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alonsosilvaallende/solara-mistral-chat-interface.ipynb

Created October 29, 2024 07:50
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solara-mistral-chat-interface.ipynb
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"cells": [
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"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
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]
},
{
"cell_type": "markdown",
"source": [
"## Chat Interface\n",
"\n",
"Let's implement a simple chat interface. To do this, we need to install `solara` and `mistralai` libraries. This demo uses `solara===1.41.0` and `mistralai===1.1.0`"
],
"metadata": {
"id": "i-CFACIiwg1D"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Un_POTbivxK0"
},
"outputs": [],
"source": [
"%pip install --quiet solara==1.41.0 mistralai==1.1.0"
]
},
{
"cell_type": "markdown",
"source": [
"Create your `Mistral` client instance using your Mistral API key stored in your Colab Secrets."
],
"metadata": {
"id": "gYGloWOwxFWt"
}
},
{
"cell_type": "code",
"source": [
"from mistralai import Mistral\n",
"from google.colab import userdata\n",
"\n",
"MISTRAL_API_KEY = userdata.get('MISTRAL_API_KEY')\n",
"client = Mistral(api_key=MISTRAL_API_KEY)"
],
"metadata": {
"id": "qWkT9Yvyw1lt"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Let's initialize a reactive variable `messages` where all the messages will be stored."
],
"metadata": {
"id": "e8vik-8V8AYM"
}
},
{
"cell_type": "code",
"source": [
"import solara as sl\n",
"from typing import List\n",
"from typing_extensions import TypedDict\n",
"\n",
"class MessageDict(TypedDict):\n",
" role: str\n",
" content: str\n",
"\n",
"messages: sl.Reactive[List[MessageDict]] = sl.reactive([])"
],
"metadata": {
"id": "eMuQcO2DxWbT"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Initially, the value of the reactive variable `messages` is an empty list."
],
"metadata": {
"id": "SEXhqMOB8Jmj"
}
},
{
"cell_type": "code",
"source": [
"messages.value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gf79os5b3Ke2",
"outputId": "f9d78f81-0906-4d5a-d18e-6f696504f8e9"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"Let's add some messages to see how to display them. For example:"
],
"metadata": {
"id": "x5dV-v4rHRNI"
}
},
{
"cell_type": "code",
"source": [
"messages.value = [\n",
" {\"role\": \"user\", \"content\": \"Hello!\"},\n",
" {\"role\": \"assistant\", \"content\": \"Hello! How can I assist you today?\"},\n",
" ]"
],
"metadata": {
"id": "7ytP8EUiHZtg"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Given a list of messages, we will display them as follows."
],
"metadata": {
"id": "WoBIFVU2G_9g"
}
},
{
"cell_type": "code",
"source": [
"@sl.component\n",
"def Page():\n",
" with sl.lab.ChatBox():\n",
" for item in messages.value:\n",
" with sl.lab.ChatMessage(\n",
" user=item[\"role\"] == \"user\",\n",
" name=\"User\" if item[\"role\"] == \"user\" else \"Assistant\"\n",
" ):\n",
" sl.Markdown(item[\"content\"])\n",
"Page()"
],
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"height": 193,
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},
"id": "V6gSs0tpHBuA",
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{
"cell_type": "markdown",
"source": [
"Given a list of messages where the last one is a user message, we query a Mistral model and retrieve the response. To make the interaction smooth, we handle it by streaming the response. For this, we define a generator."
],
"metadata": {
"id": "2dHNngad8esw"
}
},
{
"cell_type": "code",
"source": [
"def response_generator(messages):\n",
" response = client.chat.stream(model=\"open-mistral-7b\", messages=messages, max_tokens=1024, random_seed=12)\n",
" for chunk in response:\n",
" yield chunk.data.choices[0].delta.content"
],
"metadata": {
"id": "YaH60K8qxzpZ"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"To test our generator, let's add a user message to our list of messages."
],
"metadata": {
"id": "dXWAqMRn8qgw"
}
},
{
"cell_type": "code",
"source": [
"messages.value = [{\"role\": \"user\", \"content\": \"Hi!\"}]\n",
"messages.value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "80Rdihsk3XaK",
"outputId": "8846d3dd-b846-42d0-9ee8-18705e143fc9"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'role': 'user', 'content': 'Hi!'}]"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"Now, let's test that the generator is working."
],
"metadata": {
"id": "TObeONZR9Imr"
}
},
{
"cell_type": "code",
"source": [
"for chunk in response_generator(messages.value):\n",
" print(chunk)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XlltoZaG3Oi0",
"outputId": "a1c9271e-0212-4071-da38-6082251a70e7"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Hello\n",
"!\n",
" How\n",
" can\n",
" I\n",
" help\n",
" you\n",
" today\n",
"?\n",
" \n",
"😊\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Let's empty again our list of messages."
],
"metadata": {
"id": "yHAPpPRvDQBD"
}
},
{
"cell_type": "code",
"source": [
"messages.value = []"
],
"metadata": {
"id": "BQ_iLw7yDMI-"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"The following step is to retrieve the input from the user and store it in the list of messages. For this, we will use `ChatInput` from `solara`. Write a message, for example \"Hi!\"."
],
"metadata": {
"id": "iYkZgFlyBRLM"
}
},
{
"cell_type": "code",
"source": [
"def send(user_message):\n",
" messages.value = [*messages.value, {\"role\": \"user\", \"content\": user_message}]\n",
"sl.lab.ChatInput(send_callback=send)"
],
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{
"cell_type": "markdown",
"source": [
"The messages variable should contain your input."
],
"metadata": {
"id": "9PFZmMnQC9qU"
}
},
{
"cell_type": "code",
"source": [
"messages.value"
],
"metadata": {
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},
"id": "cFa8_vIg4LPt",
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},
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"messages.value = [{'role': 'user', 'content': 'Hi!'}] # In case you are running all the notebook at once"
],
"metadata": {
"id": "De0BN71dnDnm"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Notice that we want to stream the response by displaying each chunk as it is received. To achieve that, we will add each chunk as it is received to the list of messages."
],
"metadata": {
"id": "aY670AZn9a2K"
}
},
{
"cell_type": "code",
"source": [
"def add_chunk_to_ai_message(chunk: str):\n",
" messages.value = [\n",
" *messages.value[:-1],\n",
" {\n",
" \"role\": \"assistant\",\n",
" \"content\": messages.value[-1][\"content\"] + chunk,\n",
" },\n",
" ]"
],
"metadata": {
"id": "ZlUKS8Uoydlf"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Let's test that the `add_chunk_to_ai_message` is working."
],
"metadata": {
"id": "D8_UEruP-CfL"
}
},
{
"cell_type": "code",
"source": [
"messages.value = [{\"role\": \"user\", \"content\": \"Hi!\"}]\n",
"messages.value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "b0S9afLONVKr",
"outputId": "08132553-81a0-437a-90ca-eaa51ea46278"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'role': 'user', 'content': 'Hi!'}]"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"messages.value = [*messages.value, {\"role\": \"assistant\", \"content\": \"\"}]\n",
"for chunk in response_generator([{'role': 'user', 'content': 'Hi!'}]):\n",
" add_chunk_to_ai_message(chunk)\n",
" print(messages.value)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"id": "vhZ396762-pv",
"outputId": "37c1d923-b719-43f1-f7ef-57dc6c4af4d7"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': ''}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello!'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you today'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you today?'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you today? '}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you today? 😊'}]\n",
"[{'role': 'user', 'content': 'Hi!'}, {'role': 'assistant', 'content': 'Hello! How can I help you today? 😊'}]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"The messages variable contains the response"
],
"metadata": {
"id": "qOOK2YW2lqwt"
}
},
{
"cell_type": "code",
"source": [
"messages.value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Xx2vfpINlwnY",
"outputId": "aea994ce-65af-4b95-b86a-342d3288a8b9"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'role': 'user', 'content': 'Hi!'},\n",
" {'role': 'assistant', 'content': 'Hello! How can I help you today? 😊'}]"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"source": [
"We now create a task which will be activated by a change on the number of user messages."
],
"metadata": {
"id": "wCg-VABGD17u"
}
},
{
"cell_type": "code",
"source": [
"messages.value = []"
],
"metadata": {
"id": "3T35-SXnJMC7"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# import nest_asyncio\n",
"# nest_asyncio.apply()"
],
"metadata": {
"id": "etwXsoJdMvaI"
},
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@sl.component\n",
"def Page():\n",
" user_message_count = len([m for m in messages.value if m[\"role\"] == \"user\"])\n",
" def send(user_message):\n",
" messages.value = [*messages.value, {\"role\": \"user\", \"content\": user_message}]\n",
" def response(messages):\n",
" messages.value = [*messages.value, {\"role\": \"assistant\", \"content\": \"\"}]\n",
" for chunk in response_generator(messages.value[:-1]):\n",
" add_chunk_to_ai_message(chunk)\n",
" def result():\n",
" if messages.value != []:\n",
" response(messages)\n",
" result = sl.lab.use_task(result, dependencies=[user_message_count])\n",
" with sl.lab.ChatBox():\n",
" for item in messages.value:\n",
" with sl.lab.ChatMessage(\n",
" user=item[\"role\"] == \"user\",\n",
" name=\"User\" if item[\"role\"] == \"user\" else \"Assistant\"\n",
" ):\n",
" sl.Markdown(item[\"content\"])\n",
" sl.lab.ChatInput(send_callback=send)\n",
"Page()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331,
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]
},
"id": "SE1dnqwNynQx",
"outputId": "81a2134b-03e7-4071-863f-0d7c5fe2e9f9"
},
"execution_count": 30,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Html(layout=None, style_='display: none', tag='span')"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "cf75672de6904ef7a8a8bd1eabda409e"
}
},
"metadata": {
"application/vnd.jupyter.widget-view+json": {
"colab": {
"custom_widget_manager": {
"url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
}
}
}
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Cannot show ipywidgets in text"
],
"text/html": [
"Cannot show widget. You probably want to rerun the code cell above (<i>Click in the code cell, and press Shift+Enter <kbd>⇧</kbd>+<kbd>↩</kbd></i>)."
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a55af2431f0a442491754cf5e0a01a96"
}
},
"metadata": {
"application/vnd.jupyter.widget-view+json": {
"colab": {
"custom_widget_manager": {
"url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
}
}
}
}
}
]
},
{
"cell_type": "code",
"source": [
"messages.value"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eFwD5sdBPSQx",
"outputId": "c67ed484-ac66-4257-c262-34da95dfe046"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'role': 'user', 'content': 'Hi!'},\n",
" {'role': 'assistant', 'content': ''},\n",
" {'role': 'assistant',\n",
" 'content': 'Hello!Hello! How can I help you today? 😊 How can I help you today? 😊'}]"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "yrwKNOpcPaFB"
},
"execution_count": null,
"outputs": []
}
]
}
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