Local Dynamic Routes¶
Fully local Semantic Router with llama.cpp and HuggingFace Encoder¶
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.
Below is an example of using semantic router with Mistral-7B-Instruct, quantized i.
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
In [ ]:
!pip install -qU "semantic-router[local]"
If you're running on Apple silicon you can run the following to run with Metal hardware acceleration:
In [ ]:
!CMAKE_ARGS="-DLLAMA_METAL=on"
'CMAKE_ARGS' is not recognized as an internal or external command, operable program or batch file.
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
In [ ]:
! 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
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 1168 100 1168 0 0 4171 0 --:--:-- --:--:-- --:--:-- 4186
0 3918M 0 6382k 0 0 7990k 0 0:08:22 --:--:-- 0:08:22 7990k
0 3918M 0 35.0M 0 0 19.4M 0 0:03:21 0:00:01 0:03:20 28.7M
2 3918M 2 78.7M 0 0 28.1M 0 0:02:19 0:00:02 0:02:17 36.2M
3 3918M 3 123M 0 0 32.4M 0 0:02:00 0:00:03 0:01:57 39.0M
4 3918M 4 171M 0 0 35.6M 0 0:01:49 0:00:04 0:01:45 41.2M
5 3918M 5 219M 0 0 37.7M 0 0:01:43 0:00:05 0:01:38 42.5M
6 3918M 6 271M 0 0 39.9M 0 0:01:38 0:00:06 0:01:32 47.3M
8 3918M 8 326M 0 0 41.8M 0 0:01:33 0:00:07 0:01:26 49.4M
9 3918M 9 379M 0 0 42.5M 0 0:01:31 0:00:08 0:01:23 50.1M
10 3918M 10 428M 0 0 43.7M 0 0:01:29 0:00:09 0:01:20 51.5M
12 3918M 12 490M 0 0 45.3M 0 0:01:26 0:00:10 0:01:16 54.2M
13 3918M 13 543M 0 0 46.0M 0 0:01:25 0:00:11 0:01:14 54.4M
15 3918M 15 609M 0 0 47.6M 0 0:01:22 0:00:12 0:01:10 56.6M
17 3918M 17 672M 0 0 48.7M 0 0:01:20 0:00:13 0:01:07 59.8M
18 3918M 18 733M 0 0 49.5M 0 0:01:19 0:00:14 0:01:05 60.8M
20 3918M 20 800M 0 0 50.6M 0 0:01:17 0:00:15 0:01:02 62.0M
21 3918M 21 861M 0 0 51.2M 0 0:01:16 0:00:16 0:01:00 63.4M
23 3918M 23 923M 0 0 51.8M 0 0:01:15 0:00:17 0:00:58 62.7M
24 3918M 24 973M 0 0 51.7M 0 0:01:15 0:00:18 0:00:57 60.1M
26 3918M 26 1039M 0 0 52.4M 0 0:01:14 0:00:19 0:00:55 61.2M
28 3918M 28 1101M 0 0 52.9M 0 0:01:14 0:00:20 0:00:54 60.1M
29 3918M 29 1156M 0 0 53.0M 0 0:01:13 0:00:21 0:00:52 59.0M
30 3918M 30 1208M 0 0 53.0M 0 0:01:13 0:00:22 0:00:51 57.0M
32 3918M 32 1272M 0 0 53.4M 0 0:01:13 0:00:23 0:00:50 59.9M
34 3918M 34 1337M 0 0 53.9M 0 0:01:12 0:00:24 0:00:48 59.5M
35 3918M 35 1397M 0 0 54.1M 0 0:01:12 0:00:25 0:00:47 59.1M
36 3918M 36 1444M 0 0 53.9M 0 0:01:12 0:00:26 0:00:46 57.6M
38 3918M 38 1506M 0 0 54.1M 0 0:01:12 0:00:27 0:00:45 59.2M
40 3918M 40 1569M 0 0 54.4M 0 0:01:11 0:00:28 0:00:43 59.3M
41 3918M 41 1630M 0 0 54.7M 0 0:01:11 0:00:29 0:00:42 58.7M
43 3918M 43 1697M 0 0 55.1M 0 0:01:11 0:00:30 0:00:41 60.0M
44 3918M 44 1759M 0 0 55.3M 0 0:01:10 0:00:31 0:00:39 63.0M
45 3918M 45 1798M 0 0 54.8M 0 0:01:11 0:00:32 0:00:39 58.6M
47 3918M 47 1866M 0 0 55.2M 0 0:01:10 0:00:33 0:00:37 59.4M
49 3918M 49 1930M 0 0 55.4M 0 0:01:10 0:00:34 0:00:36 59.9M
50 3918M 50 1994M 0 0 55.7M 0 0:01:10 0:00:35 0:00:35 59.4M
52 3918M 52 2062M 0 0 56.0M 0 0:01:09 0:00:36 0:00:33 60.4M
54 3918M 54 2121M 0 0 56.1M 0 0:01:09 0:00:37 0:00:32 64.6M
55 3918M 55 2186M 0 0 56.3M 0 0:01:09 0:00:38 0:00:31 63.9M
57 3918M 57 2248M 0 0 56.5M 0 0:01:09 0:00:39 0:00:30 63.6M
59 3918M 59 2317M 0 0 56.8M 0 0:01:08 0:00:40 0:00:28 64.6M
60 3918M 60 2382M 0 0 57.0M 0 0:01:08 0:00:41 0:00:27 64.1M
62 3918M 62 2435M 0 0 56.8M 0 0:01:08 0:00:42 0:00:26 62.7M
63 3918M 63 2496M 0 0 56.9M 0 0:01:08 0:00:43 0:00:25 62.0M
64 3918M 64 2546M 0 0 56.8M 0 0:01:08 0:00:44 0:00:24 59.5M
66 3918M 66 2596M 0 0 56.6M 0 0:01:09 0:00:45 0:00:24 55.7M
67 3918M 67 2646M 0 0 56.5M 0 0:01:09 0:00:46 0:00:23 52.7M
69 3918M 69 2708M 0 0 56.6M 0 0:01:09 0:00:47 0:00:22 54.7M
70 3918M 70 2773M 0 0 56.8M 0 0:01:08 0:00:48 0:00:20 55.4M
72 3918M 72 2833M 0 0 56.9M 0 0:01:08 0:00:49 0:00:19 57.4M
73 3918M 73 2896M 0 0 56.9M 0 0:01:08 0:00:50 0:00:18 59.7M
75 3918M 75 2952M 0 0 56.9M 0 0:01:08 0:00:51 0:00:17 61.1M
76 3918M 76 3012M 0 0 57.0M 0 0:01:08 0:00:52 0:00:16 60.3M
78 3918M 78 3078M 0 0 57.2M 0 0:01:08 0:00:53 0:00:15 60.9M
80 3918M 80 3140M 0 0 57.3M 0 0:01:08 0:00:54 0:00:14 61.3M
81 3918M 81 3196M 0 0 57.2M 0 0:01:08 0:00:55 0:00:13 60.1M
83 3918M 83 3261M 0 0 57.4M 0 0:01:08 0:00:56 0:00:12 61.7M
84 3918M 84 3324M 0 0 57.5M 0 0:01:08 0:00:57 0:00:11 62.8M
86 3918M 86 3390M 0 0 57.6M 0 0:01:07 0:00:58 0:00:09 62.4M
88 3918M 88 3456M 0 0 57.7M 0 0:01:07 0:00:59 0:00:08 63.1M
89 3918M 89 3520M 0 0 57.9M 0 0:01:07 0:01:00 0:00:07 64.8M
91 3918M 91 3585M 0 0 58.0M 0 0:01:07 0:01:01 0:00:06 64.9M
93 3918M 93 3654M 0 0 58.1M 0 0:01:07 0:01:02 0:00:05 65.8M
94 3918M 94 3719M 0 0 58.2M 0 0:01:07 0:01:03 0:00:04 65.7M
96 3918M 96 3778M 0 0 58.3M 0 0:01:07 0:01:04 0:00:03 64.5M
97 3918M 97 3828M 0 0 58.1M 0 0:01:07 0:01:05 0:00:02 61.6M
99 3918M 99 3888M 0 0 58.2M 0 0:01:07 0:01:06 0:00:01 60.6M
100 3918M 100 3918M 0 0 58.2M 0 0:01:07 0:01:07 --:--:-- 58.8M
'ls' is not recognized as an internal or external command,
operable program or batch file.
Initializing Dynamic Routes¶
Similar to the 02-dynamic-routes.ipynb notebook, we will be initializing some dynamic routes that make use of LLMs for function calling
In [ ]:
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_schema
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]
c:\Users\Siraj\Documents\Personal\Work\Aurelio\Virtual Environments\semantic_router_3\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 from .autonotebook import tqdm as notebook_tqdm
In [ ]:
time_schema
Out[ ]:
{'name': 'get_time',
'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.',
'signature': '(timezone: str) -> str',
'output': "<class 'str'>"}
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.
In [ ]:
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, pass0)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: ifFalse, silences output fromllama.cpp
For other parameter explanation, refer to the
llama-cpp-pythonAPI Reference
In [ ]:
from semantic_router import RouteLayer
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)
rl = RouteLayer(encoder=encoder, routes=routes, llm=llm)
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from ./mistral-7b-instruct-v0.2.Q4_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-instruct-v0.2
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 11: general.file_type u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 23: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 7.24 B
llm_load_print_meta: model size = 3.83 GiB (4.54 BPW)
llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: PAD token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.15 MiB
llm_load_tensors: CPU buffer size = 3917.87 MiB
..................................................................................................
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 256.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.12 MiB
llama_new_context_with_model: CPU compute buffer size = 164.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
Model metadata: {'general.name': 'mistralai_mistral-7b-instruct-v0.2', 'general.architecture': 'llama', 'llama.context_length': '32768', 'llama.rope.dimension_count': '128', 'llama.embedding_length': '4096', 'llama.block_count': '32', 'llama.feed_forward_length': '14336', 'llama.attention.head_count': '32', 'tokenizer.ggml.eos_token_id': '2', 'general.file_type': '2', 'llama.attention.head_count_kv': '8', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.freq_base': '1000000.000000', 'tokenizer.ggml.model': 'llama', 'general.quantization_version': '2', 'tokenizer.ggml.bos_token_id': '1', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.add_bos_token': 'true', 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"}
Guessed chat format: mistral-instruct
2024-05-07 15:50:07 INFO semantic_router.utils.logger local
In [ ]:
rl("how's the weather today?")
Out[ ]:
RouteChoice(name='chitchat', function_call=None, similarity_score=None)
In [ ]:
out = rl("what's the time in New York right now?")
print(out)
get_time(**out.function_call[0])
from_string grammar:
root ::= object
object ::= [{] ws object_11 [}] ws
value ::= object | array | string | number | value_6 ws
array ::= [[] ws array_15 []] ws
string ::= ["] string_18 ["] ws
number ::= number_19 number_25 number_29 ws
value_6 ::= [t] [r] [u] [e] | [f] [a] [l] [s] [e] | [n] [u] [l] [l]
ws ::= ws_31
object_8 ::= string [:] ws value object_10
object_9 ::= [,] ws string [:] ws value
object_10 ::= object_9 object_10 |
object_11 ::= object_8 |
array_12 ::= value array_14
array_13 ::= [,] ws value
array_14 ::= array_13 array_14 |
array_15 ::= array_12 |
string_16 ::= [^"\] | [\] string_17
string_17 ::= ["\/bfnrt] | [u] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]
string_18 ::= string_16 string_18 |
number_19 ::= number_20 number_21
number_20 ::= [-] |
number_21 ::= [0-9] | [1-9] number_22
number_22 ::= [0-9] number_22 |
number_23 ::= [.] number_24
number_24 ::= [0-9] number_24 | [0-9]
number_25 ::= number_23 |
number_26 ::= [eE] number_27 number_28
number_27 ::= [-+] |
number_28 ::= [0-9] number_28 | [0-9]
number_29 ::= number_26 |
ws_30 ::= [ <U+0009><U+000A>] ws
ws_31 ::= ws_30 |
2024-05-07 15:50:08 INFO semantic_router.utils.logger Extracting function input...
2024-05-07 15:50:59 INFO semantic_router.utils.logger LLM output: {
"timezone": "America/New_York"
}
2024-05-07 15:50:59 INFO semantic_router.utils.logger Function inputs: [{'timezone': 'America/New_York'}]
name='get_time' function_call=[{'timezone': 'America/New_York'}] similarity_score=None
Out[ ]:
'07:50'
In [ ]:
out = rl("what's the time in Rome right now?")
print(out)
get_time(**out.function_call[0])
from_string grammar:
root ::= object
object ::= [{] ws object_11 [}] ws
value ::= object | array | string | number | value_6 ws
array ::= [[] ws array_15 []] ws
string ::= ["] string_18 ["] ws
number ::= number_19 number_25 number_29 ws
value_6 ::= [t] [r] [u] [e] | [f] [a] [l] [s] [e] | [n] [u] [l] [l]
ws ::= ws_31
object_8 ::= string [:] ws value object_10
object_9 ::= [,] ws string [:] ws value
object_10 ::= object_9 object_10 |
object_11 ::= object_8 |
array_12 ::= value array_14
array_13 ::= [,] ws value
array_14 ::= array_13 array_14 |
array_15 ::= array_12 |
string_16 ::= [^"\] | [\] string_17
string_17 ::= ["\/bfnrt] | [u] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]
string_18 ::= string_16 string_18 |
number_19 ::= number_20 number_21
number_20 ::= [-] |
number_21 ::= [0-9] | [1-9] number_22
number_22 ::= [0-9] number_22 |
number_23 ::= [.] number_24
number_24 ::= [0-9] number_24 | [0-9]
number_25 ::= number_23 |
number_26 ::= [eE] number_27 number_28
number_27 ::= [-+] |
number_28 ::= [0-9] number_28 | [0-9]
number_29 ::= number_26 |
ws_30 ::= [ <U+0009><U+000A>] ws
ws_31 ::= ws_30 |
2024-05-07 15:50:59 INFO semantic_router.utils.logger Extracting function input...
2024-05-07 15:51:27 INFO semantic_router.utils.logger LLM output: {"timezone": "Europe/Rome"}
2024-05-07 15:51:27 INFO semantic_router.utils.logger Function inputs: [{'timezone': 'Europe/Rome'}]
name='get_time' function_call=[{'timezone': 'Europe/Rome'}] similarity_score=None
Out[ ]:
'13:51'
In [ ]:
out = rl("what's the time in Bangkok right now?")
print(out)
get_time(**out.function_call[0])
from_string grammar:
root ::= object
object ::= [{] ws object_11 [}] ws
value ::= object | array | string | number | value_6 ws
array ::= [[] ws array_15 []] ws
string ::= ["] string_18 ["] ws
number ::= number_19 number_25 number_29 ws
value_6 ::= [t] [r] [u] [e] | [f] [a] [l] [s] [e] | [n] [u] [l] [l]
ws ::= ws_31
object_8 ::= string [:] ws value object_10
object_9 ::= [,] ws string [:] ws value
object_10 ::= object_9 object_10 |
object_11 ::= object_8 |
array_12 ::= value array_14
array_13 ::= [,] ws value
array_14 ::= array_13 array_14 |
array_15 ::= array_12 |
string_16 ::= [^"\] | [\] string_17
string_17 ::= ["\/bfnrt] | [u] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]
string_18 ::= string_16 string_18 |
number_19 ::= number_20 number_21
number_20 ::= [-] |
number_21 ::= [0-9] | [1-9] number_22
number_22 ::= [0-9] number_22 |
number_23 ::= [.] number_24
number_24 ::= [0-9] number_24 | [0-9]
number_25 ::= number_23 |
number_26 ::= [eE] number_27 number_28
number_27 ::= [-+] |
number_28 ::= [0-9] number_28 | [0-9]
number_29 ::= number_26 |
ws_30 ::= [ <U+0009><U+000A>] ws
ws_31 ::= ws_30 |
2024-05-07 15:51:27 INFO semantic_router.utils.logger Extracting function input...
2024-05-07 15:51:56 INFO semantic_router.utils.logger LLM output: {"timezone": "Asia/Bangkok"}
2024-05-07 15:51:56 INFO semantic_router.utils.logger Function inputs: [{'timezone': 'Asia/Bangkok'}]
name='get_time' function_call=[{'timezone': 'Asia/Bangkok'}] similarity_score=None
Out[ ]:
'18:51'
In [ ]:
out = rl("what's the time in Phuket right now?")
print(out)
get_time(**out.function_call[0])
from_string grammar:
root ::= object
object ::= [{] ws object_11 [}] ws
value ::= object | array | string | number | value_6 ws
array ::= [[] ws array_15 []] ws
string ::= ["] string_18 ["] ws
number ::= number_19 number_25 number_29 ws
value_6 ::= [t] [r] [u] [e] | [f] [a] [l] [s] [e] | [n] [u] [l] [l]
ws ::= ws_31
object_8 ::= string [:] ws value object_10
object_9 ::= [,] ws string [:] ws value
object_10 ::= object_9 object_10 |
object_11 ::= object_8 |
array_12 ::= value array_14
array_13 ::= [,] ws value
array_14 ::= array_13 array_14 |
array_15 ::= array_12 |
string_16 ::= [^"\] | [\] string_17
string_17 ::= ["\/bfnrt] | [u] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]
string_18 ::= string_16 string_18 |
number_19 ::= number_20 number_21
number_20 ::= [-] |
number_21 ::= [0-9] | [1-9] number_22
number_22 ::= [0-9] number_22 |
number_23 ::= [.] number_24
number_24 ::= [0-9] number_24 | [0-9]
number_25 ::= number_23 |
number_26 ::= [eE] number_27 number_28
number_27 ::= [-+] |
number_28 ::= [0-9] number_28 | [0-9]
number_29 ::= number_26 |
ws_30 ::= [ <U+0009><U+000A>] ws
ws_31 ::= ws_30 |
2024-05-07 15:51:56 INFO semantic_router.utils.logger Extracting function input...
2024-05-07 15:52:25 INFO semantic_router.utils.logger LLM output: {
"timezone": "Asia/Bangkok"
}
2024-05-07 15:52:25 INFO semantic_router.utils.logger Function inputs: [{'timezone': 'Asia/Bangkok'}]
name='get_time' function_call=[{'timezone': 'Asia/Bangkok'}] similarity_score=None
Out[ ]:
'18:52'
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