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.
Using llama.cpp also enables the use of quantized GGUF models, reducing the memory footprint of deployed models, allowing even 13-billion parameter models to run with hardware acceleration on an Apple M1 Pro chip.
Full Example
Below is an example of using semantic router with Mistral-7B-Instruct, quantized to reduce memory footprint.
Installing the library
Note: if you require hardware acceleration via BLAS, CUDA, Metal, etc. please refer to the abetlen/llama-cpp-python repository README.md
pip install -qU "semantic-router[local]"
If you’re running on Apple silicon you can run the following to compile with Metal hardware acceleration:
CMAKE_ARGS="-DLLAMA_METAL=on" pip install -qU "semantic-router[local]"
Download the Mistral 7B Instruct 4-bit GGUF files
We will be using Mistral 7B Instruct, quantized as a 4-bit GGUF file, a good balance between performance and ability to deploy on consumer hardware
!curl -L "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_0.gguf?download=true" -o ./mistral-7b-instruct-v0.2.Q4_0.gguf
!ls mistral-7b-instruct-v0.2.Q4_0.gguf
Initializing Dynamic Routes
Similar to dynamic routes in other examples, we will be initializing some dynamic routes that make use of LLMs for function calling
from datetime import datetime
from zoneinfo import ZoneInfo
from semantic_router import Route
from semantic_router.utils.function_call import get_schema
def get_time(timezone: str) -> str:
"""Finds the current time in a specific timezone.
:param timezone: The timezone to find the current time in, should
be a valid timezone from the IANA Time Zone Database like
"America/New_York" or "Europe/London". Do NOT put the place
name itself like "rome", or "new york", you must provide
the IANA format.
:type timezone: str
:return: The current time in the specified timezone."""
now = datetime.now(ZoneInfo(timezone))
return now.strftime("%H:%M")
time_schema = get_schema(get_time)
time = Route(
name="get_time",
utterances=[
"what is the time in new york city?",
"what is the time in london?",
"I live in Rome, what time is it?",
],
function_schemas=[time_schema],
)
politics = Route(
name="politics",
utterances=[
"isn't politics the best thing ever",
"why don't you tell me about your political opinions",
"don't you just love the president" "don't you just hate the president",
"they're going to destroy this country!",
"they will save the country!",
],
)
chitchat = Route(
name="chitchat",
utterances=[
"how's the weather today?",
"how are things going?",
"lovely weather today",
"the weather is horrendous",
"let's go to the chippy",
],
)
routes = [politics, chitchat, time]
Encoders
You can use alternative Encoders, however, in this example we want to showcase a fully-local Semantic Router execution, so we are going to use a HuggingFaceEncoder with sentence-transformers/all-MiniLM-L6-v2 (the default) as an embedding model.
from semantic_router.encoders import HuggingFaceEncoder
encoder = HuggingFaceEncoder()
llama.cpp LLM
From here, we can go ahead and instantiate our llama-cpp-python llama_cpp.Llama LLM, and then pass it to the semantic_router.llms.LlamaCppLLM wrapper class.
For llama_cpp.Llama, there are a couple of parameters you should pay attention to:
n_gpu_layers: how many LLM layers to offload to the GPU (if you want to offload the entire model, pass -1, and for CPU execution, pass 0)n_ctx: context size, limit the number of tokens that can be passed to the LLM (this is bounded by the model’s internal maximum context size, in this case for Mistral-7B-Instruct, 8000 tokens)verbose: if False, silences output from llama.cpp
For other parameter explanation, refer to the llama-cpp-python API Reference
# In semantic-router v0.1.0, RouteLayer has been replaced with SemanticRouter
from semantic_router import SemanticRouter
from llama_cpp import Llama
from semantic_router.llms.llamacpp import LlamaCppLLM
enable_gpu = True # offload LLM layers to the GPU (must fit in memory)
_llm = Llama(
model_path="./mistral-7b-instruct-v0.2.Q4_0.gguf",
n_gpu_layers=-1 if enable_gpu else 0,
n_ctx=2048,
)
_llm.verbose = False
llm = LlamaCppLLM(name="Mistral-7B-v0.2-Instruct", llm=_llm, max_tokens=None)
# Initialize SemanticRouter with our encoder, routes, and LLM
router = SemanticRouter(encoder=encoder, routes=routes, llm=llm)
Let’s test our router with some queries:
router("how's the weather today?")
This should output:
RouteChoice(name='chitchat', function_call=None, similarity_score=None)
Now let’s try a time-related query that will trigger our function calling:
out = router("what's the time in New York right now?")
print(out)
get_time(**out.function_call[0])
This should output something like:
name='get_time' function_call=[{'timezone': 'America/New_York'}] similarity_score=None
'07:50'
Let’s try more examples:
out = router("what's the time in Rome right now?")
print(out)
get_time(**out.function_call[0])
Output:
name='get_time' function_call=[{'timezone': 'Europe/Rome'}] similarity_score=None
'13:51'
out = router("what's the time in Bangkok right now?")
print(out)
get_time(**out.function_call[0])
Output:
name='get_time' function_call=[{'timezone': 'Asia/Bangkok'}] similarity_score=None
'18:51'
Cleanup
Once done, if you’d like to delete the downloaded model you can do so with the following:
rm ./mistral-7b-instruct-v0.2.Q4_0.gguf
