{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/07-ollama-local-execution.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/07-ollama-local-execution.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Local Dynamic Routes - With Ollama"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fully local Semantic Router with Ollama and HuggingFace Encoder\n",
"\n",
"There are many reasons users might choose to roll their own LLMs rather than use a third-party service. Whether it's due to cost, privacy or compliance, Semantic Router supports the use of \"local\" LLMs through `llama.cpp`.\n",
"\n",
"Below is an example of using semantic router which leverages Ollama in order to utilize the **OpenHermes** LLM. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need `pillow`, `torch` and `transformers` for the HuggingFace encoders."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing the Library and Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"[notice] A new release of pip is available: 23.1.2 -> 24.0\n",
"[notice] To update, run: python.exe -m pip install --upgrade pip\n"
]
}
],
"source": [
"!pip install semantic_router[local]==0.0.23 \\\n",
"pillow torch transformers"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\20240123 Semantic Router\\venvs\\semantic_router\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from semantic_router.encoders import HuggingFaceEncoder\n",
"\n",
"encoder = HuggingFaceEncoder()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define Static Routes"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from semantic_router import Route\n",
"\n",
"# we could use this as a guide for our chatbot to avoid political conversations\n",
"politics = Route(\n",
" name=\"politics\",\n",
" utterances=[\n",
" \"isn't politics the best thing ever\",\n",
" \"why don't you tell me about your political opinions\",\n",
" \"don't you just love the president\" \"don't you just hate the president\",\n",
" \"they're going to destroy this country!\",\n",
" \"they will save the country!\",\n",
" ],\n",
")\n",
"\n",
"# this could be used as an indicator to our chatbot to switch to a more\n",
"# conversational prompt\n",
"chitchat = Route(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
" \"how are things going?\",\n",
" \"lovely weather today\",\n",
" \"the weather is horrendous\",\n",
" \"let's go to the chippy\",\n",
" ],\n",
")\n",
"\n",
"# we place both of our decisions together into single list\n",
"routes = [politics, chitchat]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define Route Layer with Ollama"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-02-22 10:59:54 INFO semantic_router.utils.logger local\u001b[0m\n"
]
}
],
"source": [
"from semantic_router.layer import RouteLayer\n",
"from semantic_router.llms.ollama import OllamaLLM\n",
"\n",
"\n",
"llm = OllamaLLM(\n",
" llm_name=\"openhermes\"\n",
") # Change llm_name if you want to use a different LLM with dynamic routes.\n",
"rl = RouteLayer(encoder=encoder, routes=routes, llm=llm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test Static Routes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'politics'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rl(\"don't you love politics?\").name"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'chitchat'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rl(\"how's the weather today?\").name"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"rl(\"I'm interested in learning about llama 2\").name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test Dynamic Routes\n",
"\n",
"Dynamic routes work by associating a function with a route. If the input utterance is similar enough to the utterances of the route, such that route is chosen by the semantic router, then this triggers a secondary process: \n",
"\n",
"The LLM we specified in the `RouteLayer` (we specified Ollama, which isn't strictly an LLM, but which defaults to using the `OpenHermes` LLM), is then usde to take a `function_schema`, and the input utterance, and extract values from the input utterance which can be used as arguments for `function` described by the the `funcion_schema`. The returned values can then be used in the `function` to obtain an output.\n",
"\n",
"So, in short, it's a way of generating `function` inputs from an utterance, if that utterance matches the route utterances closely enough.\n",
"\n",
"In the below example the utterance **\"what is the time in new york city?\"** is used to trigger the \"get_time\" route, which has the `function_schema` of a likewise named `get_time()` function associated with it. Then Ollama is used to run `OpenHermes` locally, which extracts the correctly formatted IANA timezone (`\"America/New York\"`), based on this utterance and information we provide it about the `function` in the `function_schema`. The returned stirng \"America/New York\" can then be used directly in the `get_time()` function to return the actual time in New York city."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from zoneinfo import ZoneInfo\n",
"\n",
"\n",
"def get_time(timezone: str) -> str:\n",
" \"\"\"\n",
" Finds the current time in a specific timezone.\n",
"\n",
" :param timezone: The timezone to find the current time in, should\n",
" be a valid timezone from the IANA Time Zone Database like\n",
" \"America/New_York\" or \"Europe/London\". Do NOT put the place\n",
" name itself like \"rome\", or \"new york\", you must provide\n",
" the IANA format.\n",
" :type timezone: str\n",
" :return: The current time in the specified timezone.\n",
" \"\"\"\n",
" now = datetime.now(ZoneInfo(timezone))\n",
" return now.strftime(\"%H:%M\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'01:59'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_time(\"America/New_York\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'get_time',\n",
" 'description': 'Finds the current time in a specific timezone.\\n\\n:param timezone: The timezone to find the current time in, should\\n be a valid timezone from the IANA Time Zone Database like\\n \"America/New_York\" or \"Europe/London\". Do NOT put the place\\n name itself like \"rome\", or \"new york\", you must provide\\n the IANA format.\\n:type timezone: str\\n:return: The current time in the specified timezone.\\n ',\n",
" 'signature': '(timezone: str) -> str',\n",
" 'output': \"<class 'str'>\"}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from semantic_router.utils.function_call import get_schema\n",
"\n",
"schema = get_schema(get_time)\n",
"schema"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"time_route = Route(\n",
" name=\"get_time\",\n",
" utterances=[\n",
" \"what is the time in new york city?\",\n",
" \"what is the time in london?\",\n",
" \"I live in Rome, what time is it?\",\n",
" ],\n",
" function_schema=schema,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-02-22 10:59:55 INFO semantic_router.utils.logger Adding `get_time` route\u001b[0m\n"
]
}
],
"source": [
"rl.add(time_route)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-02-22 11:01:29 INFO semantic_router.utils.logger Extracting function input...\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[32m2024-02-22 11:01:32 INFO semantic_router.utils.logger LLM output: {\n",
" \"timezone\": \"America/New_York\"\n",
"}\u001b[0m\n",
"\u001b[32m2024-02-22 11:01:32 INFO semantic_router.utils.logger Function inputs: {'timezone': 'America/New_York'}\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"name='get_time' function_call={'timezone': 'America/New_York'} similarity_score=None trigger=None\n"
]
}
],
"source": [
"out = rl(\"what is the time in new york city?\")\n",
"print(out)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'02:01'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_time(**out.function_call)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "semantic_splitter_1",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}