "ed6957f0c380ba9f"
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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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"outputs": [
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{
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"data": {
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"text/plain": "{'created_timestamp': 1701173282280,\n 'finish_reason': 'stop',\n 'message': {'content': 'The result of 112 plus 22 is 144.',\n 'function_call': None,\n 'role': 'assistant'},\n 'object': 'ChatCompletion'}"
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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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"# add function message\n",
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"chat_completion = taskingai.inference.chat_completion(\n",
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" model_id=model_id,\n",
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" messages=[\n",
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" SystemMessage(\"You are a professional assistant.\"),\n",
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" UserMessage(\"What is the result of 112 plus 22?\"),\n",
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" assistant_function_call_message,\n",
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" FunctionMessage(name=fucntion_name, content=\"144\")\n",
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" ],\n",
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" functions=[function]\n",
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")\n",
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"chat_completion"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-11-28T12:08:02.319026Z",
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"start_time": "2023-11-28T12:08:00.109622Z"
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}
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},
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"id": "9df9a8b9eafa17d9"
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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",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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}

‎examples/inference/chat_completion.py‎

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import taskingai
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import os
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# Load TaskingAI API Key from environment variable
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model_id = os.environ.get("MODEL_ID")
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print(taskingai.Config.API_KEY, taskingai.Config.HOST, model_id)
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from taskingai.inference import *
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# choose an available chat_completion model from your project
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# function call
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function = Function(
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name="plus_a_and_b",
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description="Sum up a and b and return the result",
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parameters={
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"type": "object",
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"properties": {
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"a": {
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"type": "integer",
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"description": "The first number"
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},
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"b": {
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"type": "integer",
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"description": "The second number"
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}
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},
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"required": ["a", "b"]
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},
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)
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chat_completion = taskingai.inference.chat_completion(
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model_id=model_id,
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messages=[
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SystemMessage("You are a professional assistant."),
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UserMessage("What is the result of 112 plus 22?"),
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AssistantMessage(
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function_call=FunctionCall(name="plus_a_and_b", arguments={"a": 112, "b": 22}),
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),
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FunctionMessage(
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name="plus_a_and_b",
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content="144"
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)
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],
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functions=[function]
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)
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print(chat_completion.message)

‎examples/inference/text_embedding.py‎

Whitespace-only changes.

‎taskingai/assistant/message.py‎

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@@ -110,6 +110,7 @@ def create_user_message(
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def update_message(
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assistant_id: str,
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chat_id: str,
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message_id: str,
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metadata: Dict[str, str],
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) -> Message:
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)
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response: MessageUpdateResponse = api_instance.update_message(
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assistant_id=assistant_id,
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chat_id=chat_id,
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message_id=message_id,
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body=body
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)

‎taskingai/inference/__init__.py‎

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# todo
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from .chat_completion import *
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from .text_embedding import *

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