LM Format Enforcer Pydantic Program¶
Generate structured data with lm-format-enforcer via LlamaIndex.
With lm-format-enforcer, you can guarantee the output structure is correct by forcing the LLM to output desired tokens.
This is especialy helpful when you are using lower-capacity model (e.g. the current open source models), which otherwise would struggle to generate valid output that fits the desired output schema.
lm-format-enforcer supports regular expressions and JSON Schema, this demo focuses on JSON Schema. For regular expressions, see the sample regular expressions notebook.
If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
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%pip install llama-index-program-lmformatenforcer
%pip install llama-index-llms-llama-cpp
%pip install llama-index-program-lmformatenforcer
%pip install llama-index-llms-llama-cpp
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!pip install llama-index lm-format-enforcer llama-cpp-python
!pip install llama-index lm-format-enforcer llama-cpp-python
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import sys
from pydantic import BaseModel, Field
from typing import List
from llama_index.program.lmformatenforcer import (
LMFormatEnforcerPydanticProgram,
)
import sys
from pydantic import BaseModel, Field
from typing import List
from llama_index.program.lmformatenforcer import (
LMFormatEnforcerPydanticProgram,
)
Define output schema
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class Song(BaseModel):
title: str
length_seconds: int
class Album(BaseModel):
name: str
artist: str
songs: List[Song] = Field(min_items=3, max_items=10)
class Song(BaseModel):
title: str
length_seconds: int
class Album(BaseModel):
name: str
artist: str
songs: List[Song] = Field(min_items=3, max_items=10)
Create the program. We use LlamaCPP as the LLM in this demo, but HuggingFaceLLM is also supported.
Note that the prompt template has two parameters:
movie_namewhich will be used in the function calledjson_schemawhich will automatically have the JSON Schema of the output class injected into it.
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from llama_index.llms.llama_cpp import LlamaCPP
llm = LlamaCPP()
program = LMFormatEnforcerPydanticProgram(
output_cls=Album,
prompt_template_str=(
"Your response should be according to the following json schema: \n"
"{json_schema}\n"
"Generate an example album, with an artist and a list of songs. Using"
" the movie {movie_name} as inspiration. "
),
llm=llm,
verbose=True,
)
from llama_index.llms.llama_cpp import LlamaCPP
llm = LlamaCPP()
program = LMFormatEnforcerPydanticProgram(
output_cls=Album,
prompt_template_str=(
"Your response should be according to the following json schema: \n"
"{json_schema}\n"
"Generate an example album, with an artist and a list of songs. Using"
" the movie {movie_name} as inspiration. "
),
llm=llm,
verbose=True,
)
llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /mnt/wsl/PHYSICALDRIVE1p3/llama_index/models/llama-2-13b-chat.Q4_0.gguf (version GGUF V2 (latest)) llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 5120, 32000, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 2: blk.0.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 4: blk.0.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 6: blk.0.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 7: blk.0.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 9: blk.0.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 11: blk.1.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 13: blk.1.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 15: blk.1.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 16: blk.1.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 18: blk.1.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 20: blk.10.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 22: blk.10.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 24: blk.10.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 25: blk.10.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 26: blk.10.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 27: blk.10.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 29: blk.11.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 31: blk.11.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 33: blk.11.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 34: blk.11.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 35: blk.11.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 36: blk.11.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 38: blk.12.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 40: blk.12.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 42: blk.12.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 43: blk.12.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 44: blk.12.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 45: blk.12.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 47: blk.13.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 49: blk.13.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 51: blk.13.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 52: blk.13.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 53: blk.13.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 54: blk.13.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 56: blk.14.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 57: blk.14.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 58: blk.14.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 59: blk.14.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 60: blk.14.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 61: blk.14.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 62: blk.14.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - 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tensor 76: blk.3.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 77: blk.3.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 78: blk.3.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 79: blk.3.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 80: blk.3.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 81: blk.3.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 82: blk.3.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 83: blk.3.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 84: blk.4.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 85: blk.4.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 86: blk.4.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 87: blk.4.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 88: blk.4.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - 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tensor 349: blk.38.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 350: blk.38.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 351: blk.38.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 352: blk.38.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 354: blk.39.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 358: blk.39.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 360: blk.39.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 361: blk.39.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 362: output_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - kv 0: general.architecture str llama_model_loader: - kv 1: general.name str llama_model_loader: - kv 2: llama.context_length u32 llama_model_loader: - kv 3: llama.embedding_length u32 llama_model_loader: - kv 4: llama.block_count u32 llama_model_loader: - kv 5: llama.feed_forward_length u32 llama_model_loader: - kv 6: llama.rope.dimension_count u32 llama_model_loader: - kv 7: llama.attention.head_count u32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 llama_model_loader: - kv 10: general.file_type u32 llama_model_loader: - kv 11: tokenizer.ggml.model str llama_model_loader: - kv 12: tokenizer.ggml.tokens arr llama_model_loader: - kv 13: tokenizer.ggml.scores arr llama_model_loader: - kv 14: tokenizer.ggml.token_type arr llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 llama_model_loader: - kv 18: general.quantization_version u32 llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q4_0: 281 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_print_meta: format = GGUF V2 (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 = 4096 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 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: n_ff = 13824 llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 6.86 GiB (4.53 BPW) llm_load_print_meta: general.name = LLaMA v2 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: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.12 MB llm_load_tensors: mem required = 7024.01 MB ................................................................................................... llama_new_context_with_model: n_ctx = 3900 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 3046.88 MB llama_new_context_with_model: compute buffer total size = 348.18 MB AVX = 1 | 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 = 1 | VSX = 0 |
Run program to get structured output.
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output = program(movie_name="The Shining")
output = program(movie_name="The Shining")
llama_print_timings: load time = 21703.16 ms llama_print_timings: sample time = 45.01 ms / 134 runs ( 0.34 ms per token, 2976.92 tokens per second) llama_print_timings: prompt eval time = 21703.02 ms / 223 tokens ( 97.32 ms per token, 10.28 tokens per second) llama_print_timings: eval time = 20702.37 ms / 133 runs ( 155.66 ms per token, 6.42 tokens per second) llama_print_timings: total time = 43127.74 ms
The output is a valid Pydantic object that we can then use to call functions/APIs.
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output
output
Out[ ]:
Album(name='The Shining: A Musical Journey Through the Haunted Halls of the Overlook Hotel', artist='The Shining Choir', songs=[Song(title='Redrum', length_seconds=300), Song(title='All Work and No Play Makes Jack a Dull Boy', length_seconds=240), Song(title="Heeeeere's Johnny!", length_seconds=180)])
