diff --git a/04-panel-intro.ipynb b/04-panel-intro.ipynb
new file mode 100644
index 0000000..8c2545c
--- /dev/null
+++ b/04-panel-intro.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Intro to HoloViz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HoloViz is a suite of high-level Python tools that are designed to work together to make visualizing data a breeze, from conducting exploratory data analysis to deploying complex dashboards.\n",
+ "\n",
+ "The core HoloViz projects are as follows:\n",
+ "\n",
+ "- [Panel](https://panel.holoviz.org): Create interactive dashboards in Jupyter notebooks or standalone apps\n",
+ "- [hvPlot](https://hvplot.holoviz.org): Quickly and interactively explore data with a familiar API\n",
+ "- [HoloViews](https://holoviews.org): Interactive plotting experience\n",
+ "- [GeoViews](http://geoviews.org): Geographic extension of HoloViews\n",
+ "- [Datashader](https://datashader.org): Render big data images in a browser\n",
+ "- [Lumen](https://lumen.holoviz.org/): Construct no-code dashboards from simple YAML specifications\n",
+ "- [Colorcet](https://colorcet.holoviz.org/): Plot with perceptually based colormaps\n",
+ "- [Param](https://param.holoviz.org): Declaratively code in Python"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What is Panel\n",
+ "\n",
+ "Today, the focus is on Panel.\n",
+ "\n",
+ "Panel packs many pre-built frontend components that are **usable with Python**.\n",
+ "\n",
+ "That means you can convert your static Python scripts into interactive ones--**no Javascript necessary**!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import panel as pn\n",
+ "pn.extension()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Panel Tutorial\n",
+ "\n",
+ "Let's start out building an interactive app that allows the user to print a custom message.\n",
+ "\n",
+ "Currently it's hard coded to `\"Hello World\"`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Hello World!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Widget\n",
+ "\n",
+ "We can give the user more control by introducing a `TextInput` widget."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "message_input = pn.widgets.TextInput(value=\"Hello World!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interactivity\n",
+ "\n",
+ "Then, we can `pn.bind` the widget's `param.value` to the callback, `echo_message`, which simply echos the input value on change.\n",
+ "\n",
+ "Note: it's important to prefix `value` with `param`--without it, there will be no updates!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def echo_message(message):\n",
+ " return f\"{message}\"\n",
+ "\n",
+ "message_ref = pn.bind(echo_message, message=message_input.param.value)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Layout\n",
+ "\n",
+ "Next, create a simple layout to see the results.\n",
+ "\n",
+ "Try typing unique in the widget to see the message update!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pn.Column(message_input, message_ref)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Recap\n",
+ "\n",
+ "To recap, we:\n",
+ "\n",
+ "1. instantiated a widget (`TextInput`).\n",
+ "2. defined a function `echo_message`\n",
+ "3. bounded the function to the widget's *param* value\n",
+ "4. laid out the the widget and the bound reference\n",
+ "\n",
+ "\n",
+ "\n",
+ "Here's all the code cells collected into one!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import panel as pn\n",
+ "pn.extension()\n",
+ "\n",
+ "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+ "\n",
+ "def echo_message(message):\n",
+ " return f\"{message}\"\n",
+ "\n",
+ "message_ref = pn.bind(echo_message, message=message_input.param.value)\n",
+ "\n",
+ "pn.Column(message_input, message_ref)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Challenge\n",
+ "\n",
+ "Doing this repeatedly is key to creating more complex apps with Panel, so let's do a quick exercise.\n",
+ "\n",
+ "Your goal is to create a widget that will toggle the message to upper case if activated by filling out the ellipses (`...`)!\n",
+ "\n",
+ "Hint: check out the [Component gallery](https://panel.holoviz.org/reference/index.html) to see what widgets are available to accomplish this goal (one of them starts with a `T`, but there are multiple solutions!)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import panel as pn\n",
+ "pn.extension()\n",
+ "\n",
+ "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+ "toggle_upper = ... # Fill this out\n",
+ "\n",
+ "def echo_message(message, toggle_upper):\n",
+ " ... # Fill this out\n",
+ " return f\"{message}\"\n",
+ "\n",
+ "message_ref = pn.bind(echo_message, message=message_input.param.value, toggle_upper=...) # Fill this out\n",
+ "\n",
+ "pn.Column(message_input, message_ref)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Congrats on building an interactive Panel app! 🎉"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introducing Panel ChatInterface"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, introducing `pn.chat.ChatInterface`, which is a component that packages all the steps you just learned to provide convenient features for developing a Chat UI with LLMs!\n",
+ "\n",
+ "### Widget\n",
+ "\n",
+ "Try typing a message and pressing enter to send!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chat = pn.chat.ChatInterface()\n",
+ "chat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You might have noticed that it echoes the message you entered, but it doesn't reply... not fun (yet).\n",
+ "\n",
+ "### Interactivity\n",
+ "\n",
+ "To make it reply, all we have to do is set a `callback`, like `pn.bind`, but with a caveat: it needs these three arguments: `contents`, `user`, and `instance`.\n",
+ "\n",
+ "Now when you try sending a message in the chat interface, it will be echoed back in italics!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " return f\"{contents}\"\n",
+ "\n",
+ "chat.callback = echo_message"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Streaming\n",
+ "\n",
+ "You might have seen services, like OpenAI and Mistral, stream tokens as they arrive.\n",
+ "\n",
+ "We can simulate streaming tokens by looping through the contents of the user's input, concatenating the characters to the final message, and `yield`ing it in italics.\n",
+ "\n",
+ "Since there's no serious computation, it'll run too fast for us to perceive streaming--thus `time.sleep`.\n",
+ "\n",
+ "Here's the latest code collected into one (and also `callback` within instantation)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "import panel as pn\n",
+ "pn.extension()\n",
+ "\n",
+ "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " message = \"\"\n",
+ " for char in contents:\n",
+ " time.sleep(0.1) # to simulate a serious computation\n",
+ " message += char\n",
+ " yield f\"{message}\"\n",
+ "\n",
+ "chat = pn.chat.ChatInterface(callback=stream_echo_message)\n",
+ "chat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More Interactivity\n",
+ "\n",
+ "`pn.chat.ChatInterface` can be used with other widgets too!\n",
+ "\n",
+ "Here, we include a `pn.widgets.FloatSlider` to control how long to wait between each character streamed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "import panel as pn\n",
+ "pn.extension()\n",
+ "\n",
+ "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " message = \"\"\n",
+ " for char in contents:\n",
+ " time.sleep(slider.value) # to simulate a serious computation\n",
+ " message += char\n",
+ " yield f\"{message}\"\n",
+ "\n",
+ "slider = pn.widgets.FloatSlider(start=0.01, value=0.5, name=\"Sleep (s)\", align=\"center\")\n",
+ "chat = pn.chat.ChatInterface(callback=stream_echo_message, min_height=350)\n",
+ "\n",
+ "pn.Column(slider, chat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Other Inputs\n",
+ "\n",
+ "`pn.chat.ChatInterface` also supports multi-modal inputs, like images, videos, PDFs, and more!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from io import BytesIO\n",
+ "import panel as pn\n",
+ "\n",
+ "pn.extension()\n",
+ "\n",
+ "def display_info(contents: BytesIO, user: str, instance: pn.chat.ChatInterface):\n",
+ " size = len(contents.getvalue())\n",
+ " return f\"Size of input: {size / (1024 * 1024):.2f} MB\"\n",
+ "\n",
+ "file_input = pn.widgets.FileInput(accept=\".jpeg,.png,.gif,.mp4,.pdf\")\n",
+ "chat = pn.chat.ChatInterface(widgets=[file_input], callback=display_info)\n",
+ "chat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's it for a crash course on Panel! These techniques, used repeatedly, will allow you to build increasingly complex web apps with just Python.\n",
+ "\n",
+ "To learn more about `pn.chat.ChatInterface`, click [here](https://panel.holoviz.org/reference/chat/ChatInterface.html). It inherits from `pn.chat.ChatFeed`, so check that out [here](https://panel.holoviz.org/reference/chat/ChatFeed.html) too!\n",
+ "\n",
+ "For more tutorials delving into Panel in general, click [here](https://panel.holoviz.org/tutorials/index.html) or check out the app gallery [here](https://panel.holoviz.org/gallery/index.html).\n",
+ "\n",
+ "There is also a HoloViz Discourse if you want to ask questions [here](https://discourse.holoviz.org/)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
+ "language": "python",
+ "name": "conda-env-ahuang_anaconda.com-ahuang_anaconda.com-ragna-panel-presentations-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05-panel-local-llm.ipynb b/05-panel-local-llm.ipynb
new file mode 100644
index 0000000..c4266ca
--- /dev/null
+++ b/05-panel-local-llm.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Local LLMs with Panel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the [previous notebook](04-panel-intro.ipynb), `pn.chat.ChatInterface` was introduced with a callback that simply echoed the sent message.\n",
+ "\n",
+ "In this section, we will make it much more interesting by connecting a local LLM, specfically Llama-3 from earlier.\n",
+ "\n",
+ "## ExLlama2\n",
+ "\n",
+ "### Initialize\n",
+ "\n",
+ "Let's first initialize the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from local_llm import Llama38BInstruct\n",
+ "from ragna import Rag, source_storages\n",
+ "import panel as pn\n",
+ "pn.extension()\n",
+ "\n",
+ "documents = [\n",
+ " \"files/psf-report-2021.pdf\",\n",
+ " \"files/psf-report-2022.pdf\",\n",
+ "]\n",
+ "\n",
+ "chat = Rag().chat(\n",
+ " documents=documents,\n",
+ " source_storage=source_storages.Chroma,\n",
+ " assistant=Llama38BInstruct,\n",
+ ")\n",
+ "\n",
+ "await chat.prepare();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Migrate\n",
+ "\n",
+ "We can first do a test run to see if it works with the example from before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "message = await chat.answer(\"Who is the Python Developer in Residence?\", stream=True)\n",
+ "\n",
+ "async for chunk in message:\n",
+ " print(chunk, end=\"\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's migrate this functionality into `pn.chat.ChatInterface` with a callback.\n",
+ "\n",
+ "To do this, we copy paste the prior cell's code into a function, and then:\n",
+ "\n",
+ "1. prefix the `def` with `async` to make it async\n",
+ "2. replace the hard-coded string with `contents`\n",
+ "3. concatenate the chunks into a `response` string\n",
+ "4. yield the `response`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "async def reply(contents, user, instance):\n",
+ " message = await chat.answer(contents, stream=True)\n",
+ "\n",
+ " response = \"\"\n",
+ " async for chunk in message:\n",
+ " response += chunk\n",
+ " yield response\n",
+ "\n",
+ "chat_interface = pn.chat.ChatInterface(callback=reply)\n",
+ "chat_interface"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now try entering \"Who is the Python Developer in Residence?\" into the chat. It should give you a similar response as before!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LlamaCpp"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For posterity, we can use `llama-cpp-python` for quantized models too!\n",
+ "\n",
+ "`llama-cpp` can run on both CPU and GPU, and has an API that mimics OpenAI's API.\n",
+ "\n",
+ "Personally, I use it because I don't have any spare GPUs lying around and it runs extremely well on my local Mac M2 Pro! It also handles chat template formats internally so it's just a matter of specifying a the proper `chat_format` key.\n",
+ "\n",
+ "Here, we:\n",
+ "1. download the quantized model (if it doesn't exist already) in GGUF format\n",
+ "2. instantiate the model; first checking the cache\n",
+ "3. serialize all messages into `transformers` format (new)\n",
+ "4. calls the chat completion Openai-like API on the messages\n",
+ "5. stream the chunks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import llama_cpp\n",
+ "import panel as pn\n",
+ "from huggingface_hub import hf_hub_download\n",
+ "pn.extension()\n",
+ "\n",
+ "model_path = hf_hub_download(\n",
+ " \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+ " \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+ " local_dir=str(Path.home() / \"shared/analyst/models\")\n",
+ ") # 1.\n",
+ "\n",
+ "# 2.\n",
+ "if model_path in pn.state.cache:\n",
+ " llama = pn.state.cache[model_path]\n",
+ "else:\n",
+ " llama = llama_cpp.Llama(\n",
+ " model_path=model_path,\n",
+ " n_gpu_layers=-1,\n",
+ " chat_format=\"mistral-instruct\",\n",
+ " n_ctx=2048,\n",
+ " logits_all=True,\n",
+ " verbose=False,\n",
+ " )\n",
+ " pn.state.cache[model_path] = llama\n",
+ "\n",
+ "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " messages = instance.serialize() # 3.\n",
+ " message = llama.create_chat_completion_openai_v1(messages=messages, stream=True) # 4.\n",
+ "\n",
+ " response = \"\"\n",
+ " for chunk in message:\n",
+ " part = chunk.choices[0].delta.content or \"\"\n",
+ " response += part\n",
+ " yield response # 5.\n",
+ "\n",
+ "chat_interface = pn.chat.ChatInterface(callback=reply)\n",
+ "chat_interface"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can even give the model a personality by setting a system message!\n",
+ "\n",
+ "Update the callback with the a system message.\n",
+ "\n",
+ "Note, Mistral Instruct does NOT support the `system` role so we use `user` instead."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "system_message = \"You are an excessively passionate Pythonista.\"\n",
+ "\n",
+ "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " messages = [\n",
+ " {\"role\": \"user\", \"content\": system_message} # updated here\n",
+ " ] + instance.serialize()\n",
+ " message = llama.create_chat_completion_openai_v1(messages=messages, stream=True)\n",
+ "\n",
+ " response = \"\"\n",
+ " for chunk in message:\n",
+ " part = chunk.choices[0].delta.content or \"\"\n",
+ " response += part\n",
+ " yield response\n",
+ "\n",
+ "chat_interface.callback = reply\n",
+ "chat_interface"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Challenge\n",
+ "\n",
+ "Your turn! Try aggregating all you've learned to customize the personality of the chatbot on the go!\n",
+ "\n",
+ "Again, replace the ellipses with the appropriate code snippets!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import llama_cpp\n",
+ "import panel as pn\n",
+ "from pydantic import BaseModel\n",
+ "from huggingface_hub import hf_hub_download\n",
+ "\n",
+ "pn.extension()\n",
+ "\n",
+ "model_path = hf_hub_download(\n",
+ " \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+ " \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+ " local_dir=str(Path.home() / \"shared/analyst/models\")\n",
+ ")\n",
+ "\n",
+ "if model_path in pn.state.cache:\n",
+ " llama = pn.state.cache[model_path]\n",
+ "else:\n",
+ " llama = llama_cpp.Llama(\n",
+ " model_path=model_path,\n",
+ " n_gpu_layers=-1,\n",
+ " chat_format=\"mistral-instruct\",\n",
+ " n_ctx=2048,\n",
+ " logits_all=True,\n",
+ " verbose=False,\n",
+ " )\n",
+ " pn.state.cache[model_path] = llama\n",
+ "\n",
+ "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+ " messages = [\n",
+ " {\"role\": \"user\", \"content\": ...} # Fill this out\n",
+ " ] + instance.serialize()\n",
+ " message = llama.create_chat_completion_openai_v1(\n",
+ " messages=messages, stream=True\n",
+ " )\n",
+ "\n",
+ " response = \"\"\n",
+ " for chunk in message:\n",
+ " part = chunk.choices[0].delta.content or \"\"\n",
+ " response += part\n",
+ " yield response\n",
+ "\n",
+ "\n",
+ "system_input = ... # Fill this out\n",
+ "chat_interface = pn.chat.ChatInterface(callback=reply, min_height=350)\n",
+ "layout = pn.Column(\n",
+ " system_input,\n",
+ " chat_interface,\n",
+ ")\n",
+ "layout"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "That's all for now. Click [here](https://holoviz-topics.github.io/panel-chat-examples/) to see more on how you can integrate `pn.chat.ChatInterface` with other services!\n",
+ "\n",
+ "Again, there is also a HoloViz Discourse if you want to ask questions [here](https://discourse.holoviz.org/)."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
+ "language": "python",
+ "name": "conda-env-ahuang_anaconda.com-ahuang_anaconda.com-ragna-panel-presentations-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/environment.yml b/environment.yml
index a92d01a..cb6c716 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,12 +1,15 @@
name: ragna-presentations
channels:
+ - nvidia
- conda-forge
dependencies:
- python=3.11
+ - libcublas
- gxx
- pip
- pip:
- python-dotenv
+ - panel
- ragna @ git+https://github.com/Quansight/ragna@pycon
- chromadb>=0.4.13
- httpx_sse
@@ -18,9 +21,13 @@ dependencies:
- python-pptx
- tiktoken
- torch ==2.2.*
+ - openai
+ - llama-cpp-python @
+ https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.72-cu121/llama_cpp_python-0.2.72-cp311-cp311-linux_x86_64.whl
- exllamav2 @
https://github.com/turboderp/exllamav2/releases/download/v0.0.18/exllamav2-0.0.18+cu121-cp311-cp311-linux_x86_64.whl
- jupyterlab_nvdashboard
- ipykernel
- iprogress
- ipywidgets
+variables: {}
diff --git a/images/recap.png b/images/recap.png
new file mode 100644
index 0000000..01eb77a
Binary files /dev/null and b/images/recap.png differ