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Model Runtime (#1858)

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Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: Garfield Dai <dai.hai@foxmail.com>
Co-authored-by: chenhe <guchenhe@gmail.com>
Co-authored-by: jyong <jyong@dify.ai>
Co-authored-by: Joel <iamjoel007@gmail.com>
Co-authored-by: Yeuoly <admin@srmxy.cn>
This commit is contained in:
takatost
2024-01-02 23:42:00 +08:00
committed by GitHub
parent e91dd28a76
commit d069c668f8
807 changed files with 171310 additions and 23806 deletions
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34
api/services/hit_testing_service.py
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@@ -1,4 +1,3 @@
import json
import logging
import threading
import time
@@ -11,7 +10,9 @@ from langchain.schema import Document
from sklearn.manifold import TSNE
from core.embedding.cached_embedding import CacheEmbedding
from core.model_providers.model_factory import ModelFactory
from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from core.rerank.rerank import RerankRunner
from extensions.ext_database import db
from models.account import Account
from models.dataset import Dataset, DocumentSegment, DatasetQuery
@@ -47,11 +48,14 @@ class HitTestingService:
retrieval_model = dataset.retrieval_model if dataset.retrieval_model else default_retrieval_model
# get embedding model
embedding_model = ModelFactory.get_embedding_model(
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
model_provider_name=dataset.embedding_model_provider,
model_name=dataset.embedding_model
model_type=ModelType.TEXT_EMBEDDING,
provider=dataset.embedding_model_provider,
model=dataset.embedding_model
)
embeddings = CacheEmbedding(embedding_model)
all_documents = []
@@ -93,14 +97,22 @@ class HitTestingService:
thread.join()
if retrieval_model['search_method'] == 'hybrid_search':
hybrid_rerank = ModelFactory.get_reranking_model(
model_manager = ModelManager()
rerank_model_instance = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
model_provider_name=retrieval_model['reranking_model']['reranking_provider_name'],
model_name=retrieval_model['reranking_model']['reranking_model_name']
provider=retrieval_model['reranking_model']['reranking_provider_name'],
model_type=ModelType.RERANK,
model=retrieval_model['reranking_model']['reranking_model_name']
)
rerank_runner = RerankRunner(rerank_model_instance)
all_documents = rerank_runner.run(
query=query,
documents=all_documents,
score_threshold=retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None,
top_n=retrieval_model['top_k'],
user=f"account-{account.id}"
)
all_documents = hybrid_rerank.rerank(query, all_documents,
retrieval_model['score_threshold'] if retrieval_model['score_threshold_enabled'] else None,
retrieval_model['top_k'])
end = time.perf_counter()
logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")