| 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 = "Expenditure_" | |
| # 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) | |