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render notebooks
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‎notebook/agentchat_cost_token_tracking.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_client_cost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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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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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
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"\n",
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"Licensed under the MIT License.\n",
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"\n",
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"# Usage tracking with AutoGen\n",
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"## 1. Use AutoGen's OpenAIWrapper for cost estimation\n",
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"The `OpenAIWrapper` from `autogen` tracks token counts and costs of your API calls. Use the `create()` method to initiate requests and `print_usage_summary()` to retrieve a detailed usage report, including total cost and token usage for both cached and actual requests.\n",
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}
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],
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"metadata": {
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"front_matter": {
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"description": "cost calculation",
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"tags": [
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"cost"
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]
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},
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"kernelspec": {
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"display_name": "msft",
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"language": "python",

‎notebook/agentchat_dalle_and_gpt4v.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Multimodal agent chat with DALL-E and GPT-4v.",
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"tags": [
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"multimodal",
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"gpt-4v"
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]
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",

‎notebook/agentchat_function_call_code_writing.ipynb

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"front_matter": {
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"description": "Equip your agent with functions that can efficiently implement features into your software application.",
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"tags": [
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"function call",
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"code generation",
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"tool use",
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"tool/function",
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"software engineering"
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]
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},

‎notebook/agentchat_guidance.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Constrained responses via guidance.",
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"tags": [
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"guidance",
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"JSON"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_human_feedback.ipynb

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
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"attachments": {},
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"cell_type": "markdown",
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}
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],
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"metadata": {
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"front_matter": {
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"description": "Code generation, execution, debugging and human feedback.",
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"tags": [
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"human",
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"code generation"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_inception_function.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Function Inception: Enable AutoGen agents to update/remove functions during conversations.",
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"tags": [
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"function inception",
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"tool/function"
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]
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",

‎notebook/agentchat_langchain.ipynb

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{
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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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"id": "ae1f50ec",
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"metadata": {
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"id": "ae1f50ec"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_langchain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
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"attachments": {},
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"cell_type": "markdown",
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}
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],
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"metadata": {
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"front_matter": {
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"description": "Use Langchain tools.",
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"tags": [
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"langchain",
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"tool/function"
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]
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},
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"colab": {
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"provenance": []
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},

‎notebook/agentchat_lmm_gpt-4v.ipynb

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],
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"metadata": {
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"front_matter": {
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"description": "In AutoGen, leveraging multimodal models can be done through two different methodologies: MultimodalConversableAgent and VisionCapability.",
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"description": "Leveraging multimodal models through two different methodologies: MultimodalConversableAgent and VisionCapability.",
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"tags": [
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"multimodal",
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"gpt-4v"

‎notebook/agentchat_lmm_llava.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Leveraging multimodal models like llava.",
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"tags": [
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"multimodal",
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"llava"
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]
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",

‎notebook/agentchat_oai_assistant_function_call.ipynb

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"description": "This Jupyter Notebook demonstrates how to leverage OSS Insight (Open Source Software Insight) for advanced GitHub data analysis by defining `Function calls` in AutoGen for the OpenAI Assistant.",
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"tags": [
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"OpenAI Assistant",
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"function call"
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"function/tool"
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]
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},
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"kernelspec": {

‎notebook/agentchat_oai_assistant_groupchat.ipynb

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],
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"metadata": {
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"front_matter": {
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"description": "This Jupyter Notebook demonstrates how to use the GPTAssistantAgent in AutoGen's group chat mode, enabling collaborative task performance through automated chat with agents powered by LLMs, tools, or humans.",
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"description": "Use GPTAssistantAgent in group chat.",
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"tags": [
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"OpenAI Assistant",
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"group chat"

‎notebook/agentchat_oai_assistant_retrieval.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "OpenAI Assistant with retrieval augmentation.",
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"tags": [
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"RAG",
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"OpenAI Assistant"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_oai_assistant_twoagents_basic.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Two-agent chat with OpenAI assistants.",
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"tags": [
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"OpenAI Assistant"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_planning.ipynb

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"attachments": {},
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"cell_type": "markdown",
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}
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],
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"metadata": {
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"front_matter": {
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"description": "Use planning agent in a function call.",
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"tags": [
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"planning",
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"tool/function"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_sql_spider.ipynb

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}
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],
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"metadata": {
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"front_matter": {
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"description": "Natural language text to SQL query using the Spider text-to-SQL benchmark.",
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"tags": [
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"SQL",
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"tool/function"
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]
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},
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",

‎notebook/agentchat_stream.ipynb

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{
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"cells": [
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"source": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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}
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],
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"metadata": {
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"front_matter": {
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"description": "Automated continual learning from new data.",
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"tags": [
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"streaming",
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"async",
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"learning"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

‎notebook/agentchat_surfer.ipynb

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"\n",
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"It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well).\n",
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"\n",
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"The WebSurferAgent uses a combination of models. GPT-4 and GPT-3.5-turbo-16 are recommended.\n",
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"The WebSurferAgent uses a combination of models. GPT-4 and GPT-3.5-turbo-16k are recommended.\n",
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"\n",
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"Your json config should look something like the following:\n",
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"```json\n",
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}
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],
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"metadata": {
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"front_matter": {
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"description": "Browse the web with agents.",
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"tags": [
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"web",
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"tool/function"
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]
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},
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‎notebook/agentchat_teachable_oai_assistants.ipynb

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"cells": [
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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_teachability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"metadata": {
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"description": "Teach OpenAI assistants.",
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"tags": [
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"teaching",
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"OpenAI assistant"
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]
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},
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"kernelspec": {
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"display_name": "flaml",
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"language": "python",

‎notebook/agentchat_two_users.ipynb

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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"metadata": {
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"description": "Involve multiple human users via function calls and nested chat.",
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"tags": [
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"human",
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},
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‎notebook/agentchat_web_info.ipynb

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"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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}
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"metadata": {
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"front_matter": {
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"description": "Solve tasks requiring web info.",
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"tags": [
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"web",
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"code generation"
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]
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",

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