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Merge pull request #18 from debanjum/deb/save-models-to-disk-on-first-run
Save Search Models to Disk on First Run ## Why - Improve application startup time - Startup application and perform semantic search even if user offline - Use search model config in YAML file for all search types (asymmetric, symmetric, image) ## Details - Load search models from disk when available - Use search model config specified in YAML file - Add search config for Symmetric Search used by Ledger/Beancount transaction search
This commit is contained in:
Debanjum
GitHub
commit
ed7c2901f5
12 changed files with 183 additions and 75 deletions
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@ -24,12 +24,19 @@ content-type:
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embeddings-file: "tests/data/.song_embeddings.pt"
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search-type:
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symmetric:
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encoder: "sentence-transformers/paraphrase-MiniLM-L6-v2"
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cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
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model_directory: "tests/data/.symmetric"
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asymmetric:
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encoder: "sentence-transformers/msmarco-MiniLM-L-6-v3"
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cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
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model_directory: "tests/data/.asymmetric"
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image:
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encoder: "clip-ViT-B-32"
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model_directory: "tests/data/.image_encoder"
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processor:
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conversation:
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@ -130,22 +130,22 @@ def initialize_search(config: FullConfig, regenerate: bool, t: SearchType = None
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# Initialize Org Notes Search
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if (t == SearchType.Notes or t == None) and config.content_type.org:
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# Extract Entries, Generate Notes Embeddings
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model.notes_search = asymmetric.setup(config.content_type.org, regenerate=regenerate, verbose=verbose)
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model.notes_search = asymmetric.setup(config.content_type.org, search_config=config.search_type.asymmetric, regenerate=regenerate, verbose=verbose)
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# Initialize Org Music Search
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if (t == SearchType.Music or t == None) and config.content_type.music:
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# Extract Entries, Generate Music Embeddings
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model.music_search = asymmetric.setup(config.content_type.music, regenerate=regenerate, verbose=verbose)
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model.music_search = asymmetric.setup(config.content_type.music, search_config=config.search_type.asymmetric, regenerate=regenerate, verbose=verbose)
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# Initialize Ledger Search
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if (t == SearchType.Ledger or t == None) and config.content_type.ledger:
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# Extract Entries, Generate Ledger Embeddings
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model.ledger_search = symmetric_ledger.setup(config.content_type.ledger, regenerate=regenerate, verbose=verbose)
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model.ledger_search = symmetric_ledger.setup(config.content_type.ledger, search_config=config.search_type.symmetric, regenerate=regenerate, verbose=verbose)
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# Initialize Image Search
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if (t == SearchType.Image or t == None) and config.content_type.image:
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# Extract Entries, Generate Image Embeddings
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model.image_search = image_search.setup(config.content_type.image, regenerate=regenerate, verbose=verbose)
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model.image_search = image_search.setup(config.content_type.image, search_config=config.search_type.image, regenerate=regenerate, verbose=verbose)
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return model
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@ -12,18 +12,31 @@ import torch
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from sentence_transformers import SentenceTransformer, CrossEncoder, util
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# Internal Packages
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from src.utils.helpers import get_absolute_path, resolve_absolute_path
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from src.utils.helpers import get_absolute_path, resolve_absolute_path, load_model
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from src.processor.org_mode.org_to_jsonl import org_to_jsonl
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from src.utils.config import TextSearchModel
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from src.utils.rawconfig import TextSearchConfig
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from src.utils.rawconfig import AsymmetricConfig, TextSearchConfig
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def initialize_model():
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def initialize_model(search_config: AsymmetricConfig):
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"Initialize model for assymetric semantic search. That is, where query smaller than results"
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torch.set_num_threads(4)
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bi_encoder = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L-6-v3') # The bi-encoder encodes all entries to use for semantic search
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top_k = 30 # Number of entries we want to retrieve with the bi-encoder
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cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') # The cross-encoder re-ranks the results to improve quality
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# Number of entries we want to retrieve with the bi-encoder
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top_k = 30
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# The bi-encoder encodes all entries to use for semantic search
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bi_encoder = load_model(
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model_dir = search_config.model_directory,
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model_name = search_config.encoder,
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model_type = SentenceTransformer)
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# The cross-encoder re-ranks the results to improve quality
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cross_encoder = load_model(
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model_dir = search_config.model_directory,
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model_name = search_config.cross_encoder,
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model_type = CrossEncoder)
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return bi_encoder, cross_encoder, top_k
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@ -149,9 +162,9 @@ def collate_results(hits, entries, count=5):
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in hits[0:count]]
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def setup(config: TextSearchConfig, regenerate: bool, verbose: bool=False) -> TextSearchModel:
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def setup(config: TextSearchConfig, search_config: AsymmetricConfig, regenerate: bool, verbose: bool=False) -> TextSearchModel:
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# Initialize Model
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bi_encoder, cross_encoder, top_k = initialize_model()
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bi_encoder, cross_encoder, top_k = initialize_model(search_config)
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# Map notes in Org-Mode files to (compressed) JSONL formatted file
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if not resolve_absolute_path(config.compressed_jsonl).exists() or regenerate:
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@ -10,16 +10,22 @@ from tqdm import trange
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import torch
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# Internal Packages
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from src.utils.helpers import resolve_absolute_path
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from src.utils.helpers import resolve_absolute_path, load_model
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import src.utils.exiftool as exiftool
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from src.utils.config import ImageSearchModel
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from src.utils.rawconfig import ImageSearchConfig
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from src.utils.rawconfig import ImageSearchConfig, ImageSearchTypeConfig
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def initialize_model():
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def initialize_model(search_config: ImageSearchTypeConfig):
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# Initialize Model
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torch.set_num_threads(4)
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encoder = SentenceTransformer('sentence-transformers/clip-ViT-B-32') #Load the CLIP model
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# Load the CLIP model
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encoder = load_model(
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model_dir = search_config.model_directory,
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model_name = search_config.encoder,
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model_type = SentenceTransformer)
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return encoder
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@ -154,9 +160,9 @@ def collate_results(hits, image_names, image_directory, count=5):
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in hits[0:count]]
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def setup(config: ImageSearchConfig, regenerate: bool, verbose: bool=False) -> ImageSearchModel:
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def setup(config: ImageSearchConfig, search_config: ImageSearchTypeConfig, regenerate: bool, verbose: bool=False) -> ImageSearchModel:
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# Initialize Model
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encoder = initialize_model()
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encoder = initialize_model(search_config)
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# Extract Entries
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image_directory = resolve_absolute_path(config.input_directory, strict=True)
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@ -10,18 +10,31 @@ import torch
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from sentence_transformers import SentenceTransformer, CrossEncoder, util
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# Internal Packages
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from src.utils.helpers import get_absolute_path, resolve_absolute_path
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from src.utils.helpers import get_absolute_path, resolve_absolute_path, load_model
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from src.processor.ledger.beancount_to_jsonl import beancount_to_jsonl
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from src.utils.config import TextSearchModel
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from src.utils.rawconfig import TextSearchConfig
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from src.utils.rawconfig import SymmetricConfig, TextSearchConfig
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def initialize_model():
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def initialize_model(search_config: SymmetricConfig):
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"Initialize model for symmetric semantic search. That is, where query of similar size to results"
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torch.set_num_threads(4)
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bi_encoder = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2') # The encoder encodes all entries to use for semantic search
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top_k = 30 # Number of entries we want to retrieve with the bi-encoder
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cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') # The cross-encoder re-ranks the results to improve quality
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# Number of entries we want to retrieve with the bi-encoder
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top_k = 30
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# The bi-encoder encodes all entries to use for semantic search
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bi_encoder = load_model(
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model_dir = search_config.model_directory,
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model_name = search_config.encoder,
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model_type = SentenceTransformer)
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# The cross-encoder re-ranks the results to improve quality
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cross_encoder = load_model(
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model_dir = search_config.model_directory,
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model_name = search_config.cross_encoder,
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model_type = CrossEncoder)
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return bi_encoder, cross_encoder, top_k
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@ -141,9 +154,9 @@ def collate_results(hits, entries, count=5):
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in hits[0:count]]
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def setup(config: TextSearchConfig, regenerate: bool, verbose: bool) -> TextSearchModel:
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def setup(config: TextSearchConfig, search_config: SymmetricConfig, regenerate: bool, verbose: bool) -> TextSearchModel:
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# Initialize Model
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bi_encoder, cross_encoder, top_k = initialize_model()
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bi_encoder, cross_encoder, top_k = initialize_model(search_config)
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# Map notes in Org-Mode files to (compressed) JSONL formatted file
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if not resolve_absolute_path(config.compressed_jsonl).exists() or regenerate:
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@ -77,14 +77,22 @@ default_config = {
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},
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'search-type':
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{
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'symmetric':
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{
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'encoder': "sentence-transformers/paraphrase-MiniLM-L6-v2",
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'cross-encoder': "cross-encoder/ms-marco-MiniLM-L-6-v2",
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'model_directory': None
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},
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'asymmetric':
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{
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'encoder': "sentence-transformers/msmarco-MiniLM-L-6-v3",
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'cross-encoder': "cross-encoder/ms-marco-MiniLM-L-6-v2"
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'cross-encoder': "cross-encoder/ms-marco-MiniLM-L-6-v2",
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'model_directory': None
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},
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'image':
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{
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'encoder': "clip-ViT-B-32"
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'encoder': "clip-ViT-B-32",
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'model_directory': None
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},
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},
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'processor':
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@ -1,4 +1,6 @@
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# Standard Packages
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import pathlib
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from os.path import join
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def is_none_or_empty(item):
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@ -32,3 +34,20 @@ def merge_dicts(priority_dict, default_dict):
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if k not in priority_dict:
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merged_dict[k] = default_dict[k]
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return merged_dict
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def load_model(model_name, model_dir, model_type):
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"Load model from disk or huggingface"
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# Construct model path
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model_path = join(model_dir, model_name.replace("/", "_")) if model_dir is not None else None
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# Load model from model_path if it exists there
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if model_path is not None and resolve_absolute_path(model_path).exists():
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model = model_type(get_absolute_path(model_path))
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# Else load the model from the model_name
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else:
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model = model_type(model_name)
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if model_path is not None:
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model.save(model_path)
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return model
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@ -37,15 +37,23 @@ class ContentTypeConfig(ConfigBase):
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image: Optional[ImageSearchConfig]
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music: Optional[TextSearchConfig]
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class SymmetricConfig(ConfigBase):
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encoder: Optional[str]
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cross_encoder: Optional[str]
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model_directory: Optional[Path]
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class AsymmetricConfig(ConfigBase):
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encoder: Optional[str]
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cross_encoder: Optional[str]
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model_directory: Optional[Path]
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class ImageSearchTypeConfig(ConfigBase):
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encoder: Optional[str]
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model_directory: Optional[Path]
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class SearchTypeConfig(ConfigBase):
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asymmetric: Optional[AsymmetricConfig]
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symmetric: Optional[SymmetricConfig]
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image: Optional[ImageSearchTypeConfig]
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class ConversationProcessorConfig(ConfigBase):
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@ -1,51 +1,78 @@
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# Standard Packages
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import pytest
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from pathlib import Path
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from src import search_type
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# Internal Packages
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from src.search_type import asymmetric, image_search
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from src.utils.rawconfig import ContentTypeConfig, ImageSearchConfig, TextSearchConfig
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from src.utils.rawconfig import AsymmetricConfig, ContentTypeConfig, ImageSearchConfig, ImageSearchTypeConfig, SearchTypeConfig, SymmetricConfig, TextSearchConfig
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@pytest.fixture(scope='session')
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def model_dir(tmp_path_factory):
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def search_config(tmp_path_factory):
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model_dir = tmp_path_factory.mktemp('data')
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search_config = SearchTypeConfig()
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search_config.asymmetric = SymmetricConfig(
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encoder = "sentence-transformers/paraphrase-MiniLM-L6-v2",
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cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2",
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model_directory = model_dir
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)
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search_config.asymmetric = AsymmetricConfig(
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encoder = "sentence-transformers/msmarco-MiniLM-L-6-v3",
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cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2",
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model_directory = model_dir
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)
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search_config.image = ImageSearchTypeConfig(
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encoder = "clip-ViT-B-32",
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model_directory = model_dir
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)
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return search_config
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@pytest.fixture(scope='session')
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def model_dir(search_config):
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model_dir = search_config.asymmetric.model_directory
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# Generate Image Embeddings from Test Images
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search_config = ContentTypeConfig()
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search_config.image = ImageSearchConfig(
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content_config = ContentTypeConfig()
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content_config.image = ImageSearchConfig(
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input_directory = 'tests/data',
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embeddings_file = model_dir.joinpath('.image_embeddings.pt'),
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batch_size = 10,
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use_xmp_metadata = False)
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image_search.setup(search_config.image, regenerate=False, verbose=True)
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image_search.setup(content_config.image, search_config.image, regenerate=False, verbose=True)
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# Generate Notes Embeddings from Test Notes
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search_config.org = TextSearchConfig(
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content_config.org = TextSearchConfig(
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input_files = ['tests/data/main_readme.org', 'tests/data/interface_emacs_readme.org'],
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input_filter = None,
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compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'),
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embeddings_file = model_dir.joinpath('.note_embeddings.pt'))
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asymmetric.setup(search_config.org, regenerate=False, verbose=True)
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asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False, verbose=True)
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return model_dir
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@pytest.fixture(scope='session')
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def search_config(model_dir):
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search_config = ContentTypeConfig()
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search_config.org = TextSearchConfig(
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def content_config(model_dir):
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content_config = ContentTypeConfig()
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content_config.org = TextSearchConfig(
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input_files = ['tests/data/main_readme.org', 'tests/data/interface_emacs_readme.org'],
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input_filter = None,
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compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'),
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embeddings_file = model_dir.joinpath('.note_embeddings.pt'))
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search_config.image = ImageSearchConfig(
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content_config.image = ImageSearchConfig(
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input_directory = 'tests/data',
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embeddings_file = 'tests/data/.image_embeddings.pt',
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embeddings_file = model_dir.joinpath('.image_embeddings.pt'),
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batch_size = 10,
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use_xmp_metadata = False)
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return search_config
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return content_config
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@ -1,14 +1,15 @@
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# Internal Packages
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from src.main import model
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from src.search_type import asymmetric
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from src.utils.rawconfig import ContentTypeConfig, SearchTypeConfig
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# Test
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# ----------------------------------------------------------------------------------------------------
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def test_asymmetric_setup(search_config):
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def test_asymmetric_setup(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
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# Act
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# Regenerate notes embeddings during asymmetric setup
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notes_model = asymmetric.setup(search_config.org, regenerate=True)
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notes_model = asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=True)
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# Assert
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assert len(notes_model.entries) == 10
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@ -16,9 +17,9 @@ def test_asymmetric_setup(search_config):
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# ----------------------------------------------------------------------------------------------------
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def test_asymmetric_search(search_config):
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def test_asymmetric_search(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
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# Arrange
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model.notes_search = asymmetric.setup(search_config.org, regenerate=False)
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model.notes_search = asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False)
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query = "How to git install application?"
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# Act
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@ -9,7 +9,7 @@ import pytest
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from src.main import app, model, config
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from src.search_type import asymmetric, image_search
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from src.utils.helpers import resolve_absolute_path
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from src.utils.rawconfig import ContentTypeConfig
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from src.utils.rawconfig import ContentTypeConfig, SearchTypeConfig
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# Arrange
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@ -18,55 +18,60 @@ client = TestClient(app)
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# Test
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# ----------------------------------------------------------------------------------------------------
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def test_search_with_invalid_search_type():
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def test_search_with_invalid_content_type():
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# Arrange
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user_query = "How to call semantic search from Emacs?"
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# Act
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response = client.get(f"/search?q={user_query}&t=invalid_search_type")
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response = client.get(f"/search?q={user_query}&t=invalid_content_type")
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# Assert
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assert response.status_code == 422
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# ----------------------------------------------------------------------------------------------------
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def test_search_with_valid_search_type(search_config: ContentTypeConfig):
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def test_search_with_valid_content_type(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
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# Arrange
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||||
config.content_type = search_config
|
||||
config.content_type = content_config
|
||||
config.search_type = search_config
|
||||
|
||||
# config.content_type.image = search_config.image
|
||||
for search_type in ["notes", "ledger", "music", "image"]:
|
||||
for content_type in ["notes", "ledger", "music", "image"]:
|
||||
# Act
|
||||
response = client.get(f"/search?q=random&t={search_type}")
|
||||
response = client.get(f"/search?q=random&t={content_type}")
|
||||
# Assert
|
||||
assert response.status_code == 200
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_regenerate_with_invalid_search_type():
|
||||
def test_regenerate_with_invalid_content_type():
|
||||
# Act
|
||||
response = client.get(f"/regenerate?t=invalid_search_type")
|
||||
response = client.get(f"/regenerate?t=invalid_content_type")
|
||||
|
||||
# Assert
|
||||
assert response.status_code == 422
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_regenerate_with_valid_search_type(search_config: ContentTypeConfig):
|
||||
def test_regenerate_with_valid_content_type(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
config.content_type = search_config
|
||||
for search_type in ["notes", "ledger", "music", "image"]:
|
||||
config.content_type = content_config
|
||||
config.search_type = search_config
|
||||
|
||||
for content_type in ["notes", "ledger", "music", "image"]:
|
||||
# Act
|
||||
response = client.get(f"/regenerate?t={search_type}")
|
||||
response = client.get(f"/regenerate?t={content_type}")
|
||||
# Assert
|
||||
assert response.status_code == 200
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.skip(reason="Flaky test. Search doesn't always return expected image path.")
|
||||
def test_image_search(search_config: ContentTypeConfig):
|
||||
def test_image_search(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
config.content_type = search_config
|
||||
model.image_search = image_search.setup(search_config.image, regenerate=False)
|
||||
config.content_type = content_config
|
||||
config.search_type = search_config
|
||||
model.image_search = image_search.setup(content_config.image, search_config.image, regenerate=False)
|
||||
query_expected_image_pairs = [("brown kitten next to fallen plant", "kitten_park.jpg"),
|
||||
("a horse and dog on a leash", "horse_dog.jpg"),
|
||||
("A guinea pig eating grass", "guineapig_grass.jpg")]
|
||||
|
|
@ -78,16 +83,16 @@ def test_image_search(search_config: ContentTypeConfig):
|
|||
# Assert
|
||||
assert response.status_code == 200
|
||||
actual_image = Path(response.json()[0]["Entry"])
|
||||
expected_image = resolve_absolute_path(search_config.image.input_directory.joinpath(expected_image_name))
|
||||
expected_image = resolve_absolute_path(content_config.image.input_directory.joinpath(expected_image_name))
|
||||
|
||||
# Assert
|
||||
assert expected_image == actual_image
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_notes_search(search_config: ContentTypeConfig):
|
||||
def test_notes_search(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
model.notes_search = asymmetric.setup(search_config.org, regenerate=False)
|
||||
model.notes_search = asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False)
|
||||
user_query = "How to git install application?"
|
||||
|
||||
# Act
|
||||
|
|
@ -101,9 +106,9 @@ def test_notes_search(search_config: ContentTypeConfig):
|
|||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_notes_search_with_include_filter(search_config: ContentTypeConfig):
|
||||
def test_notes_search_with_include_filter(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
model.notes_search = asymmetric.setup(search_config.org, regenerate=False)
|
||||
model.notes_search = asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False)
|
||||
user_query = "How to git install application? +Emacs"
|
||||
|
||||
# Act
|
||||
|
|
@ -117,9 +122,9 @@ def test_notes_search_with_include_filter(search_config: ContentTypeConfig):
|
|||
|
||||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_notes_search_with_exclude_filter(search_config: ContentTypeConfig):
|
||||
def test_notes_search_with_exclude_filter(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
model.notes_search = asymmetric.setup(search_config.org, regenerate=False)
|
||||
model.notes_search = asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False)
|
||||
user_query = "How to git install application? -clone"
|
||||
|
||||
# Act
|
||||
|
|
|
|||
|
|
@ -5,14 +5,15 @@ import pytest
|
|||
from src.main import model
|
||||
from src.search_type import image_search
|
||||
from src.utils.helpers import resolve_absolute_path
|
||||
from src.utils.rawconfig import ContentTypeConfig, SearchTypeConfig
|
||||
|
||||
|
||||
# Test
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
def test_image_search_setup(search_config):
|
||||
def test_image_search_setup(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Act
|
||||
# Regenerate image search embeddings during image setup
|
||||
image_search_model = image_search.setup(search_config.image, regenerate=True)
|
||||
image_search_model = image_search.setup(content_config.image, search_config.image, regenerate=True)
|
||||
|
||||
# Assert
|
||||
assert len(image_search_model.image_names) == 3
|
||||
|
|
@ -21,9 +22,9 @@ def test_image_search_setup(search_config):
|
|||
|
||||
# ----------------------------------------------------------------------------------------------------
|
||||
@pytest.mark.skip(reason="results inconsistent currently")
|
||||
def test_image_search(search_config):
|
||||
def test_image_search(content_config: ContentTypeConfig, search_config: SearchTypeConfig):
|
||||
# Arrange
|
||||
model.image_search = image_search.setup(search_config.image, regenerate=False)
|
||||
model.image_search = image_search.setup(content_config.image, search_config.image, regenerate=False)
|
||||
query_expected_image_pairs = [("brown kitten next to plant", "kitten_park.jpg"),
|
||||
("horse and dog in a farm", "horse_dog.jpg"),
|
||||
("A guinea pig eating grass", "guineapig_grass.jpg")]
|
||||
|
|
@ -38,11 +39,11 @@ def test_image_search(search_config):
|
|||
results = image_search.collate_results(
|
||||
hits,
|
||||
model.image_search.image_names,
|
||||
search_config.image.input_directory,
|
||||
content_config.image.input_directory,
|
||||
count=1)
|
||||
|
||||
actual_image = results[0]["Entry"]
|
||||
expected_image = resolve_absolute_path(search_config.image.input_directory.joinpath(expected_image_name))
|
||||
expected_image = resolve_absolute_path(content_config.image.input_directory.joinpath(expected_image_name))
|
||||
|
||||
# Assert
|
||||
assert expected_image == actual_image
|
||||
|
|
|
|||
Loading…
Reference in a new issue