import logging import lancedb import os from pathlib import Path from sentence_transformers import SentenceTransformer #from FlagEmbedding import LLMEmbedder, FlagReranker # Al document present here https://github.com/FlagOpen/FlagEmbedding/tree/master #EMB_MODEL_NAME = "thenlper/gte-base" EMB_MODEL_NAME = 'BAAI/llm-embedder' task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch) #EMB_MODEL_NAME = LLMEmbedder('BAAI/llm-embedder', use_fp16=False) # Load model (automatically use GPUs) #reranker_model = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) # use_fp16 speeds up computation with a slight performance degradation #EMB_MODEL_NAME = "thenlper/gte-base" #DB_TABLE_NAME = "Huggingface_docs" DB_TABLE_NAME = "doc_embed1" # Setting up the logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) retriever = SentenceTransformer(EMB_MODEL_NAME) # db db_uri = os.path.join(Path(__file__).parents[1], ".lancedb1") db = lancedb.connect(db_uri) table = db.open_table(DB_TABLE_NAME)